NH – Natural Hazards

NH1.1 – Extreme heat events: processes, impacts and adaptation

EGU22-1204 | Presentations | NH1.1 | Highlight

Less-deadly heatwaves due to soil drought

Hendrik Wouters, Jessica Keune, Irina Y. Petrova, Chiel C. van Heerwaarden, Adriaan J. Teuling, Jeremy S. Pal, Jordi Vilà-Guerau de Arellano, and Diego G. Miralles

Global warming increases the number and severity of deadly heatwaves. Recent heatwaves often coincided with soil droughts that acted to intensify air temperature but lower air humidity. Since lowering air humidity may reduce human heat stress, the net impact of soil desiccation on the morbidity and mortality of heatwaves remains unclear. Combining weather balloon and satellite observations, atmospheric modelling, and meta-analyses of heatwave mortality, we find that soil droughts—despite their warming effect—lead to a mild reduction in heatwave lethality. More specifically, morning dry soils attenuate the afternoon heat stress anomaly by ~5%. This occurs due to reduced surface evaporation and increased entrainment of dry air from aloft. The benefit appears more pronounced during specific events, such as the Chicago 1995 and Northern U.S. 2006 and 2012 heatwaves. Likewise, our findings suggest that irrigated agriculture may intensify lethal heat stress, and question recently proposed heatwave mitigation measures involving surface moistening to increase evaporative cooling.

The manuscript of the findings is in press for Science Advances.




How to cite: Wouters, H., Keune, J., Petrova, I. Y., van Heerwaarden, C. C., Teuling, A. J., Pal, J. S., Vilà-Guerau de Arellano, J., and Miralles, D. G.: Less-deadly heatwaves due to soil drought, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1204, https://doi.org/10.5194/egusphere-egu22-1204, 2022.

EGU22-1538 | Presentations | NH1.1 | Highlight

Spring regional sea surface temperature precursors of European summer heat waves

Goratz Beobide-Arsuaga, André Düsterhus, Wolfgang A. Müller, Elizabeth A. Barnes, and Johanna Baehr

Past case studies have proposed many different spring and early summer sea surface temperature anomalies (SSTA) over the North Atlantic as precursors of European summer heat waves. Negative SSTAs in the Subpolar Gyre and western tropical Atlantic, and positive SSTAs in North Sea and Mediterranean Sea are few of the examples suggested to precede different European summer heat waves. Any robust description of North Atlantic spring SSTA precursors is further complicated by the large spatial heterogeneity of European summer heat waves and the limited number of observed events. Here, we combine the MPI-Grand Ensemble dataset with its 100 historical simulations (1850-2006) with a Neural-Network-based Explainable Artificial Intelligence method. In this unique data set, we systematically investigate the relevance of the North Atlantic spring SSTAs in preceding different types of European summer heat waves. We find that spring European regional seas provide useful information to differentiate and anticipate different types of European summer heat waves. While positive SSTAs in western Iberian Peninsula precede western European summer heat waves, positive SSTAs in the North Sea or Mediterranean Sea precede eastern European summer heat waves. The regional spring SSTAs relate to distinct soil moisture anomaly patterns in June, which resemble the location of the heat waves. These results could potentially improve seasonal prediction of European summer heat waves.

How to cite: Beobide-Arsuaga, G., Düsterhus, A., Müller, W. A., Barnes, E. A., and Baehr, J.: Spring regional sea surface temperature precursors of European summer heat waves, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1538, https://doi.org/10.5194/egusphere-egu22-1538, 2022.

EGU22-7118 | Presentations | NH1.1

The upper bound of mid-latitude extreme temperatures

Yi Zhang and William Boos

 Extreme temperatures have a wide societal impact yet remain a major uncertainty in climate projections. Past studies have identified several drivers of heatwaves, including atmospheric blocking and soil moisture-atmosphere feedback. However, it remains unknown what limits the magnitude of extreme temperatures, and a quantitative understanding of heatwaves is lacking. Here we provide a theory of mid-latitude extreme temperatures based on a convective-instability mechanism. We formulate the upper bound of the surface temperature as a function of the temperature at the 500-hPa pressure level (T500), which is supported by observations and reanalysis data. Based on this theory, we project that the annual hottest daily maximum temperature (TXx) should increase by 1.9 K for each 1 K of increase in T500 over mid-latitude land if there is no evident drying or moistening of surface air on the annual hottest days. The observed TXx trend over the past four decades between 40°N-65°N is consistent with our projection. With T500 within 40°N-65°N increasing slightly faster than the global warming, the warming rate of TXx of this region will be on average around twice of the global warming if specific humidity does not change on the hottest days. However, TXx will increase at a faster rate over regions with a decrease in specific humidity on the hottest days, and vice versa.

How to cite: Zhang, Y. and Boos, W.: The upper bound of mid-latitude extreme temperatures, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7118, https://doi.org/10.5194/egusphere-egu22-7118, 2022.

EGU22-237 | Presentations | NH1.1

Investigating the associated dynamics of 2019 Heat wave over India

Rani Devi and Krushna Chandra Gouda

India witnessed the second longest recorded heat wave during May-June 2019 causing more human deaths with the maximum temperature recorded was about 51.8oC in a place called Churu in the state of Rajasthan. The present study investigated the spatio-temporal pattern of the maximum temperature and the associate heat waves in the country. The relationship of the heat wave spread and the variables like temperature, humidity, soil moisture as well as the land use and land cover is explored. The dynamics of large scale oceanic and atmospheric features resulting advection and local heating mechanism is found to be the reason of such high intense heat wave in 2019 summer season. The anomaly of all the related weather parameters are linked with the intense maximum temperature and resultant heat wave and the hot spots are identified. The impacts of ENSO (including 'El Niño Modoki') and MJO on the longest and highest heat wave phenomena are also quantified for the year 2019. The role of soil moisture and the evapotranspiration also observed in the analysis which clearly shows lack of these parameters also triggers the intense heat wave events. This study will help in better understanding of the local heat wave dynamics and these informations can be useful for the public health interventions against the intense heat wave situations.


How to cite: Devi, R. and Gouda, K. C.: Investigating the associated dynamics of 2019 Heat wave over India, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-237, https://doi.org/10.5194/egusphere-egu22-237, 2022.

EGU22-6827 | Presentations | NH1.1

Feedback attribution to dry heatwaves over East Asia

Ye-Won Seo, Kyung-Ja Ha, and Tae-Won Park

Summer heatwave events have exhibited increasing trends, with sudden increases occurring since the early 2000s over northeastern China and along the northern boundary of Mongolia. However, the mechanism behind heatwaves remains unexplored. To quantitatively examine the feedback attribution of concurrent events related to surface temperature anomalies, the coupled atmosphere–surface climate feedback-response analysis method based on the total energy balance within the atmosphere–surface column was applied. The results demonstrate that the contributions of the latent heat flux and surface dynamic processes served as positive feedback for surface warming by reducing the heat release from the surface to the atmosphere because of deficient soil moisture based on dry conditions. Cloud feedback also led to warm temperature anomalies through increasing solar insolation caused by decreasing cloud amounts associated with anomalous high-pressure systems. In contrast, the sensible heat flux played a role in reducing the warm temperature anomalies by the emission of heat from the surface. Atmospheric dynamic feedback led to cold anomalies. The influence of ozone, surface albedo, and water vapor processes is very weak. This study provides a better understanding of combined extreme climate events in the context of radiative and dynamic feedback processes.

How to cite: Seo, Y.-W., Ha, K.-J., and Park, T.-W.: Feedback attribution to dry heatwaves over East Asia, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6827, https://doi.org/10.5194/egusphere-egu22-6827, 2022.

Heatwaves are meteorological disasters that can damage human health and reduce agricultural production when extremely high temperatures are involved. A heatwave over the Korean Peninsula in 2018 broke the temperature and duration records kept since observations began. This event caused significant socio-economic damage. High pressure in the upper atmosphere over Eastern Europe and strong convection over the western North Pacific subtropical region are major fluctuations known to strengthen heatwaves over the Korean Peninsula. This study analyzed how these factors affected predictions of the 2018 heatwave over the Korean Peninsula using the sub-seasonal to seasonal (S2S) prediction model. Of the 11 models used in the S2S prediction project, 6 were selected: CMA, ECCC, ECMWF, KMA, NCEP, and UKMO. These models underestimated the daily surface temperature from July to August 2018 compared with observations, and the prediction errors gradually increased as lead-time increased. The model that simulated significant upper-level high pressure events in Eastern Europe and convection activities in the western North Pacific subtropical region predicted surface temperatures for the Korean Peninsula that were similar to the observed values. The increase in air pressure in the upper atmosphere over Eastern Europe is related to the recent expansion of areas affected by heatwaves in Europe. Even in the S2S models, the model that accurately predicted the characteristics of the heatwave showed excellent prediction performance for the Korean Peninsula. The increase in convection activities in the western North Pacific subtropical region increased when the amplitude of phases 4–6 of the Madden–Julian Oscillation (MJO) was large and they included many days. If the S2S model simulates the characteristics of the MJO accurately, the surface temperature prediction performance for the Korean Peninsula will increase. Therefore, it is very important for the S2S model to predict these two factors accurately, particularly when predicting heatwaves similar to that which occurred over the Korean Peninsula in 2018.

This work was funded by the Korea Meteorological Administration Research and Development Program under Grant KMI2020-01212.

How to cite: Wie, J. and Moon, B.-K.: Effect of Upper-Level High Pressure in Eastern Europe and Convection Activities in the Western North Pacific Subtropical Region on the Prediction of Heatwaves over the Korean Peninsula, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6693, https://doi.org/10.5194/egusphere-egu22-6693, 2022.

EGU22-3750 | Presentations | NH1.1

Identifying drivers for heat waves using wavelets and machine learning approaches

Sebastian Buschow, Jan Keller, and Sabrina Wahl

The driving mechanisms of extreme heat events are known to live on a range of spatio-temporal scales. The occurrence and severity of a heatwave can be influenced by (a) slow variations in the ocean and sub-surface, (b) planetary tele-connections, (c) variations in the jet-stream and synoptic weather systems, as well as (d) local-scale feedbacks.

While important progress has been made on each of these individual contributions, fewer studies have attempted to draw a unified picture including them all. We approach this task with tools from classic statistical modeling, as well as image processing machine learning. With the help of wavelet-transforms, predictor variables can be separated into individual scales. Together with local variables and global principal component time-series, these potential drivers are supplied to a statistical learner with the task of reconstructing the field of heatwave occurrences. Contributions from individual scales can then directly be identified, either via variable selection before or during learning, or by measures of feature importance applied to the trained models.

We demonstrate this approach for the case of summer heatwaves in the ERA5 reanalysis. If successful, our  framework can also be transferred to other extreme events such as droughts, cold spells or wind storms.

How to cite: Buschow, S., Keller, J., and Wahl, S.: Identifying drivers for heat waves using wavelets and machine learning approaches, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3750, https://doi.org/10.5194/egusphere-egu22-3750, 2022.

EGU22-2397 | Presentations | NH1.1

Heatwave-related extreme rainfall events

Christoph Sauter, Christopher White, Hayley Fowler, and Seth Westra

Research on heatwave-related impacts typically focusses on risks to health or critical infrastructure. However, since high temperatures are an important element of convection-driven extreme rainfall events that can trigger flash floods, heatwave-induced extreme rainfall events are also important when considering heatwave impacts. Heavy rainfall events following heatwaves might alleviate the direct impacts of the heat but introduce other risks related to flash floods.

Using sub-daily rainfall observations on a global scale, we show that short duration rainfall extremes are indeed more likely to occur if preceded by a heatwave than compared to non-heatwave events. In addition, these rainfall events are more intense as well. However, this link is dependent on the region, with some locations, especially arid regions, showing no relationship between the two phenomena at all. We also investigate if hotter heatwaves are more likely to be followed by rainfall extremes. This could have implications for future heatwaves which are projected to become more intense.

How to cite: Sauter, C., White, C., Fowler, H., and Westra, S.: Heatwave-related extreme rainfall events, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2397, https://doi.org/10.5194/egusphere-egu22-2397, 2022.

EGU22-7778 | Presentations | NH1.1

The role of heatwave events on the occurrence and persistence of thermal stratification in the southern North Sea

Wei Chen, Joanna Staneva, Sebastian Grayek, Johannes Schulz-Stellenfleth, and Jens Greinert

Extremes in temperatures not only directly affect the marine environment and ecosystems but also have indirect impacts on hydrodynamics and marine life. The role of heatwave events responsible for the occurrence and persistence of thermal stratification was analysed using a fully coupled hydrodynamic and wave model within the framework of the Geesthacht Coupled cOAstal model SysTem (GCOAST) for the North Sea. The model results were assessed against satellite reprocessed data and in situ observations from field campaigns and fixed MARNET stations. To quantify the degree of stratification, a potential energy anomaly over the water column was calculated. A linear correlation existed between the air temperatures and the potential energy anomaly in the North Sea excluding the Norwegian Trench and the area south of 54◦N latitude. Contrary to the northern part of the North Sea, where the water column is stratified in the warming season each year, the southern North Sea is seasonally stratified in years when a heatwave occurs. The influences of heatwaves on the occurrence of summer stratification in the southern North Sea are mainly in the form of two aspects, i.e., a rapid rise in sea surface temperature at the early stage of the heatwave period and a relatively higher water temperature during summer than the multiyear mean. Another factor that enhances the thermal stratification in summer is the memory of the water column to cold spells earlier in the year. Differences between the seasonally stratified northern North Sea and the heatwave-induced stratified southern North Sea were attributed to changes in water depth.

How to cite: Chen, W., Staneva, J., Grayek, S., Schulz-Stellenfleth, J., and Greinert, J.: The role of heatwave events on the occurrence and persistence of thermal stratification in the southern North Sea, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7778, https://doi.org/10.5194/egusphere-egu22-7778, 2022.

EGU22-2728 | Presentations | NH1.1

A global assessment of heatwaves since 1850 in different datasets

Laura Hövel, Ralf Hand, and Stefan Brönnimann

Over the past century there was a significant increase in heatwaves in several regions around the globe. This increase is projected to continue with ongoing global warming and forms a serious risk for various ecosystems as well as human health. Changes in the occurrence and the characteristics of heatwaves since the middle of the 20th century are extensively studied in observational datasets and model simulations. However, there is a research gap concerning preindustrial (1850-1900) heatwaves and heatwaves in the early 20th century and their relation to forcings and large-scale variability modes.

In this study we analyse the occurrence of heatwaves and the spatial and temporal distribution of different heatwave characteristics since 1850 using different observational datasets (20CRv3 reanalysis, EUSTACE gridded temperature, HadEX3 and station data) and a 36-member ensemble of atmospheric model simulations. We compare preindustrial heatwaves to recent and projected heatwaves and analyse how global or local heatwave hotspots change over time.

We use a new approach, a 30-year running baseline climatology, which allows us to analyse heatwave characteristics across different centuries. Our analysis shows that the different observational datasets show a comparable distribution of heatwave characteristics. Furthermore, the atmospheric model based on observed volcanic forcings can also be used to analyse preindustrial and early 20th century heatwaves.  The agreement of the model simulations with the observational datasets allows to use the atmospheric model to analyse earlier preindustrial time periods that are not covered by observations. With our on-going analysis of preindustrial heatwaves, we consequently contribute to a better understanding of past climate extremes.


How to cite: Hövel, L., Hand, R., and Brönnimann, S.: A global assessment of heatwaves since 1850 in different datasets, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2728, https://doi.org/10.5194/egusphere-egu22-2728, 2022.

EGU22-10642 | Presentations | NH1.1

Identification of European heatwave families 

Julia Hellmig, Felix Strnad, and Bedartha Goswami

Mainly caused by anthropogenic climate change occurring heatwaves have become more frequent and extreme throughout the 21th century. Summer heatwaves over Europe are mainly caused by positive phases of the North Atlantic Oscillation (NAO) and jet stream anomalies, subsequently causing atmospheric blocking over different parts of Europe. With this work we aim to define families of European heatwaves caused by different atmospheric regimes. In the long run this could help predicting European heatwaves and their length, intensity and spatial extend. To identify European heatwaves and their spatial extend we use the graph framework DeepGraphs. Within this framework every extreme heat day isconsidered a node and a heatwave is defined as the union of all nearest neighbour nodes (which are connected by edges). 

Two clustering steps are applied to cluster the heatwave into families depending on their length, season and spatial extend. 

Our results reveal a promising way to classify European heatwaves based on their atmospheric cause which could help forecasting heatwaves in the future.

How to cite: Hellmig, J., Strnad, F., and Goswami, B.: Identification of European heatwave families , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10642, https://doi.org/10.5194/egusphere-egu22-10642, 2022.

EGU22-3666 | Presentations | NH1.1 | Highlight

European heat waves of summer 2021 in the context of past major heat waves

Ondřej Lhotka and Jan Kyselý

Climate change-induced rise in global temperatures is linked to changes in hot extremes. The recent summer of 2021 was marked by extremely high temperatures over the Mediterranean, which together with numerous wildfires considerably affected human society and natural environment. Using daily maximum temperatures from the ERA-5 reanalysis, we aim to assess the severity of heat waves in 2021 in the context of past major European heat waves (since 1950) through analysing their length, spatial extent, intensity, and overall magnitude. We show that the summer of 2021 was record-breaking in terms of total duration of heat waves and their magnitude was comparable to those in 2003 and 2010. The past two decades (2002–2021) almost completely redraw the spatial pattern of the occurrence of the historically most severe heat wave in European regions. Before 2002, heat waves of 1955, 1972, and 1994 were the most severe in many parts of Europe. Considering the whole 1950–2021 period, however, those heat waves remain as historically the most severe only over a small portion of their original area, and the map is dominated by the 2003, 2010, 2018, and 2021 events. This documents a rapid change in heat wave characteristics in Europe over the last two decades.

How to cite: Lhotka, O. and Kyselý, J.: European heat waves of summer 2021 in the context of past major heat waves, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3666, https://doi.org/10.5194/egusphere-egu22-3666, 2022.

EGU22-10558 | Presentations | NH1.1

The Decadal Variability of Extreme European Heat

Laura Suarez-Gutierrez, Wolfgang A. Müller, and Jochem Marotzke

We evaluate the contribution of the decadal to multidecadal variability in the North Atlantic climate system to impact-relevant extreme heat metrics over Europe, and how this contribution evolves in a warming world. To do this, we use the largest existing ensemble of a comprehensive, fully-coupled climate model: the 100-member Max Planck Institute Grand Ensemble (MPI-GE). MPI-GE has been shown to have one of the most adequate representations of the variability and forced response in observed temperatures in the historical record. Furthermore, the large ensemble size of MPI-GE provides the robust sampling of internal variability that is required to evaluate the contribution of variability on decadal to multidecadal timescales to low-probability, high-impact extreme events.

In our evaluation, we go beyond common metrics defining heatwave intensity or duration, and employ heat excess metrics that account for the cumulative intensity and persistence of heat per Summer beyond given thresholds. We use these cumulative heat metrics to assess excess dry heat as well as other impact-relevant aspects of heatwaves, such as hot and humid conditions and lack of night time cooling. Our preliminary results indicate that the contribution of the decadal variability in the North Atlantic, represented by the Atlantic Multidecadal Variability (AMV), contributes to differences in these metrics between positive versus negative AMV phases that are comparable to the forced changes due to anthropogenic global warming in parts of Europe. This potential for the exacerbation of such extreme conditions under positive AMV phases highlights the necessity for considering these decadal variations both in the attribution of past events as well as in our projections of future extreme heat.

How to cite: Suarez-Gutierrez, L., Müller, W. A., and Marotzke, J.: The Decadal Variability of Extreme European Heat, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10558, https://doi.org/10.5194/egusphere-egu22-10558, 2022.

EGU22-4042 | Presentations | NH1.1

thermofeel: developing an open research software project for heat stress and thermal comfort. 

Chloe Brimicombe, Tiago Quintino, Claudia Di Napoli, Florian Pappenberger, Rosalind Cornforth, and Hannah Cloke

Extreme heat is a growing risk to both human and planetary health. It is an area of research with many mathematical models that attempt to capture mostly human responses to thermal conditions. However, like many science fields software is often not developed in a reproducible manner, which adheres to the shared principles of open science, software and research. Here, we present thermofeel which is a python thermal comfort library that was developed at the European Centre for Medium-Range Weather Forecasts (ECMWF) with the dual purpose of being able to be integrated into their operational forecasting systems and allowing users of ECMWF products to be able to use the same methods with their data. In addition, hosting thermofeel on GitHub allows for future growth through open research software process in line with the fast-moving extreme heat field and gives the potential for collaboration between the ECMWF with many other user groups. Further, the development here could lead to a global heat hazard early warning system and the first forecasting results will be presented demonstrating the skill of thermal indices. Finally, thermofeel is currently in pre-operational forecasting at ECMWF and is available for everybody through pip and GitHub. This work has been funded by the European Union’s Horizon 2020 Research and Innovation programme under Grant Agreement no 824115. 

How to cite: Brimicombe, C., Quintino, T., Di Napoli, C., Pappenberger, F., Cornforth, R., and Cloke, H.: thermofeel: developing an open research software project for heat stress and thermal comfort. , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4042, https://doi.org/10.5194/egusphere-egu22-4042, 2022.

EGU22-11189 | Presentations | NH1.1 | Highlight

Probing the unfathomable: ensemble boosting for physical climate storylines of unseen heat extremes

Erich Fischer, Urs Beyerle, Claudia Gessner, Flavio Lehner, Angeline Pendergrass, Sebastian Sippel, Joel Zeder, and Reto Knutti

The Pacific Northwest heat wave is one of a series of record-shattering heat extremes that, based on the previous observational record, may have been deemed impossible. Here we address the question of whether the potential for such an extreme heat wave could have been foreseen using simulated physical climate storylines.

We use a novel approach, called ensemble boosting, in which a fully-coupled free-running climate model (CESM2) is used to develop physical storylines of very rare heat extremes under present-day conditions. In ensemble boosting, the most extreme events in an initial-condition large ensemble for the near future are re-initialized with slightly perturbed atmospheric initial conditions to efficiently generate events that are even more extreme, with the goal of sampling events with magnitudes that have not been seen before.

We demonstrate that, with this approach, CESM2 can efficiently simulate events that reach or even exceed the magnitude and duration of the 2021 Pacific Northwest heatwave anomaly. The atmospheric circulation anomalies associated with the most extreme simulated heat waves in the boosted ensemble are remarkably similar to the observed event. We further evaluate the anomalies in the surface energy and water budgets that contribute to the most intense simulated events. We conclude that based on this approach, heat waves unseen in the observational record can be simulated in models, at least in some regions. After probing this approach for the Pacific Northwest heatwave, we apply it to other mid-latitude regions where extreme heat events of much higher magnitude than has been observed are plausible in the near future.

