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Wildfires have reached an unprecedented scale in the Northern Hemisphere. The summers of 2021 and 2022 demonstrated the destructive power of wildfires especially in Northern America and Southern Europe. Global warming indicates that fire seasons will become more extreme and will extend to more temperate regions in northern latitudes in the future. Multiple studies claim that natural variability hides the trend of increasing fire danger in climate model simulations for future potentially fire-prone areas. Single Model Initial-Condition Large Ensembles (SMILEs) help scientists to distinguish climate trends from natural variability. So far, the SMILE framework has only been applied for fire danger estimation on a global scale. In this study, we use a regional SMILE of the Canadian regional climate model version 5 (CRCM5-LE) over Central Europe under the RCP 8.5 scenario from 1980 to 2099, to analyze fire danger trends in a currently not fire-prone area. We use the meteorological Canadian Fire Weather Index (FWI) as a fire danger indicator. The study area covers four heterogeneous landscapes, namely the Alps, the Alpine Foreland, the lowlands of the Southern German Escarpment and the Eastern Mountain Ranges of the Bavarian Forest. We demonstrate that the CRCM5-LE is a suitable dataset to disentangle climate trends from natural variability in a multivariate fire danger metric. Results show the strongest increases in the median (50th) and extreme (90th) percentile of the FWI in the northern parts of the study area in the summer months July and August, where high fire danger becomes the median condition and extremes occur earlier in the fire season. The southern parts of the study region are affected less strongly, but due to weaker variability in these regions, time of emergence (TOE) is reached there in the early 2040’s. In the northern parts, the climate change trend exceeds natural variability in the late 2040’s. We find that today’s threshold for a 100-year FWI event, will occur every 30 years by 2050 and every 10 years by 2099. Our results highlight Central Europe’s potential for severe fire events from a meteorological perspective and the need for fire management in the near future even in temperate regions.

Streamflow elasticity is a simple approximation of how responsive a river is to precipitation. It is represented as a ratio of the expected percentage change in streamflow for a 1 % change in precipitation. Typically estimated for the annual median streamflow, we here propose a new concept in which streamflow elasticity is estimated across the full range of streamflow percentiles in a large-sample context. This “elasticity curve” can be used to develop a more complete depiction of how streamflow responds to precipitation. We find three different elasticity curve types which characterize this relationship at the annual and seasonal timescales in the USA, based on two statistical modelling approaches, a panel regression which facilitates causal inference and a single catchment model which allows for consideration of static attributes. Type A describes catchments where low flows are the least and high flows are the most responsive to precipitation. The majority of catchments at the annual, winter, and fall timescales exhibit this behavior. Type B describes catchments where the response is relatively consistent across the flow distribution. At the seasonal timescale, many catchments experience a consistent level of response across the flow regime. This is especially true in snow-fed catchments during cold months, when the actual elasticity skews towards zero for all flow percentiles while precipitation is held in storage. Consistent response is also seen across the majority of the country during spring when streamflow is comparatively stable and in summer when evaporation demand is high and soil moisture is low. Finally, Type C describes catchments where low flows are the most responsive to precipitation change. These catchments are dominated by highly flashy low flow behavior. We show that the curve type varies separately from the magnitude of the elasticity. Finally, we demonstrate that available water storage is likely the key control which determines curve type.

Many rivers and streams are ungauged or poorly gauged and predicting streamflow in such watersheds is challenging. Although streamflow signals result from processes with different frequencies, they can be “sparse” or have a “lower-dimensional” representation in a transformed feature space. In such cases, if this appropriate feature space can be identified from streamflow data in gauged watersheds by dimensionality reduction, streamflow in poorly gauged watersheds can be predicted with a few measurements taken. This study utilized this framework, named data-driven sparse sensing (DSS), to predict daily-scale streamflow in 543 watersheds across the contiguous United States. A tailored library of features was extracted from streamflow training data in watersheds within the same climatic region, and this feature space was used to reconstruct streamflow in poorly gauged watersheds and identify the optimal timings for measurement. Among different regions, streamflow in snowmelt-dominated and baseflow-dominated watersheds (e.g., Rocky Mountains) was more effectively predicted with fewer streamflow measurements taken. The prediction efficiency in some rainfall-dominated regions, for example, New England and the Pacific coast, increased significantly with an increasing number of measurements. The spatial variability of prediction efficiency can be attributed to the process-driven mechanisms and the dimensionality of watershed dynamics. Storage-dominated systems are lower-dimensional and more predictable than rainfall-dominated systems. Measurements taken during periods with large streamflow magnitudes and/or variances are more informative and lead to better predictions. This study demonstrates that DSS can be an especially useful technique to integrate ground-based measurements with remotely sensed data for streamflow prediction, sensor placement, and watershed classification., Water Resources Research, 59 (4), ISSN:0043-1397, ISSN:1944-7973

