PICO
- Long-term changes in rainfall patterns and flood occurrence;
- Process-informed extreme value statistics;
- Interactions between rainfall distribution and catchment conditions in shaping flood patterns;
- Detection and attribution of flood hazard changes, such as atmospheric drivers, land use controls, natural water retention measures, reservoir construction, and river training;
- Changes in flood exposure: economic and demographic growth, urbanisation of flood prone areas, implementation of multi-scale risk mitigation measures (particularly structural defences);
- Changes in flood vulnerability: changes of economic, societal and technological aspects driving flood vulnerability and private precautionary measures;
- Multi-factor decomposition of observed flood damages combining the hydrological and socio-economic drivers;
- Future flood risk scenarios and the role of adaptation and mitigation strategies.
Extreme floods are intensifying in magnitude and frequency in a warming climate. While the current drivers of flood changes are well understood, large uncertainties remain regarding the most extreme floods and their drivers due to internal climate variability. This makes it difficult to disentangle changes due to interannual variability from changes due to climate change. In mountain regions such as the Alps, increasing sub-daily rainfall extremes, together with declining snowmelt contributions and evolving antecedent soil moisture conditions, are expected to substantially alter the generation processes of extreme floods. However, how these changing flood drivers jointly affect future extreme flood events in mountain regions remains poorly understood.
We therefore investigate how the generation processes of extreme floods will change in the Alps. Specifically, we examine 1) how the drivers of moderate and extreme flood events differ in a warming climate, 2) the extent to which increasing sub-daily rainfall extremes can compensate for declining snowmelt, 3) potential changes in the timing and volume of flood events, and 4) whether projected changes in flood generation processes are significant relative to internal climate variability.
To address these questions, we analyse hourly simulations from a hydrological model driven by climate projections from a single-model initial-condition large ensemble (SMILE) for 384 catchments in Switzerland and Austria. The SMILE consists of 50 ensemble members and enables a robust quantification of internal climate variability. To analyse future changes in flood generation processes, we classify the projected floods based on their drivers, including precipitation, snowmelt, soil moisture and their interplay using a flood classification framework. We further analyse flood characteristics using indicators such as time to peak, volume, peak magnitude and seasonality. Preliminary results indicate that (1) snowmelt extremes continue to play a dominant role in driving the most extreme floods at high elevations, but are less important for moderate floods; (2) floods occur more frequently under dry antecedent moisture conditions than before, while requiring higher rainfall intensities to be generated; and (3) flashiness increases more strongly for extreme floods than for moderate floods.
Improving our understanding of future changes in the generation processes of extreme floods is essential for supporting local authorities, who are deciding on how to adapt to the effects of climate change on hydrological extremes.
How to cite: Astagneau, P. C., Tarasova, L., Wood, R. R., and Brunner, M. I.: Future extreme flood generation processes in the Alps, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5039, https://doi.org/10.5194/egusphere-egu26-5039, 2026.
Floods and flash floods are among the most frequent and destructive natural hazards worldwide, and North Africa has experienced numerous severe and deadly flood events over recent decades. The magnitude, frequency, and severity of floods in this region exhibit strong spatial and temporal variability, reflecting the combined influence of basin physiography, hydrological processes, and climatic conditions. Ongoing climate change is expected to alter these controls, further complicating the understanding of flood-generation mechanisms and their evolution over time. This study aims to investigate the dominant processes driving flood generation across North Africa and to examine how climate variability and change may influence flood characteristics, trends, and severity. We analyze long-term streamflow records from 163 basins distributed across Morocco, Algeria, and Tunisia, with basin areas ranging from 23 to 20,000 km² and observation periods spanning from 1950 to 2023. Flood events are examined across a wide range of magnitudes, from frequent runoff events to rarer extreme floods, with the objective of identifying dominant flood-generation processes and potential shifts in their relative importance over time.