The ensemble boosting approach is computationally efficient, and it preserves physical consistency both in time, in space and across variables. This has the major advantages that the drivers can be directly evaluated against observed events and the generated storylines can be used in impact studies that require physical consistency, e.g. for the evaluation of humid heatwaves or compound events, for assessing wildfire risks or for ecosystem modelling.

How to cite: Fischer, E., Beyerle, U., Gessner, C., Lehner, F., Pendergrass, A., Sippel, S., Zeder, J., and Knutti, R.: Probing the unfathomable: ensemble boosting for physical climate storylines of unseen heat extremes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11189, https://doi.org/10.5194/egusphere-egu22-11189, 2022.

EGU22-2671 | Presentations | NH1.1

Future changes in African heatwaves and their drivers at the convective scale

Cathryn Birch, Lawrence Jackson, Declan Finney, John Marsham, Rachel Stratton, Simon Tucker, Sarah Chapman, Cath Senior, Richard Keane, Francoise Guichard, and Elizabeth Kendon

The future change in dry and humid heatwaves is assessed in 10 year pan-African convective scale (4.5km) and parameterised convection (25km) climate model simulations. Compared to reanalysis, the convective scale simulation is better able to represent humid heatwaves than the parameterised simulation. Model performance for dry heatwaves is much more similar. Both model configurations simulate large increases in the intensity, duration and frequency of heatwaves by 2100 under RCP8.5. Present day conditions that occur on 3 to 6 heatwave days per year will be normal by 2100, occurring on 150-180 days per year. The future change in dry heatwaves is similar in both climate model configurations, whereas the future change in humid heatwaves is 56% higher in intensity and 20% higher in frequency in the convective scale model. Dry heatwaves are associated with low rainfall, reduced cloud, increased surface shortwave heating and increased sensible heat flux. In contrast, humid heatwaves are predominately controlled by increased humidity, which is associated with increased rainfall, cloud, longwave heating and evaporation, with dry bulb temperature gaining more significance in the most humid regions. Approximately one third (32%) of present day humid heatwaves commence on wet days, suggesting the potential for compound flood-humid heat climate extremes. Moist processes are known to be better represented in convective scale models. Climate models with parameterised convection, such as those in CMIP, may underestimate the future change in humid heatwaves, which heightens the need for mitigation and adaptation strategies and indicates there may be less time available to implement them to avoid future catastrophic heat stress conditions than previously thought.

How to cite: Birch, C., Jackson, L., Finney, D., Marsham, J., Stratton, R., Tucker, S., Chapman, S., Senior, C., Keane, R., Guichard, F., and Kendon, E.: Future changes in African heatwaves and their drivers at the convective scale, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2671, https://doi.org/10.5194/egusphere-egu22-2671, 2022.

EGU22-2473 | Presentations | NH1.1

Historical and projected heat waves in Croatia

Lidija Srnec, Vjeran Magjarević, and Ivan Güttler

Introduction: Last IPCC AR6 reported with very high confidence that more frequent hot extremes will increase for the severity of heatwaves all-round the globe. It is known that heat and hot weather that can last for several days (so called heatwaves) can significantly influence human health as well as rise in heat-related deaths.

Design and methods: In this work, climate simulations obtained by regional climate model RegCM4 over Croatia are used. RegCM4 was forced by four different global climate models on 12.5 km horizontal resolution. Historical climate simulated by model is compared with observed daily data measured at Croatian meteorological stations in order to evaluate simulations. Future climate is considered by three different IPCC scenarios: the lowest RCP2.6, the middle RCP4.5 and the highest RCP8.5 emission scenario. We considered three future time slices: 2021-2050 (P1), 2031-2060 (P2) and 2041-2070 (P3).

Results: The range of climate change for maximum temperature during summer will be examined in the future time slices. We will also look into duration and number of heat waves in different parts of Croatia. Knowledge of the current situation as well as possible change in the future can help in the planning future adaptation and mitigation measures.

How to cite: Srnec, L., Magjarević, V., and Güttler, I.: Historical and projected heat waves in Croatia, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2473, https://doi.org/10.5194/egusphere-egu22-2473, 2022.

EGU22-4390 | Presentations | NH1.1

Extreme heatwaves in Europe 1950-2020: analysis of the links between meteorology, population, and impacts

Théo Mandonnet, Aglaé Jézéquel, Fabio D'Andrea, Améline Vallet, and Céline Guivarch

There is high confidence that heatwaves will become more frequent and more intense under the influence of climate change. Different definitions of heatwaves exist based on the statistical distribution of temperature, in general using thresholds and duration and extension criteria.
If one observes the overlap between these definitions and the actual human and material damage produced by heatwaves, it appears that there is low consistency between the two. In other terms, a large amplitude heatwave in the physical climatological sense may not be equivalently as large in terms of impacts.
By crossing meteorological (E-OBS), demographic (WorldPop, GHS-POP), and impact (EM-DAT) databases at the European scale, we developed indices to classify heatwaves and select extreme ones in terms of impacts. We also proposed a method to evaluate the classification abilities of these indices. Including demographic data in the indices seems central to understand the links between meteorological conditions and observed impacts.

How to cite: Mandonnet, T., Jézéquel, A., D'Andrea, F., Vallet, A., and Guivarch, C.: Extreme heatwaves in Europe 1950-2020: analysis of the links between meteorology, population, and impacts, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4390, https://doi.org/10.5194/egusphere-egu22-4390, 2022.

EGU22-13479 | Presentations | NH1.1 | Highlight

Heat vulnerability assessment and mapping for a bucolic town in the UK 

Jeetendra Sahani, Sisay E. Debele, and Prashant Kumar

With ongoing climate change, the number, frequency, and intensity of events of extremely hot days during summers called heatwaves are progressing. Vulnerability of the population is one aspect responsible for the risk induced by such heatwaves. In society, certain characteristics make one group of people more vulnerable to heatwaves than others, such as poverty, access to cooling facilities, age, gender etc. The current research identifies such vulnerability factors or indicators of population to help in devising heat management strategies. This research focuses on a small bucolic region (Guildford) in Surrey county of the United Kingdom as mostly risk or vulnerability factors are underestimated and ignored in such regions compared to city population. Twelve heat vulnerability factors or indicators (house type, sex, age, ethnicity, place density, access to central heating, residence type: communal, health condition, household composition, disability, accommodation tenure i.e. rented or owned, and education level) were selected after reviewing several literatures to include in the study based on their data availability. Census data on such vulnerability indicators at lower output scale were collected. Principal component analysis was performed, and four major principal components were identified from these 12 factors which explained most of the variance (82 %) in the data. The corresponding loading value of each of these factors were utilised to find heat vulnerability indices for each lower output area and these indices were mapped using QGIS. It was noted that not only people living in town centre which is generally considered hotter and so are highly vulnerable, but outskirt regions were also significantly vulnerable compared to other lesser vulnerable regions. Such a vulnerability map can help authorities for site focused heat mitigation strategies application, early warnings, and preparation during summers, particularly during excessively hot days i.e., heatwaves. Nature-based permanent solution can be encouraged in regions of such highly vulnerable identified regions. 

How to cite: Sahani, J., Debele, S. E., and Kumar, P.: Heat vulnerability assessment and mapping for a bucolic town in the UK , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13479, https://doi.org/10.5194/egusphere-egu22-13479, 2022.

EGU22-4129 | Presentations | NH1.1 | Highlight

Is heat stress more indicative of summer mortality than temperature alone?

Eunice Lo, Ana M. Vicedo-Cabrera, Dann Mitchell, Jonathan Buzan, and Jakob Zscheischler

Extreme high temperatures are associated with elevated human mortality risks. This is evidenced by a typically U- or J-shaped relationship between daily temperature and mortality found for most places in the world where data exist. However, high temperature is not the only contributor to heat stress. Humidity is also an important factor because it affects evaporation of sweat, which is crucial for cooling the human body in hot environments. Although various heat stress metrics, many of which are a combination of atmospheric temperature and humidity based on different physiological assumptions, have been developed to estimate heat stress, the relationship between these metrics and mortality remains unclear.

In this study, the relationships between seven heat stress metrics — wet bulb temperature, apparent temperature, discomfort index and swamp cooler temperatures at four different efficiencies [1] — and mortality are systematically assessed using well-established Distributed Lag Non-linear Models (DLNMs) [2]. The predictive powers of these metrics, as well as that of daily mean temperature, are compared for the summer season at global locations in 39 countries, where sufficient meteorological and health data are available [3]. The results of this study provide new information as to which of these metrics are most indicative of summer mortality in different locations, and whether the ‘best-fit’ heat stress metric for a location gives a substantially different mortality estimate compared to the commonly used daily mean temperature. These results have important implications for heat-health impact monitoring, developing national and international heat-health action plans, as well as for projecting future heat-related mortality under different climate change scenarios.


[1] Buzan, J. R. et al.: Implementation and comparison of a suite of heat stress metrics within the Community Land Model version 4.5. Geosci. Model Dev., 8, 151–170, 2015.

[2] Gasparrini and Armstrong: Reducing and meta-analysing estimates from distributed lag non-linear models. BMC Medical Research Methodology, 13:1, 2013.

[3] Vicedo-Cabrera, A. M. et al.: The burden of heat-related mortality attributable to recent human-induced climate change. Nature Climate Change, 11, 492–500, 2021.

How to cite: Lo, E., Vicedo-Cabrera, A. M., Mitchell, D., Buzan, J., and Zscheischler, J.: Is heat stress more indicative of summer mortality than temperature alone?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4129, https://doi.org/10.5194/egusphere-egu22-4129, 2022.

EGU22-2046 | Presentations | NH1.1 | Highlight

Exploring the association between bioclimatic indices and cardiovascular mortality: Preliminary results from Northern Greece

Anastasia Paschalidou, Kyriaki Psistaki, Paraskevi Begou, Ilias Petrou, and Ioannis M Dokas

It is well-established that exposure to extreme ambient temperatures is linked to adverse health effects associated with cardiovascular and respiratory diseases. Epidemiological studies demonstrate that the relationship between air temperature and mortality is depicted as a “U”, “V” or “J” shaped curve where the lower extrema reflect the comfort zone and mortality rises beyond a temperature threshold that is region- and population-specific and depends on various socioeconomic factors. However, temperature is not the only parameter determining thermal stress, as relative humidity, wind speed and other meteorological parameters are also known to play an important role which is often ignored. This study investigated the relationship between mortality and thermal conditions in the region of Northern Greece, using several bioclimatic indices as indicators. The data used included mean daily values of air temperature, relative humidity and wind speed and daily mortality counts due to cardiovascular diseases for the time-period 2010-2018. The following 3 thermal indices were estimated: (a) Effective Temperature (ET), (b) Normal Effective Temperature (NET) and (c) Apparent Temperature (AT). These indices were selected as they depend on typically measured variables and they can describe thermal comfort in both warm and cold environments. The association between each thermal index and mortality was studied by fitting a Poisson regression model for over-dispersed data, combined with a distributed lag non-linear model. In order to detect delayed adverse effects of low temperatures, the lag period was extended to 21 days. A “U” shape curve was found to describe the relationship between each thermal index examined and mortality, indicating the existence of a cold and a hot threshold. Thresholds were identified at 16.6oC and 31.3oC for AT, at 16.1oC and 25.5oC for ET and at 13.7oC and 24.3oC for NET. Exposure to high temperatures was found to be more hazardous compared to low temperatures. The cardiovascular mortality risk increased by 8%, 14% and 10% for each additional degree above the AT, NET and ET hot threshold, respectively. On the other hand, a degree below the AT cold threshold resulted in 1% rise in the mortality risk and 2% rise for the case of ET and NET. Furthermore, the thresholds identified for the bioclimatic indices were used to identify temperature thresholds. In all cases the cold temperature threshold lied between 18.1oC and 20.7oC, confirming that cold-mortality is not necessarily linked to the lowest temperatures. The hot temperature threshold was almost the same in all cases; 27.6oC for AT and ΝET and 27.7 for ET. On the whole, this study confirms the complexity of climate-health associations and highlights the importance of bioclimatic indices as tools to evaluate thermal stress and to feed adverse health effect prevention strategies.

ACKNOWLEDGEMENT: We acknowledge support of this work by the project “Risk and Resilience Assessment Center –Prefecture of East Macedonia and Thrace -Greece.” (MIS 5047293) which is implemented under the Action “Reinforcement of the Research and Innovation Infrastructure”, funded by the Operational Programme "Competitiveness, Entrepreneurship and Innovation" (NSRF 2014-2020) and co-financed by Greece and the European Union (European Regional Development Fund). 

How to cite: Paschalidou, A., Psistaki, K., Begou, P., Petrou, I., and Dokas, I. M.: Exploring the association between bioclimatic indices and cardiovascular mortality: Preliminary results from Northern Greece, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2046, https://doi.org/10.5194/egusphere-egu22-2046, 2022.

EGU22-4219 | Presentations | NH1.1

Perceptions of heat-health impacts and the effects of knowledge andpreventive actions by outdoor workers in Hanoi, Vietnam

Steffen Lohrey, Melissa Chua, Clemens Gros, Jerôme Faucet, and Jason K.W. Lee

Extreme heat is an increasing climate threat, most pronounced in urban areaswhere poor populations are at particular risk.Weanalyzed heat impacts and vulnerabilities of 1027 outdoorworkerswho participated in a KAP survey in Hanoi, Vietnam in 2018, and the influence of their mitigation actions, their knowledge of heat-risks, and access to early warnings.
We grouped respondents by their main income (vendors, builders, shippers, others, multiple jobs, and nonworking) and analyzed their reported heat-health impacts, taking into consideration socioeconomics, knowledge of heat impacts and preventive measures, actions taken, access to air-conditioning, drinking amounts and use of weather forecasts. We applied linear and logistic regression analyses using R.
Construction workers were younger and had less knowledge of heat-health impacts, but also reported fewer symptoms. Older females were more likely to report symptoms and visit a doctor. Access to air-conditioning in the bedroom depended on age and house ownership, but did not influence heat impacts as cooling was too expensive. Respondents who knew more heat exhaustion symptomswere more likely to report impacts (p< 0.01) or consult a doctor (p<0.05). Similarly, thosewho checkedweather updateswere more likely to report heat impacts (p< 0.01) and experienced about 0.6 more symptoms (p< 0.01). Even though occupation type did not explain heat illness, builders knewconsiderably less (40%; p<0.05) about heat than other groups butwere twice as likely to consult a doctor than street vendors (p < 0.01). Knowledge of preventive actions and taking these actions both correlated positively with reporting of heat-health symptoms, while drinking water did not reduce these symptoms (p < 0.01). Child carers and homeowners experienced income losses in heatwaves (p < 0.01). The differences support directed actions, such as dissemination of educational materials and weather forecasts for construction workers. The Red Cross assisted all groups with cooling tents, provision of drinks and health advice.

How to cite: Lohrey, S., Chua, M., Gros, C., Faucet, J., and Lee, J. K. W.: Perceptions of heat-health impacts and the effects of knowledge andpreventive actions by outdoor workers in Hanoi, Vietnam, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4219, https://doi.org/10.5194/egusphere-egu22-4219, 2022.

EGU22-4753 | Presentations | NH1.1 | Highlight

Adaptation to extreme heat in the agricultural sector – SSP-dependent scenarios for mechanization deployment

Nicole van Maanen, Anton Orlov, and Carl-Friedrich Schleussner

Climate change and increasing heat stress reduces labour productivity and supply all across the globe. In a global warming scenario of 3°C, effective labour (i.e., the combination of productivity and supply) is expected to decrease by up to 50 percentage points relative to the period 1986-2005. Central Africa, Southeast Asia and Latin America will be most affected. In these regions, the agricultural sector is still of paramount importance for livelihoods and food security and outdoor work is more common. When heat stress further increases, the capability for physical activity will reduce across a wide range of working places, primarily outdoors. Especially in low- and middle-income countries the effects of climate change will lead to a reduction in economic activity and decrease the capacity for economic growth.


Automation and mechanization of outdoor work could greatly reduce the economic costs of heat stress and counts as the most effective adaptation strategy in the agricultural- and construction sectors to climate change, but scenarios of potential future deployment of mechanization are in their infancy. Here we propose a Mechanization Deployment Index (MDI), which builds on the concept of constrained adaptative capacity reflecting a level of mechanization under the presence of socio-economic constraints compared to the maximum mechanization potential in the absence of constraints to adaptive capacity. By identifying socioeconomic variables within the framework of the Shared Socioeconomic Pathways (SSPs) that correlate with the current level of mechanization deployment, we are able to project five scenarios for future mechanization implementation alongside the SSPs. For the first time, we will be able to show how different socio-economic trajectories strongly modulate future heat stress impacts in the agriculture sector. These scenarios can be included in integrated assessments of climate change and improve the economic risk assessment in the 21st century.

How to cite: van Maanen, N., Orlov, A., and Schleussner, C.-F.: Adaptation to extreme heat in the agricultural sector – SSP-dependent scenarios for mechanization deployment, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4753, https://doi.org/10.5194/egusphere-egu22-4753, 2022.

NH1.2 – Advances in Pluvial and Fluvial Flood Forecasting, Assessment and Flood Risk Management

Gode Bola1,4, Raphael M. Tshimanga1, Jeff Neal2,  Laurence Hawker2, Mark A. Trigg3, Lukanda Mwamba4 , Paul Bates2

1 Congo Basin Water Resources Research Center (CRREBaC) & Department of Natural Resources Management, University of Kinshasa, DR Congo

2School of Geographical Sciences, University of Bristol, United Kingdom

3School of Civil Engineering, University of Leeds, United Kingdom

4General Commission of Atomic Energy, Regional Center for Nuclear Study, Kinshasa, DR Congo

Flood disasters have always been reported in the Congo Basin with significant damages to human lives, food production systems and infrastructure. Losses incurred by these damages are huge and represent a major challenge for economic expansion in developing nations. In the Congo River Basin, where the availability of in-situ data is a significant challenge, new approaches are needed to investigate flood risks and enable effective management strategies. This study uses recently developed global flood prediction data in order to produce flood risk maps for the Congo River Basin, where flood information currently does not exist. Flood hazard maps that estimate fluvial flooding at a grid cell resolution of 3 arc-seconds (~ 90 m), gridded population density data of 1 arc-second (~ 30 m) spatial resolution, and a spatial layer of infrastructure dataset are used to address flood risk at the scale of the Congo Basin. The global flood data provide different return periods of exposure to flooding in the Congo Basin and identifies flood extents. The risk analysis results are presented in terms of the percentage of population and infrastructure at flood risk for six return periods (5, 10, 20, 50, 75 and 100 years). Of the 525 administrative territories, 374 are exposed to fluvial floods, and 38 (10 %) of them are categorised as risk hotspots. Analysis shows that the most exposed territories represent 1% of total exposure which is estimated at 2.65% of the basin’s population. This study demonstrates the first and potentially most important stage in developing flood responses by determining the flood hazards areas and the population/infrastructures that would be exposed. The flood risk maps produced in this study provide information necessary to support policy decisions of flood disasters prevention, including prioritisation of interventions to reduce flood risk in the CRB.

Keywords: Flood hazard, Risk assessment, Return period, Congo River Basin


How to cite: Gode, B. B.: Multi return periods flood hazards and risks assessment in the Congo River Basin, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-248, https://doi.org/10.5194/egusphere-egu22-248, 2022.

EGU22-389 | Presentations | NH1.2

Hydraulic zoom: a hydrological/hydrodynamic downscaling framework from regional to local scale

Gabriel Narváez and Rodrigo Paiva

Flooding is the most damaging natural hazard in terms of economic and population affected. Hydrological-hydraulic models are essential tools for evaluating the risks associated with flooding since they provide a physically based approach. In this work, we propose a novel approach that takes advantage of the coverage advantages of large-scale modeling and the accurate representation of local modeling, where high-resolution data are available. A dynamic downscaling framework, so-called hydraulic zoom, has been created by coupling the local relevant discharge estimation of the large-scale models with the detailed local representation of the reach-scale models. The large-scale hydrological model (MGB) is employed for estimating the inflow, rainfall excesses, infiltration, and evaporation from open water in order to use as input into an area in which the flow is solved through the full shallow waters formulation. The HEC-RAS 2D 6.1 is applied for solving the 2D dynamic equations. Besides, HEC-RAS enables forcing rainfall excess distributed inside the 2D area by the rain-on-grid approach while also allowing incorporate evaporation and infiltration. 

The hydraulic zoom is applied in the Itajai-Açu river basin of 15000 km2 in Southern Brazil in the Santa Catarina State. The 2D area is about 833.6 km2, considering  95 km of the main river until the outlet into the sea. The 2D area modeled is highly prone to floods, recording flood events with more than 53 deaths and more than 1 million affected people only between 1983 and 2011.

Estimations from MGB and from HEC-RAS 2D (fed with the MGB outputs) are compared against observed water surface level (WSE), WSE anomalies, and flood extent. The results reveal that streamflows estimated by a regional hydrological model can be incorporated into a local model improving in mean the estimations in about 41% (0.8 m) for WSE, 29% (0.35m) for WSE anomalies, and 10% of the Fit metric for flood extent. This hydraulic zoom framework reveals greate potential of producing high-resolution flood hazard maps allowing also representing pluvial floods, with regional distribution but local resolution. 

How to cite: Narváez, G. and Paiva, R.: Hydraulic zoom: a hydrological/hydrodynamic downscaling framework from regional to local scale, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-389, https://doi.org/10.5194/egusphere-egu22-389, 2022.