Hydrologic extremes such as floods and droughts are often spatially related, which increases management challenges and potential impacts. However, these spatial relationships in high and low flows are often overlooked in risk assessments and we know little about their differences and origins. Here, we ask how spatial relationships of both types of hydrologic extremes and their potential hydro‐meteorological drivers differ and vary by season. We propose lagged upper‐ and lower‐tail correlation as a measure of extremal dependence for temporally ordered events to build complex networks of high and low flows. We compare complex networks of overall, low and high flows, determine hydro‐meteorological drivers of these networks, and map past changes in spatial relationships using a large‐sample data set in Central Europe. Our network comparison shows that low flows are correlated more strongly and over longer distances than high flows and high‐ and low‐flow networks are strongest in spring and weakest in summer. Our driver analysis shows that high‐flow dependence is most strongly governed by precipitation in winter and evapotranspiration in summer while low‐flow dependence is most strongly governed by snowmelt in winter and evapotranspiration in fall. Finally, our change analysis shows that changes in connectedness (i.e., the number of catchments a catchments shows strong flow correlations with) vary spatially and are mostly positive for high flows. We conclude that spatial flow correlations are considerable for both high and particularly low flows as a result of a combination of spatially related hydro‐meteorological drivers whose importance varies by extreme type and season., Plain Language Summary: Droughts and floods can happen in multiple locations at once with important implications for flood and drought risk. Still, the spatial relationships between events and the reasons for them are not well studied. Here, we therefore ask how spatial relationships of both types of extremes and their meteorological drivers differ and vary by season. We compare networks of overall, low and high flows, determine hydro‐meteorological drivers of these networks, and map past changes in flow dependence using a large‐sample data set in Central Europe. Our network comparison shows that low flows are correlated more strongly and over longer distances than high flows and both high‐ and low‐flow networks are strongest in spring and weakest in summer. Our driver analysis shows that high‐flow dependence is governed by precipitation dependence in winter and evapotranspiration dependence in summer and fall while low flow dependence is most strongly governed by snowmelt in winter and evapotranspiration and snowmelt in fall. Finally, our change analysis shows that changes in connectedness (i.e., the number of catchments a catchments shows strong flow correlations with) vary spatially and are mostly positive for high flows. We conclude that spatial flow correlation is considerable for both high and particularly low flows highlighting the need to consider it in risk assessments., Key Points: We propose and use a tail dependence measure to map and compare complex networks of high and low flows in Central Europe at a seasonal scale. Low flows are related more strongly and over longer distances than high flows and relationships are strong in spring and weak in summer. Seasonal flow correlation is shaped by spatial dependence in drivers with varying importance of precipitation, evaporation, and snowmelt., Swiss National Science Foundation

Timely projections of seasonal streamflow extremes can be useful for the early implementation of annual flood risk adaptation strategies. However, predicting seasonal extremes is challenging particularly under non-stationary conditions and if extremes are connected in space. The goal of this study is to implement a space-time model for projection of seasonal streamflow extremes that considers the nonstationarity and spatio-temporal dependence of high flows. We develop a space-time model to project seasonal streamflow extremes for several lead times up to 2 months using a Bayesian Hierarchical Modelling (BHM) framework. This model is based on the assumption that streamflow extremes (3-day maxima) at a set of gauge locations are realizations of a Gaussian elliptical copula and generalized extreme value (GEV) margins with nonstationary parameters. These parameters are modeled as a linear function of suitable covariates from the previous season selected using the deviance information criterion (DIC). Finally, the copula is used to generate streamflow ensembles, which capture spatio-temporal variability and uncertainty. We apply this modelling framework to predict 3-day maximum flow in spring (May-June) at seven gauges in the Upper Colorado River Basin (UCRB) with 0 to 2 months lead time. In this basin, almost all extremes that cause severe flooding occur in spring as a result of snowmelt and precipitation. Therefore, we use regional mean snow water equivalent and temperature from the preceding winter season as well as indices of large-scale climate teleconnections – ENSO, AMO, and PDO – as potential covariates for 3-day maximum flow. Our model evaluation, which is based on the comparison of different model versions and the energy skill score, indicates that the model can capture the space-time variability of extreme flow well and that model skill increases with decreasing lead time. We also find that the use of climate variables slightly enhances skill relative to using only snow information. Median projections and their uncertainties are consistent with observations thanks to the representation of spatial dependencies through covariates in the margins and a Gaussian copula. This spatio-temporal modeling framework helps to plan seasonal adaptation and preparedness measures as predictions of extreme spring flows become available 2 months before actual flood occurrence.

