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Despite considerable advances in flood forecasting during recent decades, state-of-the-art, operational flood early warning systems (FEWS) need to be equipped with near-real-time inundation and impact forecasts and their associated uncertainties. High-resolution, impact-based flood forecasts provide insightful information for better-informed decisions and tailored emergency actions. Valuable information can now be provided to local authorities for risk-based decision-making by utilising high-resolution lead-time maps and potential impacts to buildings and infrastructures. Here, we demonstrate a comprehensive floodplain inundation hindcast of the 2021 European Summer Flood illustrating these possibilities for better disaster preparedness, offering a 17-hour lead time for informed and advisable actions. A hindcast experiment of the 2021 summer flood in West Germany unveils a 17-hour lead time for preparedness and advisable action, holding promise for impact-based forecasting of inundated roads, railways and building footprint in real-time.

Floods affect more people than any other natural hazard; thus flood warning and disaster management are of utmost importance. However, the operational hydrological forecasts do not provide information about affected areas and impact but only discharge and water levels at gauges. We show that a simple hydrodynamic model operating with readily available data is able to provide highly localized information on the expected flood extent and impacts, with simulation times enabling operational flood warning. We demonstrate that such an impact forecast would have indicated the deadly potential of the 2021 flood in western Germany with sufficient lead time.

Reliable seasonal flood forecasting is vital for managing reservoirs and disaster response. This study investigates whether probabilistic forecasts of seasonal floods can be improved by integrating spectrally transformed hydroclimatic variables. We apply the Wavelet System Prediction (WASP) method to enhance climate covariates within a Generalized Extreme Value (GEV) model. Using streamflow observations from 649 European catchments, we compare forecasts using raw and spectrally transformed covariates. Results show that the transformation significantly improves forecast skill, measured by the Ranked Probability Skill Score (RPSS), especially at longer lead times. The most notable gains are observed in Northern and Western Europe, including the UK and Norway. The proposed hybrid WASP‐GEV forecasting framework, integrating spectral transformation, significantly enhanced seasonal flood forecast skills with up to 3 months of lead time. These findings highlight the potential of advanced data transformation techniques to improve hydroclimatic extreme forecasts, benefiting water resource management in a changing climate.

Hot–wet compound events, the sequential occurrence of humid hot days followed by extreme rainfall, can cause catastrophic consequences, often exceeding the impacts of the isolated occurrence of each event. The urban-coastal microclimate is confounded by complex interactions of land–sea breeze circulations, urban effects of convection and rainfall, and horizontal advection of moisture, which can favor the hot–wet compound occurrence. We present the first observational assessment (1951–2022) of summertime hot–wet compound events across global coastal megacities. We find a significant ( P  < 0.001) increase in the frequency of hot–wet compound events in both hemispheres: on average, ~3 events in the 1950s to 43 events in the 2020s. Cities with upward trends in the frequency of hot–wet compound events are situated < 30 km from coasts, with cities in the southern hemisphere showing faster hot-to-wet transition times (<3 days) than cities in the northern hemisphere. Further, 26 out of 29 sites show increased extreme precipitation, reaching 153%, when humid heat amplitude rises from the 50th to 90th percentiles. Understanding hot–wet compound interactions over the world’s coasts is highly relevant for climate change impact assessment and informing climate adaptation.