How to cite: Rachdane, M., El Khalki, E. M., Tarasova, L., and Tramblay, Y.: Flood Generation Processes in North Africa, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7213, https://doi.org/10.5194/egusphere-egu26-7213, 2026.
In British Columbia, floods predominantly occur in the late spring and summer months due to snowmelt. However, larger, damaging floods have recently occurred in the fall and winter months, raising the possibility of flood mechanisms varying seasonally. Fall and winter floods often occur after an atmospheric river event, but the weeks and months leading up to the event may play a role in setting up the flood event. For example, the devastating November 2021 floods occurred after a significant heavy precipitation event, but this event followed an extended anomalously wet period prior to the flooding. This study places that flood and others in their historical context, comparing the space-time dynamics of hydrologic conditions leading up the flood events. To identify the role of extended antecedent conditions, we performed a series of model experiments with precipitation and other meteorological variables held to climatology at different lead times. These experiments isolate the relative contribution of an extended antecedent wet condition and of a single, heavy precipitation event in determining a flood event. Such experiments highlight the climate variables of interest when adapting to a potentially changing flood regime due to climate change.
How to cite: Troy, T. J., Rai, P., and Shrestha, R.: Changes in flood drivers in British Columbia, Canada, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15232, https://doi.org/10.5194/egusphere-egu26-15232, 2026.
North China experienced some of the world’s most extreme floods, with flood peaks exhibiting pronounced heavy-tailed behavior that rivals global flood envelope curves. Unraveling the physical origins of these outliers is essential for reliable flood risk estimation and hydrologic design. Here, we analyze long-term rainfall and streamflow records (1950–2023) from 297 watersheds across North China to attribute the upper tail of flood peaks to storm structure and watershed behaviors. Although the region’s short-duration rainfall intensities are on the upper end of the rainfall spectrum, we find that the most extreme floods are not primarily driven by these high-intensity events. Instead, they are linked to long-duration events with exceptionally large accumulated totals, indicating that accumulated rainfall, rather than intensity alone, is the dominant meteorological control on catastrophic peak discharge. We further identify a threshold-type runoff response: once storm-total rainfall exceeds specific storage capacities, peak discharge increases disproportionately with additional rainfall, sharply widening the magnitude gap between extreme and ordinary floods. Our analyses suggest that this effective storage is governed largely by deep weathered-rock and aquifer systems rather than shallow soil layers. The susceptibility to this threshold behavior depends on the watershed's spatial organization. Integral geomorphic metrics reveal that watersheds in mountain-plain transition zones are structurally predisposed to rapid routing and nonlinear peak amplification once storage is surpassed. These watersheds are characterized by fan-shaped drainage networks and heterogeneous river longitudinal profiles. These findings highlight a latent catastrophic potential in watersheds where historical storms have rarely crossed these controlling thresholds. As climate change drives more persistent extreme rainfall, threshold exceedance may become more frequent, implying a heightened risk of unprecedented floods beyond historical experience.
How to cite: Zhang, J., Zhuang, J., Wang, X., Smith, J., and Yang, L.: Climatic and Geomorphic Controls on the Threshold Behavior of Upper Tail Floods, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15408, https://doi.org/10.5194/egusphere-egu26-15408, 2026.
Understanding how flood frequency changes under non-stationary hydroclimatic conditions remains a key challenge in hydrology. This study presents a Bayesian process-based framework for flood frequency analysis that explicitly accounts for the seasonal dependence of rainfall–runoff processes and their sensitivity to climate change. The approach links an event-based rainfall–runoff model with probabilistic representations of storm, soil moisture, and catchment response, allowing the joint propagation of uncertainty from climate drivers to flood quantiles. The process-based structure of the framework also enables the disentangling of individual flood-generating mechanisms, such as the upward shift of the zero-degree isotherm, long-term changes in soil moisture regimes, and variations in precipitation intensity. The framework is implemented in Austrian hotspots, i.e. groups of similar catchments, using long-term hydrometeorological records and regional climate projections (EURO-CORDEX).