EGU22-627 | Presentations | NH1.2

Urban Flood Mapping Uncertainty Justification using Variable Drop-off in RF and XGBoost Algorithms: A Case Study of Marib City, Yemen

Ali Al-Aizari, Omar AlThuwaynee, Yousef Al-Masnay, Kashif ullah, Hyuck-Jin Park, Nabil Al-Areeq, Chunli Zhao, and Xingpeng Liu ⃰

EGU22-687 | Presentations | NH1.2

QGIS-based Autonomous Process and Arc River Data Repository for Efficient Flood Inundation and Hazard Mapping 

Kyungdong Kim, Hojun You, Dongsu Kim, and Yeonghwa Gwon


Flood inundation and hazard maps have played various crucial roles in terms of municipal hazard planning, timely flood control countermeasure operation, economic levee design, and developing flood forecasting or nowcasting systems. Given that the riparian areas prone to flood conventionally imposed special cares to justify applications of recently available flood inundation or hazard assessment numerical models on top of digital elevation models of dense spatial resolution such as LiDAR irrespective of their high costs. However, laborious and time & cost-consuming processes were required to proficiently produce inundation and hazard maps, which includes preparation of geometric and hydrologic data as input for the targeted numerical model, executing the model and post-processing, and inundation and subsequent hazard mapping. For example in Korea, field surveyed geometric dataset are provided in CAD format and should have to be manually converted into cross-sectional information compatible with HEC-RAS as a numerical model, where such dataset are not managed in centralized and standardized database. Then, flood inundation and hazard maps are generated one by one based on flood stage heights simulated from the HEC-RAS, where additional tools such as HEC-GeoRAS or manual drawing against DEM are usually applied. In order to efficiently and cost-effectively provide a series of flood inundation and hazard maps automatically with minimum practitioner involvement, this study demonstrates a set of open-source based tools that automated flood and hazard mapping processes as follows: a) parse CAD files containing geometric surveys like cross-sections and store them into server-based Arc River database approachable through website; b) retrieve geometric information using RiverML from Arc River and implicitly make them compatible with HEC-RAS input format; c) execute the HEC-RAS with some designated boundary conditions and various flood discharge; d) parse HEC-RAS output in binary format and draw flood inundation and hazard map on a given DEM through a developed add-on in QGIS using Python. We found that the proposed entire autonomous processes substantially enhanced efficiency and accuracy for flood mapping. The spatial accuracy of flood inundation and hazard map after applying above processes were validated throughout comparison with an inundation trace map acquired from typhoon Nari, 2007, in Hancheon basin located in Jeju Island, Korea, where a series of inundation and hazard maps were comprehensively investigated to track the burst of flood in the extreme flood events.



This work was supported by the US Geological Survey Cooperative Grant Agreement #G19AC00257 and by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (21AWMP- B121092-06).

How to cite: Kim, K., You, H., Kim, D., and Gwon, Y.: QGIS-based Autonomous Process and Arc River Data Repository for Efficient Flood Inundation and Hazard Mapping , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-687, https://doi.org/10.5194/egusphere-egu22-687, 2022.

EGU22-1137 | Presentations | NH1.2

Investigation of Air-Bubble Screen on Reducing Scour in River Facility

Kuo-Wei Liao and Zhen-Zhi Wang

This study proposes an innovative idea to reduce scour in river structures via air-bubble screens, which does not provoke a significant impact on the ecological environment. Check dam is one of the most popular river facilities and is selected as the research target of this study. The scouring problem on the downstream side of check dam may damage its own safety and therefore, preventing the check dam from souring has been a challenge task for years. To lessen the safety impact from scouring, the existing methods often rely on using reinforced concrete structures that often, does not solve the problem but induces a series of scouring problem. Further, reinforced concrete structure may damage the river ecological environment during and after the construction. On the other hand, air-bubble screen may provide an alternative solution in solving the scouring problem without interrupting the environment. A scaled-check dam model using flume channel at Hydrotech Research Institute in NTU is conducted, and then the FLOW-3D is used to carry out numerical simulation to evaluate the effectiveness of the air-bubble screen in reducing the depth and range (or volume) of the scours. Results shown that air-bubble screen is able to effectively reduce the check dam scours. Based on results from experiments and simulations, the design principles for air-bubble screen are provided as a reference for future practice. 

How to cite: Liao, K.-W. and Wang, Z.-Z.: Investigation of Air-Bubble Screen on Reducing Scour in River Facility, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1137, https://doi.org/10.5194/egusphere-egu22-1137, 2022.

EGU22-1424 | Presentations | NH1.2

Operational hydraulic flood impact forecasting with RIM2D for improved disaster management 

Heiko Apel, Sergiy Vorogushyn, and Bruno Merz

The disastrous flood of July 2021 in Germany has shown that forecasts of river discharge or water levels at selected gauges do not provide sufficient information for timely and location specific warning of the population and targeted disaster management actions. The gauge forecasts as well as the available flood hazard maps were insufficient to assess the flood severity in downstream areas. In order to provide more actionable flood forecasts, the hydraulic model RIM2D was developed and setup for the Ahr river. It solves the inertial formulation of the shallow water equations on a regular grid, and is highly parallelized on Graphical Processor Units (GPUs). Moreover, the modelling concept is parsimonious and allows for fast model setup. We show that hydraulic simulations driven by the available hydrological gauge forecasts would have been feasible with short simulation duration. It would be possible to provide spatially explicit forecasts of inundation depths and flow velocities with sufficient lead time. Moreover, we also show that impact forecasts indicating human instability in water and building failure hazard can be additionally provided in operational mode. We argue that using these hydraulic and impact forecasts would have had a substantial impact on the flood alertness of the population and responsible authorities, enabling a better early warning and disaster management. This could eventually save lives during future extreme flash floods.

How to cite: Apel, H., Vorogushyn, S., and Merz, B.: Operational hydraulic flood impact forecasting with RIM2D for improved disaster management , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1424, https://doi.org/10.5194/egusphere-egu22-1424, 2022.

With the acceleration of urbanization, urban pluvial flooding seriously threatens urban sustainable development and human life. It is widely accepted that various landscape elements contribute to the magnitude of urban pluvial flooding. Considerable efforts investigated the universal mechanism of urban pluvial flooding by regarding the whole study area as spatial homogeneous while ignoring its local specific mechanism. The spatially heterogeneous effects of landscape elements on urban pluvial flooding remain poorly understood. Additionally, it is still unclear how the interactive effects of landscape elements affect urban pluvial flooding. In most practical situations, urban pluvial flooding is affected by multiple factors, rather than by a single factor alone. These shortcomings make it impossible to formulate urban pluvial flooding mitigation measures based on the relative contribution of various landscape elements on urban pluvial flooding. To shed some light on this topic, an innovative method that integrated the all subsect regression model, cubist regression tree, and geographical detector model is presented to spatially explicit the heterogeneous forces driving urban pluvial flooding variation and identify the pluvial flooding dominant driving forces with different local conditions. By comparing with two other commonly used regression methods (global regression model, spatial lag model), the proposed method can fully quantify this spatial non-stationarity mechanism and spatially explicit the local driving forces. Urban pluvial flooding dominant driving factors and their contribution vary with the local site conditions. Even for the same dominant factor, its contribution to pluvial flooding varies considerably in different watersheds. Based on this, local authorities can develop site-specific urban pluvial flooding mitigation strategies according to the dominant factors in different areas. The results of this study extend our scientific understanding of the site-specific mechanism of urban pluvial flooding, providing useful information for formulating more targeted and effective urban pluvial flooding mitigation strategies with different local conditions, rather than a “one-size-fits-all” policy.

How to cite: Zhang, Q., Wu, Z., Guo, G., and Tarolli, P.: How to develop site-specific urban pluvial flooding mitigation strategies? A new approach to investigating the spatial heterogeneous driving forces of urban pluvial flooding, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1573, https://doi.org/10.5194/egusphere-egu22-1573, 2022.

EGU22-2344 | Presentations | NH1.2

Deep learning approaches to study floods through river cameras

Remy Vandaele, Sarah L Dance, and Varun Ojha

The monitoring of river water-levels is essential to study floods and mitigate their risks. However, it is difficult to obtain accurate measurements of river water-levels: indeed, the river gauges commonly used to measure these levels can be overwhelmed during flood events, and their number is declining globally [1,2]. This means that the monitoring and study of floods relying on gauge station measurements can only be based on sparse and possibly inaccurate river water-level data distributed unevenly along the rivers, sometimes several kilometres away from the location of interest.

We investigate if deep learning can be used to monitor river water-levels in a more flexible and efficient way. More specifically, we apply two deep learning approaches on river cameras, which are CCTV cameras commonly used to monitor the surroundings of rivers and could be easily installed at new locations. The first approach [3,4] relies on transfer learning to train water segmentation networks able to find the water pixels within the camera images and use the number of water pixels within (regions of) the images to monitor the relative evolution of the river water-level. The second approach is based on the creation of a large dataset of 32,715 images annotated with distant gauge water-level data in order to accurately train networks able to produce river water-level indexes independent from the field of view of the cameras. 

We show that both approaches can be used as sources of river water-level data. The first approach is able to produce river water-level indexes highly correlated with ground truth river water-levels (Pearson correlation coefficient >0.94). While the second approach is less accurate (Pearson correlation coefficients between 0.8 and 0.94), it is able to produce calibrated indexes independent from the field of view of the camera. 


[1] Mishra, A. K., and Coulibaly, P. (2009), Developments in hydrometric network design: A review, Rev. Geophys., 47, RG2001, doi:10.1029/2007RG000243.

[2] Global Runoff Data Center (2016).  Global runoff data base, temporal distribution of available discharge data.  https://www.bafg.de/SharedDocs/Bilder/Bilder_GRDC/grdcStations_tornadoChart.jpg. Last visited:2021-04-26.

[3] Vandaele, R., Dance, S. L., & Ojha, V. (2020, September). Automated water segmentation and river level detection on camera images using transfer learning. In DAGM German Conference on Pattern Recognition (pp. 232-245). Springer, Cham.

[4] Vandaele, R., Dance, S. L., & Ojha, V. (2021). Deep learning for automated river-level monitoring through river camera images: an approach based on water segmentation and transfer learning. Hydrology and Earth System Sciences, 25(8), 4435-4453.

How to cite: Vandaele, R., Dance, S. L., and Ojha, V.: Deep learning approaches to study floods through river cameras, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2344, https://doi.org/10.5194/egusphere-egu22-2344, 2022.

EGU22-2418 | Presentations | NH1.2

Flood risk mapping using multi-criteria analysis (TOPSIS) model through geospatial techniques- A case study of the Navsari city, Gujarat, India

Azazkhan Ibrahimkhan Pathan, Dr. Prasit Girish Agnihotri, Dr. Saif Said, Dr. Dhruvesh Patel, Dr. Cristina Prieto, Usman Mohsini, Nilesh Patidar, Dr.Pankaj Gandhi, Khushboo Jariwala, Bojan Đurin, Mohammad Yasin Azimi, Juma Rasuli, Kalyan Dummu, Saran Raaj, Arbaaz A. Shaikh, and Muqadar Salihi

Flood is one of the most devastating natural disasters that cause enormous socioeconomic and environmental destruction. The severity of flood losses has evoked emphasis on more comprehensive and vigorous flood modeling techniques for alleviating flood damages. Flood vulnerability in Navsari is intensifying due to urbanization, industrialization, and population growth. Although there has been a significant increase in research on flood assessment at a local scale in Navsari, there remains a lack of tools developed which utilize the risk map of the city. In response to this prerequisite, in this study we have employed a GIS-based Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) multi-criteria analysis model to develop a flood risk map for Navsari city in Gujarat, India, to determine the vulnerable areas that are more susceptible to flooding. To estimate the extent of flood hazard, vulnerability, and risk intensities in terms of area covered, the city was divided into ten zones (i.e. NC1 to NC10) and classified into five classes: very high, high, moderate, low, and very low. A total of seven hazard forming spatial layers (i.e. slope, elevation, soil, rainfall, flow accumulation, distance to a river, and drainage density) and seven vulnerability forming spatial layers (i.e. female population, population density, land use, household, distance to hospital, road network density, and literacy rate) were appraised for evaluating the risk of flooding. The generated flood risk map has been compared with the extent of flood calculated based on field data collected from thirty-six random places. The outcome of the model unveiled the capability of the TOPSIS model since it capitulate low RMSE value varied between 0.95 to 0.43 and high R square value ranged from 0.78 to 0.95. The zones indicated under ‘high’ and ‘very high’ categories (i.e. NC8, NC6, NC4, NC1, NC7, and NC10) demand abrupt flood control action to alleviate the severity of flood risk and subsequent damages. The approach implemented in the study can be applied to any flood-sensitive region around the globe to accurately evaluate the risk of flood. Lastly, flood risk mapping using TOPSIS based geospatial techniques divulge the novel and efficacious approach, especially for data-sparse regions.

How to cite: Pathan, A. I., Agnihotri, Dr. P. G., Said, Dr. S., Patel, Dr. D., Prieto, Dr. C., Mohsini, U., Patidar, N., Gandhi, Dr. P., Jariwala, K., Đurin, B., Azimi, M. Y., Rasuli, J., Dummu, K., Raaj, S., Shaikh, A. A., and Salihi, M.: Flood risk mapping using multi-criteria analysis (TOPSIS) model through geospatial techniques- A case study of the Navsari city, Gujarat, India, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2418, https://doi.org/10.5194/egusphere-egu22-2418, 2022.

EGU22-2622 | Presentations | NH1.2

A complete meteo-hydrological chain to support early warning systems from weather scenarios to flooded areas: the Apollo medicane use case

Martina Lagasio, Giacomo Fagugli, Luca Ferraris, Elisabetta Fiori, Simone Gabellani, Rocco Masi, Vincenzo Mazzarella, Massimo Milelli, Andrea Parodi, Flavio Pignone, Silvia Puca, Luca Pulvirenti, Francesco Silvestro, Giuseppe Squicciarino, and Antonio Parodi

An intense Mediterranean hurricane (medicane Apollo) hit many countries during the last week of October 2021. Up to 7 people died because of the floods caused by the cyclone in Tunisia, Algeria, Malta and Italy. Apollo persisted over the same Mediterranean area from 24 October to 1 November 2021 producing flash-flood and flood episodes with very intense rainfall events, especially over eastern Sicily and Calabria on 25-26 October 2021. CIMA Foundation operated in real-time with a complete forecasting chain to predict both the Apollo medicane weather evolution and its hydrological and hydraulic impacts. The work provides support to the Italian Civil Protection Department early warning activities and in the framework of the H2020 LEXIS and E-SHAPE projects. The complete meteo-hydrological forecasting chain is composed by the cloud-resolving WRF model assimilating radar data and in situ weather stations (WRF-3DVAR), the fully distributed hydrological model Continuum, the automatic system for water detection (AUTOWADE), and the hydraulic model TELEMAC-2D. This work presents the forecasting performances of each model involved in the CIMA meteo-hydrological chain, with focus on both very short-range temporal scales (up to 6 hours ahead) and short-range forecasts (up to 48 hours ahead). The WRF-3DVAR model results showed very good predictive capability of the most intense rainfall events in terms of timing and location over Catania and Siracusa provinces in Sicily. Thus, enabling also very accurate discharge peaks and timing predictions for the creeks hydrological network peculiar of eastern Sicily. Starting from the WRF-3DVAR model predictions, the daily AUTOWADE tool run using Sentnel-1 (S1) data, was anticipated with respect to the scheduled timing to quickly produce a flood map (S1 acquisition performed on 25 October 2021 at 05UTC, flood map produced on the same day at 13UTC). Furthermore, an ad hoc tasking of the COSMO-SkyMed satellite constellation was performed, again based on the on the WRF-3DVAR predictions, to overcome the S1 data latency on eastern Sicily during the period 26-30 October 2021. Finally, the resulting automated operational mapping of floods and inland waters was integrated with the subsequent execution of the hydraulic model TELEMAC. Due to the probable frequency increase of such extreme events (in light of the ongoing climate change), the application of a complete meteo-hydrological chain presented in this work can pave the way for future applications in early warning activities in the Mediterranean areas.

How to cite: Lagasio, M., Fagugli, G., Ferraris, L., Fiori, E., Gabellani, S., Masi, R., Mazzarella, V., Milelli, M., Parodi, A., Pignone, F., Puca, S., Pulvirenti, L., Silvestro, F., Squicciarino, G., and Parodi, A.: A complete meteo-hydrological chain to support early warning systems from weather scenarios to flooded areas: the Apollo medicane use case, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2622, https://doi.org/10.5194/egusphere-egu22-2622, 2022.

EGU22-2696 | Presentations | NH1.2

Introducing ProMaIDes: A State-of-the Science Flood Risk Management Tool

Daniel Bachmann, Roman Schotten, and Shahin Khosh Bin Ghomash

Floods are natural hazards with severe socio-economic and environmental impacts on affected areas and societies every year. A chain of different processes being involved in a flooding - characterized by precipitation, topography, land use etc. - complicates the understanding of the dynamics of a flood. However, the prediction of probabilities, flood hazards, flooding extents, dike failure, consequences and understanding the ongoing processes during a flood event are important issues in flood risk management. Computational modelling is a key method in supporting flood risk management and tackling the mentioned challenges.

While several computer-based models for assisting flood risk management exist, typically they concentrate on only one component of the flood risk analysis chain such as rainfall generation, hydrological/hydraulic modelling or damage analysis. They do not merge the other components on one platform which may result in encapsulated conclusions. In recent years the availability of higher detailed data, larger study domains, more computational power and more innovative models paved the way for more effective solutions.

In this work we present ProMaIDes (Protection Measures against Inundation Decision support), an open-source, free software package for risk-based evaluation of flood risk mitigation measures1. The software package consists of numerous relevant modules for a flood risk analysis in riverine and coastal regions: the HYD-module for a hydrodynamic analysis, the DAM-module for an analysis of consequences (including economical damage, consequences to people and the disruption of critical infrastructure services), the FPL-module for the reliability analysis of dikes and dunes as well as a combining RISK-module and the decision support MADM-module. To support a user-friendly model setup, visualization of input and data results, a connection with the free QGIS-system is established by QGIS-plugins and a PostgreSQL-database as data-management system. A detailed online documentation featuring theory, application and programming is available2. A community of users is currently set-up.

In order to give a better understanding and to demonstrate the capabilities of ProMaIDes, the tool itself, but also the modules combined with case studies are shortly presented.


1 https://promaides.h2.de

2 https://promaides.myjetbrains.com/youtrack/articles/PMID-A-7/General

How to cite: Bachmann, D., Schotten, R., and Khosh Bin Ghomash, S.: Introducing ProMaIDes: A State-of-the Science Flood Risk Management Tool, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2696, https://doi.org/10.5194/egusphere-egu22-2696, 2022.

Slope instability of river dikes during floods is often driven by the evolution of groundwater pressures. Despite the temporal nature of high river water levels, pressure heads during floods are often assumed to reflect steady-state seepage conditions, leading to conservative estimates of dike slope safety. Here, we investigate the influence of transient groundwater conditions that result from variable flood wave shapes on probabilistic safety estimates of slope stability. We have sampled a large number of flood waves, aiming to maximize the variability in the flood wave shapes, and used them in a modeling chain consisting of a hydrological model (MODFLOW) and a probabilistic dike slope safety assessment (FORM). We compared the resulting time-dependent probabilistic dike safety for inner (landward) slope and outer (riverward) slope stability with the current flood safety assessment in the Netherlands. This comparison showed that current methods based on steady-state and analytical solutions seem to underestimate dike safety. Other methods, based on a design discharge wave, are more consistent with the multi-flood wave dike reliability, but their error increases at extreme water levels. In line with the temporal component of variable flood water levels, the failure probability also has a strong temporal component. Our results indicate that the highest failure probability always occurs after the river water level peak, with a delay of up to 15 days for both inner slope and outer slope stability. In addition, the uncertainty in the shape of the flood wave can be as important as the uncertainty in the geomechanical material properties for explaining the variation in dike failure probabilities. Therefore, this research strongly suggests that transient-groundwater conditions as a function of variable flood wave shapes should be incorporated in dike safety assessment. As a first step, we recommend further research on the occurrence probability of the most influential waveform characteristics, being the total flood wave volume (for the inner slope) and the total water level decrease after the peak (for the outer slope).

How to cite: van Woerkom, T., van der Krogt, M., and Bierkens, M.: On the incorporation of transient groundwater conditions resulting from variable flood wave shapes in probabilistic slope stability assessments of dikes , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3099, https://doi.org/10.5194/egusphere-egu22-3099, 2022.

EGU22-3374 | Presentations | NH1.2

Real-time Flood Forecasting Using Numerical Weather Prediction System Through NICAM-LETKF Data Assimilation in the Prek Thnot River, Cambodia

Sophal Try, Takahiro Sayama, Ty Sok, Sophea Rum Phy, and Chantha Oeurng

Flood is widely recognized as the most common and frequent natural phenomenon which currently threatens huge damage worldwide. The Prek Thnot River (PTR) in Cambodia is one of the flood-prone areas where severe floods occur every year and cause damage to residents downstream. This study aims to evaluate the forecasting performance of flooding in the PTR using near real-time datasets from satellite observation (i.e., GSMaP and GPM) and forecasted rainfall from NICAM-LETKF numerical weather prediction (so called GSMaPxNEXRA) dataset. GSMaPxNEXRA data is produced by Global Cloud Resolving Model with Data Assimilation. This study used a fully distributed rainfall-runoff-inundation (RRI) model for river discharge and water level simulations. The RRI model was calibrated and validated with gauged observed rainfall during flood events in 2000, 2001, 2007, 2010, and 2020 with satisfactory and acceptable results. The most recent flood event in 2020 was considered to evaluate real-time flood forecasting. The near real-time simulation indicated the results discharge and water level with statistical indicators KGE = 0.80 and 0.07 and r2 = 0.83 and 0.87 for GPM and KGE = 0.48 and -0.12 and r2 = 0.54 and 0.67 for GSMaP. The GPM rainfall product outperforms GSMaP rainfall in the PTR. Flood forecast from the GSMaPxNEXRA showed an accuracy with KGE = 0.79 and r2 = 0.89 (1-day forecast) to KGE = 0.66 and r2 = 0.76 (5-day forecast). On the other hand, the performance of 1-day to 5-day forecast indicated with coefficient of extrapolation (CE) and coefficient of persistence (CP) between CE = -2.62 and CP = -2.65 for 1-day forecast to CE = 0.71 and CP = -0.06 for 5-day forecast. To conclude, real-time flood forecasting in the PTR was successfully assessed and evaluated in this study; however, the accuracy of flood prediction should be further improved in the future by considering data assimilation and machine learning.

How to cite: Try, S., Sayama, T., Sok, T., Phy, S. R., and Oeurng, C.: Real-time Flood Forecasting Using Numerical Weather Prediction System Through NICAM-LETKF Data Assimilation in the Prek Thnot River, Cambodia, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3374, https://doi.org/10.5194/egusphere-egu22-3374, 2022.

