HS2.4.3 | Hydrological extremes: from droughts to floods
EDI
Hydrological extremes: from droughts to floods
Co-organized by NH1
Convener: Manuela Irene Brunner | Co-conveners: Louise Slater, Gregor Laaha, Marlies H Barendrecht, Wouter Berghuijs
Orals
| Wed, 17 Apr, 08:30–10:10 (CEST)
 
Room B, Thu, 18 Apr, 08:30–12:25 (CEST), 14:00–15:40 (CEST)
 
Room B
Posters on site
| Attendance Wed, 17 Apr, 16:15–18:00 (CEST) | Display Wed, 17 Apr, 14:00–18:00
 
Hall A
Posters virtual
| Attendance Wed, 17 Apr, 14:00–15:45 (CEST) | Display Wed, 17 Apr, 08:30–18:00
 
vHall A
Orals |
Wed, 08:30
Wed, 16:15
Wed, 14:00
Hydrological extremes (floods and droughts) have major impacts on society and ecosystems and are projected to increase in frequency and severity with climate change. These events at opposite ends of the hydrological spectrum are governed by different processes that operate on different spatial and temporal scales and require different approaches and indices to characterize them. However, there are also many similarities and links between the two types of extremes which are increasingly being studied.
This session on hydrological extremes aims to bring together the flood and drought communities to learn from the similarities and differences between flood and drought research. We aim to improve the understanding of the processes governing both types of hydrological extremes, develop robust methods for modelling and analyzing floods and droughts, assess the influence of global change on hydro-climatic extremes, and study the socio-economic and environmental impacts of both types of extremes.
We welcome submissions that present insightful flood and/or drought research, including case studies, large-sample studies, statistical hydrology, and analyses of flood or drought non-stationarity under the effects of climate-, land cover-, and other anthropogenic changes. Studies that investigate both extremes are of particular interest. We especially encourage submissions from early-career researchers.

Orals: Wed, 17 Apr | Room B

Chairpersons: Manuela Irene Brunner, Wouter Berghuijs
Droughts and floods
08:30–08:50
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EGU24-7811
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solicited
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On-site presentation
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Anne Van Loon and the PerfectSTORM team

There seem to be two contrasting views on flooding after drought. Subsurface hydrologists pose that with dry antecedent conditions there is more storage available, which leads to lower flood peaks. Surface hydrologists pose that dry, hydrophobic soils support less infiltration and more surface runoff, which leads to higher flood peaks. But which theory is true? Or can both be true? And what happens if you put people and their actions in the mix? In this presentation is discuss the scientific and empirical evidence related to drought-flood events. I draw on scientific literature, global data analysis, a review of reports and news articles, qualitative case studies, and science communication examples. I will mostly focus on hydrological processes, but also highlight some meteorological and anthropogenic aspects.

How to cite: Van Loon, A. and the PerfectSTORM team: On the drought-flood conundrum: do droughts cause more or less flooding? Let’s discuss the science, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7811, https://doi.org/10.5194/egusphere-egu24-7811, 2024.

08:50–09:00
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EGU24-9343
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ECS
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On-site presentation
Anna Kis, Rita Pongrácz, and János Adolf Szabó

Climate change affects several sectors and environmental conditions, in particular the statistical characteristics of the runoff processes of a certain watershed. As a consequence of the higher temperature values and the altered precipitation distribution, the intensity and timing of floods and droughts, as well as their severity, may change in the coming decades. In order to develop adaptation strategies and implement an adequate water management, it is necessary to project the future trends of variables that can essentially influence water management, taking into account possible climate change scenarios, including the quantification of uncertainty.

Our aim is to investigate the runoff conditions with a special focus on the frequency of critical low water levels and the different levels of flood warnings for selected river sections (i.e. Tiszabecs, Uszti Csorna, Rahiv) in the Uppest-Tisza Basin, located in Central-Eastern Europe. For this purpose, simulations with the physically based, distributed DIWA hydrological model driven by a regional climate model simulation are completed. In order to analyse the projected changes, simulations are made for a historical period (1972–2001) as well as for two future periods (2021–2050 and 2069–2098). We also investigate how the choice of the RCP scenario (i.e. RCP2.6, RCP4.5 or RCP8.5) affects the output of the hydrological simulation. In order to assess uncertainty, time series of meteorological parameters (providing inputs for the hydrological model) are generated by a weather-generator embedded in a Monte-Carlo cycle. Therefore, several hundreds of scenarios with equal probability are available, by using only one climate model. Furthermore, a bias-correction of the climate model simulation is implemented for which the weather-generator is used by fitting the crucial distribution parameters to the reference, i.e. the so-called CARPATCLIM database.

How to cite: Kis, A., Pongrácz, R., and Szabó, J. A.: Analysis of the frequency of critical low water levels and flood warning water levels for the Uppest-Tisza catchment for the 21st century, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9343, https://doi.org/10.5194/egusphere-egu24-9343, 2024.

09:00–09:10
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EGU24-18942
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On-site presentation
Maria Kireeva, Dmitriy Magritskiy, and Natalia Frolova

The study is devoted to the analysis of daily time series of river runoff in the Arctic zone of Eurasia. Unique data on daily water discharges in the closing gauges of Arctic rivers were collected and processed in the package grwat (https://cloud.r-project.org/web/packages/grwat/index.html ), which identifies genetic components of runoff. As a result, 53 runoff characteristics were obtained for each of the 25 rivers flowing into the Arctic Ocean and the contribution of snowmelt, rainfall, and groundwater components to the total runoff was analyzed. Particular attention was paid to extreme characteristics - maximum water discharges of spring freshet, rain events and minimum 1, 5, 10-averaged discharges during summer and winter.
The study of maximum water discharges has shown that, in general there are trends of decreasing annual maximums for both large and medium-sized Arctic rivers. This trend, however, is not yet statistically significant everywhere. The most intensive decrease in maximums localized in the Northern Dvina, Ob, and Yenisei rivers, for which flow regulation by reservoirs has a significant impact. For the Kolyma, Yana and Indigirka rivers, there are periods of increase in maximums and their decrease lasting 5-7 years, with a general tendency to increase during 1960-2001 up to 15-20%.
In contrast, the minimum discharges with different averaging intervals increases by 25-56 % everywhere; this trend is presumably related to the general climate warming, increased infiltration and the role of groundwater flow, and for the rivers in the eastern part of the Arctic zone - to the degradation of permafrost.
The study also included analysis of the runoff signature transformation in Arctic zone by every year, as well as on average for the modern and historical period. The typing methodology consisted in classifying hydrographs according to two main features: a) exceedance of maximum discharge relative to the average annual discharge b) the share of flood runoff volume in the total annual runoff. The analysis showed a noticeable increase in the frequency of occurrence of smoothed hydrographs on the rivers of the Arctic zone of the Asia-Pacific region, for some basins the number of such years increased by 1.5-2 times (Polui, Turukhan, Ob rivers).

How to cite: Kireeva, M., Magritskiy, D., and Frolova, N.: Changes in maximum and minimum runoff of Eurasian Arctic rivers during the climate change epoch, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18942, https://doi.org/10.5194/egusphere-egu24-18942, 2024.

09:10–09:20
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EGU24-13741
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On-site presentation
Wendy Sharples, Sur Sharmila, Bende-Michl Ulrike, Navid Ghajarnia, Katayoon Bharamian, Jiawei Hou, Christopher Pickett-Heaps, and Elisabetta Carrara

Many natural disasters in Australia are the result of compound events, where the assessment of single climate system drivers in isolation do not fully capture hydro-climate extremes. Multivariate compound events such as ‘hot and dry’ and ‘wet and windy’ events, portend a multitude of hazards from heatwaves and bushfires through to coastal inundation and floods. The multiple drivers compounding together in these events, lead to extreme conditions ripe for natural disasters to occur. Presently, compound events are negatively impacting Australia’s ability to protect its population and environmental and economic assets, as Australia tries to adjust to the greenhouse gas driven climatic shifts, with potential projected increases in hazard severity. We aim to understand the change in frequency, duration and intensity of ‘hot and dry’ and ‘wet and windy’ compound events, at current and increased global warming levels. The ‘hot and dry’ compound event is defined as the co-occurrence of SPI drought conditions, and at least 3 consecutive days of hot temperatures. The ‘wet and windy’ compound event is defined as the co-occurrence of both extreme wind and precipitation. These two compound events were chosen to begin with due to the historic severity of their associated impacts. However further research is planned to understand all types of compound events including preconditioned, and, spatially and temporally compounding, in order to fully gauge Australia’s potential vulnerability to natural disasters now and in the future.

How to cite: Sharples, W., Sharmila, S., Ulrike, B.-M., Ghajarnia, N., Bharamian, K., Hou, J., Pickett-Heaps, C., and Carrara, E.: Compound events and hydro-climate extremes – how they are impacting Australia, now and in the future, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13741, https://doi.org/10.5194/egusphere-egu24-13741, 2024.

09:20–09:30
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EGU24-361
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ECS
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On-site presentation
Debankana Bhattacharjee and Chandrika Thulaseedharan Dhanya

In recent decades, the heightened frequency of extreme hydro-meteorological events such as floods and droughts has emerged as a global concern. These events not only pose a significant threat to individual societies but also exert lasting impacts on entire ecosystems. Of particular concern is the occurrence of whiplash events, where rapid transitions from wet to dry spells or vice versa amplify the already substantial impacts on various spatial and temporal scales. This study delves into the potential risks associated with the immediate succession of dry spells following wet spells and the heightened likelihood of intense compound occurrences fueled by concentrated rainfall distribution. Spanning 7 decades from 1951 to 2019, this research employs Event Coincidence Analysis or ECA to examine the aggregated whiplash behaviour in the Indian subcontinent. Our investigation focuses on the frequency of compound whiplash events, specifically dry spells followed by wet spells. Intriguingly, the findings reveal that, on average, 45 to 60% of dry spells across the majority of India are followed by wet spells within a 3-month window or 90 days. Moreover, our analysis demonstrates that the rate of wet spells triggering subsequent dry spells surpasses the reverse scenario. Consistent with the overall trend, compound flash floods and droughts, categorised by high intensity but brief duration, have been notably prevalent from 1951 to 2019. Although the spatial coverage of these events remains relatively small, recent decades have witnessed a discernible increase of 7–9%, primarily in arid, semi-arid, and tropical monsoon regions. Limited occurrences in tropical savannahs and humid subtropical regions were also noted. While the spatial structures associated with increased whiplash frequency appear less organised compared to individual dry and wet spells, the study underscores significantly higher ratios. This suggests that, despite the modest spatial coverage, whiplash events have experienced a notable increase in frequency over the past three decades. This comprehensive analysis contributes valuable insights into the evolving landscape of hydro-meteorological extremes, emphasising the growing importance of understanding compound events for effective climate resilience and adaptation strategies.

How to cite: Bhattacharjee, D. and Dhanya, C. T.: Evolution of Hydro-Meteorological Whiplash Events (Compound Floods and Droughts) over India, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-361, https://doi.org/10.5194/egusphere-egu24-361, 2024.

09:30–09:40
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EGU24-10718
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ECS
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On-site presentation
Meera G Mohan, Arathy Nair Geetha Raveendran Nair, and Adarsh Sankaran

In response to the escalating challenges posed by climate change, this study addresses the critical need to understand the dynamics of extreme climatic events within Kerala, India. Focusing on the years spanning 1980 to 2020, specifically during 12 identified drought years within, we meticulously examine transitions between droughts and floods, recognizing the profound impact on the region's hydrological landscape. With a strategic selection of 17 stream gauge locations covering high, mid, and low lands, representing varied climatic zones, our investigation delves into the intricacies of climatic shifts. The study deals with the analysis of discernible trend of increasing frequency in extreme events over time, by employing a thorough approach incorporating statistical significance testing, frequency analysis of extreme events, and lag analysis and then to unravel the intricate relationships between streamflow and precipitation during distinct phases such as pre-drought, drought, and post-drought years. The research findings illustrate an erratic pattern in the occurrence of contradictory extremes, such as transitions between drought and flood. The timing and duration of these transitions are also found to be inconsistent, showing varying periods in-between and occasionally consecutive occurrences of the same extremes, which in turn highlights the complexity and irregularity of extreme event patterns present in Kerala. Notably, our analysis reveals a concerning trend where the frequency of extreme events is progressively increasing, indicating a higher occurrence of climatic extremes over the years. Specifically, from 2015 to 2020, the observed transitions are striking, in the case that, the total incidences of heavy rain (64.5-115.5 mm per day) were 360 across 10 months in 2015 whereas in the succeeding year (2016), followed by an unprecedented 100-year return period drought. The year 2017 again saw incidences of heavy rain climbing to a total of 360 events. Astonishingly, the anomaly continued with the recurrence of devastating floods in 2018, which persisted for a broadened period up to 2020. While extending the future dynamics for the coming decade, the study predicted the frequency and patterns of extreme events in Kerala by incorporating future General Circulation Model (GCM) precipitation data. The results indicate a substantial increase in the frequency of extreme events, coupled with the anticipated emergence of prolonged dry periods in Kerala's future hydroclimatic landscape. The integration of this data into the analysis enabled the estimation of variations in future streamflow, providing valuable insights into the evolving climatic scenario. This forward-looking approach allowed for the inference of potential patterns of extreme events over the past decade in Kerala, contributing to proactive strategies for climate resilience and adaptive water resource management in the region.

How to cite: G Mohan, M., Geetha Raveendran Nair, A. N., and Sankaran, A.: Exploring Extreme Climate Transitions in Kerala, India: A Multi-Decadal Investigation (1980-2020), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10718, https://doi.org/10.5194/egusphere-egu24-10718, 2024.

09:40–09:50
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EGU24-9626
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ECS
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On-site presentation
Junliang Qiu, Wei Zhao, Luca Brocca, and Paolo Tarolli

In 2022, Europe experienced an unprecedented drought. 2023 marked the warmest year globally on meteorological records, leading to droughts and wildfires in Greece during the summer. In July 2023, certain areas of the Mediterranean experienced sea surface temperatures 5.5°C higher than the annual average, contributing to severe summer heatwaves and wildfires in the Greek region. These conditions also provided ample thermal energy for the formation of Storm Daniel. From September 4th to 6th, Storm Daniel struck Greece, resulting in significant rainfall and flooding. Coordinated satellite monitoring revealed that the flooded area in central Greece reached 875.28 km². On September 10th, Storm Daniel hit Libya, leading to dam collapses and claiming the lives of over 11,000 people. Concurrently, the flood area in the northern deserts of Libya exceeded 1,000 km². From a global perspective, Europe has witnessed an increased frequency of extreme droughts and floods in recent years, while North Africa grapples with geopolitical instability. Climate change-induced natural disasters are further heightening the vulnerability of the Mediterranean region. Consequently, this study underscores the importance of (1) enhancing hydrological monitoring in arid and semi-arid regions of the Mediterranean, (2) developing a Mediterranean scale early warning system, and (3) stressing the imperative for the European Union and North African countries to collaboratively establish climate change adaptation strategies, aiming to avert humanitarian disasters triggered by climate crises.

How to cite: Qiu, J., Zhao, W., Brocca, L., and Tarolli, P.: From drought to Storm Daniel: an overall assessment on the fragility of the Mediterranean region, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9626, https://doi.org/10.5194/egusphere-egu24-9626, 2024.