Sequential climate hazards, such as “warm and wet” compound extremes, have direct societal implications for highly urbanized regions and agricultural production. While typically extreme temperatures and rainfall are inversely correlated during the summer, extreme humid heatwaves often lead to atmospheric instability and moisture convection, increasing the likelihood of extreme precipitation (EP). Little is known about how heatwave characteristics, such as peak intensity and duration, influence EP at a regional scale. Using high‐resolution, sub‐daily station‐based observational records over five decades (1971–2021) across India, we find a robust increase in the frequency of compound humid heat‐peak precipitation events in all seasons. Our sensitivity analysis of the impact of humid heatwave characteristics on the subsequent sub‐daily rainfall extremes reveals that, with an increase in peak heatwave intensity for a given heatwave duration, >50% of sites show an increase in the magnitude of rainfall; conversely, with an increase in heatwave duration for a given peak heatwave intensity, around 67% sites show a decline in sub‐daily rainfall extremes. An asymmetrical shift toward above‐average precipitation extremes in response to humid heat stress is mainly clustered around low‐elevation, densely populated coastal areas and the irrigation‐intensive Indo‐Gangetic Plains. Plain Language Summary Compound humid heatwave‐extreme rainfall events substantially impact society, as the sequential occurrence of such events has immense damage potential due to the limited recovery time compared to the situation where these events occur in isolation. Detecting spatiotemporal patterns in compound hot‐wet weather extremes is essential for disaster management, projecting future changes, and devising an early warning system for vulnerable populations. Using gauge‐based observations of more than five decades across India, we find a robust increase in the number of compound humid heatwave‐extreme precipitation events over climatologically homogeneous regions of India across all seasons. We analyze how the rainfall extremes depend on the characteristics of the preceding heatwave: we find that rainfall extremes increase with increasing heatwave intensity for more than 50% of sites; in contrast, rainfall extremes decrease with increasing heatwave duration for around 67% of sites. A shift toward higher rainfall in response to humid heat characteristics is apparent in densely populated coastal areas and irrigation‐dominated regions of the country. Key Points We quantify the exceedance probability of sub‐daily rainfall peaks conditioned on preceding heatwaves A significant upward trend in the frequency of compound heatwave‐extreme precipitation events is detected across all seasons Sub‐daily rainfall peaks shows notable changes in their exceedance probability in response to changing heatwave properties

Understanding and quantifying total economic impacts of flood events is essential for flood risk management and adaptation planning. Yet, detailed estimations of joint direct and indirect flood-induced economic impacts are rare. In this study an innovative modeling procedure for the joint assessment of short-term direct and indirect economic flood impacts is introduced. The procedure is applied to 19 economic sectors in eight federal states of Germany after the flood events in 2013. The assessment of the direct economic impacts is object-based and considers uncertainties associated with the hazard, the exposed objects and their vulnerability. The direct economic impacts are then coupled to a supply-side Input-Output-Model to estimate the indirect economic impacts. The procedure provides distributions of direct and indirect economic impacts which capture the associated uncertainties. The distributions of the direct economic impacts in the federal states are plausible when compared to reported values. The ratio between indirect and direct economic impacts shows that the sectors Manufacturing, Financial and Insurance activities suffered the most from indirect economic impacts. These ratios also indicate that indirect economic impacts can be almost as high as direct economic impacts. They differ strongly between the economic sectors indicating that the application of a single factor as a proxy for the indirect impacts of all economic sectors is not appropriate.

The Limpopo Basin in southern Africa is prone to droughts which affect the livelihood of millions of people in South Africa, Botswana, Zimbabwe and Mozambique. Seasonal drought early warning is thus vital for the whole region. In this study, the predictability of hydrological droughts during the main runoff period from December to May is assessed using statistical approaches. Three methods (multiple linear models, artificial neural networks, random forest regression trees) are compared in terms of their ability to forecast streamflow with up to 12 months of lead time. The following four main findings result from the study. 1. There are stations in the basin at which standardised streamflow is predictable with lead times up to 12 months. The results show high inter-station differences of forecast skill but reach a coefficient of determination as high as 0.73 (cross validated). 2. A large range of potential predictors is considered in this study, comprising well-established climate indices, customised teleconnection indices derived from sea surface temperatures and antecedent streamflow as a proxy of catchment conditions. El Niño and customised indices, representing sea surface temperature in the Atlantic and Indian oceans, prove to be important teleconnection predictors for the region. Antecedent streamflow is a strong predictor in small catchments (with median 42 % explained variance), whereas teleconnections exert a stronger influence in large catchments. 3. Multiple linear models show the best forecast skill in this study and the greatest robustness compared to artificial neural networks and random forest regression trees, despite their capabilities to represent nonlinear relationships. 4. Employed in early warning, the models can be used to forecast a specific drought level. Even if the coefficient of determination is low, the forecast models have a skill better than a climatological forecast, which is shown by analysis of receiver operating characteristics (ROCs). Seasonal statistical forecasts in the Limpopo show promising results, and thus it is recommended to employ them as complementary to existing forecasts in order to strengthen preparedness for droughts.