Extreme low and high flows can have negative economic, social, and ecological effects and are expected to become more severe in many regions due to climate change. Besides low and high flows, the whole flow regime, i.e., annual hydrograph comprised of monthly mean flows, is subject to changes. Knowledge on future changes in flow regimes is important since regimes contain information on both extremes and conditions prior to the dry and wet seasons. Changes in individual low- and high-flow characteristics as well as flow regimes under mean conditions have been thoroughly studied. In contrast, little is known about changes in extreme flow regimes. We here propose two methods for the estimation of extreme flow regimes and apply them to simulated discharge time series for future climate conditions in Switzerland. The first method relies on frequency analysis performed on annual flow duration curves. The second approach performs frequency analysis of the discharge sums of a large set of stochastically generated annual hydrographs. Both approaches were found to produce similar 100-year regime estimates when applied to a data set of 19 hydrological regions in Switzerland. Our results show that changes in both extreme low- and high-flow regimes for rainfall-dominated regions are distinct from those in melt-dominated regions. In rainfall-dominated regions, the minimum discharge of low-flow regimes decreases by up to 50 %, whilst the reduction is 25 % for high-flow regimes. In contrast, the maximum discharge of low- and high-flow regimes increases by up to 50 %. In melt-dominated regions, the changes point in the other direction than those in rainfall-dominated regions. The minimum and maximum discharges of extreme regimes increase by up to 100 % and decrease by less than 50 %, respectively. Our findings provide guidance in water resource planning and management and the extreme regime estimates are a valuable basis for climate impact studies., Hydrology and Earth System Sciences, 23 (11), ISSN:1027-5606, ISSN:1607-7938

In Alpine regions, future changes in glacier and snow cover are expected to change runoff regimes towards higher winter but lower summer discharge. The low summer discharge will coincide with the highest water demand for irrigation, and local and regional water shortages are expected to become more likely. One possible measure to adapt to these changes can be the extension of current uses of artificial reservoirs and natural lakes to the provision of water for the alleviation of water shortage. This study assesses the potential of reservoirs and natural lakes for the alleviation of water shortages in a nationwide analysis in Switzerland. To do so, we estimated water supply and demand under current and future conditions both under normal and extreme runoff regimes for 307 catchments. Water demand was assessed for various categories including drinking water, industrial use, artificial snow production, agriculture, ecological flow requirements, and hydropower production. The aggregated supply and demand estimates were used to derive water surplus/shortage estimates. These were then compared to the storage capacity of reservoirs and natural lakes within a catchment to determine the potential for alleviating summer water scarcity. Our results show that water shortage is expected mainly in the lowland region north of the Alps, and less in the Alps. In this lowland region, the potential of natural lakes for alleviating water scarcity is high. This potential is lower in the Alps where it is expected to increase or decrease under future conditions depending on the region of interest. Catchments with a high storage capacity can potentially contribute to the alleviation of water shortage downstream. We conclude that a spatial mismatch between water scarcity and storage availability exists since water stored in reservoirs on the southern side of the Alps is often not available for the use on the northern side.

Floods often affect not only a single location, but also a whole region. Flood frequency analysis should therefore be undertaken at a regional scale which requires the considerations of the dependence of events at different locations. This dependence is often neglected even though its consideration is essential to derive reliable flood estimates. A model used in regional multivariate frequency analysis should ideally consider the dependence of events at multiple sites which might show dependence in the lower and/or upper tail of the distribution. We here seek to propose a simple model that on the one hand considers this dependence with respect to the network structure of the region and on the other hand allows for the simulation of stochastic event sets at both gauged and ungauged locations. The new Fisher copula model is used for representing the spatial dependence of flood events in the nested Thur catchment in Switzerland. Flood event samples generated for the gauged stations using the Fisher copula are compared to samples generated by other dependence models allowing for modeling of multivariate data including elliptical copulas, R-vine copulas, and max-stable models. The comparison of the dependence structures of the generated samples shows that the Fisher copula is a suitable model for capturing the spatial dependence in the data. We therefore use the copula in a way such that it can be used in an interpolation context to simulate event sets comprising gauged and ungauged locations. The spatial event sets generated using the Fisher copula well capture the general dependence structure in the data and the upper tail dependence, which is of particular interest when looking at extreme flood events and when extrapolating to higher return periods. The Fisher copula was for a medium-sized catchment found to be a suitable model for the stochastic simulation of flood event sets at multiple gauged and ungauged locations.