Results show that (i) changes in flood frequency are primarily driven by projected increases in precipitation intensity, while temperature and soil moisture act as modulators or amplifiers of this signal; (ii) the expected reduction in soil moisture tends to mitigate frequent floods but has limited influence on rare events; (iii) anticipated shift of flood peaks toward spring in alpine regions due to the rising 0°C line and enhanced snowmelt contribution. The proposed methodology provides a transferable tool for assessing climate-sensitive flood hazards in non-stationary environments.
How to cite: Cafiero, L., Bertola, M., Blöschl, G., Valent, P., Laio, F., and Viglione, A.: Climate-sensitive Flood Frequency Analysis Based on Flood Event Characteristics, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7422, https://doi.org/10.5194/egusphere-egu26-7422, 2026.
Climate change is expected to intensify heavy rainfall with concomitant increases in flood hazards, yet most large-scale flood assessments remain based on daily data. While sub-daily rainfall variability differs fundamentally from daily statistics, its implications for flood generation and risk remain poorly understood at continental scales, largely due to the scarcity of long-term hourly streamflow observations. Consequently, flood hazards inferred from daily-scale analyses may be systematically underestimated. Here, a causally constrained deep learning model for hourly runoff reconstruction is developed, integrating multi-source hourly meteorological forcings with limited observed hourly streamflow and widely available daily discharge constraints. Using this model, we create a multi-decadal reconstruction of hourly runoff for nearly ten thousand basins across the continental United States. Building on these reconstructions, we provide a first assessment of how rainfall-flood relationships differ between hourly and daily timescales and investigate potential flood underestimation arising from daily-scale analyses. We show that sub-daily flood peaks can be masked when aggregated to daily resolution, and examine the temporal evolution and controlling mechanisms of this underestimation across events and catchments, with particular attention on catchment scale, intraday rainfall variability, storm duration, and aggregation timescales. This work highlights the importance of resolving sub-daily flood processes for flood risk assessment and early warning, and provides a foundation for ongoing extensions toward global hourly runoff reconstruction and large-scale sub-daily flood risk analysis under a changing climate.
How to cite: He, L., Shi, L., Shen, J., Song, W., Klotz, D., and Zscheischler, J.: Hourly rainfall-flood relationships and risks of systematic flood underestimation in daily-scale analyses, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10566, https://doi.org/10.5194/egusphere-egu26-10566, 2026.
Two approaches, namely the informed-parameter approach and the hydrologic model-chain approach, are widely used to estimate future flood quantiles while accounting for nonstationarity. In the informed-parameter approach, changes in flood quantiles are estimated by conditioning distribution parameters on physical covariates, including meteorological forcings, anthropogenic drivers, and large-scale climate modes. On the other hand, for the model chain approach, meteorological forcings are fed into the hydrological model to simulate flood peaks, which are subsequently used to estimate flood quantiles.
Both approaches have distinct advantages and limitations, such as the multiple linkages involved in the model-chain approach and the relatively simplified process representation in the informed-parameter approach. These characteristics strongly influence the uncertainty associated with flood quantile estimates. However, direct comparisons between these two widely used approaches remain limited. This study presents a preliminary comparative assessment of the informed-parameter and hydrologic model-chain approaches, focusing on how uncertainty propagates into future flood quantile estimates. Overall, the findings of this study aim to support stakeholders in understanding the challenges associated with each approach and in selecting the most suitable method for a given region.
How to cite: Ganapathy, A., Agarwal, A., and Nallasamy, N. D.: Informed-Parameter versus Hydrologic Model-Chain Approaches for Flood Quantile Estimation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19075, https://doi.org/10.5194/egusphere-egu26-19075, 2026.