09:50–10:00
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EGU24-20728
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On-site presentation
Mohammad Reza Najafi, Wooyoung Na, and Andrew Grgas-Svirac

Hydroclimatic whiplash, a lagged compound hazard combined with preceding drought (flood) and following flood (drought), may induce significant environmental, hydrological, and socio-economic impacts worldwide. North America is one of the hot spots becoming susceptible to the transitions or shifts between two extremes. These compound events are also expected to become more frequent and intense in the future under climate change. To better understand the climate influence, overall decadal changes in climate variables and related hydroclimatic swing events need to be analyzed considering two components: anthropogenic external forcing and natural internal variability. External forcing is induced by anthropogenic activities imposing greenhouse gas emissions on the climate system, resulting in the signal of global warming. Internal climate variability (ICV), also termed climate noise, is an irreducible uncertainty induced by the chaotic nature thus unpredictable evolution of the climate system. In this study, we use four single-model initial-condition large ensembles (SMILEs) under historical and future forcing scenarios (RCP8.5), CanLEAD-EWEMBI, CanLEAD-S14FD, CanRCM4-LE, and GFDL-SPEAR, to quantify the relative role of external forcing and ICV on variations in compound dry-wet swing events across North America. The SMILE enables the robust quantification of the externally forced response and internal variability via computation of ensemble statistics, provided the ensemble size is large enough. On the virtue of this advantage, the standardized precipitation evaporation index is estimated to identify dry and wet spells and their transitions based on ensemble pooling and threshold-based event extraction methods. Frequency, intensity, transition time, transition intensity, and relative role of preceding and following spells, etc. are quantified for each warming period (1.5°C-4 °C global warming levels) and compared with those of the baseline period to investigate their projected changes and trends. The relative contribution of ACC and ICV to compound dry-wet spells is quantified by the ratio of changes and trends in the ensemble mean and the spread (standard deviation) among the ensemble members of each SMILE, respectively. The results of this study suggest that hydroclimatic swing events across North America are expected to become more frequent and intensified in a warmer climate, which is induced by significant emergence of external forcing. In addition, the transition time and transition intensity are projected to be more dominated by anthropogenic forcing over ICV than other characteristics indicating that more abrupt and severe shifts can occur in the future. The findings of this study support the necessity of developing appropriate measures for mitigating the anthropogenic forcing impact because it increases the risk of lagged compound floods and droughts that can lead to severe disasters in North America.

How to cite: Najafi, M. R., Na, W., and Grgas-Svirac, A.: Relative Contribution of External Forcing and Internal Climate Variability to Lagged Dry-Wet Swing Projections in North America, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20728, https://doi.org/10.5194/egusphere-egu24-20728, 2024.

10:00–10:10
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EGU24-10781
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On-site presentation
River discharge extremes in Norwegian regulated catchments: hydrologic model simulations including human interventions
(withdrawn)
Emiliano Gelati, Sigrid J. Bakke, Kolbjørn Engeland, and Lena M. Tallaksen

Orals: Thu, 18 Apr | Room B

Chairpersons: Marlies H Barendrecht, Gregor Laaha
Droughts
08:30–08:50
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EGU24-12331
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solicited
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On-site presentation
Oldrich Rakovec, Antonio Sanchez Benitez, Helge Gößling, and Luis Samaniego

Europe has experienced a series of hot and dry weather conditions with significant socioeconomic and environmental consequences over the past decade. Here, using a novel storyline approach, we examine the extremity of the recent European droughts, and we aim to isolate the thermodynamical component of climate change from changes in atmospheric patterns, which remain controversial in climate model simulations. Our climate analysis is currently based on an ensemble (n=5) of three storyline scenarios (pre-industrial, PI; PD, present-day; 4K warming) using a CMIP6 model (AWI-CM1) with the free-troposphere winds, including the jet stream, constrained toward ERA5 data. The meteorological variables at the land surface are further used as input to a hydrological impact modelling framework using the mesoscale Hydrologic Model (mHM). 

Regarding the 2022 drought analysis, first, using our experiments, we quantify the extremity of the present-day (PD) European drought against pre-industrial (PI) simulations. The potential evapotranspiration shows an apparent increase across the entire ensemble between the PD and PI periods in all of Europe. The same increase holds for actual evapotranspiration in northern Europe and most of central Europe, while the Mediterranean shows a relative decrease of 15%; however, there is no clear separation between PD and PI ensembles. The river runoff exhibits significant reductions of 35-50% in the Mediterranean regions, while changes between -15% and 15% occur over the rest of Europe (with less agreement on the signal). 

Second, we compare how the present 2022 droughts will be further amplified under different warming-level climate scenarios. Our results suggest that the 2022 river runoff drought would be much more strongly pronounced for the 4K world concerning the PD period, by up to 50% in the Mediterranean. A clear decline, although of slightly less extremity (-15% up to -40%), would also be projected across the majority of Central Europe. These changes align with observed trends associated with anthropogenic climate change. Our ongoing efforts aim to quantify possible stress on water resources and ecosystems, by providing insights into the potential future hydrological impact of different global warming levels. The aforementioned results will be further extended to address the multi-year drought perspective during the 2018-2022 periods. 

This work was supported by funding from the Federal Ministry of Education and Research (BMBF) and the Helmholtz Research Field Earth & Environment for the Innovation Pool Project SCENIC.

How to cite: Rakovec, O., Sanchez Benitez, A., Gößling, H., and Samaniego, L.: The recent European droughts within the nudged storyline context, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12331, https://doi.org/10.5194/egusphere-egu24-12331, 2024.

08:50–09:00
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EGU24-8320
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On-site presentation
Kasi Venkatesh and Bellie Sivakumar

Agricultural drought has emerged as a significant threat to global food security and sustainable development. Despite the progress made in the identification and analysis of various characteristics for the assessment and early warning of agricultural drought, our knowledge of the mechanisms governing agricultural drought propagation remains limited. This study aims to address this gap by employing complex network theory. Specifically, the study uses complex network measures to investigate the spatial propagation of agricultural drought propagation across India during 1950–2014. Spatial drought networks are constructed using event synchronization (ES) for mild drought conditions derived from the Standardized Soil Moisture Index (SSMI) at a 3-month aggregated scale (SSMI-3). The investigation delves into the mechanisms of spatial propagation of drought, including propagation source and sink, distance and orientation using directed networks. Several metrics, including network divergence, in-degree, and out-degree, inward and outward distance, inward and outward orientation are used. These metrics play a crucial role in identifying specific locations, namely source and sink regions, propagation distance and orientation, where drought onsets extend to other areas within the regional spatial networks. The results indicate that the northwest India acts as the source region and the west central India and peninsular India act as sinks. The central and east India are identified as vulnerable regions playing crucial roles in spatial drought propagation. The results also reveal that the dominant directions of propagation lead towards the northwestern parts of India. For inward distances, shorter propagation distances of less than 50 km are observed in the peninsular, central, and some parts of the northeastern regions, while longer propagation distances are observed in the western parts of India, exceeding 150 km. For outward distances, shorter propagation distances below 20 km are observed in hilly regions, while longer propagation distances are observed in the peninsular regions of India, exceeding 80 km. These results suggest that most regions propagate droughts inward and outward, covering distances of even hundreds of kilometres. Understanding the dominant inward and outward orientation of drought propagation could play a crucial role in developing early warning systems for droughts.

How to cite: Venkatesh, K. and Sivakumar, B.: Agricultural Drought Propagation over India: A Complex Network Theory Approach , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8320, https://doi.org/10.5194/egusphere-egu24-8320, 2024.

09:00–09:10
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EGU24-4758
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ECS
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On-site presentation
Joke De Meester and Patrick Willems

Several severe drought events occurred in the past years and droughts will likely occur more frequent and be more intense in the future. Hydrological drought, which reflects the shortage of water in the river system, can lead to economic losses and can have severe negative impacts on aquatic ecosystems. Therefore being able to predict and increase insights in which rivers are more vulnerable to hydrological droughts, based on catchment characteristics and human interactions, can be of relevance for water managers. In this analysis, the drought sensitivity of rivers is predicted at a regional scale (Flanders, Belgium). Hereby the interests of multiple stakeholders is taken into account by considering four drought metrics, namely the yearly summer volume, the number of dry days, the drought intensity and drought severity. Whereby the latter three are based on the ecological flow. To predict each of these drought metrics, five models ranging from statistical to tree-based methods are applied using twelve input variables ranging from catchment characteristics to human interactions. Hereby random forest without bootstrap and XGBoost outperforms the other methods. To increase the interpretability of the results, the XGBoost models are used to calculate the SHAP and SHAP interaction values. As a result, the impact of the different input variables on the model results is assessed.

From this analysis, some general conclusions can be drawn. Irrigation is the most important variable for each of the considered drought metrics. However, not for every drought metric a clear, unique dependence between the irrigation and the drought sensitivity of a river could be observed. Rivers which have sand as dominant soil texture in their drainage area are less vulnerable to drought. When there are more human interaction in the drainage area, the river is more vulnerable to drought. Beside this, several other dependencies are observed of which many can be explained by the difference in ease of water transferability between sandy soils and clay soils. Next to this, it became clear that the impact of forest and agricultural area on the drought sensitivity of a river is complex, whereby especially its interaction with soil texture and human activities needs further investigation. The applied method can predict the drought sensitivity of a river based on catchment characteristics and human interactions, and therefore define rivers that are more vulnerable to drought. They moreover can provide additional insights in the importance of catchment characteristics and human interactions, and their relation to the drought sensitivity of a river.

How to cite: De Meester, J. and Willems, P.: Analysing spatial variability in drought sensitivity of rivers using explainable artificial intelligence, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4758, https://doi.org/10.5194/egusphere-egu24-4758, 2024.

09:10–09:20
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EGU24-13019
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ECS
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On-site presentation
Anne Hoek van Dijke, Sungmin Oh, Xin Yu, and Rene Orth

Prolonged periods of below-average precipitation decrease streamflow, deplete soil moisture and groundwater reservoirs, and affect vegetation health. These effects can last for several years even after precipitation returns to normal. This way, droughts can decrease or increase streamflow for post-drought years. These drought legacy effects were found in a few local studies, but they have not yet been studied at global scale. 
Here, we study drought legacy effects on streamflow in > 1100 catchments distributed across the globe using Long-Short Term Memory (LSTM) models. This type of data-driven model is very suitable for time-series predictions with long-term dependencies, and LSTMs are therefore frequently used to model streamflow. We train our LSTM model for each catchment to predict streamflow based on meteorological forcing data. For training, we include all available data between 1980 – 2019, but we exclude the drought legacy years (the two years after each drought year). We assume that our models do therefore not know about the drought legacy effects. After training we use the LSTM models to predict streamflow for drought legacy years. We then define the legacy effects as the difference between model errors (the difference between the predicted and measured streamflow) for drought legacy years, in comparison to the model errors for normal years.
Using this methodology, we find catchments that show no, positive, or negative drought legacy effects. In the next step we will study if these legacy effects vary along climate or land cover gradients. And we additionally include satellite data of vegetation greenness, evaporation, and terrestrial water storage in the LSTM training to study two hypotheses: 1) we find negative drought legacy effects due to a depletion of groundwater, and 2) we find positive drought legacy effects, because vegetation mortality leads to decreased evaporation after the drought.
Our study offers a new perspective on understanding drought legacy effects on streamflow using observational data and demonstrates the usefulness of machine learning in uncovering complex drought impacts. 

How to cite: Hoek van Dijke, A., Oh, S., Yu, X., and Orth, R.: Analyzing drought legacy effects on streamflow with machine learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13019, https://doi.org/10.5194/egusphere-egu24-13019, 2024.

09:20–09:30
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EGU24-11083
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ECS
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On-site presentation
Pallavi Kumari and Rajendran Vinnarasi

Rapid intense droughts (Flash Drought) under climatic warming are of widespread concern owing to their catastrophic impacts on agricultural production, eco-system, and nation’s economy. Several studies highlight the need to develop an improved understanding of flash drought to manage its effect better, However the lack of consistent definitions have limited progress toward its assessments. A number of variables, climatic drivers are generally linked to flash drought development thus it is possible that no single description might adequately capture the flash drought. However, it is crucial to make sure that the rapid onset, fast intensification, and severe nature of flash drought can be identified and distinguished from more conventional drought (longer duration) events. With the increasing use of flash drought term within the scientific community, this study presents an evidence-based result by identifying flash droughts using pentad-scale precipitation series across India. The results demonstrate that one of the factors causing and accelerating the flash drought – rapid drought intensification and lasts for shorter duration (3 pentads to 18 pentads) is the meteorological variable precipitation. The results of this study can be further utilised in the accurate characterization of flash drought and its assessment with the strong evidence of precipitation series in finding of flash drought events across the nation.

How to cite: Kumari, P. and Vinnarasi, R.: Evidence of Flash/ Rapid Drought in India based on Precipitation Deficit- A new Climatic Threat, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11083, https://doi.org/10.5194/egusphere-egu24-11083, 2024.

09:30–09:40
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EGU24-9639
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ECS
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On-site presentation
Georgios Blougouras, Markus Reichstein, Mirco Migliavacca, Alexander Brenning, and Shijie Jiang

Hydrological drought (negative streamflow anomalies) can have significant societal and ecosystem impacts, and understanding its drivers is crucial for interpreting past and present droughts, as well as assessing future drought risk. However, despite recent research advancements, a comprehensive multivariate perspective on the drivers of hydrological drought remains elusive, particularly in the context of global warming, where distributional changes in drivers could result in an increased frequency of complex, compound events. In order to address this, quantifying the contribution of each driver is necessary. In our research, we devise an interpretable machine learning framework that can explain which hydrometeorological variables contribute to streamflow predictions. This is done by encoding a conceptual hydrological model into a neural network architecture, creating a physics-encoded hybrid model that allows us to maintain physical consistency and ensure a more causal understanding. We apply our framework to numerous North American basins across spatiotemporal scales and quantify the contribution of each potential driver to identified streamflow deficit events. We also investigate the mechanisms associated with compound drivers and assess if drought drivers are becoming increasingly complex due to climate change based on the defined compoundness index.  Overall, our framework has managed to capture the contribution of diverse drought drivers to events across different hydroclimatological regimes. The results demonstrate the effectiveness of our novel method in improving hydrological drought process understanding, especially the mechanisms and severity of droughts associated with compound drivers, thereby facilitating increased preparedness for future drought risks.

How to cite: Blougouras, G., Reichstein, M., Migliavacca, M., Brenning, A., and Jiang, S.: Interpretable Machine Learning to Uncover Key Compound Drivers of Hydrological Droughts, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9639, https://doi.org/10.5194/egusphere-egu24-9639, 2024.

09:40–09:50
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EGU24-859
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ECS
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On-site presentation
Propagation of meteorological to streamflow flash droughts during summer monsoon season in India
(withdrawn after no-show)
Rajesh Singh and Vimal Mishra
09:50–10:00
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EGU24-6086
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On-site presentation
Juliette Blanchet, Guillaume Chagnaud, and Jean-Dominique Creutin

Droughts are recurrent phenomena that present a large variety of space and time patterns making rather difficult the assessment of their rarity and the comparison between events. Our study focuses on the space-time “memory effect” of meteorological drought over France using gridded precipitation from the SAFRAN reanalysis over 1950-2022. The proposed easy tool of rarity matrix analyzes how drought events build and persist across time and space. The approach is purely statistic, assuming that drought consequences over a given area depend on the probability of non exceedance (“rarity”) of antecedent rainfall accumulations. In order to cover a large spectrum of “memory effects”, we consider a continuum of accumulation periods ranging from a few weeks to several years and moving windows of size 80x80 to 480x480 km2 over France. The rarity matrix of a given year displays the most severe rarity values encountered during the year as a function of the various accumulation periods and the various spatial scales.

Over the study period of 1950-2022 we show how the shape of rarity matrix discriminate short- and long-term historical droughts, as well as regional to national droughts.

As an additional asset, the rarity matrix is also able to analyze the rarity of precipitation excess over several weeks to months or years, as it was the case in fall 2023 in France.

How to cite: Blanchet, J., Chagnaud, G., and Creutin, J.-D.: The multi-scale rarity matrix – a comprehensive tool to analyze the space-time severity of meteorological drought, with application to France, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6086, https://doi.org/10.5194/egusphere-egu24-6086, 2024.

10:00–10:10
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EGU24-1738
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ECS
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On-site presentation
Matt Grant, Anna Ukkola, Elisabeth Vogel, Sanaa Hobeichi, Andy Pitman, and Andrew Hartley

Australia is frequently exposed to considerable impacts from severe and widespread droughts. Despite this, a comprehensive understanding of the past trends and drivers of Australian droughts remains elusive. Existing studies have often characterised past trends based on changes in mean values rather than the extremes. However, given Australia’s exceptionally variable climate, this may fail to capture the full nature of the country’s drought trends. Furthermore, studies often rely on a limited number of drought indicators and may not encompass the diverse meteorological, hydrological and ecological conditions contributing to drought.