Water scarcity can have severe socio-economic and ecological consequences but can be alleviated by adapting water management plans and by increasing preparedness. Such adaptation measures require reliable estimates of potential water scarcity, which are derived at a certain temporal and spatial scale. The choice of scale might affect the outcome of water shortage assessments and conclusions drawn from them. It has been stressed that water scarcity assessments should be performed at a monthly instead of an annual scale, but it has not yet been investigated whether water scarcity assessments could profit from further increasing the temporal resolution. Furthermore, it has been shown that national scale assessments might hide regions potentially affected by water scarcity, and it remains unclear what scale nationwide assessments should focus on. In this study, we therefore investigate the effect of choosing a specific catchment size as the unit of nationwide water scarcity assessments. We compute water supply and demand on a dataset from Switzerland at two spatial scales, a regional scale consisting of large hydrological catchments and a local scale consisting of medium-sized hydrological catchments, and use them to derive water scarcity estimates. Our results show that water scarcity assessments performed at a monthly compared to an annual scale significantly affect the outcomes of water scarcity assessments, while a sub-monthly resolution does not lead to very different results than monthly assessments. In addition, our results also demonstrate that water scarcity can be over- or underestimated at a regional scale compared to at a local scale, at both a monthly and an annual scale and for both normal and extreme discharge conditions. These differences between the local and regional scale estimates can be explained by the within-region variability of local catchment characteristics, i.e. differences between local and regional estimates are larger in regions with diverse local catchment characteristics. We conclude that regional-scale assessments might not be sufficient in regions with highly variable physiographical and meteorological catchment characteristics and/or differences in water demand, infrastructure, and management. Therefore, water scarcity assessments should be conducted at a scale where the variability in these factors is small.

Predictions of floods, droughts, and fast drought‐flood transitions are required at different time scales to develop management strategies targeted at minimizing negative societal and economic impacts. Forecasts at daily and seasonal scale are vital for early warning, estimation of event frequency for hydraulic design, and long‐term projections for developing adaptation strategies to future conditions. All three types of predictions—forecasts, frequency estimates, and projections—typically treat droughts and floods independently, even though both types of extremes can be studied using related approaches and have similar challenges. In this review, we (a) identify challenges common to drought and flood prediction and their joint assessment and (b) discuss tractable approaches to tackle these challenges. We group challenges related to flood and drought prediction into four interrelated categories: data, process understanding, modeling and prediction, and human–water interactions. Data‐related challenges include data availability and event definition. Process‐related challenges include the multivariate and spatial characteristics of extremes, non‐stationarities, and future changes in extremes. Modeling challenges arise in frequency analysis, stochastic, hydrological, earth system, and hydraulic modeling. Challenges with respect to human–water interactions lie in establishing links to impacts, representing human–water interactions, and science communication. We discuss potential ways of tackling these challenges including exploiting new data sources, studying droughts and floods in a joint framework, studying societal influences and compounding drivers, developing continuous stochastic models or non‐stationary models, and obtaining stakeholder feedback. Tackling one or several of these challenges will improve flood and drought predictions and help to minimize the negative impacts of extreme events. This article is categorized under: Science of Water > Science of Water, Drought and flood modeling and prediction challenges related to (a) data, (b) process understanding, (c) modeling and prediction, and (d) human–water interactions. image, Swiss National Science Foundation http://dx.doi.org/10.13039/501100001711

In recent years, drought impacts have been more severe and frequent than past impacts throughout Europe. Due to the heterogeneity of Europe’s hydro- climatological situation as well as the multiple nations on the continent, drought events and their impacts vary with respect to location, sector, extent, duration and scale. In order to understand recent effects of drought and their possible drivers, national representatives distributed a uniform questionnaire to water management stakeholders of 28 contributing countries. Here, we focus on obtaining information on stakeholders’ drought perception,impacts, and current management strategies on a national and sub-national scale. With the survey, we analyse how strong the relationship between perceptions and actual hazard information is. Actual drought hazard information from the European Drought Observatory for the years 2018 and 2019 is compared with the questionnaire’s results. The results of the study highlight the diversity among national drought perceptions and the value of already existing drought management strategies. An absence of coordinated drought management is mostly attributed to a lack of resources and macro- governmental guidance. Supported by the national perspectives, possible macro-governmental pathways to increase national and sub-national awareness and resilience are discussed. The results support the need for national drought policies, which could be pushed forward with international drought management directives.

Up to 2003, a collective memory on severe drought-events in Switzerland has been pretty much inexistent. There has been no targeted research on hydrological drought and no early detection instrument was available for guiding decision makers. Research within the framework of the National Research Program 61 (www.nrp61.ch) “Sustainable Water Use” (2010-2013) provided the cornerstones for prototyping the early drought detection platform www.drought.ch. In June 2013, the platform was launched and provided useful information during two severe drought events in 2015 and 2018. At the same time, awareness about future increases in the frequency of such events has been confirmed by several studies considering future streamflow projections. Drought and water scarcity are now found on the list of the most threatening hazards for Switzerland. Several political initiatives call for increased efforts in the deployment of a national early warning system for critical droughts. This led to the proposition, that www.drought.ch should be integrated as the main tool for official national drought warnings in Switzerland. This contribution summarizes the 10-year process of developing the drought warning system drought.ch from technology readiness level 1 (TRL 1, “basic principles observed”) to TRL 8 (“system complete and qualified”). TRL1 started in 2010 with a two-stage dialogue with stakeholders from different sectors including national administration, hydropower, forestry, agriculture, and river navigation. TRL 3 (“experimental proof of concept”) began in 2013. Over the years, the initial focus on drought-specific monitoring of precipitation, streamflow, lake levels, groundwater levels, soil moisture deficit, snow resources, and dryness in forests and stream temperatures has been expanded to advanced countrywide sub-seasonal ensemble prediction of drought-parameters. The last major upgrade was the deployment of monthly forecasts (issued twice a week) during the extreme summer drought in 2018. Analyses of public drought perception after the 2018 event demonstrated that TRL 8 has been achieved, i.e. that the drought platform is useful.