EGU22-4201 | Presentations | NH1.2

Rainfall threshold curves and machine learning approaches for pluvial flood forecasting based on local news reports in Croatia

Nino Krvavica, Bojana Horvat, Ivana Marinović, and Ante Šiljeg

This study presents a forecasting model for pluvial flooding in the city of Zadar, Croatia, where a huge mesoscale convective system recently caused massive pluvial flooding and widespread property damage. Flood forecasting approaches based on hydrologic-hydraulic models require a large set of accurate data to provide reliable simulations. They also require many simulations, which can be computationally expensive and time consuming. Therefore, we are investigating the possibility of using a data-driven approach based on local news reports of pluvial flooding combined with a local high-resolution rain gauge. To this end, we considered two different computational approaches. The first - a conventional one - is based on rainfall threshold curves that define the critical rainfall depth for different time periods above which flooding is likely to occur. The second approach is based on machine learning and a classification problem - predicting whether accumulated rainfall depths over different time periods will lead to pluvial flooding. For the second approach, we considered 10 different methods that belong to five categories of machine learning typically used for classification problems. They are logistic regression, support vector machine, discriminant analysis, decision trees, and nearest neighbours. After a careful analysis, we defined rainfall threshold curves for Zadar that can be used for an early warning system and flood forecasting. We show that some machine learning models can provide slightly more accurate predictions than the threshold curve, with quadratic discriminant analysis being the most successful method for this purpose. Overall, this study shows that flood forecasting based on news reports in the city of Zadar can be a reliable approach. The analysis conducted in this study has laid the foundation for the implementation of an early warning system and pluvial flood forecasting in the Croatian coastal area.

How to cite: Krvavica, N., Horvat, B., Marinović, I., and Šiljeg, A.: Rainfall threshold curves and machine learning approaches for pluvial flood forecasting based on local news reports in Croatia, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4201, https://doi.org/10.5194/egusphere-egu22-4201, 2022.

EGU22-4210 | Presentations | NH1.2

An Integrated Approach of AHP-GIS Based Dam Site Suitability Mapping - A Noval Approach for Flood Alleviating Measures

Saran Raaj, Azazkhan Pathan, Usman Mohseni, Nilesh Patidar, Khushboo jariwala, Nitin Kachhawa, Dr. P.G Agnihotri, Dr. Dhruvesh Patel, Dr. Cristina Prieto, Dr. Pankaj Gandhi, and Dr. Bojan Đurin

Surat is a district that has seen numerous floods and high rainfall over the last two decades. The solution to the problem, and the primary aim of this study, is to construct a storage facility, such as a dam, as part of flood prevention measures. The concept of multi-criteria decision making (MCDM) is now widely employed for everyday real-life challenges. Recent advancements and diverse approaches in geographic information systems (GIS) and remote sensing, along with the MCDM technique, will enable us to make an informed decision about where to build a dam site location model (DSLM). The Analytic Hierarchy Process (AHP) is the most frequently utilised MCDM technique for resolving water-related issues. To produce DSLM, ten thematic layers were considered: precipitation, stream order, geomorphology, geology, LULC, soil, distance to road, elevation, slope, and major fault fracture. Precipitation and stream order were the two most important elements affecting the DSLM. The weights of the thematic map layers were determined using the analytical hierarchy process (AHP) technique. These thematic maps and weights are used to perform overlay analysis, resulting in a suitability map with five classes ranging from high to low suitability. Three main sites have been selected as the best candidates for the construction of a new dam. By implementing this low-cost strategy, we may be able to reduce the amount of effort required in the traditional method of dam site selection while increasing decision-makers' accuracy. Approximately 14% of the Surat district is classified as a very high adaptability area, while 27.2 percent is classified as a high suitability area. This method can be applied all over the world to locate possible dam sites, which can be helpful for flood mitigation measures. In addition to that, the presented approach unveiled the scientific method for flood mitigation measures, which are in immediate demand all over the globe, especially in data-scarce regions.

How to cite: Raaj, S., Pathan, A., Mohseni, U., Patidar, N., jariwala, K., Kachhawa, N., Agnihotri, Dr. P. G., Patel, Dr. D., Prieto, Dr. C., Gandhi, Dr. P., and Đurin, Dr. B.: An Integrated Approach of AHP-GIS Based Dam Site Suitability Mapping - A Noval Approach for Flood Alleviating Measures, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4210, https://doi.org/10.5194/egusphere-egu22-4210, 2022.

EGU22-4345 | Presentations | NH1.2

Towards Urban Flood Susceptibility Mapping Using Data-Driven Models

Omar Seleem, Georgy Ayzel, Arthur Costa Tomaz de Souza, Axel Bronstert, and Maik Heistermann

Both frequency and severity of urban pluvial floods have been increasing due to rapid urbanization and climate change. Hydrological and two dimensional (2D) hydrodynamic models are still too computationally demanding to be used for real-time applications for large urban areas (i.e. flood management scale). As an alternative, data-driven models could be used for flood susceptibility mapping. This study evaluated and compared the performance of image-based models represented by a convolutional neural network (CNN) and point-based models represented by an artificial neural network (ANN), a random forest (RF) and a support vector machine (SVM) with regard to the spatial resolution of the input data. We also examined model transferability. Eleven variables representing topography, anthropogenic aspects and precipitation were selected to predict flood susceptibility mapping. The results showed that: (1) all models were skilful with a minimum area under the curve AUC = 0.87. (2) The RF models outperformed the other models for all spatial resolutions. (3) The CNN models were superior in terms of transferability. (4) Aspect and elevation were the most important factors for flood susceptibility mapping for image-based and point-based models respectively.

How to cite: Seleem, O., Ayzel, G., Costa Tomaz de Souza, A., Bronstert, A., and Heistermann, M.: Towards Urban Flood Susceptibility Mapping Using Data-Driven Models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4345, https://doi.org/10.5194/egusphere-egu22-4345, 2022.

EGU22-4771 | Presentations | NH1.2

Application of frequency ratio modelling technique for predictive flooded area susceptibility mapping using remote sensing and GIS

Khushboo Jariwala, Prasit Agnihotri, Dhruvesh Patel, Azaz Pathan, Usman Mohseni, and Nilesh Patidar

Coastal areas are directly vulnerable to natural disasters like floods, which causes massive damages to natural resources and human resources. Dam induces floods can be devastating for surrounding low lying areas. Bharuch is a district with substantial industrial growth, and intended human activities were causing an imbalance in natural resources for planning and fulfilling other demands. Floods can be devastating concerning the Bharuch district's social, economic, and environmental perspectives. The proper analysis becomes very important to reduce the impact and find mitigation measuring techniques. I did flood susceptibility mapping using the frequency ratio model for the six sub-districts of the area. The susceptibility of a flood was analysed using the frequency ratio model by considering nine different independent variables (land use/land cover, elevation, slope, topographic wetness index, surface runoff, lithology, distance from the main river, soil texture, river network) through weighted-based bivariate probability values. In total, 151 historical floods were reported. I took locations for this study, from which I used 72 locations for susceptibility mapping. I combined the independent variables and historic flood locations to prepare a frequency ratio database for flood susceptibility mapping. The developed frequency ratio was varied from 0 to 13.2 and reclassified into five flood vulnerability zones, namely, very low (less than 0.99), low (0.99-2.04), moderate (2.04-5.58), high (5.58-13.2) and very high susceptibility (more than 13.2). The flood susceptibility analysis will be a valuable and efficient tool for local government administrators, researchers, and planners to devise flood mitigation plans.

Keywords: Flood Susceptibility · Flood · Frequency Ratio · Vulnerability · Bharuch

How to cite: Jariwala, K., Agnihotri, P., Patel, D., Pathan, A., Mohseni, U., and Patidar, N.: Application of frequency ratio modelling technique for predictive flooded area susceptibility mapping using remote sensing and GIS, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4771, https://doi.org/10.5194/egusphere-egu22-4771, 2022.

Flood is one of the most devastating natural disasters. The damages of flood usually vary with the consideration of different factors (depth, duration, velocity, materials of infrastructures) of flooding. Therefore, flood damage estimation is a complex process. Most of previous studies considered only flood depth in developing flood damage functions for residential houses. However, the consideration of other flood parameters such as flood duration and flood velocity are also crucial to estimate flood damage more reliably. Therefore, this study aimed to consider various flood parameters such as flood depth, flood duration, and flood velocity in development of flood damage functions for residential houses.  In this study, the Teesta River Basin in Bangladesh was chosen as the study area. A detailed household questionnaire survey was conducted in flood-affected areas of Lalmonirhat and Rangpur districts (administrative unit of Bangladesh) to collect data of 2017 and 2019 flood events.  Most of the houses in the surveyed flood-affected areas are composed of mud base and side wall of corrugated iron sheets (called “MC type”). For each house, the questionnaire aimed to identify the flood information (flood depth, flood duration, the qualitative representation of flood velocity), household structure information (area, plinth height, ceiling height), structural damage mechanism and the required amount of material with labor work to repair the damage after each flood event. Using the survey data, we have developed depth-damage functions for MC type of house by considering different flood velocity and flood duration combinations. The newly developed depth-damage functions can generalize thresholds of flood depth, flood velocity and flood duration that are responsible for specific type of structural damages (mud removal from the base, mud removal from the base together with side wall instability, full structure instability) of MC type house. Finally, a grid-based approach through the integration of new depth-damage functions with hydrologic-hydraulic model (RRI) and Nays2DFlood Solver (iRIC software) simulation results has been developed to estimate the total flood damage for MC type houses in flood-affected areas of the Teesta River Basin. This comprehensive method can be easily used to derive the depth-damage functions and estimation of total damage for other types of houses if enough surveyed data can be obtained from the field.

Keywords: Flood damage estimation, Depth-damage function, MC type house, Hydrologic-hydraulic model

How to cite: Haque, S., Ikeuchi, K., Shrestha, B. B., and Minamide, M.: Generalizing flood damage mechanism processes of MC Type houses by developing comprehensive flood damage estimation method for Teesta River Basin, Bangladesh , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5110, https://doi.org/10.5194/egusphere-egu22-5110, 2022.

EGU22-6322 | Presentations | NH1.2

Development of a loosely coupled geomatic-hydrological modeling approach for flood inundation mapping in small watersheds 

Zainab El Batti, Etienne Foulon, Camila Gordon, and Alain Rousseau

In Québec, Canada, extraordinary spring conditions in 2017 and 2019 have provided major incentives for the provincial government to commission the updating of current flood inundation maps. Indeed, some of these maps, dating back as far as the 1980’s, do not adequately reflect actual flood risks. Classical hydrodynamic models, such as HEC-RAS (1D, mixed, or full 2D), are generally used to perform the mapping, but they do require significant expertise, hydrometric data, and high-resolution bathymetric surveys. Given the need for updating flood inundation maps and reducing the associated financial costs (data collection and human resources), there is an emerging demand for simplified conceptual methods. In recent years, several models have been developed to fulfill this need, including the geomatic Height Above the Nearest Drainage (HAND) method which solely relies on a the digital elevation model (DEM).

This project aims at expanding upon earlier work carried out with HAND which was designed to compute the required water height to flood any DEM pixel of a watershed. The information provided by HAND along with the application of the Manning equation allow for the construction of a synthetic rating curve for any homogeneous river reach. This methodological approach has been used to come up with first-instance flood inundation mapping of large rivers in conterminous United States with a matching rate reaching 90% when compared to the use of HEC-RAS. However, to our knowledge, this has not been assessed for small rivers, and our goal here is to validate this simplified conceptual approach using two small watersheds (less than 200 km²) in Quebec.

The results of this study show that the ensuing synthetic rating curves for small rivers are consistent with river hydraulics (Froude numbers meeting the subcritical flow requirement behind the use of Manning equation) and in-situ derived rating curves of six hydrometric stations. The results also demonstrate the relevance of this approach when comparing the use of HAND with HEC-RAS 2D for the hydrographic networks of the two watersheds given flows simulated by a semi-distributed hydrological model (i.e., HYDROTEL). For this demonstration, the forcing data include the precipitation and temperature time series of the Canadian precipitation analysis system. Preliminary results indicate good performances (hitting rate above 60%) for the pilot river watersheds which are located in a data-sparse region.

While the preliminary results illustrate the potential to produce first-instance flood inundation mapping solely based on a DEM and simulated streamflows, future work will contribute to the advancement of our understanding of flood risks in poorly-gauged watersheds. HAND-derived inundation mapping will be further analyzed and compared to HEC-RAS-2D applications (i.e., the diffusion-wave equations), although the presence of complex urban infrastructures such as culverts, pipes, or bridges may represent a major challenge for the proposed approach. We believe a modeling continuum based on hydrological modeling and HAND-derived flood inundation mapping will inform and strengthen land management planning and contribute to the elaboration of public safety protocols.

How to cite: El Batti, Z., Foulon, E., Gordon, C., and Rousseau, A.: Development of a loosely coupled geomatic-hydrological modeling approach for flood inundation mapping in small watersheds , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6322, https://doi.org/10.5194/egusphere-egu22-6322, 2022.

Floods, the most frequent and severe of natural disasters worldwide, inflict significant social, environmental and fiscal impacts, including: loss of human life, damage to natural habitats and damage to infrastructure. Flood risk mapping can be used to mitigate these impacts as it provides a holistic approach to identifying flood prone areas by simultaneously considering socioeconomic and environmental indicators. This research compares the performance of two multi-criteria decision making methods, and one Machine Learning (ML) method in the development of flood risk mapping. This approach was first developed and validated for the Don River watershed in the Greater Toronto Area and subsequently extended to several other watersheds across Southern Ontario. Remote sensing data such as Digital Elevation Models and landuse and lancover datasets were used to develop the environmental flood hazard extent, and combined together with socioeconomic indicators, flood risk maps were developed using subjective and objective weighting schemes in a GIS analysis. The subjective maps were produced using the Analytical Hierarchy Process (AHP), the objective maps were produced using the Shannon Entropy method and the ML maps were produced using Artificial Neural Networks. The accuracy of these maps was compared against the floodplain map of the Don River. For a range of flood risk severity, where 1 was very low risk and 5 was very high risk, the AHP maps were superior in identifying areas where flood risk severity was 4 or greater. Conversely, the Entropy maps were superior in identifying areas where flood hazard risk was 5, however the difference in accuracy for both scenarios was marginal between the two methods. The accuracy of the ML maps showed marginal superior performance under both scenarios in comparison to the multi-criteria maps. Additionally, the uncertainty in the combination of flood risk indicators was quantified through a sensitivity analysis focusing on the discretization of the number of classes in each indicator dataset. The outcome of this research provides an accurate and simplified alternative to using hydrological and hydraulic models, especially when insufficient data limits the use of hydrological and hydraulic models. Future research should focus on an optimisation approach to the discretization of classes in indicator datasets.

How to cite: Khalid, R. and Khan, U. T.: A comparison of multi-criteria and machine learning weighting for flood risk assessment in the Southern Ontario, Canada, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6574, https://doi.org/10.5194/egusphere-egu22-6574, 2022.

EGU22-6636 | Presentations | NH1.2

Flood flow modelling coupled with ML-based land cover detection from UAV and satellite river imagery

Takuya Sato, Shuji Iwami, and Hitoshi Miyamoto

This research examined a new method for coupling flood flow modelling with the machine learning (ML)-based land cover detection from the Unmanned Aerial Vehicle (UAV) and satellite river imagery. We examined a 2 km river channel section with a gravel bed in the Kurobe River, Japan. The method used Random Forests (RF) for riverine land cover detection with the satellite images' RGBs and Near InfraRed (NIRs). In the process, the UAV images were used effectively to train the RF in several small portions of the river channel where the types of riverine land cover were precise. Using these UAV images with the corresponding feature values (i.e., RGBs and NIRs) of the satellite images made it possible to create the training data with high accuracy for land cover detection. The results indicated that combining the high- and low-resolution images in the RF could effectively detect waters, gravel/sand, trees, and grasses from the satellite images with a certain degree of accuracy. Its F-measure, consisting of precision and recall rates, had high enough with 0.8. Then, the ML-based land covers were coupled with a flood flow model. In the coupling, the results of the detected riverine land covers were converted into the roughness coefficients of the two-dimensional flood flow analysis. The flood flow simulation could reproduce the velocity field and water surface profile of flood flows with high accuracy. These results strongly suggest the effectiveness of coupling the current flood flow modelling with the ML-based land cover detection for grasping the most vulnerable portions in river flood management.

How to cite: Sato, T., Iwami, S., and Miyamoto, H.: Flood flow modelling coupled with ML-based land cover detection from UAV and satellite river imagery, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6636, https://doi.org/10.5194/egusphere-egu22-6636, 2022.

EGU22-7247 | Presentations | NH1.2

Flood early warning can significantly mitigate monetary damage

Heidi Kreibich, Paul Hudson, and Bruno Merz

Flood warning systems have a long track record of protecting human lives, but monetary damage continue to increase. Knowledge about the effectiveness of early flood warnings in reducing monetary damage is sparse, especially at the individual level. To gain more knowledge in this area, we analyse a dataset that is unique in terms of detailed information on warning reception and monetary damage at the property level. The dataset contains 4,468 damage cases from six flood events in Germany. We show quantitatively that early flood warnings are only effective in reducing monetary damage if people know what to do when they receive the warning (with at least one hour's notice). The average reduction in contents damage is 4 percentage points, which corresponds to a reduction of EUR 3,800 for the average warning recipient. This is substantial compared to the mean contents damage ratio of 21% and an absolute contents damage of 17,000 EUR. For the building damage ratio, the average reduction is 2 percentage points, which corresponds to a damage reduction of EUR 10,000. This is a remarkable reduction compared to the mean building damage ratio of 11% and a mean absolute building damage of 48,000 EUR. We also show that particularly long-term preparedness is related to people knowing what to do when they receive a warning. Risk communication, training and (financial) support for private preparedness are thus effective in mitigating flood damage in two ways: through precautionary measures and more effective emergency measures.

How to cite: Kreibich, H., Hudson, P., and Merz, B.: Flood early warning can significantly mitigate monetary damage, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7247, https://doi.org/10.5194/egusphere-egu22-7247, 2022.

EGU22-8450 | Presentations | NH1.2

Improving resilience through a surface water flooding decision support system

Heather Forbes, John Bevington, Andy Evans, Andrew Gubbin, Kay Shelton, Richard Smith, and Elizabeth Wood

Flood Foresight is JBA’s strategic flood monitoring and forecasting system, providing flood inundation and depth estimates across the UK and Ireland at 30m resolution up to 10-days ahead of fluvial flood events. It consists of Flood Monitoring (based on observed discharges from river gauge telemetry) and Flood Forecasting (based on simulated discharge from a rainfall-runoff model) modules.

Recently, Flood Foresight has been expanded to provide asset alerting around heavy rainfall and surface water (pluvial) flooding, demonstrated in a proof-of-concept system on behalf of Network Rail during a Small Business Research Initiative project funded by Department for Transport and delivered by InnovateUK.

The surface water flood forecasting system is now running in real time using high resolution ensemble rainfall forecasts from Met Eireann (IREPS).  This system represents a major advance in the availability of information indicating the risk to rail infrastructure across Great Britain.  Taking advantage of ensemble rainfall forecasts, it is possible to give an indication of where rain might happen and the severity of that rain (in comparison to historical rainfall amounts), and also to provide an indication of the confidence in that forecast.  This concept is crucial to the handling of intense rainfall events, due to their inherent lack of predictability.  The presentation of mapped likelihood information for both rainfall and surface water flooding forecasts provides users with spatial context for the asset alerts.  It allows them to see the extent and uncertainty in the location of the intense rainfall event. 

The system has been developed to run autonomously using rainfall forecasts as they are provided by Met Eireann, via FTP.  Therefore the resulting asset alert information is always available, and always presents the most up-to-date information.  This gives asset managers the ability to access the information at a time that is convenient to them, but also the system can provide alerts when assets are identified as at risk as the information becomes available. 

The forecast data is available beyond 36 hours into the future, providing sufficient lead time for asset managers to coordinate responses and mobilise staff and equipment, if needed.  The temporal resolution of the forecast information is high at short lead times (i.e.  hourly for the first 6 hours), decreasing as lead time increases (after 24 hours the information is 6 hourly, further reducing to 12 hourly when longer lead time forecasts are available).  This decreasing temporal resolution with longer lead times allows for increased uncertainty in the timing of events further in the future to be obscured to the user, reducing confusion if the timing changes with subsequent forecasts. 

The proof-of-concept system focuses on the rail industry, however it is extensible to other sectors where population, assets or infrastructure are vulnerable to surface water flooding. Flood impact data and associated alerts can be customised based on a client’s asset portfolio and their incident management needs.

The presentation will explore heavy rainfall events evaluated during the proof-of-concept demonstrations, describing the information the Flood Foresight system could have provided ahead of, and during the event.

How to cite: Forbes, H., Bevington, J., Evans, A., Gubbin, A., Shelton, K., Smith, R., and Wood, E.: Improving resilience through a surface water flooding decision support system, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8450, https://doi.org/10.5194/egusphere-egu22-8450, 2022.

EGU22-8823 | Presentations | NH1.2

Improved flood predictions by combining satellite observations, topographic information and rainfall spatial data using deep learning

Rocco Palmitessa, Oliver Gyldenberg Hjermitslev, Heidi Egeberg Johansen, Karsten Arnbjerg-Nielsen, Peter Bauer‐Gottwein, Peter Steen Mikkelsen, and Roland Löwe

Flood warning systems are needed to plan mitigation measures and inform response strategies. The extent and dynamics of floods are typically predicted using physics-based hydrological models, which are computationally expensive and data assimilation is difficult. Deep-learning models can overcome these limitations, enabling fast predictions informed by multiple sources of data. Studies show this can be achieved while retaining or improving the level of detail and accuracy previously attainable. We, therefore, propose a deep-learning flood forecasting tool that combines multiple sources of readily available data to quickly generate flood extent maps, which can inform warnings.

We train a neural network with U-NET architecture consisting of encoder and decoder convolutional modules. In the encoder module, features are extracted from the input and the data is downsampled to reduce complexity. Subsequently, the data is upsampled back to the original dimension in the decoder module and each 10 by 10 m pixel of the output image represents a flood prediction. The input to the neural network includes radar rainfall observations, LIDAR topographic scans, soil type and land use maps, groundwater depth simulations and previous inundation maps. All inputs are individually normalized and pre-processed. The rainfall observations are temporally aggregated to various intervals, hydrological features are highlighted in the topographic scans, and soil types and uses are grouped into categories.

The model is trained and evaluated against a set of maps of surface water extent derived from Synthetic Aperture Radar (SAR) satellite observations. The predictions are scored against the target images by computing the critical success index (CSI), which measures the percentage of correct predictions among the total predicted of observed flooded areas. Permanent water bodies and areas where flooding is not captured by the satellite images (e.g. in forests) are masked out during both training and evaluation. The model is trained on a set of flooding events that occurred between 2018 and 2020 within the Jammerbugt Municipality in northern Denmark, which extends for about 850 km2. The model is validated on spatially independent data and tested on temporally independent events from the same study area.