This work explores past drought trends in Australia using multiple drought indicators. We analyse changes in traditional drought metrics, including precipitation, runoff and soil moisture, defining droughts as time periods below the 15th percentile. We complement these metrics with an impacts-based drought indicator built from government drought reports using machine learning. We explore the drivers of drought trends using explainable machine learning methods, and consider multiple drivers including large-scale climate features, land surface properties and past conditions. Using a diverse range of metrics allows for a more comprehensive analysis of drought changes experienced over the past decades and will provide greater insight into the main drivers behind Australian droughts.

How to cite: Grant, M., Ukkola, A., Vogel, E., Hobeichi, S., Pitman, A., and Hartley, A.: Understanding past changes in Australian droughts and their drivers, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1738, https://doi.org/10.5194/egusphere-egu24-1738, 2024.

Coffee break
Chairpersons: Louise Slater, Wouter Berghuijs
Floods
10:45–11:05
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EGU24-5997
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ECS
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solicited
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Highlight
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On-site presentation
Miriam Bertola and Günter Blöschl and the Team members

Megafloods that far exceed previously observed records at a given location can take citizens and flood managers by surprise. Existing methods based on local and regional information rarely go beyond national borders and cannot predict these floods well because of limited data on megafloods, and because flood generation processes of such extremes differ from those of smaller, more frequently observed events. Here we analyse the most comprehensive dataset of annual maximum discharges in Europe available to date, to assess whether recent locally surprising megafloods could have been anticipated using observations in hydrologically similar catchments across the continent.

We base our analysis on annual maximum river discharge observations from 8023 gauging stations for the period 1810–2021. We identify about 500 “target” catchments where recent (i.e., after 1999) megafloods have occurred that are surprising based on local data. We perform a hindcast experiment of predicting their peak discharge with regional envelope curves, using flood observations from similar “donor” catchments up to the year before their occurrence. From this group of donor catchments we construct an envelope curve which we compare with the megaflood that occurred later in the target catchments. We repeat this analysis for all the detected megafloods in the target catchments.

Our analysis shows that, in 95.5% of the target catchments, the discharge of the envelope is larger than that of the observed megaflood, suggesting that, from a European perspective, almost none of the events can be considered a regional surprise. Similar results are obtained by repeating the analysis on two consecutive sub-periods, indicating that megafloods have not changed much in time relative to their spatial variability. In conclusion, our findings show that recent megafloods could have been anticipated from observations in other parts of Europe, which would not be possible using only national data.

How to cite: Bertola, M. and Blöschl, G. and the Team members: Surprising megafloods in Europe – learning from the big picture, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5997, https://doi.org/10.5194/egusphere-egu24-5997, 2024.

11:05–11:15
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EGU24-6166
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ECS
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On-site presentation
Elena Macdonald, Bruno Merz, Viet Dung Nguyen, and Sergiy Vorogushyn

The distributions of many observed time series of daily precipitation and streamflow show heavy tail behaviour. This means that the occurrence of extreme events has a higher probability than would be the case if the tail was receding exponentially. To avoid underestimating extreme flood events in their occurrence probability or their magnitude, a robust estimation of the tail behaviour is required. However, this is often hindered due to the limited length of time series. One way of overcoming this is to enhance the understanding of the processes that govern the tail behaviour of flood peak distributions. Here, we analyse how the spatial variability of rainfall and runoff generation along with the tail behaviour of rainfall affect the flood peak tail behaviour in catchments of various size. To do so, a modelling chain consisting of a stochastic weather generator and a conceptual rainfall-runoff model is used. For a large synthetic catchment (>100,000 km²), long time series of daily rainfall with varying tail behaviour and varying degree of spatial variability are generated and used as input for the rainfall-runoff model. In the rainfall-runoff model, spatially variable runoff is generated by setting respective model parameters accordingly. The tail behaviour of the simulated precipitation and streamflow time series is characterized with the shape parameter of the Generalized Extreme Value (GEV) distribution.

Our analysis shows that heavy-tailed rainfall tends to result in heavy-tailed flood peak distributions, independent of the catchment size. In contrast, first results regarding the effect of the spatial variability of rainfall on flood peak tail behaviour indicate that this relation varies with the size of the catchment. In large catchments, attenuating effects, for example through river routing, might have a stronger impact than in small basins. Regarding the runoff generation, the tail of flood peak distributions tends to be heavier when a fast runoff component is triggered simultaneously in a larger share of the catchment rather than when this is the case only very localized. This in turn is linked to more homogeneous catchment characteristics and rainfall patterns. The results of this study can help with improving the estimation of occurrence probabilities of extreme flood events.

How to cite: Macdonald, E., Merz, B., Nguyen, V. D., and Vorogushyn, S.: Heavy-tailed flood peak distributions: What is the effect of the spatial variability of rainfall and runoff generation?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6166, https://doi.org/10.5194/egusphere-egu24-6166, 2024.

11:15–11:25
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EGU24-18032
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ECS
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On-site presentation
Bora Shehu and Axel Bronstert

The extreme weather conditions of July 2021 caused major flooding’s in multiple tributaries of the Meuse and Rhine rivers. Particularly the Ahr Valley in Germany was greatly affected, where exceptional damage and severe human loss was registered. Since then, several studies have been conducted to understand the extremity, the major driving forces, the particular mechanisms of this flood and the possible impacts of climate change on the generation of such an event. Here the main objective is to perform a hydrological analysis of the July 2021 Ahr event and discuss the challenges in modelling or analysing this event.

First, we show that particularly the rainfall field is associated with high uncertainty, as seen by the high variability between the different rainfall products available. The average areal rainfall volume can differ between products with as much as 50mm/day, which constitutes almost 55% of the rainfall volume estimated by Radolan. To capture the full uncertainty-range of the rainfall field, rainfall simulations conditioned both on radar and station observations are implemented.  

Next, based on rainfall simulations and reconstructed discharge data, runoff coefficients (Rc) are shown to be ranging between 0.6 to 1.2 (median 0.7). These values are clearly higher than expected in continental climate (Rc ~ 0.20-0.51) and the latest 100-year return flood observed in 2016 (with Rc ~ 0.4). The high lower range suggests, that the dominant processes have changed, with slower components of surface runoff shifting to faster ones. This agrees well with the observed traces of erosion, surface water and flow paths in parts of the catchment.

Lastly, the reconstructed discharge data are also subjected to uncertainty due to lack of observations and the non-representativeness of the stage-discharge curve during the flood wave. Hence, high Rc values do not only originate from underestimated rainfall but as well from possible overestimated flood volume. For this purpose, discharge was estimated with the Larsim model. As expected due to the change of the dominant processes, the pre-event parameter set underestimates considerably the flood volume, while the post-calibration one agrees better with the reconstructed data. On both cases, the computed runoff coefficient ranges between 0.4 to 0.7.

To conclude, extraordinary events such as the July 2021 in Ahr Catchment, are accompanied with high uncertainty and as such are difficult to be analysed or modelled. Nevertheless, the results dictate that the surface runoff played an important role in July 2021. At the same time, it is clear that the landscape still has a considerable retention effect, between 30% and 50%, even for such a heavy rainfall.

How to cite: Shehu, B. and Bronstert, A.: Challenges in analysing and modelling extreme floods: The case study of Ahr catchment, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18032, https://doi.org/10.5194/egusphere-egu24-18032, 2024.

11:25–11:35
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EGU24-3964
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On-site presentation
Michel Lang, Jérôme Le Coz, Felipe Mendez-Rios, Perrine Guillemin, David Penot, and Didier Scopel

The safety of nuclear power plants in France is assessed based on 1000-year flood estimates, with a safety factor accounting for uncertainty. Previous studies on the Garonne River near Agen, France, used threshold exceedance values from a continuous 85 year-long series at two hydrometric stations: Malause (1915-1966) and Lamagistère (1967-2000), and historical data at Agen since 1770. The estimate of the design flood was very sensitive to the choice of an Exponential or of a Generalized Pareto distribution, yielding 12 600 and 16 000 m3/s, respectively. This communication presents a more comprehensive study based on a GEV distribution fitted from the annual maximum values of a continuous series since 1852 (adding Agen 1852-1914) and historical data at Agen since 1435. The statistical framework accounts for both discharge and sampling uncertainty components. The first uncertainty component is about 3% for the recent years and 35% for the oldest years. The statistical framework is able to account for a multiplicative error on rating curves. This leads to corrections in peak discharge values, with better agreement between historical data at Agen and hydrometric data at Malause-Lamagistère. The final estimate of the design flood is around 10 500-11 600 m3/s, without or with the largest known historical flood of 1435. It confirms the safety of the nuclear plant, based on extensive historical information.

How to cite: Lang, M., Le Coz, J., Mendez-Rios, F., Guillemin, P., Penot, D., and Scopel, D.: Extreme flood analysis on the Garonne river at Agen, using historical information since 1435, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3964, https://doi.org/10.5194/egusphere-egu24-3964, 2024.

11:35–11:45
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EGU24-17464
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On-site presentation
Yeshewatesfa Hundecha, Jonas Olsson, Lennart Simonsson, and Jörgen Rosberg

Understanding the nature of flooding of a region is key for flood management. The impact of flooding depends on how spatially extensive it is and this, in turn, is influenced by the processes generating the flood. In this study, we investigated the relative importance of rainfall and snowmelt in the generation of floods of different magnitudes and characterized their spatial patterns in different climate regions of Sweden. We generated a large number of spatially diverse extreme river flow scenarios across Sweden that are statistically consistent with the observations by employing a multi-site weather generator and a highly resolved semi-distributed hydrological model. The extreme flows within each of the main rivers were classified based on their generating meteorological forcing and the spatial distribution of the flow magnitudes was assessed. The results reveal that rainfall is the main contributor of extreme flows of all magnitudes in the southern part followed by rain-on-snow, while in the northern part, rain-on-snow is the main process resulting in extreme flows followed by rainfall. Pure snowmelt is the least contributor of extremes in all regions and its contribution decreases with increasing magnitude of the flow. The proportion of events generated by rainfall increases with the magnitude of the flow in all regions. Extremes of lower magnitudes are generally more spatially widespread than the higher extremes and events generated by snowmelt and rain-on-snow are spatially more widespread than events generated by rainfall.

The possible impact of climate change was also assessed by generating extreme flows for end-of-century climate change scenarios by perturbing the weather inputs generated by the weather generator using data from a set of regional climate models and using them to force the hydrologic model. The results show that the main generating processes in each region remain the same. However, the proportion of rainfall generated events will be markedly higher than under the present climate. 

How to cite: Hundecha, Y., Olsson, J., Simonsson, L., and Rosberg, J.: Patterns of extreme high flows in relation to their dominant generating processes across Sweden, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17464, https://doi.org/10.5194/egusphere-egu24-17464, 2024.

11:45–11:55
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EGU24-14203
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Virtual presentation
Development and Performance of a Continuous Simulation Modelling Approach for Design Flood Estimation in South Africa 
(withdrawn)
Jeff Smithers
11:55–12:05
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EGU24-18180
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ECS
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On-site presentation
Diverse flood characteristics over southern High Mountain Asia under different synoptic patterns
(withdrawn)
Yanxin Zhu
12:05–12:15
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EGU24-14963
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ECS
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On-site presentation
Li Liu, Yue-Ping Xu, and Haiting Gu

Reliable Tropical Cyclone (TC) precipitation and flood nowcasting play an important role in disaster prevention and mitigation. Especially for small-scale reservoirs, timely and accurate inflow forecasts are required to provide safe space for capturing high flows without having to resort to hazardous and damaging releases. Numerous studies have investigated the ability of deep learning in TC precipitation nowcasts. However, few of them focus on the skill of deep-learned TC precipitation forecasts in inflow flood forecasts. In this study, a novel framework is developed by introducing TC track information together with antecedent precipitation in the Convolution LSTM model (PTC-ConvLSTM). The ConvLSTM forecast precipitation is then input to an event-based Xinanjiang hydrological model for inflow flood forecasting, and the propagation of errors from TC track forecasts to inflow forecasts is further analyzed. The results show that TC track information enables a further 5% improvement compared to outputs from ConvLSTM with only precipitation information. PTC-ConvLSTM precipitation nowcasts present a probability of detection (POD) greater than 0.34 for a threshold of 5mm/h in a lead time of 6h. The nowcasts-driven flood forecasts have an NSE greater than 0 with a lead time of 5h at least. It is also indicated that the 100km error in TC track forecasts could generally result in a 10% degradation in precipitation forecasts and a further 8% deterioration in the driven flood forecasts. The effectiveness of our model indicates that the precipitation nowcasts from deep learning have strong applicability in disaster mitigation.

How to cite: Liu, L., Xu, Y.-P., and Gu, H.: Enhanced tropical cyclone inflow flood forecasts by using deep learning and spatial‑temporal information, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14963, https://doi.org/10.5194/egusphere-egu24-14963, 2024.

12:15–12:25
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EGU24-14209
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On-site presentation
Paul Wagner and Nicola Fohrer

The Harz mountains in Germany have experienced floods and droughts in recent years. On the one hand, a major flood event affected the city of Goslar in 2017, and on the other hand, drought conditions since 2018 have led to tree mortality in the forested catchment area upstream. The frequency of such extreme events is expected to increase as a result of climate change. Here, we aim at assessing the impacts of the ongoing tree mortality on hydrology. To this end, we employ the ecohydrological model SWAT+ to assess changes in water balance components. The model is specifically calibrated for extreme conditions by evaluating the model performance for different segments of the flow duration curve. Satellite-derived changes in forest cover are used to assess the impact on water balance components. The analysis of the model performance indicates that the calibration strategy improved model performance for drought conditions. Furthermore, first model results indicate that tree mortality led to a decrease in evapotranspiration and an increase in surface runoff. The spatial assessment suggests stronger effects at the sub-catchment scale than at the catchment scale. However, the faster response of the catchment due to tree mortality potentially increases the severity of flood events and the flood risk in downstream areas. Therefore, afforestation with climate-resilient trees is needed to improve both flood and drought resilience in the Harz mountains.

How to cite: Wagner, P. and Fohrer, N.: Impacts of hydrological extremes in a mountainous forest catchment: Experiences from the Harz mountains, Germany, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14209, https://doi.org/10.5194/egusphere-egu24-14209, 2024.

Lunch break
Chairpersons: Gregor Laaha, Manuela Irene Brunner
Climate change & water management
14:00–14:10
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EGU24-12436
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ECS
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On-site presentation
Demian Vusimusi Mukansi, Jeff Smithers, Katelyn Johnson, Thomas Kjeldsen, and Macdex Mutema

In this study, the annual maximum streamflow from 14 stations in KwaZulu-Natal, along the East Coast of South Africa, were analysed. Trends were investigated using the non-parametric Mann-Kendall test and the Sen Slope tests, and the results indicate that the annual maximum streamflow has been decreasing in magnitude at 78 % of stations. Extreme value analysis was performed using both stationary and non-stationary models using time and rainfall as covariates. The results show that the stationary models are superior to non-stationary models at most stations with time as a covariate. Where possible, streamflow stations were linked with rainfall stations to determine the impact of rainfall on annual maximum streamflow. The results indicate that the non-stationary model incorporating observed rainfall as a covariate performed better than the stationary and non-stationary models with only time as a covariate. Therefore, incorporating rainfall in design flood estimation should be considered to account for non-stationary trends and to mitigate the risk of failure of hydraulic structures. Regional magnification factors to account for non-stationarity were not investigated further in this study as the majority of the stations showed a negative trend, which means the application of a regional magnification factor will result in a reduction of the magnitude of the estimated design floods.

How to cite: Mukansi, D. V., Smithers, J., Johnson, K., Kjeldsen, T., and Mutema, M.: Detecting Trends In Hydrological Extremes And Non-Stationary Extreme Value Analysis Of Flood Data In Kwazulu-Natal, South Africa, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12436, https://doi.org/10.5194/egusphere-egu24-12436, 2024.

