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The July 2021 flood in central Europe was one of the five costliest disasters in Europe in the last half century, with an estimated total damage of EUR 32 billion. The aim of this study is to analyze and assess the flood within an interdisciplinary approach along its entire process chain: the synoptic setting of the atmospheric pressure fields, the processes causing the high rainfall totals, the extraordinary streamflows and water levels in the affected catchments, the hydro-morphological effects, and the impacts on infrastructure and society. In addition, we address the question of what measures are possible to generate added value to early response management in the immediate aftermath of a disaster. The superposition of several factors resulted in widespread extreme precipitation totals and water levels well beyond a 100-year event: slow propagation of the low pressure system Bernd, convection embedded in a mesoscale precipitation field, unusually moist air masses associated with a significant positive anomaly in sea surface temperature over the Baltic Sea, wet soils, and steep terrain in the affected catchments. Various hydro-morphodynamic processes as well as changes in valley morphology observed during the event exacerbated the impact of the flood. Relevant effects included, among many others, the occurrence of extreme landscape erosion, rapidly evolving erosion and scour processes in the channel network and urban space, recruitment of debris from the natural and urban landscape, and deposition and clogging of bottlenecks in the channel network with eventual collapse. The estimation of inundation areas as well as the derived damage assessments were carried out during or directly after the flood and show the potential of near-real-time forensic disaster analyses for crisis management, emergency personnel on-site, and the provision of relief supplies. This study is part one of a two-paper series. The second part (Ludwig et al., 2022) puts the July 2021 flood into a historical context and into the context of climate change.

As Europe is faced with increasing droughts and extreme precipitation, countries are taking measures to adapt to these changes. It is challenging, however, to navigate through the wide range of possible measures, taking into account the efficacy, economic impact and social justice aspects of these measures, as well as the governance requirements for implementing them. This article presents the approach of selecting and analysing adaptation measures to increasing extreme weather events caused by ongoing climate change that was developed and applied in the H2020 project BINGO (Bringing Innovation to Ongoing Water Management). The purpose of this project is (a) to develop an integrated participatory approach for selecting and evaluating adaptation measures, (b) to apply and evaluate the approach across six case-study river basins across Europe, and (c) to support decision-making towards adaptation capturing the diversity, the different circumstances and challenges river basins face across Europe. It combines three analyses: governance, socio-economic and social justice The governance analysis focuses on the requirements associated with the measures and the extent to which these requirements are met at the research sites. The socio-economic impact focuses on the efficacy of the measures in reducing the risks and the broad range of tools available to compare the measures on their societal impact. Finally, a tentative social justice analysis focuses on the distributive impacts of the adaptation measures. In the summary of results, we give an overview of the outcome of the different analyses. In the conclusion, we briefly assess the main pros and cons of the different analyses that were conducted. The main conclusion is that although the research sites were very different in both the challenges and the institutional context, the approach presented here yielded decision-relevant outcomes. IWW Water Centre, Moritzstr. 26, 45476, Mülheim an der Ruhr, Germany, CA extern

The occurrence of dry periods on the Wupper catchment has increased in the last decades in conjunction with the shifting of the precipitation regime. In the frame of the Horizon 2020 project BINGO (Bringing INnovation to onGOing water management), the effects of climate change scenarios on the water cycle in the Wupper catchment area were investigated. To quantify these effects, a set of hydrological models (NASIM and SWAT) has been set-up, calibrated, and validated for the upper part of the Dhünn River catchment area – Wupper River's main tributary. This sub-catchment corresponds to one of the inflows to the Große Dhünn Reservoir (GDR), the second largest drinking water reservoir in Germany. Both models were driven with climate data from decadal predictions, which have been selected instead of IPCC-RCP scenarios, as they provide a more realistic assumption of climate variability for the next 10 years. Ten decadal members based on the MiKlip (Mittelfristige Klimaprognose – medium-term climate prediction) framework have been prepared for the time span of 2015 to 2024. Additionally, a simulation with TALSIM-NG (a reservoir-oriented hydrological model) was carried out to obtain future reservoir storage. Special focus was given to identify observed trends and compare them to future trends. Past hydro-meteorological extreme dry periods were evaluated based on observed data. Standardized Precipitation Index (SPI), Standardized Precipitation-Evapotranspiration Index (SPEI), and Standardized Runoff Index (SRI) were estimated for different seasons to determine if they were abnormally dry or wet. SPI, SPEI, and SRI were also calculated with decadal predictions to evaluate future extreme dry periods. Uncertainties in climate data predictions are one of the greatest challenges. Observed and forecast time series were compared by means of statistical tests in order to assess uncertainties in climate data predictions. Also, the application of two hydrological models aims to determine potential uncertainties, so that predictions are more reliable. Results indicate that SRI might be more appropriate to estimate drought periods for the study area in the frame of reservoir management – where inflow rates are of crucial importance – as this index quantifies losses in runoff formation processes. In terms of inflow rates to GDR, future changes indicate a reduction in runoff for the spring season, while an increment during winter. On the other hand, a clear change in pattern for fall and summer seasons remains uncertain. Simulations of GDR reservoir volume with different climate scenarios show that water stress by the end of 2024 is not unlikely, so sustainable adaptation measures should be further considered. Effectively managing the GDR will become consequently more complex.