Hydrological regimes are increasingly altered by the combined influences of climate variability, climate change, and anthropogenic interventions, challenging the traditional assumption of stationarity in flood frequency analysis (FFA). For sustainable water resources planning and flood risk management, it is crucial to consider non-stationary nature of flood behaviour. In this study, we examine the spatiotemporal aspects of flood pattern in the Upper Narmada Basin, utilizing a non-stationary flood frequency framework that incorporates the non- stationarity behaviour due to climate variability, climate change and reservoir influence. We develop single-covariate (SC) and multi-covariate (MC) non-stationary models based on Generalized Additive Models for Location, Scale, and Shape (GAMLSS), incorporating climate indices, reservoir metrics, and time as predictors. This study prioritizes the estimation of non-stationary return periods in scenarios driven by climate variability, applying the Expected Waiting Time (EWT) method. The findings show significant non-stationarity in flood return period caused by both climate variability and reservoir influence. This leads in significant variations from return levels calculated under stationary assumptions. The results underscore the risk of underestimating or overestimating flood risks when depending on conventional stationary FFA in a dynamic climate. Thus, it is vital to refine flood return levels with non-stationarity measures for effective and sustainable hydrological planning in climate-sensitive and regulated river basins.
How to cite: Badika, P. and Agarwal, A.: Estimating Climate-Driven Non-Stationary Flood Return Periods Using the GAMLSS model and EWT approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16834, https://doi.org/10.5194/egusphere-egu26-16834, 2026.
While climate change continues to exacerbate extreme weather events worldwide, their impacts can be further amplified or dampened by socio-economic drivers that influence local exposure and vulnerability. These drivers, such as population growth and economic development, are dynamic and influence the impact of extreme events on global and local scales. For example, population growth in flood-prone areas can heighten exposure, whereas economic development can increase economic losses while improving the capacity to cope with a disaster and thus reduce vulnerability. A novel method to quantify the effect of climate change and socio-economic drivers on the impact of these events is through impact attribution assessments.
This research expands a storyline attribution framework for quantifying the effects on climate change on the hazard and impact of compound flooding to also include the effect of socio-economic drivers. An event-based approach for compound flooding from tropical cyclone (TC) Idai in Mozambique is used to disentangle the effect of historical climate and population change. TC Idai hit Mozambique in 2019 and caused over 600 fatalities, affected over 1.8 million people, resulting in $3 billion in damages. Idai is used as a case study, representing an extremely destructive compound flood event in a underrepresented, data-poor, and highly vulnerable region.
Compound flooding is modelled using a state-of-the-art hydrodynamic modelling chain that combines the Super-Fast INundation for coastS (SFINCS) model with the hydrodynamic model Delft3D Flexible Mesh and hydrological model wflow. The climate drivers of compound flooding from TCs that are known to be affected by climate change, such as precipitation, wind and sea-level rise, are adjusted to create scenarios with the climate trend removed. Present-day and historical population scenarios are used as exposure data to assess the effect of population change based on a novel harmonized global population dataset (Paprotny, 2025). By modelling multiple factual and counterfactual scenarios, in which drivers are adjusted individually and jointly, we disentangle their respective contributions to the affected population and fatalities.
This approach advances event-based impact attribution by incorporating non-climatic drivers into assessments of compound flood impacts from TCs. The framework relies solely on global datasets and open-source software which allows worldwide applications, including highly impacted but data-scarce and often underrepresented regions.
How to cite: Vertegaal, D., van den Hurk, B., Couasnon, A., Paprotny, D., and Muis, S.: Storyline impact attribution of climate and exposure drivers of compound flood impact from tropical cyclone Idai in Mozambique, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1264, https://doi.org/10.5194/egusphere-egu26-1264, 2026.