Grouping catchments according to their seasonal streamflow or flood behavior can be essential in regionalization studies, climate impact assessments, or model choice and evaluation. Classical clustering approaches often rely on a selection of indices derived from streamflow/flood hydrographs to identify groups of similar hydrographs and ignore valuable information provided through the temporal (auto-)correlation pattern. To exploit this temporal information, we propose a functional clustering approach to identify catchments with similar streamflow regimes or flood hydrographs. Functional data clustering expresses hydrograph shapes as continuous functions by projecting them onto a set of basis functions (here B-splines) and clusters the resulting basis coefficients using classical clustering algorithms such as hierarchical or k-means clustering. We apply this functional clustering approach to (1) a large set of catchments in the United States in order to identify regions with similar streamflow regimes and (2) a large set of catchments in Switzerland in order to identify regions with similar flood reactivity. We show that both the streamflow regime and flood reactivity regions are not only similar in terms of their streamflow/hydrograph behavior but also in terms of physiography and climate. We use the streamflow regime clusters derived using functional data clustering to assess future streamflow regime changes in the United States and demonstrate that they are beneficial in climate impact assessments, e.g. to indicate which types of catchments are particularly prone to future change. Further, we use the flood reactivity regions in a regionalization study to derive design hydrographs in ungauged catchments. We conclude that functional clustering approaches are beneficial in climate impact assessments and regionalization studies and might potentially also be valuable to cluster other types of hydrological phenomena such as drought events or long-term streamflow behavior.

Droughts can seriously challenge water management if they have large spatial extents. These extents may change in a warming climate along with changes in underlying hydro-meteorological drivers. Therefore, we ask (1) how streamflow drought spatial extent has changed over the period 1981-2018 in the United States, (2) which physical drivers govern drought spatial extent, and (3) whether/how the importance of these drivers has changed over time. We analyze temporal changes in streamflow drought extents and their drivers using drought events extracted for 671 catchments in the conterminous United States using a variable threshold-level approach. Drought spatial extents are determined as the percentage of catchments affected by drought during a certain month. Then, important drivers are identified by determining the spatial percentage overlap of the area under streamflow drought with precipitation droughts, temperature anomalies, snow-water-equivalent deficits, and soil moisture deficits. Finally, the spatial extent and overlap time series are used in a trend analysis to determine changes in drought spatial extent and to identify changes in the importance of different variables as drivers of drought spatial extent. Our analyses show that (1) drought spatial extents have increased, mainly because of increases in the extent of small droughts; (2) drought extents overall substantially overlap with soil moisture deficits and the relationship of drought to precipitation and temperature varies seasonally; (3) the importance of temperature as a driver of drought extent has increased over time. We therefore conclude that continued global warming may further increase the probability of spatially compounding drought events, which requires adaptation of regional drought management strategies.

Compound hot and dry events can lead to severe impacts whose severity may depend on their time scale and spatial extent. Despite their potential importance, the climatological characteristics of these joint events have received little attention regardless of growing interest in climate change impacts on compound events. Here, we ask how event time scale relates to (1) spatial patterns of compound hot-dry events in the United States, (2) the spatial extent of compound hot-dry events, and (3) the importance of temperature and precipitation as drivers of compound event occurrence. To study such rare spatial and multivariate events, we introduce a multi-site multi-variable weather generator (PRSim.weather), which enables generation of a large number of spatial compound hot-dry events. We show that the stochastic model realistically simulates distributional and temporal autocorrelation characteristics of temperature and precipitation at single sites, dependencies between the two variables, spatial correlation patterns, and spatial heat and drought indicators and their co-occurrence probabilities. The results of our compound event analysis demonstrate that (1) the Northwestern and Southeastern United States are most susceptible to compound hot-dry events independent of time scale and susceptibility decreases with increasing time scale, (2) the spatial extent and time scale of compound events are strongly related with sub-seasonal events (1–3 months) showing the largest spatial extents, and (3) the importance of temperature and precipitation as drivers of compound events varies with time scale where temperature is most important at short and precipitation at seasonal time scales. We conclude that time scale is an important factor to be considered in compound event assessments and suggest that climate change impact assessments should consider several instead of a single time scale when looking at future changes in compound event characteristics. The largest future changes may be expected for short compound events because of their strong relation to temperature.