The proposed model yielded up to ~60% CSI with the test dataset, which is comparable to existing flood screening approaches. The test data included both fluvial and pluvial flooding as well as observed surface water in coastal areas. Large flooded areas were correctly predicted, while false negatives were frequently obtained for smaller areas. The overall performance of the proposed method is expected to improve by further tuning the model hyperparameters and by treating separately flood processes with different dynamics (e.g. pluvial vs. fluvial vs. coastal). These tradeoffs are compensated by the minimal computational time required to generate predictions once the model has been trained. Also, it is expected that the model can easily be transferred to other locations since it relies on local topographic information. The additional advantage of using a deep-learning approach is the ability to easily integrate alternative and additional data sources, which enables, for example, longer-term flood warnings driven by rainfall forecasts instead of observations.

How to cite: Palmitessa, R., Hjermitslev, O. G., Johansen, H. E., Arnbjerg-Nielsen, K., Bauer‐Gottwein, P., Mikkelsen, P. S., and Löwe, R.: Improved flood predictions by combining satellite observations, topographic information and rainfall spatial data using deep learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8823, https://doi.org/10.5194/egusphere-egu22-8823, 2022.

EGU22-9037 | Presentations | NH1.2

Improvement of Disaster Management Approaches in Japan Using Paddy Field

Debanjali Saha, Kazuo Oki, Koshi Yoshida, and Hideaki Kamiya

Japan has a history of major natural disasters, mostly due to its geographical characteristics and topographic features. Major typhoons and floods cause severe damages to lives, properties and important infrastructure, which may increase in future due to climate change. Therefore, sustainable and cost-effective disaster management strategies are of timely requirement, and paddy fields in the river flood plain areas of Japan can be effectively utilized in this regard. After the paddy harvest season, most paddy fields remain unused for a few months and during this time it can work as storage reservoir with minor interventions. During intense rainfall, water can be stored within the paddy field bunds if the drainage outlets are kept closed for some time. Thus, contribution of rainwater to the river can be lessened, resulting river discharge reduction to some extent and protecting important areas from flood damages. The potential of paddy fields in Japan as storage reservoir is not well represented in any research that involves hydrological modelling. This study is performed to assess the impact of using paddy fields for river discharge and inundation reduction, through hydrological model simulation. Two major river basins in Japan, Abukuma river in Fukushima prefecture and Chikuma river in Nagano prefecture are selected as study areas. Paddy field covers 15-20% of watershed areas of these rivers and most of these fields are very close to the main river stream, which indicates their fair potential to store rainwater and contribute to discharge reduction. A global hydrological and water resources model named ‘H08’ is used in this study to simulate river discharge for two scenarios, where one is the control scenario with no storage of water within the paddy field and another is storing rainwater within the exiting or extended paddy bunds. Simulations are performed for 2018 and 2019 to compare the normal flood year and extreme event (a super typhoon occurred in Japan in 2019). Observed and simulated discharge is compared for model calibration and results show better correlation in the upstream section of the rivers. More adjustment of model parameters is still necessary for better calibration. Simulation results show that for 2018, Abukuma river experienced 21-25% decrease in river discharge when water is stored within the conventional 25cm paddy bund. The reduction increased up to 35% when the paddy bunds are assumed to be extended up to 50cm in height. Similar results are observed for Chikuma river basin. For 2019, discharge shows 10-15% decrease for 25cm paddy bunds and around 20% reduction for proposed 50cm bund. With this discharge reduction potential, if paddy field bunds can be extended up to 50cm with a working public-private partnership, where farmers are aware of the advantages of utilizing unused paddy fields as such an effective means of flood management, then this strategy can be considered a sustainable and cost-effective way of disaster management, where the existing land-cover will act as a natural means of storage reservoir. Moreover, this sustainable strategy can be adopted in other countries having similar geographical features as Japan.

How to cite: Saha, D., Oki, K., Yoshida, K., and Kamiya, H.: Improvement of Disaster Management Approaches in Japan Using Paddy Field, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9037, https://doi.org/10.5194/egusphere-egu22-9037, 2022.

EGU22-10674 | Presentations | NH1.2

Towards development of a seamless probabilistic flood inundation map for extreme flood events across Australian catchments

Katayoon Bahramian, Wendy Sharples, Christoph Rudiger, S L Kesav Unnithan, Basudev Biswal, Elisabetta Carrara, and Zaved Khan

Floods in Australia are among the most costly and deadly natural disasters causing significant material damage, injury, and death. Effective emergency management to reduce the devastating consequences of flooding depends on the accuracy and reliability of forecasts. Effective infrastructure planning for flood mitigation depends on the accuracy and reliability of future projections. Flood inundation mapping is a tool widely used for flood mitigation purposes by providing information on flood event characteristics such as occurrence, magnitude, timing, and spatial extent. However, information derived from flood inundation maps is subject to uncertainties in each step of a complex modelling chain, including uncertainties in hydro-meteorological and observational datasets, digital elevation models and representation of rivers, as well as over-simplification of hydrological and hydraulic processes. Therefore, relying on a purely deterministic representation of flood characteristics may lead to poor decision making. Probabilistic flood maps are capable of accounting for uncertainty by estimating the probability of a certain area being flooded, which is a recommended approach for risk-based decision making. In addition, providing probabilistic flood map information encompassing past, present, and future, will improve Australia’s resilience to flood events and target infrastructure spending. Generation of seamless probabilistic flood maps in an operational setting, particularly at a continental scale, needs to be supported with an integrated and consistent set of hydro-meteorological datasets across timescales and catchments.  

The aim of this study is to develop a seamless probabilistic flood inundation mapping framework for near-future to far future floods across flood-prone Australian catchments. We take advantage of products from the Australian Water Outlook (AWO: awo.bom.gov.au), a water service that provides nationally consistent water information since 1911 until the present as well as long-term projections out to 2100. In this framework, large rainfall events are detected based on ensemble forecasts or projections from AWO using a threshold analysis. After detection of a potential flood, an event-based hydrological model (URBS) is initialised to generate an ensemble of river reach hydrographs in a Monte Carlo framework where the parameterisation of the catchment wetness is informed by historical flood events for the catchment. This enables uncertainty from ensemble rainfall and catchment losses to be quantified and incorporated within the hydrograph generation step. Lastly, we combine remotely sensed data with topographic and river network information to map the flood extent, using the height above nearest drainage (HAND) method. This framework will be tested for two major flood events in February 2020 and March 2021 at Hawkesbury Nepean Valley catchment located in New South Wales, Australia, which, due to significantly different antecedent conditions, had dissimilar flood characteristics, thereby demonstrating the suitability of the framework.

How to cite: Bahramian, K., Sharples, W., Rudiger, C., Unnithan, S. L. K., Biswal, B., Carrara, E., and Khan, Z.: Towards development of a seamless probabilistic flood inundation map for extreme flood events across Australian catchments, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10674, https://doi.org/10.5194/egusphere-egu22-10674, 2022.

EGU22-10803 | Presentations | NH1.2

Sensitivity analysis of network structure in missing streamflow data complementation using Bidirectional Short-Term Memory network

Takeyoshi Nagasato, Kei Ishida, Daiju Sakaguchi, Motoki Amagasaki, and Masato Kiyama

Streamflow data based on the observation may be partially missing due to flood or malfunction of the measuring equipment. Here, it is important to complement the missing flow rate with high accuracy for water resource management and flood risk management. Various statistical approaches such as linear regression and multiple regression models have been proposed as methods for complementing missing flow rates. Among the statistical methods, deep learning has been rapidly evolved with the improvement of computational equipment. Then, deep learning methods have achieved remarkable success in various fields. It may indicate that there is a possibility that the missing flow rate can be complemented with high accuracy by using the deep learning method. Therefore, this study implemented deep learning for missing streamflow complementation. In addition, because the network structure of deep learning may have a great influence on estimation accuracy, this study conducted a sensitivity analysis of the network structure. Among the deep learning methods, Bidirectional LSTM (Bi-LSTM) was implemented in this study. Bi-LSTM is a kind of LSTM that can learn long-term dependence of time series data. Bi-LSTM learns data in both forward and backward directions, compared to Unidirectional LSTM which learns data forward directions. As for the input data, both hourly streamflow and precipitation data were used. For model learning and evaluation, missing streamflow data were artificially generated. The results show that Bi-LSTM can complement the flow rate with high accuracy. It also showed the importance of optimizing the network structure.

How to cite: Nagasato, T., Ishida, K., Sakaguchi, D., Amagasaki, M., and Kiyama, M.: Sensitivity analysis of network structure in missing streamflow data complementation using Bidirectional Short-Term Memory network, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10803, https://doi.org/10.5194/egusphere-egu22-10803, 2022.

EGU22-10827 | Presentations | NH1.2

A Numerically-integrated Approach for Residential Flood Loss Estimation at the Community Level

Rubayet Bin Mostafiz, Ayat Al Assi, Carol Friedland, Robert Rohli, and Md Adilur Rahim

Evaluating average annual loss (AAL) is an essential component of assessing and minimizing future flood risk. A robust method for quantifying flood AAL is needed to provide valuable information for stakeholder decision-making. Several recent studies suggest that the numerical integration method can provide meaningful AAL estimates since this technique includes the full loss‐exceedance probability of flood. While past research focuses on applying the numerical integration method on a single, one-family residential house, calculations across space are necessary for assessing community vulnerability. This research develops a computational framework in MATLAB for integrating across the full loss-exceedance probability curve through space to evaluate flood AAL for multiple single-family homes, including loss to the structure, content, and time spent in refurbishing it (i.e., use), over a case-study census block in Jefferson Parish, Louisiana, USA. To further inform flood mitigation planning, the AAL is also calculated for one, two, three, and four feet of freeboard and separately for each owner-occupant type of residence (i.e., homeowner, landlord, and tenant). Although previous studies provided essential information related to the structure and content loss for one type for ownership-occupant type (homeowner), the wider scope of this study allows for consideration of the use loss and the remaining ownership-occupant types. Results show that individual building AAL varies within a community because of its building attributes. Besides, results highlight the difference of AALs by owner-occupant type, with homeowners generally bearing the highest total AAL and tenants incurring the lowest total AALs. A simple elevation of only one foot can decrease the AAL by as much as 90 percent. A sensitivity analysis underscores the importance of using the exact values of the base flood elevation (BFE) compared to rounding to the nearest integer and excluding damage lower than first flood height (FFH) in the depth-damage functions (DDFs). Expanding the application of the numerical integration method to a broad-scale study area may enhance validity and accuracy as compensating errors are likely to make bulk estimates more reasonable, which might augment its utility at the community level. In general, such techniques improve resilience to flood, the costliest natural hazard, by assisting in better understanding of risk with and without mitigation efforts. 


How to cite: Mostafiz, R. B., Assi, A. A., Friedland, C., Rohli, R., and Rahim, M. A.: A Numerically-integrated Approach for Residential Flood Loss Estimation at the Community Level, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10827, https://doi.org/10.5194/egusphere-egu22-10827, 2022.

EGU22-10891 | Presentations | NH1.2

Improvement of river flow estimation accuracy using ensemble learning stacking

Daiju Sakaguchi, Kei Ishida, Takeyoshi Nagasato, Motoki Amagasaki, and Masato Kiyama

In recent years, disasters are more frequent and enormous due to global warming. In the field of hydrology, high-precision rainfall-runoff modeling is required. Recently, deep learning has been applied to rainfall-runoff modeling and shows high accuracy. It is also expected that the accuracy will be improved by using ensemble learning for deep learning. This study tried to improve the accuracy of river flow estimation by performing ensemble learning for deep learning. Stacking was used as the ensemble learning method. For deep learning, LSTM, CNN, and MLP was used and compared. XGBoost was used as the learning device used for ensemble learning. The target area was the Tedori River basin in Ishikawa Prefecture, Japan. In deep learning, the input data were daily average precipitation and temperature. In deep learning and ensemble learning, the target data was the daily average river flow. RMSE was used as the evaluation index. As a result, the accuracy was the highest after ensemble learning when using LSTM. It shows that the selection of the learning device is important for ensemble learning.

How to cite: Sakaguchi, D., Ishida, K., Nagasato, T., Amagasaki, M., and Kiyama, M.: Improvement of river flow estimation accuracy using ensemble learning stacking, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10891, https://doi.org/10.5194/egusphere-egu22-10891, 2022.

In recent years, climate change intensifies heavy rainfall, resulting in annual flood damage. Population is increasing worldwide, and urbanization is expected to continue expanding. Under these circumstances, once an inundation occurs, the damage is expected to be more extensive than ever before. Therefore, in this study, we are analyzing the effects of DEM resolution and land use data, which are the calculation conditions for inundation calculations in flood forecasting, on inundation characteristics such as inundation magnitude and duration during large-scale inundation.

 In this paper, the target watershed was the Tone River in Japan, where major floods have occurred in the past, and the analysis was conducted in the plain area. DEM data and land use data are important factors in determining inundation characteristics; The higher the resolution of the DEM data, the better it can represent the microtopography, which in turn affects the inundation flow. Also, land use data determines the roughness coefficient, which affects the velocity of floodwaters, and the infiltration capacity and initial loss into the ground. In this paper, The DEM data were analyzed with resolutions of 5m, 25m, 50, 100m, and 250m. The land use data for the years 1978, 1987, 1997, 2006 and 2016 were used to analyze the inundation characteristics due to increasing urbanization.

The results of inundation analysis with different resolutions of DEM data show that the resolution has no significant effect on the inundation rate. However, as for the inundation area, the larger the mesh size, the larger the inundation area, which is expected to be caused by the homogenization of DEM data. It was also found that as urbanization progresses, the inundation area spreads faster. In addition, the urbanization process affects the diminishing period of inundation rather than the expansion process, because it loses the function of infiltration capacity, and the inundation depth is less likely to decrease.

How to cite: Koyama, N. and Yamada, T.: Analysis of inundation characteristics under various computational conditions for large-scale flood forecasting, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10930, https://doi.org/10.5194/egusphere-egu22-10930, 2022.

EGU22-10940 | Presentations | NH1.2

An Improved Micro Scale Average Annual Flood Loss Implementation Approach  

Md Adilur Rahim, Ehab S Gnan, Carol J Friedland, Rubayet Bin Mostafiz, and Robert V Rohli

Average annual loss (AAL) is used as the basis for the evaluation of risk mitigation measures.  However, the current AAL implementations in flood risk assessment have several shortcomings. For instance, results generated using Riemann trapezoids for the available return periods of a site are typically gross approximations, especially when damage changes rapidly with depth. Monte Carlo simulations offer improvements in precision but at the expense of being computationally intensive. The log-linear method that extrapolates losses to higher return periods and performs piece-wise Riemann sum with these limited return periods can fail to capture the non-linear flood behavior. This paper presents an improved implementation that quantifies AAL at the micro-scale level including the full range of loss‐exceedance probabilities. To demonstrate the methodology, the financial benefit of increasing the lowest floor elevation for a one-story single-family residence is assessed. Several depth-damage functions (DDFs) are selected and compared to examine the variability in AAL results related to the DDF choice. Results demonstrate the need for an AAL estimate that includes the full loss‐exceedance probabilities. Results also highlight the need to assess flood risk at the micro-scale level for a more localized and accurate assessment, whereupon the estimate can be expanded to broader-scale risk estimations with a higher degree of accuracy. The more realistic AAL estimates results could encourage homeowners and communities to take action and support government decision-makers by investing in flood mitigation and considering building code changes.

How to cite: Rahim, M. A., Gnan, E. S., Friedland, C. J., Mostafiz, R. B., and Rohli, R. V.: An Improved Micro Scale Average Annual Flood Loss Implementation Approach  , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10940, https://doi.org/10.5194/egusphere-egu22-10940, 2022.

EGU22-12328 | Presentations | NH1.2

Flood susceptibility mapping using an Artificial Neural Network model: the case study of Southern Italy

Michele Del Vecchio, Filippo Balestra, Maria Antonia Pedone, Dina Pirone, Danilo Spina, Salvatore Manfreda, and Giovanni Menduni

EGU22-12430 | Presentations | NH1.2

Modelling the natural flood management in medium scale lowland catchments in Thames Basin (UK)

Heou Maleki Badjana, Anne Verhoef, Hannah Cloke, Stefan Julich, Patrick McGuire, Carla Camargos, and Joanna Clark

Natural flood management (NFM) is widely promoted and adopted as an effective way of managing flood risks. However, there remain many unknowns especially on its effectiveness at medium and large scales. This study has first analysed the consistency of a modelling framework that integrates the Soil and Water Assessment Tool (SWAT) model for simulating the land based NFM in two medium scale lowland catchments within the Thames River basin (UK). Afterwards, it has assessed the effectiveness of NFM in these catchments using broadscale hypothetical scenarios. The results show that it is possible to model land-based NFM in medium scale catchments but this is highly dependent on the one hand on catchment landscape characteristics and on the other hand on the availability and quality of necessary input datasets, model choice, configuration, parametrisation and calibration and uncertainty analysis techniques. Furthermore, the NFM effects vary across the catchments and landscapes characteristics. Afforestation seems to provide less effect on large flood events in terms of reducing the peak flows compared to small events. The implementation of crop rotation scenarios, depending on the crop choice and tillage practice may lead to the increase of the peak flows. Overall, this study showed that NFM modelling in medium catchments is not straightforward and prior to any task, an extensive analysis needs to be carried out to understand the datasets, the model, and processes configuration as well as different calibration and uncertainties analysis techniques. Moreover, the choice of woodland planting only as NFM measure will require an extensive work within the catchment to produce an effect which suggests that to better minimise the flood risk, the combination with other measures that can reduce the amount of flow reaching the river channel or delay the timing of the peak flow (eg. leaky barriers) would be necessary.

How to cite: Badjana, H. M., Verhoef, A., Cloke, H., Julich, S., McGuire, P., Camargos, C., and Clark, J.: Modelling the natural flood management in medium scale lowland catchments in Thames Basin (UK), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12430, https://doi.org/10.5194/egusphere-egu22-12430, 2022.

NH1.4 – Extreme meteorological and hydrological events induced by severe weather and climate change

EGU22-12077 | Presentations | NH1.4

Weather Extremes in the Euro Atlantic Region: Assessment and Impacts

Margarida L. R. Liberato and Alexandre M. Ramos

Despite being major sources of hazards and having impacts on local and national populations, environment and economies, processes involved in extremes’ intensification and generation of disastrous impacts, such as extreme and widespread dry and wet events or flash flooding, are not fully understood yet. Therefore, the goal of WEx-Atlantic project is to perform research, to improve knowledge on weather extremes in the North Atlantic European sector and to communicate it to society. Considered extremes are strong winds and heavy hydrometeorological (HM – dry and wet) events associated with extratropical cyclones (EC), frontal systems and atmospheric rivers (AR).

WEx-Atlantic contributes to improve our understanding on the assessment of weather systems and the underlying physical mechanisms, variability and expected changes under global warming, as well as meteorological, environmental (e.g. forest) and socioeconomic (e.g. renewable wind energy and power grid) impacts on Portugal including the Macaronesia Islands.

WEx-Atlantic applies state-of-the-art techniques to detect and track weather systems, including AR, mid-latitude systems and weather types to reanalysis datasets as well as to GCMs. Here a review of WEx-Atlantic research and new contribution is presented.


This work was supported by project “Weather Extremes in the Euro Atlantic Region: Assessment and Impacts—WEx-Atlantic” (PTDC/CTA-MET/29233/2017; LISBOA-01-0145-FEDER-029233, NORTE-01-0145-FEDER-029233) funded by Fundação para a Ciência e a Tecnologia, Portugal (FCT). Alexandre. M. Ramos was supported by the FCT Scientific Employment Stimulus 2017 (CEECIND/00027/2017).


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How to cite: Liberato, M. L. R. and Ramos, A. M.: Weather Extremes in the Euro Atlantic Region: Assessment and Impacts, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12077, https://doi.org/10.5194/egusphere-egu22-12077, 2022.

EGU22-2532 | Presentations | NH1.4

Using Vertical Integrated Liquid Density from a Weather Radar Network to Nowcast Severe Events

Laura Esbrí, Tomeu Rigo, M. Carmen Llasat, and Antonio Parodi

This contribution has the main goal of identifying, characterizing, tracking and nowcasting severe thunderstorms using the Density of the Vertical Integrated Liquid (DVIL). The DVIL can synthesize all the volumetric information of a column of the weather radar in a 2D plane. This is, it estimates the quantity of precipitable liquid water in the column but, besides, it reduces the dependency on the height of the column. This point becomes crucial to give an appropriate weight of potential danger to thunderstorms that occurred out of the typical convective season. . This is particularly useful to improve the decision-making and early warning in critical environments and infrastructures, like airports and air traffic management (ATM). The usage of DVIL has multiple advantages, for instance, reducing the computational time consumed on the analysis of large areas. Also, to obtain a good and simple description of the potentially dangerous thunderstorms, and to have an easily integrating into other systems for ATM decision making. The main disadvantage is a less precise characterization of the atmospheric objects than with the whole radar volumetric data. Nevertheless, the differences are scarce and do not produce any significant inconvenience in the procedure. The algorithm first identifies those areas exceeding a DVIL threshold, which is established for thunderstorms with a certain probability of producing severe weather. The characterization module turns out simpler than in other methodologies because of the data type (2D instead of 3D reflectivity fields), but it can be combined with other data types if needed. The tracking and nowcasting procedure obtain the past trajectory of the thunderstorm and then use it to weather forecast from 5 to the next 60 minutes, with 5 minutes steps. Different convective episodes that have affected the proximity of Italian and Spanish airports have been analysed to evaluate the following points: (1) the performance of the correct identification of potentially dangerous thunderstorms, (2) the capability of tracking the path and characterizing the life cycle of those storms, and (3) the ability of the nowcasting to correctly forecast the time and the most dangerous area.

This project has received funding from the SESAR Joint Undertaking under grant agreement No 892362, SINOPTICA-H2020 (Satellite-borne and IN-situ Observations to Predict the Initiation of Convection for Air traffic management) project.

How to cite: Esbrí, L., Rigo, T., Llasat, M. C., and Parodi, A.: Using Vertical Integrated Liquid Density from a Weather Radar Network to Nowcast Severe Events, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2532, https://doi.org/10.5194/egusphere-egu22-2532, 2022.