South Africa experienced its most severe flood disaster in recorded history on 11–12 April 2022, when extreme rainfall triggered catastrophic flooding in the greater Durban area, resulting in 544 fatalities. Durban, like many southern African cities, is characterised by rapid urbanisation, the expansion of informal settlements in flood-prone areas, and limited adaptive capacity, leading to high flood risk during heavy rainfall events. Understanding how these interacting drivers shape flood hazard and risk, and disentangling the role of anthropogenic climate change from other controls, remains a key challenge. While attribution studies are increasingly capable of quantifying the effects of climate change on extreme rainfall, equivalent assessments for river flood magnitudes remain scarce. A direct translation of rainfall intensification into flood severity cannot be assumed, as flood generation is strongly modulated by antecedent catchment conditions, runoff processes and river network characteristics. This study applies a hydro-climatic, event-based attribution framework to investigate the April 2022 Durban flood. An ensemble of atmospheric model reconstructions of rainfall during 11-12 April 2022 is used to drive a hydrological model under counterfactual cooler and factual warmer (present-day) climate conditions. By explicitly representing catchment processes, the framework allows us to assess how climate change altered flood magnitude and timing (rather to focus only on rainfall), and to explore the sensitivity of flood magnitude to pre-event hydrological conditions. We find that peak flow in the Mlazi River was substantially increased by climate-change-amplified rainfall totals, thereby establishing a direct causal link between anthropogenic greenhouse gas forcing and the 2022 Durban flood. The results contribute to understanding the relative roles of atmospheric forcing and catchment controls in shaping extreme flood outcomes. Conducted as part of the WaRisCo project within the Water Security in Africa (WASA) programme, this study provides an urgent message for the need for climate-smart Disaster Risk Reduction as well as for longer-term adaptation in the greater Durban area.
How to cite: Biskop, S., Schreiter, F., Kralisch, S., and Engelbrecht, F.: Event-based Attribution of the April 2022 Durban Flood, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14584, https://doi.org/10.5194/egusphere-egu26-14584, 2026.
In recent decades, floods have been the most frequent and impactful disasters worldwide, prompting renewed interest in the role of existing hydraulic infrastructures as adaptation measures. Large reservoirs can locally attenuate flood peaks by temporarily storing excess water, but their effectiveness at broader spatial scales remains poorly quantified. Here, we assess the national scale impact of large reservoirs on flood peak attenuation and population exposure in Italy by accounting for both the spatial variability of their effects and the combined influence of multiple reservoirs. We analyze large reservoirs in Italy using a physically based, spatially distributed index that quantifies flood peak reduction along the entire river network. The method represents the combined effect of multiple reservoirs through an equivalent reservoir concept, accounting for travel times, downstream unregulated contributions, and reservoir flood storage capacity. We compute discharge-weighted and population-weighted indices of flood peak reduction under current conditions and under hypothetical retrofit scenarios with increased flood storage capacity. Results show that, while reservoirs can strongly attenuate flood peaks locally (up to 70-80% immediately downstream), their average impact at the national scale is limited. Furthermore, increasing reservoir flood storage capacity only marginally improves this effect. The limited effectiveness is primarily controlled by the spatial configuration of reservoirs within the Italian river network and the location of population centers relative to regulated reaches. These findings indicate that existing reservoirs alone are insufficient as a systemic flood adaptation strategy in Italy, and that effective risk reduction requires spatially coordinated, catchment scale combinations of measures.
How to cite: Cipollini, S., Pomarico, I., Volpi, E., and Fiori, A.: Controls on the Impact of Large Reservoirs on Flood Peaks and Population Exposure at the National Scale in Italy, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5835, https://doi.org/10.5194/egusphere-egu26-5835, 2026.
This study explores the Mahnitaš torrential watercourse near Siverić, Croatia, as a unique case illustrating the convergence of Austro-Hungarian torrent regulation practices and early Japanese SABO engineering.
Mahnitaš, an ephemeral tributary of the Čikola River, drains a steep and relatively small catchment in eastern Šibenik-Knin County and exhibits pronounced torrential behavior, characterized by rapid runoff response and short-duration, high-magnitude flow events during intense rainfall. Intensive industrialization at the beginning of the 20th century substantially increased anthropogenic pressure on the natural environment. Mining activities, widespread deforestation, excavation, and the construction of transport infrastructure significantly modified surface runoff conditions, disrupted natural drainage networks, and increased sediment availability. These changes intensified erosion processes, sediment transport, and peak discharges within the catchment. Consequently, the Mahnitaš torrent evolved into a significant hydrological hazard, posing increasing risks to mining facilities, transport corridors, and nearby settlements, thereby necessitating systematic torrent regulation measures to mitigate flood hazards and sediment-related impacts.