Floods cause large damages, especially if they affect large regions. Assessments of current, local and regional flood hazards and their future changes often involve the use of hydrologic models. However, uncertainties in simulated floods can be considerable and yield unreliable hazard and climate change impact assessments. A reliable hydrologic model ideally reproduces both local flood characteristics and spatial aspects of flooding, which is, however, not guaranteed especially when using standard model calibration metrics. In this paper we investigate how flood timing, magnitude and spatial variability are represented by an ensemble of hydrological models when calibrated on streamflow using the Kling–Gupta efficiency metric, an increasingly common metric of hydrologic model performance. We compare how four well-known models (SAC, HBV, VIC, and mHM) represent (1) flood characteristics and their spatial patterns; and (2) how they translate changes in meteorologic variables that trigger floods into changes in flood magnitudes. Our results show that both the modeling of local and spatial flood characteristics is challenging. They further show that changes in precipitation and temperature are not necessarily well translated to changes in flood flow, which makes local and regional flood hazard assessments even more difficult for future conditions. We conclude that models calibrated on integrated metrics such as the Kling–Gupta efficiency alone have limited reliability in flood hazard assessments, in particular in regional and future assessments, and suggest the development of alternative process-based and spatial evaluation metrics.

In recent years, the adverse effects of drought have been experienced and perceived more severely and frequently all over Europe. These impacts are a result of the drought hazard and the socio-economic and ecological vulnerability. Due to the heterogeneity of Europe’s hydro-climatology and its cultural, political, social and economic diversity , the socio-economic and ecological impacts vary not only with respect to the extent, duration and severity of the drought, but also with the characteristics of affected societies, economic sectors and ecosystems. The lack of understanding the spatio-temporal differences in the drivers of drought risk hinders the successful mitigation of future impacts, and the design of suitable reactive and proactive drought action plans. Therefore, this study describes the European drought events of 2018 and 2019 beyond the hazard. The hypothesis to be proven is that similar hazard conditions result in different impacts due to national and sub-national differences in drought vulnerability, perception and drought-risk management. Based on research in 35 European countries, comparable national datasets on drought management and perception are established. For each of these countries, a uniform questionnaire was distributed to water management-related stakeholders at different administrative levels. A major focus of the questions was the perception and impacts of the recent droughts and current management strategies on a national and sub-national scale. The results of the questionnaires are also compared to country-scale profiles of past drought events for different drought types, i.e. meteorological, soil moisture, hydrological and vegetation drought, which were established based on information derived from the European Drought Observatory indicator system.The results highlight a large diversity in the national perception of drought as a natural hazard and its impacts; but also a different spatial extent of 2018/2019 drought events At the same time, existing drought management strategies are shown to increase national and sub-national resilience. The study, therefore, calls for international exchange and mutual learning to improve national and international drought governance and management.

Mountains play an essential role in storing water and providing it to downstream regions and are therefore commonly referred to as ‘water towers of the world’. In particular, they provide runoff in the lowlands’ low flow season by contributing snow- and glacier melt. Mountain runoff thus plays an important role in achieving the UN Sustainable Development Goals (SDGs), in particular regarding water, food, and energy. However, the mountains’ water provision service is strongly challenged by climate change leading to the retreat and volume loss of glaciers, rising snow lines, and changes in precipitation amount and variability. One potential strategy for addressing these changes is the construction of new water reservoirs or the adjustment of current reservoir management strategies. These strategies need to take account of various, eventually competing water uses rooted in different sectors relevant at different scales and governments with different economic interests.We investigate the governance process related to the planning of a future reservoir in one of the most important water towers of the world, the European Alps. We ask why and how governance processes can lead to a coordination gap between upstream reservoir planning and the development of strategies allowing for the alleviation of downstream water shortage. We show on a case study in the Swiss Alps, that downstream water deficits could potentially be covered through a newly constructed upstream reservoir if management strategies were flexible enough. However, additional water uses than hydropower were not taken into account in the governance processes leading to the provision of a concession for the new reservoir. Instead, the decision-making within a participative process was influenced by (a) a lack of knowledge and data, (b) an interest to increase renewable energy production, (c) a focus on environmental agreements, and (d) economic interests. We conclude that upstream and downstream water demands need to be balanced in governance processes. Such balancing can be achieved by clarifying process design and by evaluating who can lead such complex processes with actors from different governments and sectors under the umbrella of non-uniform and incoherent institutions.

Flooding can affect large regions leading to high economic and societal costs. Estimating regional flood risk is crucial for developing adaptation strategies, public awareness policies, and protection structures. Yet, estimating regional flood hazard is not trivial because of the few large flood events observed. Here, we derive regional flood hazard estimates for large river basins in the United States by using a stochastic streamflow generator. This allows us to increase the number of flood events available for the analysis and to investigate the simultaneous occurrence of flooding in different parts of a river basin. We propose the continuous, stochastic simulation approach (PRSim.wave), which combines a non-parametric spatio-temporal model based on the wavelet transform with the parametric kappa distribution. The model reproduces the temporal and distributional characteristics of streamflow at individual sites and retains the spatial dependencies between sites even for spatial extremes. We use PRSim.wave to generate long and spatially consistent time series of daily discharge for a large set of catchments in the conterminous United States. For each catchment, we extract flood events from the simulated series using a peak-over-threshold approach to derive a spatial dataset of flood occurrences. Using this dataset, we estimate how probable it is that a certain percentage of stations within a specific river basin is jointly flooded. We show that: (1) there are strong regional differences in the likelihood of joint and potentially widespread flooding and (2) there are spatial differences in regional flood hazard estimates which could not be derived from observed data only. We deem our approach a valuable tool for water managers and policy makers to make informed decisions on the risk of widespread flooding.