14:10–14:20
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EGU24-11404
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ECS
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On-site presentation
Carles Beneyto, José Ángel Aranda, and Félix Francés

The present work presents a novel methodology based on the use of stochastic Weather Generators (WG) for the estimation of high return period floods under climate change scenarios. Starting from the premise that the 30-years climate projections, commonly used for future flood studies, do not provide enough information to obtain accurate extreme quantile estimations (especially in arid and semi-arid climates), we propose to exploit the available information by performing a regional study of maximum precipitation of the bias-corrected climate projections (mid-term and long-term), the outputs of which will improve the WG implementation.

This methodology has been applied in a case study, Rambla de la Viuda (Spain), a typical Mediterranean ephemeral river located in eastern Spain. The river is ca. 36 km in length and 1513 km2 in catchment surface, with a remarked variability: large floods are a significant element of this irregular hydrological regime, producing up to 80% of annual discharge volume. Precipitation and temperatures were obtained from the EUROCORDEX project: twelve combinations of Global Circulation Models and Regional Circulation Models were evaluated for a RCP8.5 emissions scenario.

The results obtained shown a clear increase in maximum and minimum temperatures for both projections (up to 3.6ºC), this increase being greater for the long-term projection, where the heat waves intensify importantly in both magnitude and frequency. In terms of precipitation, the results are similar, with precipitation quantiles increasing for practically all models and for both projections, although slightly reducing the annual amount of precipitation. The long synthetic series of precipitation that fed a fully-distributed hydrological model translated into substantial shifts in the river flows regimes, presenting, in general, lower flows during the year but increasing the frequency and magnitude of extreme flood events, reaching 100 years return period quantile values up to 58% higher at the river outlet and up to 130% at a smaller upper subcatchment. 

These results have demonstrated the solidity and effectiveness of the proposed methodology. In the field of meteorological modeling, the results have been consistent and satisfactory, demonstrating the methodology's ability to accurately represent the complexities of extreme climate patterns. Likewise, in the hydrological field, the methodology has exhibited an effective capacity to represent and simulate the processes related to the water cycle, offering coherent and satisfactory results in the estimation of low frequency flood events under climate change scenarios. This consistency in the robustness of the methodology, both in meteorological and hydrological modeling, supports its applicability and reliability in diverse environments and climatic conditions.

How to cite: Beneyto, C., Aranda, J. Á., and Francés, F.: On the use of weather generators for the estimation oflow-frequency floods under climate change scenarios, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11404, https://doi.org/10.5194/egusphere-egu24-11404, 2024.

14:20–14:30
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EGU24-1108
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On-site presentation
Egemen Firat, Buse Özer, Koray K. Yılmaz, Gülçin Türkkan Karaoğlu, and Esra Fitoz

In flood hazard and risk assessment studies, modeling is generally done by examining the hydrometeorological events that have developed by using past datasets. Recently, increasing rainfall per unit time due to climate change may cause flash floods. Hydrographs, which are input to 1D/2D hydrodynamic models, are also likely to change as a result of climate change. Hydrological calculations based on past data may underestimate the predicted values. Therefore, flood risks produced from the results of flood depth and hazard models may also remain at low values. In this study, firstly, a hydrological modeling study was carried out on the streams in Haramdere Basin by using hydrometeorological measurements between 2010-2022 and hydrographs were produced for Q2, Q5, Q10, Q25, Q50, Q100, Q500 and Q1000 returning periods. In mapping studies, river structures, stream geometry, digital surface and terrain model were determined using ground measurements and flight data. Then, flood depth and hazard maps were created with 1D and 2D hydrodynamic models. Economic risk calculation was made using these maps. Then, RCP8.5 scenarios known as having high precipitation anomalies for all climate models included in CMIP6 were re-run in the hydrological model. In this way, flow data were generated for each climate model RCP8.5 scenario. Then, 3 different climate change impacts (worst, medium and best) for Haramidere Basin in regards to flood hazard and risk will be revealed by analyzing the rainfall and runoff extremes produced from the hydrological model for all climate models. In the worst case scenario, the climate model with the highest rainfall and runoff extremes, in the medium case, the average of the values calculated from all climate models and in the best case scenario, the climate model with the lowest extremes will be selected. In this way, climate models specific to this basin will be determined for these 3 different Scenarios. Then, from these selected climate models, coefficients will be determined to be used in hydrological calculations for the effect of climate change on flooding events. Finally, flood risk calculations will be made for these 3 scenarios and the economic value of climate change in terms of flood risk will be quantified by comparing with the flood risk calculated with the measurements between 2010-2022.

How to cite: Firat, E., Özer, B., Yılmaz, K. K., Türkkan Karaoğlu, G., and Fitoz, E.: Climate Change Effects on Flood Hazard and Risk in Harami̇dere Basin, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1108, https://doi.org/10.5194/egusphere-egu24-1108, 2024.

14:30–14:40
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EGU24-9553
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ECS
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On-site presentation
Aparna Chandrasekar, Friedrich Boeing, Andreas Marx, Oldrich Rakovec, Sebastian Mueller, Ehsan Sharifi, Jeisson Javier Leal Rojas, Luis Samaniego, and Stephan Thober

Climate change is altering the water cycle from the global to the local scale. The increase in temperatures and changing precipitation patterns intensify not only mean values, but also the frequency and severity of extreme weather events, leading to alterations in water availability and distribution.

This study assesses the impact of climate change on flood patterns (maximum annual river discharge) in Germany. Climate models ranging from different spatial scales will be compared for the five largest German catchments outlets (including headwaters). The climate model ensembles from the EURO-CORDEX initiative, and the ICON climate model from the Destination Earth Initiative / NextGEMS project will be used along with the mHM (mhm-ufz.org) model. The river discharge values produced from the mHM model will be used to calculate the Q90 (90th percentile of daily discharge) and the Qmax (maximum annual discharge) parameters.

Initial results from the EURO-CORDEX initiative predict a 5-15% reduction in Q90 and Qmax in the summer half year, and a 5-30% increase in Q90 and Qmax in the winter half year, in the alpine regions in Germany. In the Elbe and Oder catchments (north-eastern part of Germany) there in a greater increase in Q90 and Qmax in the summer half year than the winter half year. This increase becomes more prominent with increasing warming. However, there is a large spread in the ensemble predictions, with uncertainty reducing with increasing warming. These parameters and results will be compared with the results from the ICON climate model to understand the contribution of spatial and/or temporal resoltion towards flood prediction.

How to cite: Chandrasekar, A., Boeing, F., Marx, A., Rakovec, O., Mueller, S., Sharifi, E., Leal Rojas, J. J., Samaniego, L., and Thober, S.: Climate adaptation to change in high-flows: Comparison of high-resolution climate model projections, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9553, https://doi.org/10.5194/egusphere-egu24-9553, 2024.

14:40–14:50
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EGU24-855
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ECS
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On-site presentation
Devika Chandrababu Salini and Bellie Sivakumar

Droughts pose substantial challenges to water resources, ecosystems, and agriculture. Climate change is anticipated to result in more frequent and greater magnitude droughts in the future. The present study assesses meteorological droughts in India under climate change conditions using a complex networks-based approach. The Standardized Precipitation Index (SPI) values at a duration of 1, 3, 6, and 12 months are used to assess the meteorological droughts. Observed precipitation data from the India Meteorological Department (IMD) and precipitation outputs from 53 GCMs participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) are used. The data considered are at a spatial resolution of 1° x 1°, covering a total of 288 grids across India. The Shortest Path Length is used as a network measure to rank the GCMs. First, the network is constructed by treating each grid as a node and identifying the links between any pair of grids according to certain threshold conditions in correlations in SPI values. Next, the GCMs are individually ranked for each of the 288 grids based on the difference in the shortest path length between the observed and GCM-simulated SPI networks. Then, the Group Decision-Making (GDM) approach is applied toidentify the top-performing GCMs across all the 288 grids. Finally, the inclusion of a comprehensive rating metric (RM) value provides a unified approach to combine the ranks obtained for GCMs across various duration (1, 3, 6, and 12 months). The results indicate that NorESM2-MM, CESM2-FV2, KACE-1-0-G, SAM0-UNICON, and CMCC-CM2-SR5 are the top five models in terms of performance. Data from these five models are then studied using Event Synchronization (ES) to uncover the spatial connections in drought events across space. This novel approach contributes to a better understanding of the spatial dynamics of meteorological droughts, especially under climate change.

How to cite: Chandrababu Salini, D. and Sivakumar, B.: Meteorological Droughts in India under Climate Change Conditions: A Complex Networks-based Approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-855, https://doi.org/10.5194/egusphere-egu24-855, 2024.

14:50–15:00
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EGU24-4127
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ECS
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On-site presentation
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Wilson Chan, Maliko Tanguy, Amulya Chevuturi, and Jamie Hannaford

Hydrological drought frequency and severity is projected to increase for the UK. However, there is not yet robust observational evidence for decreasing river flows and increasing hydrological drought severity. This lack of evidence may stem from short observational records, human influences on river flows and internal climate variability. As a result, river flow trends in the past and in the near-term may be different to the trend induced by long-term climate change. This lack of congruency poses significant challenges for decision-makers faced with uncertain future projections on the one hand and an apparent lack of observed changes on the other: underscoring the need for approaches that bridge this gap. Single-Model-Initial-Condition-Large Ensembles (SMILEs) provide an ideal opportunity to reconcile past observations and future projections as they isolate the effect of internal climate variability. Here, we use the 50-member CRCM5 12km SMILE to drive GR6J catchment hydrological models for 190 catchments across Great Britain. Results show that observed trends in precipitation and river flows are within the spread of the large ensemble, which includes both robust wetting and drying trends over the historical period that could have arisen from internal climate variability. We further estimate the time of emergence for each catchment, i.e. the decade at which river flow changes exceed natural climate variability. Winter river flows increase with warming and are estimated to exceed natural climate variability before the 2050s for many catchments, with implications for flood risk. Summer river flows are estimated to reduce with warming, including hotspots in southwest Britain with an early time of emergence, exacerbating existing pressures on water resources. Autumn flows for catchments in southeast England are estimated to decrease but are not estimated to exceed natural climate variability until late 21st century. Establishing water management and adaptation strategies is crucial well in advance of catchments reaching their time of emergence (i.e. before a statistically significant trend is detectable). These results highlight the potential to use SMILEs to explore plausible alternative realisations and explore storylines of low-likelihood, high-impact hydrological extremes.

How to cite: Chan, W., Tanguy, M., Chevuturi, A., and Hannaford, J.: Emerging river flow and hydrological drought trends in Great Britain, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4127, https://doi.org/10.5194/egusphere-egu24-4127, 2024.

15:00–15:10
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EGU24-15522
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ECS
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On-site presentation
Pratik Chakraborty, Sophie De Kock, Pierre Archambeau, Michel Pirotton, Sébastien Erpicum, and Benjamin Dewals

Dams, prevalent globally as major hydraulic structures, play essential roles in water supply, hydroelectric power, and flood management. However, they are known to significantly transform hydrological regimes by, among others, regulating flood and base flow dynamics. This, in turn, necessitates a meticulous understanding of the nature of these alterations.

Focused on the Eupen dam in Belgium, this study examines its storage dynamics in relation to moderate and extreme flood events. The study analyses time-series of inflow and outflow discharges at the dam for the period from 1995 to 2023. The inclusion of the July 2021 extreme event provides valuable insights into the dam’s performance (or lack thereof) during such mega-events. Notable aspects of the methodology include adjustments for an ungauged sub-basin, the use of a Savitsky-Golay filter to refine (field-)data quality without compromising peak details and a fundamental mass-balance approach to compute outflow data from the inflow time-series.

The examination of 18 flood events during this period reveals significant findings: the dam's ability to reduce peak discharge by 8.6 to 91%, delay peak discharge by up to 68 hours, decrease flood volume by 2 to 94%, and reduce the rising rate by 1.09 to 11.16 times. Distinctly, the study also reveals a strong correlation between the damping ratio of the flood wave and the ratio of the volumes of the incoming flood to that available in the reservoir (at the start of an event). The outcomes of the flood frequency analysis are also presented and interpreted in detail.

The present study features a marked shift from existing dam-effects research, wherein the analysis is often focused on mean annual flow characteristics or aggregated data across numerous dams. It highlights the rewards of such a singular case study, in terms of being able to scrutinise individual flood events. This, in turn, provides the scope to understand more complex underlying conditions that prompt a dam's effects on streamflow characteristics. Finally, this research evidences the benefits provided by dam reservoirs on flood wave damping, but also their limits in doing so.

How to cite: Chakraborty, P., De Kock, S., Archambeau, P., Pirotton, M., Erpicum, S., and Dewals, B.: Flood control capacity of a large reservoir under moderate and extreme flood conditions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15522, https://doi.org/10.5194/egusphere-egu24-15522, 2024.

15:10–15:20
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EGU24-10595
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ECS
|
On-site presentation
National-scale modelling analysis of drought characteristics and propagation under anthropogenic impacts in largest basins of China
(withdrawn after no-show)
Hao haoran, Dong ningpeng, and Yang mingxiang
15:20–15:30
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EGU24-5903
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On-site presentation
Sergio Martín Vicente Serrano, Ahmed El Kenawy, Dhais Peña-Angulo, Jorge Lorenzo-Lacruz, Conor Murphy, Jamie Hannaford, Simon Dadson, Kerstin Stahl, Iván Noguera, Magí Franquesa, Beatriz Fernández-Duque, and Fernando Domínguez-Castro

This research examines the changes in annual streamflow across Europe from 1962 to 2017, with a specific focus on the correlation between streamflow trends and climate dynamics, as well as physiographic and land cover characteristics. The spatial distribution of streamflow trends aligns closely with climate patterns, suggesting a climate-related influence. However, a detailed analysis at the basin scale reveals that the significant decline in streamflow in southern Europe cannot be solely attributed to climate dynamics. Instead, a discernible negative trend linked to non-climate factors becomes apparent. Specifically, our study indicates that the primary drivers of negative streamflow trends in southern Europe, especially during dry years, are forest growth and irrigated agriculture. This is attributed to the higher proportion of green water consumption compared to blue water generation. These findings hold substantial implications, particularly in the context of widely adopted nature-based solutions for addressing climate change. This includes concerns about carbon sequestration through forests and the planned expansion of irrigated agricultural lands in central and northern European countries to meet growing crop water demands. Such developments may potentially reduce the availability of water resources, leading to an increased frequency and severity of low flow periods.

How to cite: Vicente Serrano, S. M., El Kenawy, A., Peña-Angulo, D., Lorenzo-Lacruz, J., Murphy, C., Hannaford, J., Dadson, S., Stahl, K., Noguera, I., Franquesa, M., Fernández-Duque, B., and Domínguez-Castro, F.: The expansion of forests and the practice of irrigated agriculture contribute to reduced river flows in southern Europe during dry years, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5903, https://doi.org/10.5194/egusphere-egu24-5903, 2024.

15:30–15:40
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EGU24-15136
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On-site presentation
Giuseppe Formetta and Martin Morlot

Heatwaves and droughts are among the natural hazards with frequencies and severities expected to increase due to climate change. Furthermore, they are responsible for a large range of social and economic impacts, such as agricultural losses, energy shortages, heat related mortality, etc. Previous works have shown that co-occurring drought and heatwave events lead to higher significant socio-economic damages compared to independent events. However, limited knowledge is available on quantifying spatial patterns of co-occurring droughts and heatwaves events, their severity, and frequency of occurrence, especially at high spatial and temporal resolution.

The aim of this study is to quantify spatio-temporal changes of compound drought and heat wave events in a large anthropized alpine Italian basin, the Adige basin, located in the North of Italy, with area greater than 10,000km2 and containing a wide range of elevation from 160m to 3905m. We quantify changes in single and multiple drought and heat wave hazards during the period 1980-2018, based on hydrological simulations performed using a recently produced hydrological digital twin model at high spatial (5 km2) and temporal (daily) resolution. The model also includes artificial reservoirs and the combination of high resolution hydrological modeling and compound hazard estimation framework has a key advantage that: i) it captures single hazard evolution at daily time scale and ii) explicitly estimate the dependence between co-occurred events directly mapping critical susceptible regions.