EGU22-11732 | Presentations | NH1.4

Factors leading to the formation of tornadoes: statistical links emerging from a large dataset

Piero Lionello, Roberto Ingrosso, M.Marcello Miglietta, and Gianfausto Salvadori

The dynamics of tornadoes include large vorticity in the lower troposphere and an intense updraft, whose combination may result in their formation. In this study we investigate the possibility of using a statistical relation for their description. In fact, the nonlinearity, complexity and fine scale of these processes presently prevents their simulation in the atmospheric circulation models currently used for weather forecasts and climate projections. Here we use a large dataset of tornadoes observed in the USA and Europe and the data of ERA5 (ECMWF ReAnalysis 5) to establish a statistical link between the occurrence of tornadoes and factors whose values can be extracted from atmospheric circulation models. The values of CAPE (convective available potential energy), WS (wind shear in the lower troposphere), SRH (storm relative helicity) and LCL (lifting condensation level) of the high resolution (about 30km) ERA5 data have been considered. The analysis shows all these variables are significantly linked to the formation of tornadoes with WS and CAPE being the most relevant ones. The analysis is an extension of a former study (Ingrosso et al., 2020, 10.3390/atmos11030301) based on a dataset of tornadoes events much larger than previously, on higher resolution atmospheric data, and more prognostic variables. The results provide a new expression for the probability of occurrence of tornadoes that can be used for forecasting their likelihood with potential applications to their predictions and future changes of their frequency.

How to cite: Lionello, P., Ingrosso, R., Miglietta, M. M., and Salvadori, G.: Factors leading to the formation of tornadoes: statistical links emerging from a large dataset, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11732, https://doi.org/10.5194/egusphere-egu22-11732, 2022.

EGU22-12589 | Presentations | NH1.4

Analysis of GNSS sensed slant wet delay during the severe weather events in central Europe

Addisu Hunegnaw, Hüseyin Duman, Gunnar Elgered, Jan Dousa, and Norman Teferle

Over the last few decades, anthropogenic greenhouse gas emissions have increased the frequency of climatological anomalies such as temperature, precipitation, and evapotranspiration. It is noticed that the frequency and severity of the intense precipitation signify a greater susceptibility to flash flooding. Flash flooding continues to be a major threat to European cities, with devastating mortality and considerable damage to urban infrastructure. As a result, accurate forecasting of future extreme precipitation events is critical for natural hazard mitigation. A network of ground-based GNSS receivers enables the measurement of integrated water vapour along slant pathways providing three-dimensional water vapour distributions. This study aims to demonstrate how GNSS sensing of the troposphere can be used to monitor the rapid and extreme weather events that occurred in central Europe in June 2013 and resulted in flash floods and property damage. We recovered one-way slant wet delay (SWD) by adding GNSS post-fit phase residuals, representing the troposphere's higher-order inhomogeneity. Nonetheless, noise in the GNSS phase observable caused by site-specific multipath can significantly affect the SWD from individual satellites. To overcome the problem, we employ a suitable averaging strategy for stacking post-fit phase residuals obtained from the PPP processing strategy to generate site-specific multipath corrections maps (MPS). The spatial stacking is carried out in congruent cells with an optimal resolution in elevation and azimuth at the local horizon but with decreasing azimuth resolution as the elevation angle increases. This permits an approximately equal number of observations allocated to each cell. The spatio-temporal fluctuations in the SWD as measured by GNSS closely mirrored the moisture field associated with severe weather events in central Europe, i.e., a brief rise prior to the main rain events, followed by a rapid decline once the storms passed. Furthermore, we validated the one-way SWD between ground-based water-vapour radiometry (WVR) and GNSS-derived SWD for different elevation angles.


How to cite: Hunegnaw, A., Duman, H., Elgered, G., Dousa, J., and Teferle, N.: Analysis of GNSS sensed slant wet delay during the severe weather events in central Europe, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12589, https://doi.org/10.5194/egusphere-egu22-12589, 2022.

EGU22-12168 | Presentations | NH1.4

Estimating tropical cyclone rainfall using the STORM dataset

Natalie Lord, Nadia Bloemendaal, Ivan Haigh, Niall Quinn, Pete Uhe, and Chris Sampson

EGU22-1758 | Presentations | NH1.4

The role of cyclones and PV cutoffs for the occurrence of unusually long wet spells in Europe

Matthias Röthlisberger, Barbara Scherrer, Andries Jan de Vries, and Raphael Portmann

The synoptic dynamics leading to the longest wet spells in Europe are so far poorly investigated, despite these events’ potentially large societal impacts. In this study we examine the role of cyclones and PV cutoffs for unusually long wet spells in Europe, defined as the 20 longest uninterrupted periods with at least 5 mm daily accumulated precipitation at each ERA-Interim grid point in Europe (this set of spells is hereafter referred to as S20). The S20 occur predominantly in summer over the eastern continent, in winter over the North Atlantic, in winter or fall over the Atlantic, and in fall over the Mediterranean and European inland seas. Four case studies reveal archetypal synoptic storylines for long wet spells: (a) A seven-day wet spell near Moscow, Russia, is associated with a single slow-moving cutoff-cyclone couple; (b) a 15-day wet spell in Norway features a total of nine rapidly passing extratropical cyclones and illustrates serial cyclone clustering as a second storyline; (c) a 12-day wet spell in Tuscany, Italy, is associated with a single but very large cutoff-complex, which is replenished multiple times by a sequence of recurrent anticyclonic wave breaking events over the North Atlantic and western Europe; and (d) a 17-day wet spell in the Balkans features intermittent periods of diurnal convective precipitation in an environment of weak synoptic forcing and recurrent passages of upper-level troughs and PV cutoffs and thus also highlights the role of diurnal convection for long wet spells over land. A systematic analysis of cyclone and cutoff occurrences during the S20 reveals considerable spatial variability in their respective role for the S20. For instance, cyclones and cutoffs are present anywhere between 10% and 90%, and 20% and 70% of the S20 time steps, respectively, depending on the geographical region. However, overall both cyclones and cutoffs, appear in larger number and at a higher rate during the S20 compared to climatology. Furthermore, in the Mediterranean, the PV cutoffs and cyclones are significantly slower moving and/or longer-lived during the S20 compared to climatology. Our study thus documents for the first time the palette of synoptic storylines accompanying unusually long wet spells across Europe, which is a prerequisite for developing an understanding of how these events might change in a warming climate and for evaluating the ability of climate models to realistically simulate the synoptic processes relevant to these events.

How to cite: Röthlisberger, M., Scherrer, B., de Vries, A. J., and Portmann, R.: The role of cyclones and PV cutoffs for the occurrence of unusually long wet spells in Europe, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1758, https://doi.org/10.5194/egusphere-egu22-1758, 2022.

EGU22-9357 | Presentations | NH1.4

Characterization and nowcasting of severe weather events over Milano Malpensa

Aikaterini Anesiadou, Sandy Chkeir, and Riccardo Biondi

Extreme weather events in Europe have increased in frequency and intensity in the last decades, especially in some areas like Alpes and Balkans, and is expected to increase even more in the upcoming years due to the climate change. Monitoring and forecasting the severe weather events locally developed and in a short time range is very challenging but also very important for aviation safety. Several studies have been made for studying the pre-convective environment, however there are still gaps in the knowledge of the dynamical processes of regional and short duration deep convective systems.

This study is implemented within the SESAR ALARM project and focuses on the analysis of the pre-convective and convective environment in support to the air traffic management and air traffic control. The work focuses in the detection, analysis and nowcasting of severe weather events in a selected hotspot: the area of Milano Malpensa airport in Italy. We have used the data from 28 weather stations, 8 GNSS stations, radar and lightning detectors, in the period 2010-2020 to train a nowcasting algorithm and to characterize the pre-convective environment.

Our first results for different locations in the area of interest, show on average that the root mean square error of the rainfall prediction lie in the range 0.1029-0.2838 mm and 0.2720-0.7815 m/s for the wind speed prediction. Our algorithm shows the best rain predictive performance in the next 10 minutes higher than 90%, and higher than 80% in the next 30 minutes. Moreover, the pre-convective environment analysis shows that all the cases with wind field divergence never show an increasing trend of GNSS Zenith Total Delay before the event.

How to cite: Anesiadou, A., Chkeir, S., and Biondi, R.: Characterization and nowcasting of severe weather events over Milano Malpensa, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9357, https://doi.org/10.5194/egusphere-egu22-9357, 2022.

EGU22-1314 | Presentations | NH1.4

Amplification of annual and diurnal cycles of alpine lightning over the past four decades

Thorsten Simon, Georg J. Mayr, Deborah Morgenstern, Nikolaus Umlauf, and Achim Zeileis

Motivation: The response of lightning to a changing climate is not fully understood. Historic trends of proxies known for fostering convective environments suggest an increase of lightning over large parts of Europe. Since lightning results from the interaction of processes on many scales, as many of these processes as possible must be considered for a comprehensive answer.

Objectives: Our aim is a probabilistic reconstruction of summer lightning over the European Eastern Alps down to its seasonally varying diurnal cycle. This necessitates consideration of many processes which becomes feasible by combining a statistical learning approach with several recent scientific achievements: Decade-long seamless lightning measurements by the Austrian Lightning Detection & Information System (ALDIS) and hourly reanalyses of atmospheric conditions including cloud micro-physics within the fifth generation ECMWF atmospheric reanalysis (ERA5).

Methods: These two data sets have been linked by the statistical learning approach called generalized additive model (GAM). GAMs are capable to identify nonlinear relationships between the target variable (lightning yes/no) and explanatory variables (ERA5). The most important explanatory variables have been selected objectively using a combination of stability selection and gradient boosting. This objective selection has reduced the pool of 85 potential ERA5 variables to the 9 most important ones. This reduced set still represents a large variety of processes including favorable environments for thunderstorms, charge separation and trigger mechanisms. The performance of the resulting GAM has been tested using cross-validation over the period of 2010-2019. 

Results: With the resulting GAM lightning for the Eastern Alps and their surroundings has been reconstructed over a period of four decades (1979-2019). The most intense changes occurred over the high Alps where lightning activity doubled in the past decade compared to the 1980s. There, the lightning season reaches a higher maximum and starts one month earlier. Diurnally, the peak is up to 50% stronger with more lightning strikes in the afternoon and evening hours. Signals along the southern and northern alpine rim are similar but weaker whereas the flatlands north of the Alps have no significant trend.

How to cite: Simon, T., Mayr, G. J., Morgenstern, D., Umlauf, N., and Zeileis, A.: Amplification of annual and diurnal cycles of alpine lightning over the past four decades, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1314, https://doi.org/10.5194/egusphere-egu22-1314, 2022.

EGU22-4102 | Presentations | NH1.4

Modelling hail probability over Italy using a machine learning approach

Riccardo Hénin, Veronica Torralba, Antonio Cantelli, Enrico Scoccimarro, Stefano Materia, and Silvio Gualdi

Hail is a meteorological phenomenon with adverse impacts affecting multiple socio-economic sectors such as agriculture, renewable energy and insurance (e.g. Púčik et al., 2019; Martius et al., 2018; Macdonald et al., 2016). The mitigation of the hail-related risk in particularly sensitive regions such as Italy has fostered hail research, aiming at a deeper understanding of the favorable environmental conditions for hail formation and the improvement of hail forecasting skills (Mohr and Kunz, 2013). Nevertheless, one of the major limitations for the study of long-term hail variability is the inherent difficulty in measuring all the hail occurrences and the consequent scarce temporal and spatial coverage of hail observations (Mohr et al., 2015). Therefore, in this study, the Probability Density Functions (PDFs) of several large-scale meteorological variables and convective indices from the ERA5 reanalysis are considered instead, with the aim of describing a conditioned hail probability, following the statistical method by Prein and Holland (2018). Then, the best set of variables to be used as predictors in the hail model are selected with a machine learning approach, based on a genetic algorithm. The model output is an estimation of the hail probability over Italy in the 1979-2020 period, on a 30x30 km grid. The model is validated over the Friuli-Venezia-Giulia region through an independent dataset based on hail pads. The estimated hail probability has been used to characterize the seasonality, long-term variability and trends of the hail frequency and to investigate the potential large-scale drivers of hailstorms over Italy. 



Púčik, T., Castellano, C., Groenemeijer, P., Kühne, T., Rädler, A. T., Antonescu, B., & Faust, E. (2019). Large hail incidence and its economic and societal impacts across Europe. Monthly Weather Review, 147(11), 3901-3916. doi: 10.1175/MWR-D-19-0204.1.

Martius, O., Hering, A., Kunz, M., Manzato, A., Mohr, S., Nisi, L., & Trefalt, S. (2018). Challenges and recent advances in hail research. Bulletin of the American Meteorological Society, 99(3), ES51-ES54. doi: 10.1175/BAMS-D-17-0207.1.

Macdonald, H., Infield, D., Nash, D. H., & Stack, M. M. (2016). Mapping hail meteorological observations for prediction of erosion in wind turbines. Wind Energy, 19(4), 777-784. doi: 10.1002/we.1854.

Mohr, S., Kunz, M., & Geyer, B. (2015). Hail potential in Europe based on a regional climate model hindcast. Geophysical Research Letters, 42(24), 10-904. doi:10.1002/2015GL067118.

Prein, A. F., & Holland, G. J. (2018). Global estimates of damaging hail hazard. Weather and Climate Extremes, 22, 10-23. doi: 10.1016/j.wace.2018.10.004.


How to cite: Hénin, R., Torralba, V., Cantelli, A., Scoccimarro, E., Materia, S., and Gualdi, S.: Modelling hail probability over Italy using a machine learning approach, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4102, https://doi.org/10.5194/egusphere-egu22-4102, 2022.

EGU22-7214 | Presentations | NH1.4

Forecasting Large Hail Using Logistic Models and the ECMWF Ensemble Prediction System

Francesco Battaglioli, Pieter Groenemeijer, and Ivan Tsonesvky

An additive logistic regression model for large hail was developed based on convective parameters from ERA5 reanalysis, severe weather reports from the European Severe Weather Database (ESWD), and lightning observations from the Met Office Arrival Time Difference network (ATDnet). This model was shown to accurately reproduce the spatial distribution and the seasonal cycle of observed hail events in Europe. A spatial map of the modelled mean distribution for hail > 2 cm will be presented.

To explore the value of this approach to medium-range forecasting, a similar statistical model was developed using four predictor parameters available from the ECMWF Ensemble Prediction System (EPS) reforecasts: Mixed Layer CAPE, Deep Layer Shear, Mixed Layer Mixing Ratio and the Wet Bulb Zero Height. Probabilistic large hail predictions were created for all available 11-member ensemble forecasts (2008 to 2019), for lead times from 12 to 228 hours.

First, we evaluated the model’s predictive skill depending on the forecast lead time using the Area Under the ROC Curve (AUC) as a validation score. For forecasts up to two to three days, the model highlights a very high predictive skill (AUC > 0.95). Furthermore, the model retains a high predictive skill even for extended forecasts (AUC = 0.85 at 180 hours lead time) showing that it can identify regions with hail potential well in advance. Second, we compared the forecast spatial probabilities at various lead times with observed hail occurrence focusing on a few recent hail outbreaks. Finally, our four-dimensional model was compared with logistic models based on composite parameters such as the Significant Hail Parameter (SHP) and the product of CAPE and Deep Layer Shear (CAPESHEAR). The four-dimensional model outperformed these composite-based ones at lead times up to four days. The high AUC scores show that this model could improve short-medium range hail forecasts. Preliminary application of this approach to other convective hazards such as convective wind gusts will be presented as well.

How to cite: Battaglioli, F., Groenemeijer, P., and Tsonesvky, I.: Forecasting Large Hail Using Logistic Models and the ECMWF Ensemble Prediction System, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7214, https://doi.org/10.5194/egusphere-egu22-7214, 2022.

EGU22-10975 | Presentations | NH1.4

Observational and numerical study of a giant hailstorm in Attica, Greece, on October 4, 2019

Georgios Papavasileiou, Vasiliki Kotroni, Konstantinos Lagouvardos, and Theodore M. Giannaros

On October 4, 2019, giant hailstones of 11 cm were reported in northern parts of Attica in southern Greece. During the same day, multiple large hail reports of hailstones larger than 3 cm as well as 5 tornadoes were reported in the European Severe Weather Database along the track of a long lived supercell thunderstorm that formed over northeastern Peloponnese and moved northeastwards to Attica and Euboea. In this study, we investigate the synoptic and mesoscale weather conditions that led to this rare event by using upper-air measurements from the Athens International Airport, satellite retrievals from METEOSAT, and reanalysis data from ERA5. 

Furthermore, the predictability of this rare event is studied through high-resolution simulations performed with BOLAM, MOLOCH and WRF-ARW models, which are used operationally by the METEO unit at the National Observatory of Athens. The models were able to reproduce the mesoscale environment associated with these severe weather events, showing a highly unstable environment in Saronic gulf with more than 3000 J kg-1 MLCAPE overlapped by more than 25 m s-1 0–6 km Bulk Shear. However, the models were not able to fairly reproduce the triggering, track and timing of the supercell formation highlighting the great uncertainties associated with the initiation of deep moist convection over areas with complex terrain. Here, we attempt to constrain these uncertainties by applying a diagnostic tool for predicting hail size using an ensemble of high resolution simulations and we discuss its operational usage. 

How to cite: Papavasileiou, G., Kotroni, V., Lagouvardos, K., and Giannaros, T. M.: Observational and numerical study of a giant hailstorm in Attica, Greece, on October 4, 2019, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10975, https://doi.org/10.5194/egusphere-egu22-10975, 2022.

EGU22-8612 | Presentations | NH1.4

A gap filled procedure for the analysis of frost days in southern Italy

Ilaria Guagliardi, Tommaso Caloiero, Giuseppe Pappagallo, and Emanuele Barca

EGU22-7030 | Presentations | NH1.4

Madden–Julian Oscillation related to the prolonged heavy rainfall in East Asia in 2020

Byung-Kwon Moon, Jieun Wie, and Jinhee Kang

In East Asia, unusually long-term and heavy rainfall in 2020 resulted in concentrated socio-economic damage and flooding. In this study, the characteristics of the Madden–Julian Oscillation (MJO) related to the prediction of heavy rainfall in East Asia were analyzed using the sub-seasonal to seasonal (S2S) prediction model. In 2020, unusually high precipitation fell in East Asia, compared to an average year, for an extended time. Precipitation was concentrated from the end of June to the middle of August; therefore, the analysis was carried out with an initial model date of July 2, 2020, while the lead-time was selected 1–31 day (July 3 to August 1). The model underestimated cumulative precipitation compared to observations, with KMA and UKMO having the lowest errors and ECMWF and CMA having the largest errors. The 850-hPa position altitude and wind field anomaly was analyzed and averaged over the prediction period. The results revealed that models with large errors showed different locations for the western and northern boundaries of the high pressure in the western North Pacific region, relative to observations, or else underestimated the size of the high-pressure zone. Based on the MJO prediction phases for July in the S2S models, models with good precipitation prediction performance in East Asia mainly showed phases 1–3 that were similar to observations and their amplitudes were also large. In contrast, models with poor prediction performance exhibited fewer instances of phases 1–3 on strong days or their amplitudes were small. This suggests that if an S2S model predicts the characteristics of the MJO accurately, similar to observations, it could improve predictions of summer precipitation in East Asia.

This work was funded by the Korea Meteorological Administration Research and Development Program under Grant KMI2020-01212.

How to cite: Moon, B.-K., Wie, J., and Kang, J.: Madden–Julian Oscillation related to the prolonged heavy rainfall in East Asia in 2020, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7030, https://doi.org/10.5194/egusphere-egu22-7030, 2022.

EGU22-11944 | Presentations | NH1.4

Weather circulation patterns associated with extreme precipitation events in Italy

Wazita Scott, Marco Gaetani, and Giorgia Fosser

In the last years, many countries in Europe have been experiencing an increased frequency of extreme precipitation leading to natural disasters like floods and landslides. In Italy, the majority of the country’s natural disasters have been related to extreme precipitation. Floods and landslides have led to the country experiencing great loss in its social and economic structure. Early warning systems are important to stakeholders such as Disaster Risk Managers to make informed decisions in relation to a forecasted disaster.

Extreme precipitation is often associated with specific circulation patterns. Precursor information about atmospheric circulation patterns can therefore act as an indicator of an oncoming extreme precipitation event. The objective of this work is to identify the weather circulation patterns associated with extreme precipitation events over Italy.

E-OBS precipitation datasets were used to identify the most intense extreme precipitation events for each season for the period 1990-2020 across Italy. Mean sea level pressure and 500 hPa geopotential height from the ERA5 dataset were used to identify circulation anomalies associated with the extreme events. The analysis is performed by clustering extreme precipitation events into three homogeneous climatic zones in Italy defined following the Köppen-Geiger classification.

Results show that extreme precipitation events are always associated with an intense low pressure system located within the Euro-Mediterranean region. Depending on the location of precipitation extremes across different climatic zones, low pressure location changes, also modifying the atmospheric circulation and the associated moisture transport. Namely, for precipitation extremes occurring in the Italian peninsula, the low pressure is located in central-western Europe, while for extremes in Sardinia and Sicily, low pressure is in the Mediterranean. 

How to cite: Scott, W., Gaetani, M., and Fosser, G.: Weather circulation patterns associated with extreme precipitation events in Italy, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11944, https://doi.org/10.5194/egusphere-egu22-11944, 2022.

EGU22-8193 | Presentations | NH1.4

Sensitivity of WRF microphysics schemes to Convective Permitting Simulation of January 2017 Heavy Rainfall Events in Southern Thailand

Yi Wang, Netsanet Alamirew, Jimy Dudhia, Changhai Liu, Cherith Moses, and Kanchana Nakhapakorn

Heavy precipitation is a major natural hazard that can have severe impacts.  In response to global warming, the character of heavy precipitation is expected to change. Projections of the future hydrologic cycle, especially of heavy precipitation, are uncertain. Especially at the regional scale, different data sources, such as different ensembles of global and regional climate models (GCMs and RCMs), provide sometimes conflicting conclusions. Therefore, it is even more important to investigate where differences between ensembles lie and to which processes they can be attributed.

A precipitation scaling (introduced by Paul O’Gorman) is used to disentangle thermodynamic and dynamic contributions in extreme precipitation. In this work, we compare the results of CMIP5 and CMIP6 and focus on climate change signals between the periods 1971-2000 and 2071-2100 over Europe. The thermodynamic component provides homogeneous signals across Europe with a rise in extreme precipitation of about 7 %/K. In contrast, the dynamic component shows no spatial homogeneous results where the dynamic contribution can even modify the thermodynamic signal. The spread between the models within one ensemble is much larger. However, based on initial analyses, the spread in the CMIP6 models appears to have become smaller compared to CMIP5. This means, understanding the dynamic changes is the key to understanding the differences between the ensembles.

As a next step, to analyze the discrepancy between CMIP5 and CMIP6 in terms of atmospheric circulation changes, we look into three atmospheric drivers: tropical and polar amplification of global warming and changes in stratospheric vortex strength.  