This paper outlines the hydromorphological characteristics of the catchment, reviews historical torrent control measures, and examines the role of check dams in erosion control and sediment management. Special emphasis is placed on the Austro–Japanese exchange of technical knowledge at the beginning of the 20th century, particularly the work of Kitao Moroto, whose documentation of the Mahnitaš torrent provides rare visual and technical evidence of early torrent control practices. The study highlights Mahnitaš as an important historical and technical link in the development of modern sediment disaster prevention and SABO engineering.
How to cite: Oskoruš, D., Andrić, I., Loborec, J., and Meaški, H.: The Mahnitaš Torrent in Croatia as a Historical Link Between Austro-Hungarian Torrent Control and Japanese SABO Engineering, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20857, https://doi.org/10.5194/egusphere-egu26-20857, 2026.
HANZE (Historical Analysis of Natural Hazards in Europe) is a publicly available database that brings together information on flood events and their impacts in Europe. The initial version of the database was created in 2017, and two updates have been published since then.
The current version of the database (HANZE v3 beta) contains 2,687 records of flash, river, coastal and compound floods (1870–March 2025). This is over 1,100 events more than in its original version, which covered the time range from 1870 to 2016. At the same time, since the release of HANZE v2, the spatial coverage of the data has been expanded from 37 to 42 European countries.
The database contains the location (using the European Union’s Nomenclature of Territorial Units for Statistics – NUTS level 3), time and quantitative data on the impacts of past floods. Each of the implemented updates has also included changes in the NUTS classification, up to the current 2024 edition. Information on events was obtained from a variety of sources, including scientific publications, international and national disaster databases, government reports, and news reports. The qualification of a flood for the database was determined by meeting at least one of the following criteria: (1) an area of at least 10km2 was flooded; (2) at least one person died; (3) at least 200 people or 50 households were affected by the flood; (4) the value of the losses was at least 1 million euro.
The next update, which is currently being prepared, will include data up to the end of 2025. New elements will also be introduced, such as recording flood event type using the 2025 Hazard Information Profiles (HIPs), developed by the United Nations Office for Disaster Risk Reduction and the International Science Council. Annual updates of HANZE are planned, which will be accessible through the European Commission’s Risk Data Hub (https://drmkc.jrc.ec.europa.eu/risk-data-hub) and the HANZE website (https://naturalhazards.eu/). The current work is part of a larger update that will include windstorms and wildfires, which will be used to update the database on attribution of floods to climate and socioeconomic change.
In this contribution we present the recent and future updates in the structure and data collection process in HANZE, as well as discuss the spatial and temporal distribution of floods and their impacts in Europe between 1870 and 2025.
How to cite: Sicińska, A., Wróblewski, K., Tanwari, K., Giza, A., Terefenko, P., Śledziowski, J., Corbane, C., Roeslin, S., and Paprotny, D.: Evolution of the HANZE flood impact database and preliminary results for 1870-2025, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7047, https://doi.org/10.5194/egusphere-egu26-7047, 2026.
Floods are among the most severe natural hazards globally, with around 1.81-2.3 billion people currently exposed to 1-in-100-year events. Understanding the evolution of flood risk is important for targeting investments and policies to address the drivers that dominate in each period, ensuring effective risk reduction. This requires disentangling the relative contribution multiple drivers to understand their behavior and adapt strategies as their relative importance changes. This study focuses on two key factors: population dynamics and climate change.