Preliminary results show increasing trends in number and severity of compound heat waves and drought events. Ongoing work aim to quantify the spatial distribution of the analysed compound events and the exposure in terms of population impacted and main land cover types. The proposed modeling framework may help improve the prediction and assessment of occurrences of compound heat waves and droughts events and the possible implementation of mitigation actions. The authors are supported by the WATERSTEM MUR PRIN 2020 (Prot. Number 20202WF53Z) and the COACH-WAT PRIN 2022 (Prot. Number 2022FXJ3NN).

How to cite: Formetta, G. and Morlot, M.: Compound heatwave and drought hazard quantification in a large anthropized alpine basin., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15136, https://doi.org/10.5194/egusphere-egu24-15136, 2024.

Posters on site: Wed, 17 Apr, 16:15–18:00 | Hall A

Display time: Wed, 17 Apr, 14:00–Wed, 17 Apr, 18:00
Chairpersons: Louise Slater, Manuela Irene Brunner
A.43
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EGU24-18
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ECS
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Lalani Jayaweera, Conrad Wasko, Rory Nathan, and Fiona Johnson

Action needs to be taken in response to the changes in future flood risk due to the impact of global warming on the magnitude and frequency of extreme rainfalls. Projected changes in extreme rainfalls can be used to estimate the associated changes in design flood estimates using Intensity-Frequency-Duration (IFD) curves in combination with event-based flood models. IFD curves are estimated from records of historical annual maxima across different storm durations and exceedance probabilities. Past studies investigating changes in extreme rainfall across Australia have been limited in scope as they have focused on single durations, single exceedance probabilities, or limited regional extents. This means that we do not yet have a comprehensive understanding of how projected changes in extreme rainfalls impact on IFD curves.

Here, to fill this gap, we investigate the changes in extreme rainfall changes across different storm durations and exceedance probabilities across 42 stations which span the entire continent of Australia. We begin with examining the trend in annual maximum rainfall across 16 different storm durations (6 min to 7 day) using the Theil-Sen slope estimator, testing for statistical significance using the Mann-Kendall test. To extrapolate 1% annual exceedance probability, we fit non-stationary Generalized Extreme Value Distributions (GEVs) at each site. Non-stationarity was assessed by varying the location parameter, varying the scale parameter, and varying both the location and scale parameters as a linear trend in time.

We find that the short duration (<1 hr) annual maximum rainfalls have increased across Australia, but longer duration annual maxima (>1 hr and 1 day) show fewer positive trends with some sites exhibiting negative trends. Based on Akaike Information Criteria, the GEV models which varied either the location parameter, or both the scale and location parameters, were found to be superior. However, when changes in quantile estimates were examined for rare exceedance probabilities (up to the 1 in 100 AEP), it was found the GEV model which only varied the location parameter was unable to capture the increased rate of change in extreme rainfalls. Accordingly, we conclude that changes in extreme rainfalls is best represented by non-stationary models that incorporate changes in both location and scale parameters.

How to cite: Jayaweera, L., Wasko, C., Nathan, R., and Johnson, F.: Non-stationary design rainfalls for Australia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18, https://doi.org/10.5194/egusphere-egu24-18, 2024.

A.44
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EGU24-445
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ECS
Anju K.v. and Srinivas V.v.

The global hydrological cycle is substantially influenced by climate change, leading to notable alterations in hydroclimatic extremes. This encompasses extreme precipitation and temperature events, ultimately amplifying the frequency and intensity of floods. Analyzing the trends in floods and the related covariates provides insight into regional patterns of flood changes and shifts in flood generation mechanisms within the selected catchments. An improved understanding of the processes driving the historical changes in this natural hazard can provide basic information to enhance our preparation and mitigation efforts. Differences in significant trends (non-stationarities) in the magnitude and frequency of flood-related characteristics are determined for the river basins of Peninsular India through analysis of AMS (Annual Maximum Series) and POT (Peaks Over Threshold) series of streamflow over the period 1979–2019. Scrutiny of the trend detection results provided a better understanding of the strengths and limitations of AMS and PDS approaches in analyzing flood characteristics. Non-stationarity in the flood peaks is attributed to precipitation and temperature dynamics. This is accomplished by developing Generalised Pareto regression models to establish a relationship between the flood peaks and basin-averaged precipitation and temperature at different time scales preceding the flood events. Our findings emphasize the importance of understanding climatic conditions driving flood events and incorporating the same for assessing hydroclimatic risks with changing climate patterns, ultimately fostering more resilient and sustainable strategies.

How to cite: K.v., A. and V.v., S.: Detection and attribution of non-stationarity of flood characteristics across the Peninsular Basins of India, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-445, https://doi.org/10.5194/egusphere-egu24-445, 2024.

A.45
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EGU24-1095
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ECS
Consecutive Extreme Dry Days of the growing season in the Vojvodina region: Observations and Projections
(withdrawn)
Jovana Bezdan, Atila Bezdan, and Boško Blagojević
A.46
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EGU24-1125
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ECS
Abinesh Ganapathy, Bruno Merz, Sergiy Vorogushyn, and Ankit Agarwal

Traditional flood frequency analysis assumes that the probability distribution is stationary over time. However, this assumption has been challenged, given widespread changes in catchments and climate. One of the inherent handicaps of the stationarity assumption is its non-inclusion of changes in extremes associated with future climatic conditions. To overcome this handicap, climate covariates can be incorporated into the estimation of flood probability through the non-stationary Climate-Informed Flood Frequency Analysis (CIFFA). The CIFFA methodology comprises 1) selection of predictands (usually seasonal maxima), 2) identification of suitable predictors (large-scale climate indices), and 3) derivation of a statistical link between predictands and predictors. Since CIFFA typically considers the flood peaks in the dominant season, its applicability to gauges, where flood extremes occur in several seasons, is limited. Here, we develop and test a novel non-stationary Climate-Informed-Seasonal-Mixing approach across various European basins. In the proposed Climate-Informed-Seasonal-Mixing approach, we fit the seasonal peak distribution (boreal seasons) with the location parameter conditioned on the selected covariate using the Bayesian Inference. The best climate covariates for each season among a set of predictors are identified based on widely applicable information criterion (WAIC), which computes log posterior predictive density and adjusts the overfitting using the effective number of parameters. Even the traditional stationary model could be a preferred model for any season if it has a minimum WAIC value. Following the estimation of seasonal distribution parameters, the annual flood quantiles are derived by multiplicatively mixing all the seasonal distributions. In order to demonstrate the performance of the proposed approach, we split the entire period into calibration and validation sets, fitting the model based only on calibration samples. The projected quantiles during the validation period are then compared with a benchmark model (traditional model fitted solely with validation samples). Our results suggest that for many gauges, the flood quantiles estimated by the proposed Climate-Informed-Seasonal-Mixing approach align with the baseline estimates where the traditional approaches fall short.

How to cite: Ganapathy, A., Merz, B., Vorogushyn, S., and Agarwal, A.: Climate-Informed-Seasonal Mixing Approach to Estimate Flood Quantiles, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1125, https://doi.org/10.5194/egusphere-egu24-1125, 2024.

A.47
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EGU24-1235
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ECS
Georgina Falster and Sloan Coats

Australia’s Murray-Darling Basin experienced three consecutive years of meteorological drought across 2017–2019, collectively named the ‘Tinderbox Drought’. Rainfall deficits during the three-year drought were focussed in the Australian cool season (April to September), and deficits in both the cool season and the annual total were unprecedented in the instrumental record. However, at ~120 years long, Australian rainfall records are not long enough to have captured the full possible range of variability, particularly for multi-year extreme events. That is, observations are an incomplete sampling of the full possible range of rainfall variability. Climate model simulations may provide longer timeseries, however climate models have known biases in Australian rainfall (Grose et al. 2020). Therefore, to determine if the Tinderbox Drought was outside the expected range of internal variability, we constructed Linear Inverse Models (LIMs) that simulate internal variability in Australian rainfall and associated global sea surface temperature (SST) anomalies. We used the LIMs to produce 10000-year-long rainfall records that emulate the stationary statistics of observed Australian rainfall, hence reflecting more of the full possible range of variability.

 

Overall, we find that rainfall deficits were most severe 1) in the northern Murray-Darling Basin; and 2) during the final year of the drought (2019). Global SST anomalies during the drought mostly did not resemble the pattern that is most reliably associated with low rainfall over the Murray-Darling Basin (warm anomalies in the central tropical Pacific and the western Indian Ocean). In fact, global SST anomalies observed during the Tinderbox Drought are not reliably associated with negative rainfall anomalies across the Murray-Darling Basin—this is particularly the case for the first two years of the drought. In terms of single-year rainfall anomalies, the only aspect of the Tinderbox Drought that was beyond the expected natural range was annual-total rainfall over the northern Murray-Darling Basin during 2019. However, when considered in terms of basin-wide rainfall over the full three years, negative anomalies during the Tinderbox Drought were beyond the expected natural range in terms of both cool season and annual rainfall. This suggests an anthropogenic contribution to the severity of the drought. Additionally, we find that Linear Inverse Models are a valuable tool for estimating whether or not an observed extreme rainfall event falls within the expected natural range.

References

Grose, M. R., Narsey, S., Delage, F. P., Dowdy, A. J., Bador, M., Boschat, G., et al. (2020). Insights from CMIP6 for Australia's future climate. Earth's Future, 8, e2019EF001469.

How to cite: Falster, G. and Coats, S.: How unusual was Australia's 2017-2019 Tinderbox Drought?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1235, https://doi.org/10.5194/egusphere-egu24-1235, 2024.

A.48
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EGU24-1772
Spatiotemporal analysis of paleofloods in the Negev region (Israel): insights into past climate
(withdrawn)
Motti Zohar
A.49
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EGU24-4044
Lena M. Tallaksen, Henny A.J. Van Lanen, Jamie Hannaford, Hege Hisdal, Daniel G. Kingston, Gregor Laaha, Christel Prudhomme, James H. Stagge, Kerstin Stahl, Anne F. Van Loon, and Niko Wanders

Drought is a worldwide phenomenon that originates from a prolonged deficiency in precipitation, often combined with high evaporation, over an extended region. The resultant meteorological water balance deficiency may cause a hydrological drought to develop into below normal levels of streamflow, lakes, and groundwater. Contemporary knowledge and experiences from an international team of drought experts are consolidated in a textbook (Tallaksen and van Lanen et al., 2023), which builds on an earlier edition from 2004 (URL 1), with significant new material added. An updated synthesis was requested given the high relevance and severe impacts of drought seen in many regions of the world in recent years, along with the increasing knowledge gained over the last two decades. A majority of these studies focus on climate and climatology approaches, whereas the textbook addresses hydrological drought in particular. The textbook consists of three parts; Part I (Drought as a natural hazard) discusses the drought phenomenon, its main features, regional diversity and controlling processes. Part II (Estimation methods) presents contemporary approaches to drought estimation, including data and hydrological drought characteristics, statistical analysis of drought series, incl. frequency analysis, time series analysis and regionalisation procedures, as well as process-based modelling. Part III (Living with drought) addresses aspects related to the interactions between water and people. Topics include historical and future drought, how human interventions influence drought, drought impacts and Drought Early Warning Systems. Knowledge and experiences shared in the book are from regions all over the world although somewhat biased to Europe and rivers that flow most of the year.

This presentation aims to introduce the textbook, its motivation and content to a wide audience. The textbook is supported with worked examples and self-guided tours that are elaborated more extensively on GitHub. Worked examples include online procedures, code, and details of the calculation procedures that enable readers to repeat calculations in a stepwise manner, either with their own data or by using online datasets, and we encourage user’s feedbacks and experiences in testing these. Self-guided tours are demonstrations of advanced methodologies that involve several calculation steps and are given as online presentations. Four datasets are included on GitHub; an international, a regional and two local datasets. The international dataset illustrates the drought phenomenon and its diversity across the world, whereas regional data and local aspects of drought are studied using a combination of hydroclimatological time series and catchment information. Hopefully, the textbook will contribute to an increased awareness of one of our main natural hazards, and thereby increase the preparedness and resilience of society to drought.

How to cite: Tallaksen, L. M., Van Lanen, H. A. J., Hannaford, J., Hisdal, H., Kingston, D. G., Laaha, G., Prudhomme, C., Stagge, J. H., Stahl, K., Van Loon, A. F., and Wanders, N.: HYDROLOGICAL DROUGHT – Processes and Estimation Methods for Streamflow and Groundwater, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4044, https://doi.org/10.5194/egusphere-egu24-4044, 2024.

A.50
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EGU24-4778
Assessment of Waterlogging Hazard in the Vojvodina Region (Northern Serbia)
(withdrawn)
Atila Bezdan, Jovana Bezdan, and Boško Blagojević
A.51
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EGU24-5969
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ECS
Yanick Dups, Daniela Pavia Santolamazza, Philipp Staufer, and Henning Lebrenz

In Switzerland, low flows are described by the five percent quantile denoted by Q347. This threshold value not only has consequences for the planning, but also necessitates authorities to adjust the operation of pertinent infrastructure to mitigate ecological impacts on watercourses. Given a discharge timeseries spanning at least a ten-year period, determination of the Q347 can be done using the duration curve. Typically, said timeseries are not available for smaller catchments necessitating the estimation of the threshold value Q347. In Switzerland, the utilization of multiple linear regression has been established to estimate the area-specific discharge q347.

The primary objective of these investigations is to estimate the Q347 value for 383 ungauged catchments in the Canton of Solothurn, each covering an area less than 100 km². Daily discharge, precipitation and temperature timeseries ranging from 1990 to 2020 were collected from 56 gauged catchments smaller than 500 km² surrounding the target area. 30 “static” parameters delineating geometry, topography, geology, land use, and drainage along with nine “climatic” parameters describing temperatures, precipitation distributions, and potential evapotranspiration were defined and computed to characterize gauged and ungauged catchments. Alongside comparing three regression methods, coupled with two adjustment techniques supplementing truncated discharge timeseries, three parameter selection methods are evaluated. The validation of the proposed models shows reduced errors and increased linear correlations between estimated and observed values compared to currently applied models. Notably, a spatially more homogeneous yet catchment-specific distribution of estimated values is observable. Particularly when timeseries remain unadjusted or adjustment is done using the Antecedent Precipitation Index (API) and the flow duration curve from a donor basin (Ridolfi, E.; Kumar, H.; Bárdossy, A., 2020), the proposed models yield promising results.

Furthermore, the temporal variability of low flow events for the glacier-free catchments in the study area has been analysed. The frequency of low flow events below the threshold systematically increased over the last 30 years, while the 10-year Q347 value of said catchments has systematically decreased in the same period. The increase in low flow days leads to large errors in the estimation of the Q347 value, especially when its estimation is based on truncated timeseries. As further changes in runoff behaviour are to be expected due to climate change, extending the definition of "low flow" to include event duration and intensity alongside a fixed threshold value could offer a more suitable description.

How to cite: Dups, Y., Pavia Santolamazza, D., Staufer, P., and Lebrenz, H.: New estimation models for determining the Q347, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5969, https://doi.org/10.5194/egusphere-egu24-5969, 2024.

A.52
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EGU24-8350
Yong-Jun Lin, Hsiang-Kuan Chang, Jihn-Sung Lai, and Yih-Chi Tan

There have been frequent reports of subway station flooding incidents in recent years. For instance, on October 30, 2012, Hurricane Sandy in the United States caused a storm surge combined with astronomical tide that submerged seven subway lines in New York City. It was the most severe disaster in the New York City subway system. On July 20, 2021, a flooding incident occurred in Zhengzhou, Henan Province, China, with a record hourly rainfall of 201.9mm. The heavy rain caused severe water accumulation at the Wulongkou yard of Zhengzhou Metro Line 5 and its surrounding areas. The temporary flood barrier was breached, allowing water to flood into the subway, with a maximum water depth of 1.75 meters inside the carriages and the flooding length extending approximately 1 kilometer.