How to cite: Ritzhaupt, N. and Maraun, D.: Differences in the regional pattern of projected future changes in extreme precipitation over Europe are driven by the dynamic contribution, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7209, https://doi.org/10.5194/egusphere-egu22-7209, 2022.

EGU22-4588 | Presentations | NH1.4

A Causality-guided Approach for Predicting Future Changes in Extreme Rainfall over China Using Known Large-scale Modes

Kelvin S. Ng, Gregor C. Leckebusch, and Kevin Hodges

Over the past few decades, while several advancements in improving the performance of global climate models (GCMs), such as predicting mean climate,  have been made, predicting extreme rainfall events related to Mei-yu fronts (MYFs) and tropical cyclones (TCs) remains an open challenge. This is partially due the coarse spatial resolution of the GCMs that restricts their ability to represent extreme events and the associated processes on relevant spatial scales. This poses a problem for stakeholders as a failure to take appropriate precautionary action before the occurrence of extreme events can have disastrous consequences. Although the spatial resolutions of typical GCMs are too coarse to simulate extreme precipitation accurately, they are more likely to be able to simulate large-scale climate modes (LSCMs) better. Given that the activities of MYFs and TCs are linked to LSCMs, we can make use of these causal connections between LSCMs and extreme rainfall associated with MYFs/TCs to construct useful prediction models. This can then be applied to the outputs of climate GCM simulations to increase our capability in predicting extreme rainfall in the future.

In this presentation, we demonstrate a novel technique based on causality-guided statistical models (CGSMs) to assess the projected future changes of extreme rainfall associated with MYFs and TCs over China using the CMIP6 historical and SSP585 scenario simulations for four selected models. First, we show that CGSMs, which are constructed using historical observations and reanalysis, have good performance in modelling historical observations. Then we compare extreme rainfall related to MYFs/TCs from the CMIP6 historical direct output of the selected models with the CGSMs predictions. Our results show that the climatological patterns of CMIP6 direct historical outputs are different to the observed climatological patterns. Yet, CGSMs driven by CMIP6 LSCMs can produce similar patterns as the observed climatology. For the projected change under the SSP585 scenario, projections based on CGSMs provide a more coherent picture than CMIP6 direct model outputs. This shows the potential of causality-guided approach in coarse resolution climate model outputs. The implication and potential use of this approach is also discussed.

How to cite: Ng, K. S., Leckebusch, G. C., and Hodges, K.: A Causality-guided Approach for Predicting Future Changes in Extreme Rainfall over China Using Known Large-scale Modes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4588, https://doi.org/10.5194/egusphere-egu22-4588, 2022.

EGU22-8566 | Presentations | NH1.4

Storm-type specific scaling of sub-daily precipitation with temperature over the North Atlantic and Europe

Jennifer Catto, Phil Sansom, and David Stephenson

Sub-daily precipitation extremes are expected to increase in intensity in a warming climate, at a rate higher than that expected from the Clausius Clapeyron scaling. Depending on the region, these precipitation extremes can be caused by different weather system types, such as extratropical or tropical cyclones, fronts, and thunderstorms. In this study we use a storm typology, based on the objective identification of cyclone, fronts and thunderstorms, to add insight to the scaling relationship between temperature and extreme precipitation.

We use 6-hourly information on the type of weather system present at each grid box over the North Atlantic and European region from ERA5 (1981-2000) during boreal winter (DJF). The mean hourly 2-m dew-point temperature over the 6 hours closest to the weather system type, and the maximum of the hourly precipitation over the same period are then used to estimate the scaling of the precipitation extremes with temperature for each storm type. Preliminary results using quantile regression we find significantly larger scaling for weather systems including thunderstorms (greater than CC scaling) than for those that do not. We also find that for the most common weather systems over Northern Europe (front only and cyclone and front together), the scaling of extreme precipitation with temperature is below CC scaling. The future impacts of the extreme precipitation events will depend on the future changes in the frequency of different weather system types as well as the temperature scaling.

How to cite: Catto, J., Sansom, P., and Stephenson, D.: Storm-type specific scaling of sub-daily precipitation with temperature over the North Atlantic and Europe, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8566, https://doi.org/10.5194/egusphere-egu22-8566, 2022.

Extreme, large-scale precipitation events can lead to extreme river floodings which are one of the most dangerous weather events for society when occurring in highly populated areas. However, the largest impacts are caused by very rare events with return periods on the order of 100 years. To do a quantitative and robust analysis of daily 100-year precipitation events, observational time series are typically too short. Therefore, an approach is applied here in which operational ensemble prediction data from the ECMWF are used to generate a large pool of simulated, but realistic daily precipitation events (corresponding to 1200 years of data) from which several 100-year events can be analysed. For five different major Central European river catchments, composite analyses show that 100-year precipitation events in all catchments are typically associated with an upper-level trough moving into Central Europe 24h to 48h before the occurrence of the events. During the 24h before the events, details in the progression of the trough and the location of the associated surface cyclone determine in which catchment extreme precipitation occurs. A comparison to composite analyses of less extreme precipitation events shows that dynamical mechanisms such as an amplified mid-tropospheric trough/cut off are more important for an intensification of precipitation events in the Danube and Oder catchments while in the Elbe, Rhine and Weser/Ems catchments thermodynamical mechanisms such as a larger moisture flux are more important. The question how a warmer climate will affect the dynamical processes of such extreme precipitation events will be investigated in a follow-up study.

How to cite: Ruff, F. and Pfahl, S.: Dynamical analysis of large-scale 100-year precipitation events over Central European river catchments and their differences to less extreme events, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4205, https://doi.org/10.5194/egusphere-egu22-4205, 2022.

Climate change has a significant role in increasing extreme precipitation, including the intensity, frequency, and magnitude of events due to increases in atmospheric moisture and climate variability. This means that future increases in floods due to climate change must be considered in the construction of flood defenses, as well as the planning of new infrastructure and hydraulic structures. Previous approaches for stress testing the design of flood defenses have relied on the scenario neutral approach and the use of harmonic functions to represent changes in the seasonality and mean of precipitation. Such approaches may inadequately account for changes in extreme precipitation, especially in runoff dominated catchments. Here, we adapt the scenario neutral approach by integrating a discrete wavelet transform (DWT) to develop the flood response surface. Such an approach allows evaluation of flood sensitivity to high and low frequency components of precipitation. Using 39 catchments in Ireland, we examine the sensitivity of flooding (QT20) to changes in low and high frequency precipitation and air temperature. A sensitivity domain of 525 extreme precipitation scenarios is applied by combining 21 low frequency and 25 high frequency sets of precipitation and air temperature changes, with short duration frequency incorporated in each harmonic wavelet function. Clustering and discriminant analysis are used to create a typology of catchment sensitivity based on generated response surfaces, the mean of annual maximum precipitation, and the mean of annual maximum flows. Results allow characterization of catchment sensitivity in gauged and ungauged locations and the integration of a wider spectrum of precipitation changes when assessing sensitivity allowances for climate change.  


How to cite: Meresa, H. and Murphy, C.: Evaluating flood sensitivity to changes in high and low frequency precipitation using a discrete wavelet transform , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13038, https://doi.org/10.5194/egusphere-egu22-13038, 2022.

EGU22-3090 | Presentations | NH1.4

Flood analysis using HEC-RAS: The case study of Majalaya, Indonesia under the CMIP6 projection

Faizal Immaddudin Wira Rohmat, Ioanna Stamataki, Zulfaqar Sa'adi, and Djelia Fitriani

Flooding is a natural disaster with extremely wide-reaching impacts and is a recurring problem in Indonesia. Whilst possible impacts of climate change are expected to aggravate flood risk in already flood-vulnerable areas, many countries struggle to achieve the United Nations’ (UN) 2030 Sustainable Development Goals (SDGs) to achieve a better and more sustainable future for all. Using the case study of Majalaya, Indonesia, the authors investigated the impact of climate change and climate variability on urban flood risk through science-based spatio-temporal flood simulations. Based on the ensemble of 17 General Circulation Models (GCMs) CMIP6, the near-future (2021 to 2050) flood projection under Shared Socioeconomic Pathways (SSPs) 2.6 (low forcing), 4.5 (medium forcing) and 8.5 (high end forcing) common to historical (1981 to 2014) was simulated. The area’s future risk of flooding was then investigated and adaptation measures were suggested for reducing and mitigating worsening flood conditions. A numerical model was developed in HEC-RAS that represented the city of Majalaya and the results were combined with the ensemble of climate projections to enable the assessment of the effects of flooding due to the combined effect of climate change and urbanisation. The model was calibrated using historical stream gauge records and past extreme flood inundation boundaries. Using the model’s output metrics (e.g. flood depth, velocity) and local demographic data, the project aims then to use a vulnerability assessment framework to quantify the impact of climate change on flood risk. The modelling results will allow for spatio-temporal mapping of the flood-prone areas in Majalaya, which will help reduce risk and vulnerability for disadvantaged populations. The development of flood vulnerability maps and future flood risk projections will assist the government in developing land-use and flood prevention management policies. This research area, drawing from the combination of flood modelling and the use of climate projections, allows for an assessment of future flood risk scenarios of the city of Majalaya and paves new avenues towards future research.

How to cite: Rohmat, F. I. W., Stamataki, I., Sa'adi, Z., and Fitriani, D.: Flood analysis using HEC-RAS: The case study of Majalaya, Indonesia under the CMIP6 projection, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3090, https://doi.org/10.5194/egusphere-egu22-3090, 2022.

EGU22-5815 | Presentations | NH1.4

Parameter exploration for hydrological hazard interactions in a data-scarce catchment.

Pablo López, Liz Holcombe, Katerina Michaelides, and Jeremy Phillips

Extreme rainfall events are increasing the frequency of hydrological hazards such as landslides, debris flow, and erosion processes. Understanding the coupling of these hazards is still a challenging task, current methodologies often take a single hazard approach without integrating the mechanisms that describe the influence of one hazard on another under the same rainfall event. Physically-based distributed models have overcome these limitations incorporating the coupling of hillslope-hydrological processes that influence the interactions of hydrological hazards at the catchment scale. Nonetheless, within these models, the physical characteristics of the catchment domain are subject to a large spatial variability increasing the uncertainty in the parameters that influence the interaction of these hazards, hindering their representation in data-scarce catchments. The aim of this study is to elaborate an experimental design to parameterize a physically-distributed model to identify the parameters that have an acceptable influence in representing and describing hydrological hazard interactions under a data-scarce environment.

The study area is set in the Soufriere catchment in Saint Lucia, which recorded multiple landslides and debris flows with impacts on catchment erosion triggered by Hurricane Tomas in October 2010. The OpenLISEM model was used to estimate the parameters that influenced the triggering of hydrological hazards that occurred during Hurricane Tomas. The parameter estimation was performed through a Global Sensitivity Analysis (GSA) All-At-a-Time (ATT) to assess simultaneously under 144 simulations the estimation of hydrological and geotechnical parameters. The parameters subject to Sensitivity Analysis were saturated moisture content, saturated hydraulic conductivity, soil cohesion, and internal friction angle. The results were verified through the Sorensen-Dice coefficient. The coefficient was calculated through a spatial overlapping method between landslide simulated areas and landslide inventory areas corresponding to the Hurricane Tomas triggered landslides obtained from the British Geological Survey (2014). The results indicated that the representation of landslides, debris flows, and erosion processes on the OpenLISEM model highly depend on the quality of the input data. The latter was confirmed by the Sorensen-Dice coefficient indicated low spatial overlap values between the simulations performed. Nevertheless, the response of the OpenLISEM model to an acceptable landslide representation similar to the landslides triggered by Hurricane Tomas was influenced in the first place by the soil cohesion and internal friction angle and in the second place by the saturated moisture content and saturated hydraulic conductivity. The identification of these parameters introduces an improvement to provide an acceptable representation of hydrological hazards interactions given the data available in a data-scarce environment.

How to cite: López, P., Holcombe, L., Michaelides, K., and Phillips, J.: Parameter exploration for hydrological hazard interactions in a data-scarce catchment., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5815, https://doi.org/10.5194/egusphere-egu22-5815, 2022.

The evaluation of the resilience of flood protection systems requires the assessment of the impact of climate change scenarios on future flood regimes. Due to the high computational effort and to the scarcity of hourly climate modelling chains, expected changes in future floods are often simulated by hydrological models on a daily basis, even for basins with short response times, where hourly simulations would be needed.

In this work, the expected occurrence and magnitude of future flood events is modelled through the coupling of bias-corrected local climate scenarios at hourly time scale and continuous rainfall-runoff modelling, in reference to the Panaro river (one of the OpenAir Laboratories in the OPERANDUM H2020 project), a tributary of the Po River in the Apennines.

The investigation exploits hourly precipitation and daily max/min temperature (used for interpolation at hourly scale) timeseries for a subset of climate modelling chains included in the EURO-CORDEX initiative through the dynamical downscaling of Global Climate Models under the RCP 8.5 concentration scenario. The comparison with observed spatial fields obtained from weather stations and from gridded E-OBS products allows to assess the biases affecting the climate raw data.

The Scaled Distribution Mapping (SDM) bias correction procedure (Switanek et al. 2017), that preserves raw climate model projected changes in the bias-corrected series, is then applied to adjust the raw model output towards observations.

A semi-distributed, continuously simulating rainfall-runoff model is parameterised on the basis of the observed meteorological and streamflow time-series, especially focusing on the reproduction of past flood events. The model is then run to reproduce the continuous hourly streamflow time-series in the Panaro river over past and future decades, providing in input i) observed meteorological forcing based on ground stations, ii) raw and bias-corrected climate scenarios over the control period, iii) bias-corrected climate scenarios for the future decades. Finally, the flood events are extracted from the continuous streamflow simulations and the changes in the flood signals expected over the future decades are analysed, in terms of both peaks and volumes.



Switanek, M. B., Troch, P. A., Castro, C. L., Leuprecht, A., Chang, H.-I., Mukherjee, R., and Demaria, E. M. C.: Scaled distribution mapping: a bias correction method that preserves raw climate model projected changes, Hydrol. Earth Syst. Sci., 21, 2649–2666, https://doi.org/10.5194/hess-21-2649-2017, 2017.

How to cite: Toth, E., Neri, M., Reder, A., and Rianna, G.: Future occurrence and magnitude of flood events in the Panaro River (Northern Italy): coupling bias-corrected hourly climate scenarios and semi-distributed rainfall-runoff modelling, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6503, https://doi.org/10.5194/egusphere-egu22-6503, 2022.

EGU22-9985 | Presentations | NH1.4

Flood sedimentological records off the south Portuguese coasts

Pedro Costa and the RV Meteor M-152 scientific team

In the present climate change scenario, the perception regarding the frequency and magnitude of flood events is changing. Nevertheless, to establish return periods and flooding patterns it is important to expand the time-window of observation beyond the historical period. To achieve this purpose, it is crucial to use the sedimentological record of alluvial plains and river banks. However, anthropogenic activities have disrupted the sedimentary dynamics thus interfering with the geomorphological settings and their stratigraphy’s. An alternative setting is the shallow nearshore, below storm wave base, where potentially stratigraphy is better preserved.

After a campaign on board RV Meteor, a group of sediment cores were collected offshore the south Portuguese coast. These cores cover the Holocene Epoch and consist essentially on alternations of silty bioclastic layers with some sandy units rich in quartz and bioclasts. The vertical variation of several sedimentological proxies allowed the differentiation of disruptive events, mostly related with extreme marine inundations or possibly linked with abrupt fluvial discharges.

Here we present some preliminary results based on grain-size and compositional analysis (XRD) and attempt to establish a chronology of those events. The preliminary data interpretation seems to suggest an increase in the flood record over the last 1000 years. However, this observation needs further support from other locations in the area and also requires a better understanding of post-depositional processes that affect the record of thin muddy layers on the nearshore stratigraphy.


This work was supported by projects OnOff - PTDC/CTAGEO/28941/2017 – financed by FCT. and FCT/UIDB/50019/2020 - IDL, also funded by FCT.

How to cite: Costa, P. and the RV Meteor M-152 scientific team: Flood sedimentological records off the south Portuguese coasts, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9985, https://doi.org/10.5194/egusphere-egu22-9985, 2022.

EGU22-1843 | Presentations | NH1.4

The impact of compound drought and heatwave events on the unprecedented 2020 fire season in the Pantanal, Brazil

Renata Libonati, João L Geirinhas, Patrícia S Silva, Ana Russo, Julia A Rodrigues, Liz B C Belem, Joana Nogueira, Fabio O Roque, Carlos C DaCamara, Ana M B Nunes, Jose A Marengo, and Ricardo M Trigo

The year of 2020 was characterised by an unprecedented fire season in Pantanal, the largest continuous tropical wetland, located in south-western Brazil. This event was the largest ever recorded over, at least, the last two decades, reaching an amount of 3.9 million ha and affecting 17 million vertebrates1,2. Recent evidence points out that this event resulted from a complex interplay between human, landscape, and meteorological factors3,4. Indeed, much of the Pantanal has been affected by severe dry conditions since 2019, with 2020’s drought being the most extreme and widespread ever recorded in the last 70 years5,6. The drought condition was maintained at record levels during most of the year of 2021, following the climate change scenarios expected for this region7. Prior to this comprehensive assessment, the 2020’s fire season has been analyzed at the univariate level of a single climate event, not considering the co-occurrence of extreme and persistent temperatures with soil dryness conditions. Here, we show that the influence of land–atmosphere feedbacks contributed decisively to the simultaneous occurrence of dry and hot spells, exacerbating fire risk. These hot spells, with maximum temperatures 6 ºC above-average were associated with the prevalence of the ideal synoptic conditions for strong atmospheric heating, large evaporation rates and precipitation deficits4. We stress that more than half of the burned area during the fire season occurred during compound drought-heatwave conditions. The synergistic effect between fuel availability and weather-hydrological conditions was particularly acute in the vulnerable northern forested areas. These findings are relevant for integrated fire management in the Pantanal as well as within a broader context, as the driving mechanisms apply across other ecosystems, implying further efforts for monitoring and predicting such extreme events.



[1] Garcia, L.C, et al.. Record-breaking wildfires in the world’s largest continuous tropical wetland: Integrative fire management is urgently needed for both biodiversity and humans. J. Environ. Manage. 2021, 293, 112870.

[2] Tomas, W. M., et al. Counting the dead: 17 million vertebrates directly killed by the 2020’s wildfires in the Pantanal wetland, Brazil. Sci. Rep. accepted.

[3] Libonati, R.; et al. Rescue Brazil’s burning Pantanal wetlands. Nature. 2020, 588, 217–219.

[4] Libonati, R., et al. Assessing the role of compound drought and heatwave events on unprecedented 2020 wildfires in the Pantanal. Environmental Research Letters. 2022, 17, 1.

[5] Thielen, D., et al. The Pantanal under Siege—On the Origin, Dynamics and Forecast of the Megadrought Severely Affecting the Largest Wetland in the World. Water. 2021, 13(21), 3034.

[6] Marengo, J.A., et al. Extreme Drought in the Brazilian Pantanal in 2019–2020: Characterization, Causes, and Impacts. Front. Water. 2021, 0, 13.

[7] Gomes, G.D.; et al.. Projections of subcontinental changes in seasonal precipitation over the two major river basins in South America under an extreme climate scenario. Clim. Dyn. 2021, 1-23.


This work was supported by Project Rede Pantanal from the Ministry of Science, Technology and Innovations of Brazil (FINEP grant 01.20.0201.00). R.L. was supported by CNPq [grant 305159/2018–6] and FAPERJ [grant E26/202.714/2019]

How to cite: Libonati, R., Geirinhas, J. L., Silva, P. S., Russo, A., Rodrigues, J. A., Belem, L. B. C., Nogueira, J., Roque, F. O., DaCamara, C. C., Nunes, A. M. B., Marengo, J. A., and Trigo, R. M.: The impact of compound drought and heatwave events on the unprecedented 2020 fire season in the Pantanal, Brazil, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1843, https://doi.org/10.5194/egusphere-egu22-1843, 2022.

EGU22-4353 | Presentations | NH1.4

How does the rise of atmospheric water demand affect flash drought development in Spain?

Iván Noguera, Fernando Domínguez-Castro, and Sergio M. Vicente-Serrano

Flash droughts are distinguished by a rapid development and intensification, which increase the potential drought impacts on natural and socio-economic systems. In recent years, a great effort has been made to identify and quantify this type of events in different regions of the world using different metrics. We developed a methodology to analyze the flash droughts based on SPEI at short-time scale (1-month) and high-frequency data (weekly). Thus, we characterized the occurrence of flash drought in Spain over the period 1961-2018 and showed that flash drought is a frequent phenomenon (40% of all droughts were characterized by rapid development), which exhibit a great spatiotemporal variability. The northern regions, where a higher frequency of flash droughts was found, showed negative trends in the frequency of flash droughts, while the central and southern regions subject to fewer flash drought events showed generally positive trends. Usually, the flash drought is associated with severe precipitation deficits and/or anomalous increases in atmospheric evaporative demand (AED), but while the role of precipitation seems obvious and essential, the role played by AED in triggering or reinforcing flash drought episodes is much more complex and exhibits important spatial and temporal contrasts. In Spain, the effect of AED is mainly restricted to water-limited regions and the warm season, but its role is minimal in energy-limited regions and in cold periods in which precipitation deficits are the main cause of flash drought development. However, the contribution of the AED on the development of flash droughts has increased notably over the last six decades, thus becoming a decisive driver in explaining the occurrence of the latest flash droughts in some regions of Spain. These findings have strong implications for proper understanding of the recent spatiotemporal behavior of flash droughts in Spain and illustrate how this type of event can be related to global warming processes.

How to cite: Noguera, I., Domínguez-Castro, F., and Vicente-Serrano, S. M.: How does the rise of atmospheric water demand affect flash drought development in Spain?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4353, https://doi.org/10.5194/egusphere-egu22-4353, 2022.

EGU22-9831 | Presentations | NH1.4

A comprehensive study of the extreme heat and drought of the 2018 European summer 

Efi Rousi, Andreas Fink, and Laura Suarez-Gutierrez and the ClimXtreme project

The summer of 2018 was an extraordinary extreme season in Europe bringing simultaneous, widespread and coherent extremes of heat and drought in large parts of the continent with extensive impacts on agriculture, forests, water supply, and large financial losses. Joining different areas of expertise available within the German ClimXtreme project (https://www.climxtreme.net/index.php/en/), we present a comprehensive analysis of the 2018 extreme European summer in terms of heat and drought.