We conduct a national-scale assessment spanning 230 years (1870-2100) using the probabilistic large-scale flood risk model RESCUE-FR to quantify how these drivers have shaped historical trends and will influence future flood exposure in Italy. Our analysis reveals a transition of dominant flood risk drivers. From 1870 to 2000, demographic changes dominated: population growth and migration into flood-prone areas drove an increase in exposure, while climate conditions remained relatively stable. Instead, the future presents a starkly different picture: climate change will be the dominant driver; specifically starting from the second part of this century (year 2060), climate change alone accounts for 57.5% of the projected increase in exposed population, while demographic growth contributes only 12.7%, despite Italy's total population being projected to decline after 2030.
Our findings demonstrate that flood risk management in Italy should adapt to this evolving landscape: from managing exposure in known flood zones, and preventing development in areas that will become vulnerable under future climate scenarios.
How to cite: Pavesi, L., Salinas, J. L., Zanardo, S., Sassi, M., Hilbert, A., Volpi, E., and Fiori, A.: The evolution of flood risk in Italy across two centuries: disentangling risk drivers to understand past trends and future issues, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18993, https://doi.org/10.5194/egusphere-egu26-18993, 2026.
Vulnerability assessment is one of the key steps in any disaster risk reduction strategy to minimize the impacts of a disaster. The increasing population, urbanization, and climate change necessitate the need for identifying population at greater risk. This study develops a composite flood vulnerability index (FVI) that incorporates game theory for strategic policy making decisions. This framework has been applied to the selected regions in Kerala affected by the 2018 flood. The influences of methodological choices using various sensitive analyses have been incorporated in developing the index. The inductive and deductive approaches such as Principal Component Analysis and Analytical Hierarchy Process are respectively used for the selection and weighting of indicators. Based on preliminary literature studies, around 30 indicators are selected to represent exposure, sensitivity, and adaptive capacity. The theoretical game theory model explores decision impacts by flood policy makers, and incentive structures are assessed based on logical scenarios and literature assumptions. The proposed framework addresses the methodological gaps in traditional flood vulnerability approaches and thus providing actionable insights to the decision makers for policy recommendations and flood preparedness.
How to cite: Moorthy, G. and Agarwal, A.: Modelling Framework for Composite Vulnerability Index incorporating Game Theory , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20573, https://doi.org/10.5194/egusphere-egu26-20573, 2026.
Hydrological memory refers to previous wetness or dryness, which fundamentally alters the ecosystem's response to a disturbance. It describes how antecedent catchment wetness persists over time and amplifies subsequent flood response. This temporal compounding mechanism poses significant challenges for flood forecasting and reservoir operations in Himalayan basins where climate change is intensifying precipitation variability. The August 2023 Punjab floods, which impacted over 12,000 settlements and resulted in 65 fatalities, demonstrate the influence of hydrological memory in dam-regulated systems. This study employs a hydromet-to-hydraulics framework that begins with atmospheric analysis and goes all the way down to HEC-RAS hydrodynamic modelling and demographic impact assessment. This approach integrates hydrodynamic modelling with forecast aware dam operations to quantify flood exposure patterns.
A detailed spatiotemporal analysis shows that heavy rain in July raised the soil moisture levels in the Beas Basin significantly. The hydrological memory increased the likelihood of August floods, despite the rainfall received being less than the seasonal average. The study employed HEC-RAS hydrodynamic modelling integrated with 2011 census data to evaluate the effectiveness of dam operations. The results showed that controlled dam operations reduced the downstream population exposure by 80%. We proposed a Genetic Algorithm-based optimisation framework with piecewise penalty functions based on SWAT-generated inflow forecasts from GFS precipitation data. It showed that forecast-informed dam operations can more effectively balance flood mitigation with water conservation objectives than manual management. This integrated framework underscores the essential requirement for reservoir management systems that incorporate catchment memory states through continuous soil moisture monitoring and precipitation forecasting.
How to cite: Pathania, A. and Gupta, V.: Hydrological Memory in the Himalayan Compound Floods: A Hydromet-to-Hydraulics Framework for Adaptive Flood Risk Management, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19550, https://doi.org/10.5194/egusphere-egu26-19550, 2026.