In 2001, Typhoon Nari caused flooding at the Taipei Station, with 16 MRT stations also inundated. Surface roads were extensively flooded, and the Taiwan Railways Administration stations in Taipei, Wanhua, and Banqiao were submerged, resulting in a 90-day suspension of the Taipei MRT station. How to quickly evaluate the impacts of subway station flooding is crucial for the extreme weather in the future.

Therefore, this study utilized the US EPA SWMM to simulate the flooding situation of the Tamsui-Xindian line during Typhoon Nari in 2001. The SWMM calculations showed varying degrees of flooding at different stations at different times. For example, Guting Station was not affected by human intervention, while the simulated flooding depth at Taipei Station was only 0.09 m different from the actual depth. Additionally, the September 17, 2001 flood profile at 17:34 showed that Taipei Station was submerged, with water flowing to Zhongshan and Shuanglian stations. The National Taiwan University Hospital station experienced minimal flooding due to its higher elevation. The simulation also displayed the water ingress situation at different stations at various times. However, there were some inaccuracies due to the lack of detailed flood progression and inflow data and the use of a simplified station model. Nonetheless, the overall simulation results reflected the related flooding process.

How to cite: Lin, Y.-J., Chang, H.-K., Lai, J.-S., and Tan, Y.-C.: Subway Flooding Simulation with US EPA SWMM: A Case Study of the Tamsui-Xindian Line During Taiwan's 2001 Typhoon Nari, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8350, https://doi.org/10.5194/egusphere-egu24-8350, 2024.

A.53
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EGU24-8460
Hossein Tabari

The aridity of a region plays a pivotal role in shaping a diverse range of hydrological processes, encompassing critical aspects such as the sensitivity of evaporation to variations in temperature and precipitation, water use efficiency, and the intricate interactions between precipitation, soil moisture, and evaporation. These processes, in turn, influence the response of hydrological extremes, such as drought and flood, to global warming. Understanding the impact of aridity on these extreme events in the context of changing climate conditions across global terrestrial ecosystems is essential for comprehending water availability and ecological resilience in different regions. This study investigates the relationships of changes in drought and flood intensities for the end of the twenty-first century with background aridity across global terrestrial ecosystems. Background aridity is quantified using an aridity index, calculated as the ratio between precipitation and evaporation. Drought is characterized by the standardized precipitation index (SPI), and flood by fitting a generalized extreme value distribution (GEV) to the annual maxima flow time series of the Inter-Sectoral Impact Model Intercomparison Project models. The results show opposite responses of drought and floods to background aridity under climate change across global terrestrial ecosystems. As aridity decreases from dry to wet regions, the intensification of flood events in the future is expected to increase. In contrast, drought intensification is more pronounced in dry and semi-dry regions. These findings hold significant implications for developing effective and region-specific water resource management policies to address hydrological extremes in a changing climate.

How to cite: Tabari, H.: Contrasting responses of drought and floods to background aridity in a changing climate across global terrestrial ecosystems, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8460, https://doi.org/10.5194/egusphere-egu24-8460, 2024.

A.54
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EGU24-9058
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ECS
Friedrich Boeing, Andreas Marx, Thorsten Wagener, Luis Samaniego, Oldrich Rakovec, Rohini Kumar, and Sabine Attinger

It is projected that the likelyhood and duration of extreme soil moisture (SM) droughts will increase in Germany under future warming scenarios. Annual precipitation changes are small under climate change in Germany with increases in winter and decreasing precipitation in summer for some parts of Germany. Generally, the climate ensemble spread in the future precipitation signal is large. Furthermore, impacts of SM droughts depend largely on the soil volume evaluated. We identified a gradient of stronger soil drying in shallow SM compared to deeper SM under global warming, leading to different effects on shallow-rooted vegetation compared to deep-rooted vegetation (agriculture versus forestry). In addition, spatial characteristics such as soil properties can strongly influence the dynamics of SM and thus shape the response of SM drought to changing meteorological conditions. 
In this work we evaluate the impact of the considered soil depth and spatial features on simulated changes in SM droughts in Germany. We compare this influence to the uncertainty in meteorological changes. We use a large climate ensemble based on Euro-Cordex regional climate model simulations, which were bias-adjusted and spatially disaggregated to run the mesoscale hydrological model (mHM) (mhm-ufz.org) with a high spatial resolution of 1.2x1.2km. 
This work aims to expand the picture of climate change impacts on SM droughts in Germany. The results can contribute to an improved definition of sector-specific drought indicators that will support national efforts to ensure climate change resilient water management.

How to cite: Boeing, F., Marx, A., Wagener, T., Samaniego, L., Rakovec, O., Kumar, R., and Attinger, S.: Evaluation of soil moisture droughts under climate change in Germany, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9058, https://doi.org/10.5194/egusphere-egu24-9058, 2024.

A.55
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EGU24-9215
|
ECS
Maria Grazia Zanoni, Marius Floriancic, Hansjörg Seybold, and James W. Kirchner

Switzerland relies significantly on sustainable water management to meet its diverse socio-economic and environmental needs. As climate change introduces heightened uncertainty in weather patterns, accurate forecasting of extreme flows from climatic data has become essential for efficient water resource management in the country. Furthermore, these events are likely shaped by nonlinear hydroclimatic and compound conditions distinct from typical average cases. A thorough understanding of these phenomena is therefore crucial for effective adaptation to changing climatic conditions. In this regard, data-driven techniques, such as Machine Learning algorithms, have proven capable of extracting knowledge from vast amounts of data, providing valuable insights into the underlying climate and societal dynamics driving extreme flow events.

The aim of the present study is therefore twofold. First, we evaluate the ability of a Dense feed-forward Neural Network (DNN) model to predict drought and peak flow events in Switzerland based on anthropogenic, environmental and climatic data. On the other side, we investigate the role of each driver in the prediction and we study the temporal trends of the target and the features. The analysis was conducted on a large dataset consisting of daily discharge data from more than 400 sites across the country, from 1999 to 2019.  First, we evaluated the flow distribution at each individual site, considering only the extreme events and developing two distinct DNN models for droughts and for peaks. The DNN performed better in modeling droughts, achieving in the test set a mean Nash-Sutcliffe efficiency coefficient of 0.6 and a mean Kling-Gupta efficiency coefficient of 0.8, compared to 0.1 and 0.38, respectively, for the peaks.  A sensitivity analysis of the features, such as the cumulative precipitation and mean air temperature in the preceding weeks of the event, was performed. In addition, we delved into a detailed examination of the temporal trends of the climatic drivers and the extreme flow rates over the 20 years of the study. In the subsequent phase of the project, we explored a multi-site modeling approach to address the issue of the DNN model's poor performance in predicting peak flows.  We introduced geographic, land use and other anthropogenic factors specific to each watershed. 

By revealing the predictive potential of data-driven models, this study serves as a valuable foundation and resource for addressing extreme flow events and the hydroclimatic and anthropogenic patterns behind them.

How to cite: Zanoni, M. G., Floriancic, M., Seybold, H., and Kirchner, J. W.: Exploring extreme flow events and associated patterns in Switzerland: a Dense feed-forward Neural Network approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9215, https://doi.org/10.5194/egusphere-egu24-9215, 2024.

A.56
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EGU24-10393
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ECS
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Alexandre Devers, Joël Gailhard, Sylvie Parey, and Stéphanie Froidurot

Understanding and quantifying severe low flows is crucial for the management of hydropower or thermal power plants. Moreover, low flows are strongly related to the climatic regime and will be affected by climate change. Therefore, we propose a modelling chain to estimate severe low flow values for several human-influenced catchments in France, both under current and future climate.

Firstly, a bivariate weather generator (Touron, 2019) of daily temperature and precipitation, representing the average of 28 catchments spread out over France, was trained, and used to generate 1000 meteorological time series over a 30-year period. Average daily precipitation and temperature are then spatially disaggregated to produce 1000 local time series for each of the 28 catchments using an analogue approach. Thirdly, MORDOR-SD a lumped conceptual rainfall-runoff model, developed and used at EDF (Garavaglia et al. 2017), combined with upstream-downstream propagation and water management module was forced by the 1000 local meteorological time series. The resulting 1000 time series of simulated river flows are then used to calculate an empirical rare percentile estimate of low flows across 12 large catchments of interest.

The methodology is applied on historical period (1981-2010) using precipitation and temperature observations to train the weather generator. The robustness of the method is evaluated by comparing return levels of low flows obtained through the proposed method and the ones estimated through river flow observations available. Finally, to assess the impact of climate change, the weather generator is also trained/used with 5 downscaled climate projections from the CMIP5 experiments corresponding to: (1) the historical period (1981-2010) and, (2) 4 storylines representing different levels of warming/drying (2036-2065).

The comparison over the historical period has shown the relative agreement between simulated and observed severe low flows. Furthermore, under future conditions, the climatic differences between the 4 storylines lead to logical differences in the estimation of severe low flows, i.e. warmer/drier storylines lead to lower estimation of severe low flows.

How to cite: Devers, A., Gailhard, J., Parey, S., and Froidurot, S.: Estimating severe low flows on human-influenced catchments by combining weather generator, analogue spatial disaggregation, and hydrological modelling under historical and future climate., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10393, https://doi.org/10.5194/egusphere-egu24-10393, 2024.

A.57
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EGU24-11499
|
ECS
Eduardo Muñoz-Castro, Bailey Anderson, Paul Astagneau, Joren Janzing, Pablo A. Mendoza, and Manuela I. Brunner

Extreme hydrometeorological events such as streamflow droughts and floods may have severe impacts on infrastructure, agriculture, water supply, and hydropower generation, as well as social and political systems. Even though such impacts can be enhanced if the two types of events occur consecutively, the occurrence and drivers of drought-to-flood transitions are not well understood. Here, we ask: ‘How do the properties of drought-to-flood transitions change with different meteorological drivers and initial hydrologic conditions?’ To address this question, we configure the PCR-GlobWB hydrological model in a suite of near-natural gauged catchments, included in the quasi-global large sample dataset CARAVAN, that comprise different hydroclimatic conditions and physiographic characteristics. We run numerical experiments to understand the sensitivity of consecutive drought-to-flood properties (e.g., duration, extension, intensity, etc.) to different driver scenarios. Additionally, we perform, for each catchment, a flux-mapping analysis to explore whether different combinations of drivers can lead to a similar catchment response through different combinations of fluxes. Finally, we define clusters of catchments with similar drivers and sensitivities of consecutive hydrological extremes to the different stress tests. Ongoing analyses suggest that the drivers of drought-to-flood transitions vary substantially across catchments.

How to cite: Muñoz-Castro, E., Anderson, B., Astagneau, P., Janzing, J., Mendoza, P. A., and Brunner, M. I.: The role of meteorological drivers and initial hydrologic conditions on streamflow drought-to-flood transition events, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11499, https://doi.org/10.5194/egusphere-egu24-11499, 2024.

A.58
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EGU24-12387
David Pino, Josep Barriendos, Mariano Barriendos, Carles Balasch, Jordi Tuset, and Laia Andreu-Hayles

The current context of climate change is leading to an increase in hydroclimate variability in the Mediterranean region. This situation is resulting in more frequent and longer dry periods but also in an increase of torrential rainfall events. Current situation justifies the study of the behaviour of droughts and floods from an integrated long-term perspective.

This study aims to study droughts, floods and their interaction during the 19th century in Catalonia using historical and administrative documentary sources. The 19th century corresponds to a climatic period of transition between the Little Ice Age and the current climatic period that includes the appearance of different climatic forcing factors such as solar minimums and extraordinary volcanic eruptions.

In Catalonia 19th century stands out for having some of the most important droughts recorded in the instrumental series of Barcelona (1812-1825), along with experiencing some notable catastrophic flood events. Administrative documentary data allowed us to study at daily resolution flood episodes such as in August 1842, May 1853, September 1874 and January 1898, together with the duration and frequency of drought episodes. Complementarily, in order to characterize the atmospheric general patterns during these episodes, we also generated daily barometric synoptic maps using old instrumental pressure data from different points of Europe. This approach provided the identification of different atmospheric anomalies driving these extreme hydrometeorological events.

How to cite: Pino, D., Barriendos, J., Barriendos, M., Balasch, C., Tuset, J., and Andreu-Hayles, L.: Drought and flood episodes during the 19th Century in Catalonia (NE Iberian Peninsula) , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12387, https://doi.org/10.5194/egusphere-egu24-12387, 2024.

A.59
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EGU24-13029
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ECS
Ryan van der Heijden, Ali Dadkhah, Amin Aghababaei, Xueyi Li, Eniola Webster-Esho, Prabhakar Clement, Mandar Dewoolkar, Ehsan Ghazanfari, Norm Jones, Gustavious Williams, and Donna Rizzo

Groundwater and surface water are interconnected in most climatic regions. Baseflow, the contribution of streamflow not directly associated with precipitation forcing, is a critical component of streamflow prediction and water resource allocation. Baseflow is often considered to be a low-frequency component of streamflow and many of the methods for estimating it are based on this premise. The climatic and physiographic attributes of a region will contribute to the low-flow behavior of its surface waterways. For example, baseflow in a snowmelt-driven basin may produce a distinct hydrologic signature compared to baseflow in a precipitation-driven basin.

In this study, we developed a unique metric based on the variable drought threshold method (VDTM) for characterizing historical streamflow timeseries and performed cluster analysis on a large set of gages in the continental United States (CONUS). Our study goal was to observe correlations between low-flow characteristics and distinct hydrologic, physiographic, and climatic regions to provide insight into the underlying mechanisms influencing baseflow.

The VDTM applies a non-exceedance percentile (NEP) computed based on the distribution of flow recorded at a stream gage over a given time frame (i.e., month, season) throughout the complete record of measurement. This study used daily streamflow records for 1,462 reference quality gages across the CONUS from the USGS GAGES-II data set; each gage contained at least 20 years of complete daily streamflow measurements. We computed the 10th NEP for each month at all 1,462 gages and normalized this value by the mean streamflow to develop the parameter r10. We performed K-means clustering on the monthly r10 values, forming seven clusters of low-flow behavior.

We observed clusters with distinct low-flow behavior across different ecoregions related to possible mechanisms driving streamflow and baseflow in those regions. For example, a cluster located in the intermountain-west shows unique behavior largely seen nowhere else in the CONUS, possibly a result of the predominantly snowmelt-driven shallow subsurface flow that contributes to baseflow seen in that region. Conversely, clusters located in the Pacific Northwest and parts of the Appalachians show a different behavior, possibly a result of the predominantly rainfall-driven streamflow observed in those regions. Principal components analysis suggests that the critical months associated with clustered gages are during the summer (June, July) and winter (January, February).

The spatial distribution of the clusters largely adheres to the defined physiographic and climatic regions of the CONUS despite the absence of any physiographic or climatic variables used for clustering, suggesting a possible linkage between these attributes and the low-flow behavior of surface waterways. Analysis of the trend and magnitude of r10 may provide insight into whether (and when) a stream is losing water to or gaining water from groundwater as well as the magnitude of the transfer. The results of this study suggest that using NEPs and the r10 metric may be an effective method for defining regionalization based on low-flow metrics.

How to cite: van der Heijden, R., Dadkhah, A., Aghababaei, A., Li, X., Webster-Esho, E., Clement, P., Dewoolkar, M., Ghazanfari, E., Jones, N., Williams, G., and Rizzo, D.: Variable Drought Threshold Method for Low-Flow Behavior Reveals Distinct Clustering Across the Continental United States, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13029, https://doi.org/10.5194/egusphere-egu24-13029, 2024.

A.60
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EGU24-14737
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ECS
Nandana Dilip K, Urmin Vegad, and Vimal Mishra

Flash Floods are one of the crucial disasters in India which causes high mortality and damage due to its sudden onset and devastating impact. These events are projected to increase in India due to the warming climate and increasing unplanned urbanization.  However, India still lacks a robust analysis on flash flood susceptibility at a subbasin scale. In our study, we have considered meteorological and geomorphological factors to improve the susceptibility mapping, as flash floods are the result of high intensity rainfall in a short period of time and the geomorphology of the basin. We analyzed 17 different geomorphological factors of drainage, relief and areal aspects. Further, we calculated the flashiness index for all the subbasins within India using the model simulated streamflow. We forced a hydrodynamic routing model with reanalysis data to simulate streamflow at the subbasin outlets. We prepared subbasin-level flash flood susceptibility maps based on geomorphology, flashiness index and a combination of both. The integrated use of geomorphology and meteorology will provide a more robust framework for identifying the flash flood prone subbasins in India. This will help the authorities in focusing on the probable regions to plan mitigation strategies.  