First, we define the events using different traditional, as well as, novel metrics. Then, we present a comprehensive dynamical analysis of the background atmospheric state, in order to better understand the events by bringing together different approaches. First results indicate that the summer of 2018 was characterized by persistent NAO+ conditions, which favored the occurrence and persistence of a Eurasian double jet stream structure. Both of those features contribute to the occurrence of heat extremes in western and central Europe. Additionally, positive blocking frequency anomalies were present over Scandinavia, which favored the intense heatwave in the region. An analysis of Rossby wave activity during the 2018 summer shows an eastward propagation of Rossby wave packets from the Pacific towards the Atlantic and the European continent already at the end of June and before the initiation of the heatwave over Scandinavia. When the peak over the Iberia occurs, there is no pronounced Rossby wave activity, which highlights the different mechanisms involved, i.e., subtropical ridges and Saharan air intrusions.

Low-frequency precursors, such as SSTs and soil moisture in spring, and their role in shaping those extreme events are also analyzed. A conspicuous tripolar SST anomaly pattern over the N. Atlantic, consisting of a cold blob south of Greenland and Iceland, was prominent starting in early spring. At the same time, a severe soil moisture depletion over Germany between April and July reflects the persistently warm and dry conditions in spring 2018 that caused anomalously dry soils in summer.

Last but not least, a tailored attribution study is presented, comparing the 2018 central European heatwave with similar events in the MPI Grande Ensemble and in CMIP6 models. To provide tailored information for this study, the event was defined as the maximum daily temperature in Germany averaged over different lengths of periods of consecutive days to account for the prolonged heat that characterized the summer of 2018. According to the MPI-GE almost every summer will be more extreme than 2018 under a 2˚C warmer world.

As heat and drought conditions are likely to become more frequent and intense under anthropogenic climate change, we argue that the scientific community can benefit from such comprehensive and transdisciplinary studies.

How to cite: Rousi, E., Fink, A., and Suarez-Gutierrez, L. and the ClimXtreme project: A comprehensive study of the extreme heat and drought of the 2018 European summer , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9831, https://doi.org/10.5194/egusphere-egu22-9831, 2022.

EGU22-359 | Presentations | NH1.4

Is climate change to blame for rising climatic disasters mortality in Nepal?

Dipesh Chapagain, Luna Bharati, and Christian Borgemeister

Human mortality and economic losses due to climatic disasters have been rising globally. Several studies argue that this upward trend is due to rapid growth in the population and wealth exposed to disasters. Others argue that rising extreme weather events due to anthropogenic climate change are responsible for the increase. Hence, the causes of the increase in disaster impacts remain elusive. Disaster impacts are higher in low-income countries, but existing studies are mostly from developed countries or at the cross-country level. This study will assess the attribution of rising climatic disaster mortality to indicators of climatic hazards, exposure, and vulnerability at the subnational scale in a low-income country, using Nepal as a case study. 
This empirical study at the scale of 753 local administrative units of Nepal will follow a regression-based approach that will overcome the limitations of the commonly used loss normalization approach in studying the attribution of disaster-induced loss and damage.

In Nepal, landslides and floods account for more than two-thirds of the total climatic disaster mortality. Hence, we will use the past 30 years (1991-2020) landslides and floods mortality data from DesInventar and Nepal's Disaster Risk Reduction portal as the dependent variable. As explanatory variables to represent climatic hazards, we will estimate and use mean and extreme precipitation indices from observational data by the Department of Hydrology and Meteorology Nepal. We will use the local unit’s population as a proxy of disaster exposure. Socio-economic and environmental indicators such as annual per capita income, percentage of people with access to mobile phones and internet, land cover distribution, and slope will be used as indicators of vulnerability. Exposure and vulnerability indicators data will be accessed from Nepal’s Central Bureau of Statistics and other sources. This study is expected to identify indicators of climatic hazards, exposure, and vulnerability that could explain the spatial and temporal variability of climatic disaster mortality in Nepal. Similarly, it will provide new insights on the role of climate change on rising climatic disaster mortality from the low-income countries’ context.

How to cite: Chapagain, D., Bharati, L., and Borgemeister, C.: Is climate change to blame for rising climatic disasters mortality in Nepal?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-359, https://doi.org/10.5194/egusphere-egu22-359, 2022.

EGU22-11093 | Presentations | NH1.4

Quantifying the hydrological responses of future climate changes on a large scale river basin in India

Shaini Naha, Miguel Angel Rico Ramirez, and Rafael Rosolem

The serious hydrological consequences of climate change faced by developing countries like India show regional variability. Understanding these regional hydrologic impacts has a crucial role in the management of water resources. Mahanadi river basin (MRB) is a major large-scale river basin in India that is predicted to face severe floods under future climate change scenarios. Commonly, climate change impacts are simulated for a specific decade, specific scenario, or specific climate model in the future. We, however, employed an arguably more objective, approach that would identify the impacts of all possible combinations of specific change within the possible mean annual temperature and precipitation 2-dimensional scenario space (derived from thirteen CMIP6 models) on the hydrological responses. CMIP6 is the recent generation of climate models, released to overcome the drawbacks of the previous generation CMIP5 models such as under/overestimating the monsoon characteristics over the Indian subcontinent. Our methodological approach also involves using an ensemble of VIC models, representing the overall model uncertainty due to parameter value choices, in conjunction with these climate projections, instead of using a single calibrated model to predict the hydrological responses. The climate projections show an overall change in mean annual precipitation and mean annual average temperature that ranges from -5 to +105% and 0-7◦C respectively. This has resulted in significant changes in both mean annual flows and peak flows of up to 2849 and 29,776 m3s-1 respectively. Uncertainties associated with the model parameters, of up to 1211 m3s-1 are observed in the predicted peak flow magnitudes, which is considerably higher than in predicted annual flow magnitudes. Our findings indicate that precipitation mainly controls the future predicted flows in the basin. This study has provided a set of results on the likely future behavior of the MRB mean annual and peak flows under the CMIP6 climate projections. Future projections of hydrologic variables, along with the associated model parameter uncertainties can help with better hydrologic impact assessment and developing adaptation strategies for MRB in India.

Keywords: Climate change, CMIP6, VIC, Mahanadi river basin, flows

How to cite: Naha, S., Rico Ramirez, M. A., and Rosolem, R.: Quantifying the hydrological responses of future climate changes on a large scale river basin in India, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11093, https://doi.org/10.5194/egusphere-egu22-11093, 2022.

EGU22-7449 | Presentations | NH1.4

Safeguarding heritage sites from hydrometeorological extremes: the Santa Croce district in Florence

Paolo Tamagnone, Enrica Caporali, and Alessandro Sidoti

Humankind is currently living in an era governed by continuous climate warm-up and unstoppable urbanization, in which the ongoing climate change is leading to an exacerbation of hydrometeorological events. With an intensification of magnitude and frequency of extreme rainfall events, engineers and scientists are striving to develop methodologies and strategies to effectively defend people and assets from pluvial flooding. Pluvial floods produced by local, intense, and fast rainstorms cause the surcharge of urban drainage systems inducing the inundation of streets and buildings before the runoff reaches the receptor watercourse. Pluvial flood damage has been defined as an ‘invisible hazard’ but it increasingly weighs on the budget of direct flood losses, raising the costs incurred by flood damages. Besides the tangible losses, the costs may be even higher when the intangible share is considered, such as the potential loss of heritage held in ancient towns. For this reason, the inestimable cultural and artistic heritage preserved in historical buildings require a high-level of protection against hazards induced by natural calamities. The present study investigates extreme rainfall-related impacts and hazards threatening the cultural heritage situated in the most vulnerable areas of the Santa Croce district (Florence, Italy). The district hosts some of the most important buildings of the city: the National Central Library of Florence and the Opera di Santa Croce. The geographical location of this monumental complex makes the cultural heritage guarded inside of it dangerously exposed to multiple sources of flood hazard. Firstly, river flooding due to the proximity to the Arno River (this area has been already harshly damaged by the catastrophic flood in 1966). Secondly, flooding by sewage since that the internal drainage network is linked with one of the main sewer conduits of the city. Then, surface runoff flowing down from the headwater. Considering this framework, the pluvial flood hazard assessment is performed using a 1D/2D dual drainage model specifically implemented to simulate all hydraulic phenomena occurring both on the surface and through the sewer network. The analysis comprehends a series of scenarios designed to simulate the impact of hydrometeorological extremes on the study area and each possible concatenation of consequences or failures. The hydraulic model incorporates different layers of information: the high-resolution digital surface model of the area and buildings, the public sewer network, and the internal rainfall collection system of the district. Geometrical features and technical specifications of the sewer network have been retrieved from detailed field surveys and research in historical archives. Model’s outcomes allow identifying the critical nodes within the drainage network, delineating the most vulnerable areas, and prioritizing the rescue efforts in case of severe cloudbursts. Results may help site managers to improve the efficiency of their hazard management and emergency plans. Furthermore, the study intends to propose suitable technical solutions for safeguarding the cultural heritage where designing intrusive engineering works hardly fits within the historical urban context.

How to cite: Tamagnone, P., Caporali, E., and Sidoti, A.: Safeguarding heritage sites from hydrometeorological extremes: the Santa Croce district in Florence, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7449, https://doi.org/10.5194/egusphere-egu22-7449, 2022.

EGU22-12481 | Presentations | NH1.4

Chennai’s urban river systems – environmental changes, anthropogenic pollution and flood-induced remobilization

Luisa Bellanova, Piero Bellanova, Jan Schwarzbauer, Frank Lehmkuhl, Philipp Schulte, and Klaus Reicherter

With a projected increase in frequency and magnitude of extreme weather events, the fast-growing coastal population centers of the Asian Global South experience a higher susceptibility to flood-related pollution. This is fueled by rapid land-use changes, urbanization, a multitude of emission sources, as well as anthropogenic- and flood-induced remobilization and relocation of pollutants. To yield a more comprehensive understanding of riverine and coastal floods in conjunction with these rapid urban and land-use changes, their impact on the environment and the health risks posed to local communities, sedimentary archives need to be studied.

Meandering through densely populated urban areas, Chennai’s rivers (Cooum and Adyar) and coastal systems have been affected by monsoon-induced floods (e.g., 2015 South Indian floods) and the 2004 Indian Ocean tsunami. Simultaneously, Chennai experienced an explosive population growth over the past 30 years, with the coinciding changes in land-use, urbanization, anthropogenic alterations to aquatic systems (e.g., damming, dredging), and (unregulated) environmental pollution. Especially the missing regulations, as well as growing volumes of sewage and physical waste have an enormous toll on the aquatic systems, but also pose threats by remobilization during floods.

To investigate potential flood-induced strata and chemostratigraphic changes over time, a total of nine sediment profiles along the Adyar and Cooum rivers are subject to GC-MS analyses of organic pollutants in correlation to stratigraphic changes in the obtained sediment profiles.

First results indicate that organic pollutants, such as petrogenic compounds (hopanes, PAHs), urban wastewater compounds (LABs, DEHA, methyl-triclosane), technical compounds (Mesamoll®, DPE, NBFA) and pesticides (e.g., DDX) allow for the identification of past flooding events and their characterization in terms of release and distribution of pollution. These proxies are used to assess (chemo-)stratigraphical alterations preserved in these sedimentary archives. However, sedimentary archives in fast-growing, urbanized environments are influenced by physical anthropogenic alterations leading to superimpositions or a hiatus in the sedimentary archives, thus hampering with the (chemo-)stratigraphic reconstruction of past flooding events and environmental changes.

How to cite: Bellanova, L., Bellanova, P., Schwarzbauer, J., Lehmkuhl, F., Schulte, P., and Reicherter, K.: Chennai’s urban river systems – environmental changes, anthropogenic pollution and flood-induced remobilization, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12481, https://doi.org/10.5194/egusphere-egu22-12481, 2022.

EGU22-11221 | Presentations | NH1.4

Moisture origin of the extreme precipitation event in Western Europe in July 2021

Imme Benedict, Florian Polak, Thomas Vermeulen, and Chris Weijenborg

From the 12th to the 15th of July 2021, Western Europe was confronted with an abnormal amount of precipitation leading to extreme floods and enormous damage in western Germany, Belgium, Luxembourg and the Netherlands. Locally, almost thrice as much as the monthly precipitation amount was observed, up to 175 mm in two days. The large-scale weather pattern in Western Europe was characterised by an intense and stationary upper-level cut-off low.

In this study the atmospheric conditions resulting in this extreme precipitation are investigated, with a focus on understanding the enhanced moisture supply leading to the extreme precipitation amounts. Previous to the event, the Baltic area experienced a significant heatwave, and it was hypothesized that due to high evaporation rates more humid air over this region would be transported towards western Europe to result in these enormous amounts of rain.

We analysed the moisture origin of the extreme precipitation with the Lagrangian trajectory diagnostic LAGRANTO applied to both re-analysis data (ERA5) and simulations with the non-hydrostatic weather research and forecasting model (WRF). Both models represent the case rather well. In addition, the impact on precipitation by adapting the sea surface temperature (SST) of both the Baltic and the Mediterranean Sea was studied using WRF. This analysis showed that SST changes in the Mediterranean had the largest impact on precipitation in western Europe. Furthermore, first results indicate that the Mediterranean Sea, which had a positive SST anomaly of 2˚C, was the main moisture source preceding the precipitation event, contrasting our initial hypothesis.

How to cite: Benedict, I., Polak, F., Vermeulen, T., and Weijenborg, C.: Moisture origin of the extreme precipitation event in Western Europe in July 2021, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11221, https://doi.org/10.5194/egusphere-egu22-11221, 2022.

EGU22-9520 | Presentations | NH1.4

The seismic footprint of the devastating July 2021 Ahr Valley flood, Germany

Michael Dietze, Rainer Bell, Thomas Hoffmann, and Lothar Schrott

Valley confined floods are a major hazard. In contrast to large river floods with day long warning time, they can evolve within minutes to hours, exhibit higher flow velocities and drive large amounts of debris into populated places. While many Alpine communities have developed mitigation, early warning and rapid response schemes for this natural hazard type, these measures are virtually unknown in Central European upland regions. Beyond flood protection, lacking measurement infrastructure also prevents retrospective collection of event anatomy data, which would be key to understand the evolution of an event and, hence to improve our response to future hazards.

The 14–15 July 2021 flood that hit the Ahr valley in the Eifel mountains, west Germany, was a drastic example of the potential of such valley confined floods. A wall of water flushed through the deeply incised valley, flooding more than 15 towns and affecting 42,000 people, resulting in the highest number of casualties in Germany since 1962. All gauges along the main channel were destroyed while the flood hydrograph was still on the rising limb and grid power loss interrupted collection and transmission of data from other potential sensors.

Here, we use data from a single seismic station near the town of Ahrweiler, originally deployed for earthquake seismology. Despite grid power cutoff around 23:19 CEST, the station recorded the arrival of the fast rising limb of the flood. We show how even an incomplete record of a single station not set up for flood early warning can be used to infer crucial and timely information about the flood: propagation velocity, water level and debris transport rate. We argue that installing a network of a few distributed low cost seismic sensors could have improved flood early warning and near real time provision of kinetic flood data. More importantly, such a network would be the key for improved response actions for future floods, deemed more likely in Central Europe under the currently changing climate conditions.

How to cite: Dietze, M., Bell, R., Hoffmann, T., and Schrott, L.: The seismic footprint of the devastating July 2021 Ahr Valley flood, Germany, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9520, https://doi.org/10.5194/egusphere-egu22-9520, 2022.

EGU22-2312 | Presentations | NH1.4

Erosion of arable land during the July 2021 flood event in Erftstadt-Blessem, Germany: understanding groundwater sapping

Joel Mohren, Matthias Ritter, Steven A. Binnie, and Tibor J. Dunai

Although fluvial erosion is predominantly governed by surface driven fluvial incision, more exotic erosional processes can significantly contribute to the fluvial shaping of landscapes. To this group belongs sapping caused by concentrated groundwater discharge, which can form a very distinct type of topography (characterised e.g. by the development of theatre-shaped channel heads). Fluvial erosion through sapping occurs where groundwater encounters a rapid change in elevation (i.e. across scarps, cliffs), and it is highly modulated by the physical properties of the solid. Groundwater sapping is, for example, promoted by inhomogeneities of permeability and/or lithological composition of the subsurface, which is often prevalent in sedimentary deposits and along contact boundaries between different lithological units. Consequently, topography shaped by groundwater sapping can be found in many places on Earth and even on Mars, and the formation of these landscapes can integrate over thousands to millions of years. However, in some regions, such as coastal areas, groundwater sapping has been reported to be associated with severe soil loss and high erosion rates on the order of tens of metres per day.

A similar magnitude of soil loss could be observed close to the village of Erftstadt-Blessem, Germany, as caused by severe flooding, peaking the 15th of July 2021. Here, intense rain events caused the formation of local drainage networks towards a gravel pit located to the north of the village. As a consequence, adjacent arable land was subject to intense backward incision, thereby eroding the underlying Quaternary sediments. The erosion formed drainage networks that appear to resemble characteristic groundwater sapping. This fluvial topography was largely preserved after the flooding, thus providing the opportunity to decipher the processes involved in the formation of these features. We use Structure-from-Motion Multi-View Stereo (SfM-MVS) photogrammetry to reconstruct the drainage geometry based on drone imagery (provided by the Kreisverbindungskommando Köln, M. Wiese; additional SfM-MVS photogrammetry data provided by ESRI Deutschland GmbH, T. Gersthofer) and photographs taken in the field using a handheld camera. The data is subsequently used to characterise the drainage networks and to compare the topography to other groundwater sapping landscapes on Earth and on Mars. Additionally, we intend to perform grain size analyses of the different sediment layers and to quantify fallout 239+240Pu in selected samples to asses the physical properties of the substratum and to trace the fate of the radionuclides during the flood event. Our aim is that our data will contribute to a better understanding of how groundwater sapping processes operate over time and to assess the importance of individual factors (e.g. substrate properties, vegetation cover and -type) on the severity of erosion. The outcome could thus not only be important for modelling terrestrial and extra-terrestrial processes but has also practical applications to the loss of arable land and the effects of outburst flooding.

How to cite: Mohren, J., Ritter, M., Binnie, S. A., and Dunai, T. J.: Erosion of arable land during the July 2021 flood event in Erftstadt-Blessem, Germany: understanding groundwater sapping, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2312, https://doi.org/10.5194/egusphere-egu22-2312, 2022.

EGU22-5114 | Presentations | NH1.4 | Highlight

Sediment pollution and morphodynamics of an extreme event: Examples from the July 2021 flood event from the Inde River catchment in North Rhine-Westphalia 

Frank Lehmkuhl, Verena Esser, Philipp Schulte, Alexandra Weber, Stefanie Wolf, and Holger Schüttrumpf

Extreme precipitation and discharge between July 13th and 16th 2021 caused serious flooding with bank erosion, including damages to infrastructure and buildings nearby the Eifel mountain region. Especially the small town of Stolberg and Eschweiler in the Inde River catchment were heavily affected. On-site investigation along the Inde River and its tributary, the Vichtbach creek, after the flood event show that mainly coarse sediments were remobilized and accumulated in the upper and middle reaches. The water masses mobilized not only sediments including gravel but also large objects like broken down trees and cars. In contrast, silty sediments were deposited in the lower reaches.

The Stolberg region is a former mining area with related industries resulting in contaminated soils and tailings close to the floodplains (Esser et al. 2020). Therefore, our investigations also focus on pollution by sediment-bound heavy metals and their distribution in the floodplains before and after this event. Flood sediment samples were taken immediately after the extreme flood event. Based on the results of flood-related pollution monitoring, conducted between 2016 and 2019 (Esser, 2020), the impact of the extreme event in July can be evaluated. During the July flood event, an exceptional amount of pollutants was remobilized. In addition to an increase in pollutants on the modern floodplain, wider areas of older and higher floodplains (Altauen) were also affected.

Esser, V. (2020): Untersuchungen zur fluvialen Morphodynamik und zur rezenten Schadstoffausbreitung in Flusssystemen - Beispiele aus der Grenzregion Belgien, Niederlande und Deutschland. PhD-Thesis, RWTH Aachen University.

Esser, V., Buchty-Lemke, M., Schulte, P., Podzun, L.S., Lehmkuhl, F. (2020): Signatures of recent pollution profiles in comparable Central European rivers - Examples from the International River Basin District Meuse. Catena 193: 104646. https://doi.org/10.1016/j.catena.2020.104646

How to cite: Lehmkuhl, F., Esser, V., Schulte, P., Weber, A., Wolf, S., and Schüttrumpf, H.: Sediment pollution and morphodynamics of an extreme event: Examples from the July 2021 flood event from the Inde River catchment in North Rhine-Westphalia , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5114, https://doi.org/10.5194/egusphere-egu22-5114, 2022.

Due to extreme precipitation and runoff, severe flooding occurred in Germany in the summer of 2021 (July 13th–16th). In the catchment area of the Rur river, especially along its tributaries Inde and Wurm, but also along the Rur itself, this flood caused severe destruction and impacts on modern and older floodplains and anthropogenic utilized areas. This led to the acute and unusual input of harmful organic pollutants, as well as the remobilization and relocation of old burdens.

Particularly floodplains are of central importance during such flood events as their natural functions include water, sediment, and nutrient retention, as well as the self-purification of water bodies. The focus of this investigation was therefore on the importance and relevance of natural floodplains during and after the 2021 summer flood. For this purpose, 16 different floodplains distributed throughout the Rur’s course were sampled immediately after the flood. The objectives were to determine pollutant concentrations, distribution, and accumulation, as well as the identification of potential pollution sources. In this context, the results of previous floodplain sampling and regular monitoring of the river’s sediments are also considered.

Preliminary results indicate elevated concentrations of several organic pollutant groups, including PAHs (polycyclic aromatic hydrocarbons), PCBs (polychlorinated biphenyls), and LABs (linear alkylbenzenes). These substances are indicators of petrogenic pollution, historical (old burdens) and current heavy industry in the catchment area, and, of wastewater and urban pollution, respectively.

By considering these indicators and identifying emission sources (e.g., wastewater treatment plants, destructed infrastructure and industry along the main river and its tributaries) and accumulation areas that are relevant for remobilization, statements can be obtained about the high dynamics of the flood event. Furthermore, the importance of natural floodplains for the accumulation and remobilization of organic pollutants, but also the self-purification of water bodies is thus investigated and emphasized. This is of great importance for the holistic assessment of the fate and behaviour of organic pollutants as well as for the estimation of short- and long-term environmental risks and hazards related to (extreme) flood events.

How to cite: Schwanen, C., Bellanova, P., and Schwarzbauer, J.: The 2021 Flood Disaster in Germany – Distribution, remobilization and accumulation of organic pollutants along the natural floodplains of the Rur river, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12558, https://doi.org/10.5194/egusphere-egu22-12558, 2022.