Regional flood and drought events often have more severe impacts than localized events in terms of damages and costs, the number of affected people, and habitat changes. Understanding which regions may be jointly affected by such extreme events can help us to derive reliable regional risk estimates, plan and manage resource flows, and develop suitable adaptation measures. However, the spatial dimension of droughts and floods is often neglected when deriving hazard estimates and we know little about the processes governing their spatial dependencies. Therefore, we investigate how and why spatial dependencies in droughts and floods vary seasonally and regionally over the United States. We aim to gain new insight into processes governing spatial dependencies of droughts and floods by contrasting their regional and seasonal patterns.To map regions with a similar seasonal flood and drought behavior, respectively, we introduce a measure of connectedness, which quantifies the number of catchments with which a specific catchment co-experiences flood or drought events. We then summarize the spatial dependencies by identifying regions with a similar flood behavior and regions with a similar drought behavior. To do so, we use a hierarchical clustering procedure on the F-madogram, which is a measure of spatial dependence for extremes. We look at regional and seasonal differences in spatial dependence both for floods and droughts and subsequently compare the two phenomena.We find that spatial dependence is over all seasons stronger for droughts than for floods. Both types of extremes, however, show regional and seasonal differences in spatial connectedness. Droughts show the strongest spatial dependence in fall. In contrast, the Rocky Mountains show the highest spatial dependence of droughts in winter because of snow accumulation. Very low spatial dependence is found in spring. The seasonal, spatial dependence patterns of floods are opposed to the one of droughts. Spatial flood dependence is highest in spring, especially in mountainous areas, high in winter at the Pacific coast and the Appalachian Mountains, and high in summer in the Rocky Mountains. In contrast, spatial connectedness is very weak in fall. We conclude that spatial dependence patterns are stronger for droughts than floods because of the slower processes and longer durations associated with the phenomenon. Furthermore, we conclude that both meteorological and land surface processes such as snowmelt and the availability of soil moisture shape the spatial dependence patterns of each extreme.

Stochastically generated streamflow time series are used for various water management and hazard estimation applications. They provide realizations of plausible but as yet unobserved streamflow time series with the same temporal and distributional characteristics as the observed data. However, the representation of non-stationarities and spatial dependence among sites remains a challenge in stochastic modeling. We investigate whether the use of frequency-domain instead of time-domain models allows for the joint simulation of realistic, continuous streamflow time series at daily resolution and spatial extremes at multiple sites. To do so, we propose the stochastic simulation approach called Phase Randomization Simulation using wavelets (PRSim.wave) which combines an empirical spatio-temporal model based on the wavelet transform and phase randomization with the flexible four-parameter kappa distribution. The approach consists of five steps: (1) derivation of random phases, (2) fitting of the kappa distribution, (3) wavelet transform, (4) inverse wavelet transform, and (5) transformation to kappa distribution. We apply and evaluate PRSim.wave on a large set of 671 catchments in the contiguous United States. We show that this approach allows for the generation of realistic time series at multiple sites exhibiting short- and long-range dependence, non-stationarities, and unobserved extreme events. Our evaluation results strongly suggest that the flexible, continuous simulation approach is potentially valuable for a diverse range of water management applications where the reproduction of spatial dependencies is of interest. Examples include the development of regional water management plans, the estimation of regional flood or drought risk, or the estimation of regional hydropower potential. Highlights. Stochastic simulation of continuous streamflow time series using an empirical, wavelet-based, spatio-temporal model in combination with the parametric kappa distribution. Generation of stochastic time series at multiple sites showing temporal short- and long-range dependence, non-stationarities, and spatial dependence in extreme events. Implementation of PRSim.wave in R package PRSim: Stochastic Simulation of Streamflow Time Series using Phase Randomization.