How to cite: Dilip K, N., Vegad, U., and Mishra, V.: Identifying Flash Flood-Prone Subbasins in India Using Geomorphological and Meteorological Parameters , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14737, https://doi.org/10.5194/egusphere-egu24-14737, 2024.

A.61
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EGU24-14703
Wonjin Kim, Si Jung Choi, and Seong Kyu Kang

ABSTRACT

This study focuses on the seasonal patterns of low flow in the Nakdong River basin (23,635 km2), considering its vital role as a seasonal phenomenon and integral component of the flow regime. Low flow, derived from groundwater discharge or surface discharge from lakes and reservoirs, exhibits varying magnitudes and durations under seasonal changes, thereby holding significant implications for agricultural activities, aquatic species, and water quality. In the absence of gauge stations for small streams, Soil and Water Assessment Tool (SWAT) was employed to ensure reliable simulation for low flow along the target watershed, and the model was calibrated for the period of ten years (2010~2019) using observed data from multipurpose dams and multifunctional weirs within the target watershed. Based on the model results, spatio-temporal variations of low flow were estimated, and seasonality indices were adopted by means of understanding and analysing low flow characteristics. The indices include seasonlity histograms (SHs) depicting the monthly distribution of low flows, seasonlity index (SI) representing the average timing of low flows within a year, and seasonality ratio (SR) showing the ratio of summer to winter low flows. Subsequently, seasonal patterns of low flow in target watershed were evaluated under three indices to figure out the response of low flow in relation to watershed characteristics and climate variability.

 

Acknowledgements

Research for this paper was carried out under the KICT Research Program, Development of IWRM-Korea Technical Convergence Platform Based on Digital New Deal) funded by the Ministry of Science and ICT.

How to cite: Kim, W., Choi, S. J., and Kang, S. K.: Evaluating the seasonal patterns of low flow in  Nakdong River basin using SWAT, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14703, https://doi.org/10.5194/egusphere-egu24-14703, 2024.

A.62
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EGU24-9081
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ECS
Lea Augustin and Thomas Baumann

Co-management strategies for floods and droughts offer a promising solution for dealing with two extremes that are increasingly close in time and space. Techniques originally developed for drought prevention, such as managed groundwater recharge (MAR), could use floods as a source of water (Flood-MAR) to simultaneously protect against flooding.

The project Smart-SWS aims to develop this concept further by capturing the flood waves in a river and infiltrating them into aquifers nearby. Subsurface storage is created through geotechnical measures in the aquifer. This storage could secure the seasonal water supply while protecting downstream settlements from flooding.

The main attributes of Smart-SWS sites mirror the overall objective: On the one hand, potential sites for such a system are located in areas that are regularly flooded and, at the same time, have problems with groundwater scarcity. In order to infiltrate large volumes of water into the aquifer and store this water for extended periods, the characteristics of the aquifer, the surface, and the water source must be taken into account to assess the suitability of these sites.

In this work, we have identified suitable sites for such an underground flood storage system by applying a GIS-based multi-criteria decision analysis (MCDA). The workflow for the suitability mapping is based on publicly available data and implemented in Python. The results are shown for the administrative district of Swabia in Bavaria, Germany, where approximately 35% of the study area was identified as having varying degrees of suitability. The robustness of the MCDA is validated with a sensitivity analysis, and the results are checked against expert opinions based on field data.

How to cite: Augustin, L. and Baumann, T.: Suitability Mapping for Subsurface Floodwater Storage Schemes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9081, https://doi.org/10.5194/egusphere-egu24-9081, 2024.

A.63
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EGU24-16427
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ECS
Marie Sæteren, Kolbjørn Engeland, Ånund S. Kvambekk, and Lena M. Tallaksen

The dynamic breakup of river ice can initiate ice runs where large masses of ice floes accumulate as ice jams. These ice jams can cause severe inundation and infrastructure damage. Several Norwegian rivers are prone to ice run events, however there are currently no models available in Norway for predicting this specific hydrological phenomenon. Ice-related problems are often dealt with on a site-to-site basis and rely heavily on local knowledge. Other countries, such as Canada and Sweden, have implemented statistical, machine learning and process-based modelling approaches. Being able to accurately predict the timing and severity of ice run and ice jam events improves the ability to take suitable mitigation measures and limit negative consequences. The aim of this work is to develop a model to predict ice run events in two Norwegian rivers, the Beiarn River and the Stjørdal River, and thereby address the need for predicting this hydrological hazard.

The work presented here is part of a master thesis study that will be completed by May 2023. Both Stjørdal and Beiarn River have been monitored by NVE in the latter half of the 20th century, and the timing and severity of historical ice run events are obtained from this data. The predictors are given by hydrometeorological and ice thickness data, both observed and modelled. The Distance Distribution Dynamics (DDD) model developed by NVE is used for simulating daily discharge, and a simple ice growth model from NVE is used for modelling ice thickness. The prediction model itself is a work in progress, initially taking a logistic regression approach. If time allows, other approaches within machine learning such as random forest will be attempted. The dataset is severely imbalanced given the rarity of ice run events and the limited length of the observed series. Different methods are evaluated in terms of their ability to deal with this issue. The ultimate objective of this project is to develop a model providing daily probabilistic forecasts of the likelihood of ice run events in the coming days.

How to cite: Sæteren, M., Engeland, K., Kvambekk, Å. S., and Tallaksen, L. M.: Development of a river breakup prediction model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16427, https://doi.org/10.5194/egusphere-egu24-16427, 2024.

A.64
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EGU24-19269
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ECS
Aman Kumar and Dhyan Singh Arya

Hydroclimatic Whiplash events refer to the extreme variability characterising the rapid transitions from one hydroclimatic extreme to another, occurring in consecutive periods. Rapid transition from one extreme to the other. The compound consecutive extremes impact often exceeds the magnitude of individual events given their occurrence at distinct times. This study introduces a comprehensive investigation into Intraseasonal compound whiplash occurrences in India, focusing on the rapid shifts between drought/heat and pluvial conditions. The data used in the study is taken from IMD precipitation of 0.25˚ x 0.25˚ and temperature at 1˚x 1˚ for the time span 1901 to 2022. This study involves distinct thresholds for duration and intensity to identify the heat dry and wet events. Dry events are characterised by prolonged low rainfall and sustained minimum temperatures throughout the dry period. Conversely, wet events exhibit high intensity within relatively shorter durations. Emphasising the 70th percentile for temperature thresholds acknowledges that extreme conditions in each component aren't mandatory for a compound event's occurrence. Our study delves into the frequency of individual extremes and compound whiplash occurrences, calculating swing severity using mean temperature quantiles for warm/dry spells alongside precipitation anomalies. The Mann-Kendall test and Sen’s slope is used for the check frequency and severity evolution at the grid level. Results highlight diverse regions witnessing increasing trends in wet and dry events, signifying a notable surge in compound whiplash incidents. This is especially worrying in areas that have typically been dry because the increase in rain can disrupt the usual climate there. This concerning trend raises alarms for local ecosystems, water resources, and socio-economic activities. Recognising these evolving patterns is critical for making strategies and long-term planning in the recent climate variability.

How to cite: Kumar, A. and Arya, D. S.: Dynamics of Intraseasonal Compound Whiplash Event: A Retrospective Analysis in India, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19269, https://doi.org/10.5194/egusphere-egu24-19269, 2024.

A.65
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EGU24-17249
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ECS
Amit Kumar, Jamie Hannaford, Stephen Turner, Lucy J. Barker, Harry Dixon, Adam Griffin, Gayatri Suman, and Rachael Armitage

With hydrological extremes becoming more frequent and intense in a changing world, the impact on livelihoods, infrastructure, and economies is crucial. River flow data is a valuable resource and can be used to understand and analyse trends in both flow and extreme events. It is essential to systematically examine trends and anomalies within river flow across the globe. To capture the true natural trends, the river flow data should be from natural catchments and free from anthropogenic influences, such as the construction of dams, alterations in land use, and extraction of water from rivers. Special attention must be directed towards delineating these factors to enhance our understanding of the complex dynamics governing river systems. 

Existing challenges in attributing trends in river flows to climate change demands for a comprehensive, worldwide Reference Hydrometric Network (RHN) with minimal human impacts, to ensure integrity of climate change signals in river flow data. This global initiative, the Reference Observatory of Basins for INternational hydrological climate change detection (ROBIN) is a global collaboration to bring together the first global RHN. Currently consisting of partners from almost 30 countries spanning every continent, the first iteration of the ROBIN dataset is now available – a consistently defined network of over 3,000 near-natural catchments. 

The ROBIN team estimated the first truly global analysis of trends in river flows using near-natural catchments for periods of 40 (1975-2016) and 60 (1956-2016) years. This research showcases the first global drought assessment using the subset of ROBIN network, investigating variations in river flow trends and their impact on drought events, and trends at a global scale. The research focused on the spatial and temporal variability of trends and drought characteristics in different countries and hydro-belts across the ROBIN network. It also shows the great potential of serving as benchmark for future hydrological trend assessments. 

Efforts are ongoing to broaden the ROBIN network to bring together more countries, incorporating additional catchments representing diverse geographical characteristics. With the support of international organizations such as WMO, UNESCO, and IPCC, ROBIN establishes the groundwork for a sustainable network of catchments, enabling comprehensive assessments of climate-induced trends, variability, and occurrences of drought on a global scale. This initiative makes a substantial contribution to enhancing our understanding of the impact of climate change on river flows and the corresponding global patterns of drought. 

How to cite: Kumar, A., Hannaford, J., Turner, S., Barker, L. J., Dixon, H., Griffin, A., Suman, G., and Armitage, R.: Global trend and drought analysis of near-natural river flows: The ROBIN Initiative, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17249, https://doi.org/10.5194/egusphere-egu24-17249, 2024.

A.66
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EGU24-17185
Francesco Chiaravalloti, Roberto Coscarelli, and Tommaso Caloiero

Heavy precipitation events are likely to become more frequent in most parts of Europe; yet, records of hourly precipitation are often insufficient to study trends and changes in heavy rainfall. Atmospheric reanalyses are an important source of long-term meteorological data, often considered as a solution to overcome the unavailability of direct measurements. The reanalysis procedure makes use of a large amount of heterogeneous historical observations, both sensed and remotely measured (in situ, satellite, etc), assimilated within a dynamical model to reconstruct the state of the atmosphere, land surface and oceans in the past. Among the available reanalyses, the ERA5 dataset released by the ECMWF, can be considered one of the state-of-the-art products. Atmospheric and surface variables are provided hourly, from 1950 to almost real time, with a horizontal resolution of 31 km. The land model of the ERA5, driven by the downscaled meteorological forcing from the lowest ERA5 model level, and with an elevation correction for the thermodynamic near-surface state, is also used to derive the ERA5-land dataset, characterized by a higher spatial resolution (9 km) and finer precipitation distribution details.

In this paper, data from the ERA5-land reanalysis dataset were used to characterize the 1-hour maximum yearly rainfall values in Italy. Specifically, 3215 grid series of 1-hour rainfall for the period 1950-2020 have been first extracted. Then, for each grid series the 71 1-hour maximum yearly rainfall values have been evaluated. Moreover, the time frame 1950-2020 has been divided into several intervals, and for each one, the frequency distribution of the months recording the annual maxima was calculated. Finally, a cluster analysis has been performed to evaluate the area with a similar monthly distribution of these values. Results showed that, considering the data over the whole of Italy, the monthly distribution of occurrences of annual maxima of 1-hr rainfall is characterized by a peak in September occurring in all the time windows considered. Furthermore, clustering cells with a similar distribution of annual hourly rainfall maxima, using k-means, allowed to identify three groups characterised by different months with the highest frequency of occurrence of the maximum.

This work was funded by the Next Generation EU - Italian NRRP, Mission 4, Component 2, Investment 1.5, call for the creation and strengthening of 'Innovation Ecosystems', building 'Territorial R&D Leaders' (Directorial Decree n. 2021/3277) - project Tech4You - Technologies for climate change adaptation and quality of life improvement, n. ECS0000009. This work reflects only the authors’ views and opinions, neither the Ministry for University and Research nor the European Commission can be considered responsible for them.

How to cite: Chiaravalloti, F., Coscarelli, R., and Caloiero, T.: Using ERA-5 reanalysis to characterize extreme rainfall in Italy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17185, https://doi.org/10.5194/egusphere-egu24-17185, 2024.

A.67
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EGU24-17479
Louis Héraut, Michel Lang, Benjamin Renard, Éric Sauquet, and Jean-Philippe Vidal

Analysing the significance of trends in hydrological variables across different components of the streamflow regime, from low flows to high flows, provides an overview of the state of a region in the context of ongoing global changes. This information is crucial for decision-making regarding adaptation but also for evaluating hydrological projections.

 

MAKAHO (MAnn-Kendall Analysis of Hydrological Observations) is an interactive cartographic visualization system designed to examine trends in hydrometric observations from the 232 stations belonging to the French Reference Hydrometric Network (Giuntoli et al., 2013). These stations show a high measurement quality, time series with a historical depth of over 30 years, and they crucially gauge near-natural catchments. The statistical test used for trend detection is a variant of the Mann-Kendall test accounting for first-order autocorrelation. The trend slope is provided by the Theil-Sen estimator.

 

The hydrological situation in France shows a marked contrast between the northern and southern regions. Between 1968 and 2020, 22 % of stations show a significantly trend in the annual maximum daily streamflow at the 90 % confidence level. Of these stations, 27 % exhibit an upward trend, with an average increase of 13 % per decade. Almost all of these stations are located in the northern part of the country.

 

This north-south divide is also visible for low flows, with the demarcation line extending further north. 39 % of stations show a decreasing trend in the annual minimum monthly discharge, with an average intensity of about 11 % per decade. The signal in the northern part of the country is less significant. The duration of low flows has significantly increased in the south, particularly in the southwest, with an average of more than ten days per decade, reaching almost a month in extreme cases.

 

The tool, developed using the R Shiny library, takes the form of an online graphical interface (https://makaho.sk8.inrae.fr/). It enables direct communication with the R Exstat package (https://github.com/super-lou/EXstat), which is essential for data aggregation and trend analysis. Calculations are performed on the fly, allowing greater customisation of analyses. MAKAHO users can choose the analysis period, the hydrological variable (from low flows to high flows), display time series for the variable of interest and extract summary sheets for a set of hydrometric stations. The interactive map and graphs allow switching from an overview to a detailed view of the results for each station. MAKAHO has been designed based on previous research projects involving stakeholders to encourage water managers to develop robust strategies for adapting to climate change and has received financial support from the French Ministry of Ecology.

 

Giuntoli, I., Renard, B., Vidal, J.-P., and Bard, A. (2013). Low flows in france and their relationship to large-scale climate indices. Journal of Hydrology, 482:105–118. https:/doi.org/10.1016/j.jhydrol.2012.12.038

How to cite: Héraut, L., Lang, M., Renard, B., Sauquet, É., and Vidal, J.-P.: MAKAHO: An interactive cartographic Tool for Trend Analysis of hydrological extremes in France , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17479, https://doi.org/10.5194/egusphere-egu24-17479, 2024.