Lake sediments are a valuable archive to document past flood occurrence and magnitude, and their evolution over centuries to millennia. This information has the potential to greatly improve current flood design and risk assessment approaches, which are hampered by the shortness and scarcity of gauge records. For this reason, paleoflood hydrology from lake sediments received fast-growing attention over the last decade. This allowed an extensive development of experience and methodologies and, thereby, the reconstruction of paleoflood series with increasingly higher accuracy. In this review, we provide up-to-date knowledge on flood sedimentary processes and systems, as well as on state-of-the-art methods for reconstructing and interpreting paleoflood records. We also discuss possible perspectives in the field of paleoflood hydrology from lake sediments by highlighting the remaining challenges. This review intends to guide the research interest in documenting past floods from lake sediments. In particular, we offer here guidance supported by the literature in how: to choose the most appropriate lake in a given region, to find the best suited sedimentary environments to take the cores, to identify flood deposits in the sedimentary sequence, to distinguish them from other instantaneous deposits, and finally, to rigorously interpret the flood chronicle thus produced.

The link between streamflow extremes and climatology has been widely studied in recent decades. However, a study investigating the effect of large-scale circulation variations on the distribution of seasonal discharge extremes at the European level is missing. Here we fit a climate-informed generalized extreme value (GEV) distribution to about 600 streamflow records in Europe for each of the standard seasons, i.e., to winter, spring, summer and autumn maxima, and compare it with the classical GEV distribution with parameters invariant in time. The study adopts a Bayesian framework and covers the period 1950 to 2016. Five indices with proven influence on the European climate are examined independently as covariates, namely the North Atlantic Oscillation (NAO), the east Atlantic pattern (EA), the east Atlantic–western Russian pattern (EA/WR), the Scandinavia pattern (SCA) and the polar–Eurasian pattern (POL). It is found that for a high percentage of stations the climate-informed model is preferred to the classical model. Particularly for NAO during winter, a strong influence on streamflow extremes is detected for large parts of Europe (preferred to the classical GEV distribution for 46 % of the stations). Climate-informed fits are characterized by spatial coherence and form patterns that resemble relations between the climate indices and seasonal precipitation, suggesting a prominent role of the considered circulation modes for flood generation. For certain regions, such as northwestern Scandinavia and the British Isles, yearly variations of the mean seasonal climate indices result in considerably different extreme value distributions and thus in highly different flood estimates for individual years that can also persist for longer time periods.

We investigate whether the distribution of maximum seasonal streamflow is significantly affected by catchment or climate state of the season/month ahead. We fit the Generalized Extreme Value (GEV) distribution to extreme seasonal streamflow for around 600 stations across Europe by conditioning the GEV location and scale parameters on 14 indices, which represent the season-ahead climate or catchment state. The comparison of these climate-informed models with the classical GEV distribution, with time-constant parameters, suggests that there is a substantial potential for seasonal forecasting of flood probabilities. The potential varies between seasons and regions. Overall, the season-ahead catchment wetness shows the highest potential, although climate indices based on large-scale atmospheric circulation, sea surface temperature or sea ice concentration also show some skill for certain regions and seasons. Spatially coherent patterns and a substantial fraction of climate-informed models are promising signs towards early alerts to increase flood preparedness already a season ahead.