The climatological changes that have been forming for years have led to several different effects within the Wupper catchment area: the increasing of strong to extremely pronounced convective events lead on one hand (exacerbated by the strong topography within the “Bergische Land” region) to major damage events (e.g. in 2018 with several million of known damage costs), but also mean lower inflows to the drinking and usage water reservoirs due to the high level of interception and evaporation potential of the natural catchment areas (high proportion of forests, drinking water protection zones). The prolonged dry periods exacerbate the problem, because of the reduced groundwater recharge. While the annual precipitation is in the normal range, the changes in the distribution (in intensity, duration and seasons) cause big changes. In addition to the water volume capacity, this also affects the water quality of the streams and reservoirs. Resulting low levels in the reservoirs (often in combination with high temperature) e.g. to blue-green algae growth and require further efforts to achieve the needed water quality.Purely stationary approaches are not sufficient for describing the processes properly and to transfer the results in a way that decision makers can understand the characteristics. Only a relative change of single precipitation periods in of percentage, cannot give any reference to the resulting effects and impacts. In addition, the different kind of data sets for hydrological and limnological impact modeling makes it difficult to compare the results. Historical point measurements (such as from climate stations or levels), areal (grid-based) historical recording e.g. precipitation by rain radar or soil parameters by satellite, weather forecasts in the range of hours to months and climate forecasts (e.g. decadal) or scenarios are each self-sufficient data sets, but must be linked in order to be able to derive appropriate measures.Therefor methods to correlate past critical situations with indices / predictors, which are statistically sufficiently robust are suitable. This will enable us to make statements for the development in future periods and to represent changes in an impact-related manner. The presentation shows examples of how such approaches can be implemented for the phenomena described above (heavy rain / drought).

Given that long time series for temporally highly resolved precipitation observations are rarely available, it is necessary to pool information to obtain reliable estimates of the distribution of extreme precipitation, especially for short durations. In this study, we use a duration-dependent generalized extreme value distribution (d-GEV) with orthogonal polynomials of longitude and latitude as spatial covariates, allowing us to pool information between durations and stations. We determine the polynomial orders with stepwise forward regression and cross-validated likelihood as a model selection criterion. The Wupper River catchment in the west of Germany serves as a case study area. It allows us to estimate return level maps for arbitrary durations, as well as intensity&ndash, duration&ndash, frequency curves at any location&mdash, also ungauged&mdash, in the research area. The main focus of the study is evaluating the model performance in detail using the Quantile Skill Index, a measure derived from the popular Quantile Skill Score. We find that the d-GEV with spatial covariates is an improvement for the modeling of rare events. However, the model shows limitations concerning the modeling of short durations d&le, 30min. For ungauged sites, the model performs on average as good as a generalized extreme value distribution with parameters estimated individually at the gauged stations with observation time series of 30&ndash, 35 years available.

Damage after rainfall events requires detailed analysis of causes and rainfall extremity for the determination of liability: the question who has to pay for what. Recent years have shown that damage has increased, but also the insight that extreme events call for the whole municipal community and not only the single citizen or sewer agency. The determination which rainfall category occurred requires thorough inspection of all available data. This includes weather radar, station data, flow data and damage data. It is important to be able to rely on quality controlled high resolution data for detailed analyses in mainly urban areas. This contribution presents important corner stones on the road towards a more harmonized way of evaluating damage producing precipitation events.

We examine rainfall characteristics of convective and front extreme events in high spatio-temporal resolution (5 minutes, 1x1 km) over an area of 1824 km2 covering the catchment of the Wupperverband, North Rhine-Westphalia. The main focus of the analysis is a description of the complexity of hourly and daily extreme rainfall with the purpose of identifying suitable characteristics that can be used in a spatial weather generator of similar resolution. The spatial and temporal properties of the extreme events are explored by means of principal component analysis, cluster analysis, and linear models. For each method a set of 17 variables are used to describe the properties of each event, e.g. duration, maximum volumes, spatial coverage and heterogeneity, and movement of cells. A total of 5-9 dimensions can be found in the data, which can be interpreted as a rough indication of how many independent variables a weather generator should employ. Both principal component analysis and cluster analysis show patterns that are in accordance with our understanding of physical properties of rainfall. In particular it seems that the differences between hourly and daily extremes can be described by relatively simple scaling across the set of variables, i.e. the level of each variable varies signicantly, but not the overall structure of the spatial precipitation. The analysis show that there is a good potential for making a spatial weather generator for high spatio-temporal precipitation for precipitation extremes. Before the method can be employed further work is necessary to describe non-linear correlation between the variables and also the tracking algorithm employed needs to be improved.