A.68
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EGU24-18080
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ECS
Gholamreza Nikravesh, Alfonso Senatore, and Giuseppe Mendicino

This contribution proposes an integrated analysis of climate regime trends in southern Italy (Calabria Region), focusing on both extreme precipitation and temperature events. Provided several precipitation and temperature observations available in the period 1955-2023 for a relatively dense monitoring network (approximately a rain gauge station per 110 km2 and a temperature station per 100 km2), four precipitation-related variables like total precipitation (PRCPTOT), maximum one-day precipitation (RX1day), maximum five-day precipitation (RX5day) and Consecutive Dry Days (CDD) were chosen. Also, three temperature-based variables were selected, i.e., the maximum of the maximum daily temperatures (TXx), the mean of the mean daily temperatures (Tmean), and the minimum of the minimum daily temperatures (TNn). The trends of these seven selected variables were assessed and combined through three approaches at the annual and seasonal scales, considering each available monitoring station (namely, 134 precipitation and 148 temperature stations). First, we combined PRCPTOT and RX1day to highlight which stations have an increased probability of both drought and flood risks, developing a novel integrated climate regime index (ICRI). Then, we considered the three temperature indices, TXx, Tmean, and TNn, to pinpoint stations that have experienced more robust rising trends. The third analysis combined PRCPTOT, RX1day and temperature (using alternatively TXx, TNn and Tmean) to investigate the compound risk of flood, drought and, to a certain extent, wildfires. The results indicate a rather homogeneous increase of all temperature-related variables, especially starting from 1990, and that since 1955, a considerable number of stations have experienced increasing trends for RX1day and falling trends for PRCTOT. Therefore, most of the territory of the region is more likely to confront water stress, flood and forest fires.

How to cite: Nikravesh, G., Senatore, A., and Mendicino, G.: Investigation of combined regional trends of extreme precipitation and temperature in southern Italy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18080, https://doi.org/10.5194/egusphere-egu24-18080, 2024.

A.69
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EGU24-16296
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ECS
Injila Hamid and Vinayakam Jothiprakash

Three homogeneity tests were carried out on the lower Columbia River basin namely, Standard normal homogeneity test, Pettit test and Buishand test for daily gridded rainfall data having spatial resolution of 0.5o spanning a period of 43 years from 1980 to 2022. These tests were employed to estimate the breakpoint year for each grid and plotted on a map for spatial visualization. It was observed that a close relation follows between the elevation of a place, its changepoint year and the land use land cover of that area. The elevation of an area affects the direction of propagation of moisture laden winds that are developed over the Southwestern part of US from Pacific Ocean and gulf of California. And eventually governing where and how much precipitation they will bring. Additionally, the land cover of an area governs the amount of evapotranspiration and hence the pressure difference between the moisture laden winds and the atmosphere over that land cover. When the daily precipitation records of 4 decades were analysed for homogeneity and changepoint year observed spatially, it was noted that a particular elevation and land cover showed similar breakpoint and a trend is being followed. This study provides a novel way of understanding the behaviour of changing precipitation patterns taking into account the long-term variability of 4 decades.

Keywords: Homogeneity test, Change point analysis, Land use land cover, Lower Columbia River basin

How to cite: Hamid, I. and Jothiprakash, V.: Analysing the relation between changepoint year, elevation, and land cover over lower Columbia River basin in North America, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16296, https://doi.org/10.5194/egusphere-egu24-16296, 2024.

Posters virtual: Wed, 17 Apr, 14:00–15:45 | vHall A

Display time: Wed, 17 Apr, 08:30–Wed, 17 Apr, 18:00
Chairpersons: Wouter Berghuijs, Marlies H Barendrecht
vA.8
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EGU24-960
Meghomala Ghosal, Somil Swarnkar, and Soumya Kundu

The intensified warming conditions have substantially impacted the occurrence, duration, and magnitude of severe hydroclimatic events worldwide. Consequently, economic conditions have experienced considerable influence in the past decades. Droughts, in particular, are complex catastrophic events, rendering them extremely unpredictable and hard to comprehend. It is a gradual and prolonged catastrophe marked by insufficient rainfall, leading to a scarcity of water. In addition, drought is often defined as a period of reduced rainfall resulting in water shortage. It is frequently assessed by examining combinations of many factors, such as precipitation, temperature, and soil moisture. Specifically, hydrological droughts are precisely characterized as prolonged periods when water levels in rivers and streams fall below a preset threshold value. Furthermore, frequent occurrences of hydrological drought pose a significant threat to freshwater resources. Thus, identifying the spatiotemporal characteristics of preceding droughts is crucial for the effective management of future water resources. Hence, this work focuses on analyzing the spatial and temporal patterns of hydrological drought events that occurred between 1964 and 2020 in the Godavari River Basin (GRB) located in the peninsular area of India. The GRB has an area of roughly 0.3 million square kilometers, making it the biggest river basin in peninsular India. Over the last several decades, the GRB has been confronted with severe drought conditions. Therefore, the present analysis utilized the dataset of daily observed water discharge data collected at 21 gauging stations by the Central Water Commission (CWC). In addition to eliminating minor droughts and aggregating droughts, the 'Variable Threshold' concept is utilized to derive hydrological drought characteristics at various stations, including intensity, deficit, and duration. According to our findings, significant spatial and temporal variation is evident in the regional hydrological drought characteristics of the GRB. Additionally, flash drought conditions have been reported at multiple stations. The results derived from this research contribute to the advancement of knowledge regarding the spatiotemporal patterns of droughts in the GRB.

How to cite: Ghosal, M., Swarnkar, S., and Kundu, S.: Examining the spatial and temporal characteristics of hydrological drought in the largest basin of the Indian Peninsula, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-960, https://doi.org/10.5194/egusphere-egu24-960, 2024.

vA.9
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EGU24-980
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ECS
Shreejit Pandey, Somil Swarnkar, and Soumya Kundu

The intensification of the hydrological cycle is a consequence of the rising global temperatures caused by global warming. This has worsened extreme hydrological occurrences, including floods. Flooding is a substantial global hazard that endangers human livelihoods, infrastructure, and economies. Furthermore, the combination of rising temperatures and human activities has significantly modified the flood patterns that have been documented worldwide by several scientists. More precisely, a substantial area of the Indian sub-continent is greatly impacted by regular instances of flooding. Previous studies have indicated an increase in both the magnitude and frequency of flood events in the Indian river basins during the past several decades. The Godavari River Basin (GRB), which is the biggest peninsular basin in India with an area of 312,812 square kilometers, has been prone to frequent and devastating flood events in recent decades. Nevertheless, the comprehensive flood attributes, such as the maximum intensity, total amount, and length, in the GRB remain unidentified. Hence, in this study, we have employed the peak-over-threshold and Master Recession Curve (MRC) techniques to evaluate the flood features in the GRB. We have utilized the daily recorded water flow information obtained from the Central Water Commission (CWC) from 21 gauging stations in the Godavari River Basin (GRB). The 21 gauging stations are categorized into four main geographical zones. The results of our research indicate that there are notable differences in the regional flood characteristics of the GRB in terms of both spatial and temporal scales. The majority of stations in the GRB exhibit substantial fluctuations in flood characteristics after 1995. More precisely, the western GRB exhibits a notable decrease in the amount, length, and intensity of floods after 1995. The data suggest that human actions have a significant role in the flood generation process in the western GRB area. The conclusions derived from this research will be valuable to policymakers and many stakeholders in their efforts to reduce flooding and promote equitable growth in the GRB.

How to cite: Pandey, S., Swarnkar, S., and Kundu, S.: Spatio-temporal characteristics of floods in the largest basin of the Indian Peninsula, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-980, https://doi.org/10.5194/egusphere-egu24-980, 2024.

vA.10
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EGU24-1256
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ECS
Guillermo Barrientos, Rafael Rubilar, Efrain Duarte, and Alberto Paredes

Persistent drought events frequently intensify the aridity of ecosystems and cause catchments runoff depletion. Here, using large and long-term data sets of meteorological and hydrologic variables (precipitation, runoff, temperature and potential evapotranspiration) investigated the major causes that modulated catchment runoff depletion between years 1980 and 2020 in southern central Chile. We identify the hydrological years where aridity index intensified and analyzed its relationship with annual runoff, and evaluated the effect of annual evaporation index and annual aridity index on water balance of 44 catchments with different precipitation regimes located between 35° and 40°S. Our results showed that observed precipitation and runoff significantly decreased between 1980 and 2020 in 64% of the catchments in the study area. Potential evapotranspiration increased significantly in 39% of the catchments. The runoff value decreased as the aridity index increased from 0.3 to 6.7, and the Budyko curve captured 98.5% of the annual variability of all catchments. Furthermore, for an extreme aridity index (e.g. 6.5), potential evapotranspiration far exceeds mean annual runoff and precipitation. Catchment runoff is modulated by the aridity index, which is a key indicator of insufficient precipitation. As expected, for any type of drought, precipitation and evapotranspiration are key factors modulating catchment runoff response. Hydrological years in which precipitation decreased, showed a decreased runoff trend. This result suggest that meteorological droughts tend to significantly decrease observed runoff. However, our results suggest that runoff in catchments, under consecutive years of water stress, will suffer from an even more severe water deficit in today’s rapidly changing global climate with negative impacts on ecosystem services and human activities.

How to cite: Barrientos, G., Rubilar, R., Duarte, E., and Paredes, A.: Runoff variation and progressive aridity during drought in catchments in southern-central Chile, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1256, https://doi.org/10.5194/egusphere-egu24-1256, 2024.

vA.11
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EGU24-14818
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ECS
Ashish Pathania and Vivek Gupta

Extreme weather events significantly impact the economy, agriculture, infrastructure, and ecosystems of a region. According to the Center for Science and Environment (CSE), extreme weather events caused the loss of nearly 3000 lives, 2 million hectares of crops, and the death of 90,000 cattle in India in 2023 alone. Effective mitigation and adaptation strategies for the region necessitate a reliable forecasting system. The spatiotemporal interactions of several hydroclimatic components at different scales make it difficult to provide reliable forecasts for a region with multiple climate zones. The present research proposes an encoder-decoder-based deep learning framework with an attention mechanism to develop a reliable forecasting model. Attention frameworks have exhibited considerable potential in learning contextual awareness within the time series domain which will help in identifying the temporal dependencies between the meteorological variables and extreme events. The proposed architecture of the forecasting model is made interpretable as it is crucial to comprehend the underlying mechanism of climatic extremes. It recognizes the contributing variables influencing the intensity and frequency of extreme events. The study employed 0.12° × 0.12° high-resolution IMDAA (Indian Monsoon Data Assimilation and Analysis) dataset encompassing climatic variables like precipitation and temperature.

Various studies have supported the association of the ENSO parameters with the anomalous climatic conditions over India. The present study also aims to ascertain the distinct contributions of ENSO variables through the implementation of an interpretable framework. Explainability results underscore the significance of precipitation patterns while forecasting drought conditions in the region. Moreover, the results highlight the complex interaction of climatic variables that affect the intensity of the extremes.

How to cite: Pathania, A. and Gupta, V.: Beyond the Extremes: Interpretable insights based on Attention framework , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14818, https://doi.org/10.5194/egusphere-egu24-14818, 2024.

vA.12
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EGU24-8302
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ECS
Nikhil Kumar, Evan G.R. Davies, Manish Kumar Goyal, and Monireh Faramarzi

Precipitation extremes are expected to rise in a warming climate; however, the corresponding increases in flood magnitudes remain a complex and underexplored issue. This study employs the annual maxima approach to assess the relationship between extreme precipitation and floods, using a process-based hydrological model, the Soil & Water Assessment Tool (SWAT), in four river basins of central India (Brahmani and Baitarni, Subarnarekha, Mahanadi and Narmada) for past (1984-2014) and future (2030-2060 and 2070-2100). First, the SWAT models underwent rigorous data selection (climate and land cover data), calibration and validation to ensure a reliable representation of the hydrologic conditions of these basins at a daily scale, based on observations from 26 hydrometric stations for the 1988–2019 period. Second, climate projections from four CMIP6 GCMs were statistically downscaled using Bias Correction/Constructed Analogues with Quantile mapping reordering (BCCAQ) for the SSP245 and SSP585 scenarios. Finally, the SWAT models were used to project future changes in extreme precipitation and flood characteristics in the selected river basins. Considering both daily model performance (Nash-Sutcliffe Efficiency-NSE > 0.60) and catchment representativeness, we selected 10 from 26 hydrometric stations for the extreme value analysis. The analysis of the ensemble mean of the 95th percentile of four GCMs and the modelled 20-year return levels show a future increase in both precipitation (0.27 to 27.93 % and 6.19 to 50.06 %) and discharge (1.31 to 50.35 % and 5.42 to 100.73 %) at 6 out of 10 selected stations, with a more significant increase under the SSP585 scenario than the SSP245 scenario, highlighting a clear link between increased precipitation and discharge The modelling framework developed in this study will improve understanding of processes involved and the thresholds at which the central Indian catchments correspond to extreme precipitation. The findings will help the projection of future flood risks and could help to shape effective adaptation strategies in the region.

How to cite: Kumar, N., G.R. Davies, E., Kumar Goyal, M., and Faramarzi, M.: Projected changes in extreme precipitation and floods in central India, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8302, https://doi.org/10.5194/egusphere-egu24-8302, 2024.

vA.13
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EGU24-20795
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ECS
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Trinath Mahato and Manish Kumar

Desertification is a worldwide issue receiving broad attention due to deforestation, climate change, and land abuse. In India, nearly 81.4 million ha are undergoing the desertification process. A long-term assessment of the key drivers of desertification and land degradation (DLD) was done over the state of Jharkhand in the central highlands of India. The region is highly vulnerable to desertification and land degradation due to its unique geographical and climatic features, with 68.77% (5.48 Mha) of the total geographical area of 7.97 Mha undergoing DLD. This study aims to quantify desertification in Jharkhand using various satellite imageries and supervised classification using machine learning (ML) techniques. The results showed five distinct classes of DLD cases, i.e., severe, intense, moderate, light, and no desertification. The severe and intense class areas make up about 5.11 Mha (64.43%) of the total geographic area (TGA). The moderate and light classes of DLD make up 0.93 Mha (11.79%) and 1.40 Mha (17.73%) of TGA, respectively. Remarkably, the districts of Giridih, Gumla, Ranchi, Dumka, Jamtara, Deoghar, Garhwa, and Palamu are considered to be the most prone regions to DLD. This study will help to demonstrate the application of remote sensing techniques to quantify DLD-prone regions and severity over the regions, which can help policymakers manage the local administrative bodies and state government departments to demarcate the region to continuously monitor and lay policies to tackle desertification.

Keywords: Desertification, Central Highlands, GEE, Random Forest, Vulnerability, Machine Learning

How to cite: Mahato, T. and Kumar, M.: Understanding the Drivers of Desertification and Land Degradation (DLD) over the Central Highlands of India, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20795, https://doi.org/10.5194/egusphere-egu24-20795, 2024.

vA.14
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EGU24-15968
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ECS
Amit Singh and Sagar Chavan

Regional flood frequency analysis (RFFA) helps in estimating the design flood at ungauged locations within a hydrologically homogeneous region. LH moment is a statistical method often used in hydrology for estimating distribution parameter. The LH moment are the linear combination of higher probability weighted moment. It offers an alternative to traditional moments and is particularly useful when dealing with skewed distributions. The Godavari River, one of the major river systems in India, experiences varying hydro climatic conditions across the basin. This study presents a comprehensive regional flood frequency analysis (RFFA) conducted in the Godavari River basin employing LH moment as a robust statistical tool. The present study incorporates the formation of region using region of Influence approach (ROI) approach. In this study, five probability distributions namely generalized extreme value (GEV), generalized logistic (GLO), Pearson Type III (PE3), Generalized Normal (GNO) and generalized Pareto (GPA) are considered for performing RFFA for estimating ungauged flood quantiles corresponding to various return periods (e.g., 50, 100, and, 200 years) in the Godavari River basin. The discordancy measure and heterogeneity measure in LH-Moment framework are considered for screening of peak flow data and checking the heterogeneity of the region formed using ROI. The suitability of GEV, GLO, PE3, GNO, and GPA distribution is judged through the LH-moment ratio diagram and the Z-statistic criteria. The performance of LH-moment based RFFA is evaluated through Leave-One-Out Cross Validation (LOOCV). Results indicate that the LH-moment based RFFA yields more reliable estimates of flood quantiles.

How to cite: Singh, A. and Chavan, S.: Regional Flood Frequency Analysis Utilizing LH-moment based framework for Godavari River Basin, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15968, https://doi.org/10.5194/egusphere-egu24-15968, 2024.