Streamflow regimes are changing and expected to further change under the influence of climate change with potential impacts on flow variability and the seasonality of extremes. However, not all types of regimes are going to change in the same way. Climate change impact assessments can therefore benefit from identifying classes of catchments with similar streamflow regimes. Traditional catchment classification approaches have focused on specific meteorological and/or streamflow indices usually neglecting the temporal information stored in the data. The aim of this study is two-fold: (1) develop a catchment classification scheme that allows for the incorporation of such temporal information and (2) use the scheme to evaluate changes in future flow regimes. We use the developed classification scheme, which relies on a functional data representation, to cluster a large set of catchments in the conterminous United States (CONUS) according to their mean annual hydrographs. We identify five regime classes that summarize the behavior of catchments in the CONUS: (1) Intermittent regime, (2) weak winter regime, (3) strong winter regime, (4) New Year's regime, and (5) melt regime. Our results show that these spatially contiguous classes are not only similar in terms of their regimes, but also their flood and drought behavior, as well as their physiographical and meteorological characteristics. We therefore deem the functional regime classes valuable for a number of applications going beyond change assessments including model validation studies or the prediction of streamflow characteristics in ungauged basins. To assess future regime changes, we use simulated discharge time series obtained from the Variable Infiltration Capacity hydrologic model driven with meteorological time series generated by five general circulation models. A comparison of the future regime classes derived from these simulations with current classes shows that robust regime changes are expected only for currently melt-influenced regions in the Rocky Mountains. These changes in mountainous, upstream regions may require the adaptation of water management strategies to ensure sufficient water supply in dependent downstream regions.

Design hydrographs described by peak discharge, hydrograph volume, and hydrograph shapeare essential for engineering tasks involving storage. Such design hydrographs are inherently uncertain asare classical flood estimates focusing on peak discharge only. Various sources of uncertainty contribute tothe total uncertainty of synthetic design hydrographs for gauged and ungauged catchments. These com-prise model uncertainties, sampling uncertainty, and uncertainty due to the choice of a regionalizationmethod. A quantification of the uncertainties associated with flood estimates is essential for reliable deci-sion making and allows for the identification of important uncertainty sources. We therefore propose anuncertainty assessment framework for the quantification of the uncertainty associated with synthetic designhydrographs. The framework is based on bootstrap simulations and consists of three levels of complexity.On the first level, we assess the uncertainty due to individual uncertainty sources. On the second level, wequantify the total uncertainty of design hydrographs for gauged catchments and the total uncertainty ofregionalizing them to ungauged catchments but independently from the construction uncertainty. On thethird level, we assess the coupled uncertainty of synthetic design hydrographs in ungauged catchments,jointly considering construction and regionalization uncertainty. We find that the most important sources ofuncertainty in design hydrograph construction are the record length and the choice of the flood samplingstrategy. The total uncertainty of design hydrographs in ungauged catchments depends on the catchmentproperties and is not negligible in our case.

Design flood estimates for a given return period are required in both gauged and ungauged catchments for hydraulic design and risk assessments. Contrary to classical design estimates, synthetic design hydrographs provide not only information on the peak magnitude of events but also on the corresponding hydrograph volumes together with the hydrograph shapes. In this study, we tested different regionalization approaches to transfer parameters of synthetic design hydrographs from gauged to ungauged catchments. These approaches include classical regionalization methods such as linear regression techniques, spatial methods, and methods based on the formation of homogeneous regions. In addition to these classical approaches, we tested nonlinear regression models not commonly used in hydrological regionalization studies, such as random forest, bagging, and boosting. We found that parameters related to the magnitude of the design event can be regionalized well using both linear and nonlinear regression techniques using catchment area, length of the main channel, maximum precipitation intensity, and relief energy as explanatory variables. The hydrograph shape, however, was found to be more difficult to regionalize due to its high variability within a catchment. Such variability might be better represented by looking at flood-type specific synthetic design hydrographs.

Traditional design flood estimation approaches have focused on peak discharges and have often neglected other hydrograph characteristics such as hydrograph volume and shape. Synthetic design hydrograph estimation procedures overcome this deficiency by jointly considering peak discharge, hydrograph volume, and shape. Such procedures have recently been extended to allow for the consideration of process variability within a catchment by a flood-type specific construction of design hydrographs. However, they depend on observed runoff time series and are not directly applicable in ungauged catchments where such series are not available. To obtain reliable flood estimates, there is a need for an approach that allows for the consideration of process variability in the construction of synthetic design hydrographs in ungauged catchments. In this study, we therefore propose an approach that combines a bivariate index flood approach with event-type specific synthetic design hydrograph construction. First, regions of similar flood reactivity are delineated and a classification rule that enables the assignment of ungauged catchments to one of these reactivity regions is established. Second, event-type specific synthetic design hydrographs are constructed using the pooled data divided by event type from the corresponding reactivity region in a bivariate index flood procedure. The approach was tested and validated on a dataset of 163 Swiss catchments. The results indicated that 1) random forest is a suitable classification model for the assignment of an ungauged catchment to one of the reactivity regions, 2) the combination of a bivariate index flood approach and event-type specific synthetic design hydrograph construction enables the consideration of event types in ungauged catchments, and 3) the use of probabilistic class memberships in regional synthetic design hydrograph construction helps to alleviate the problem of misclassification. Event-type specific synthetic design hydrograph sets enable the inclusion of process variability into design flood estimation and can be used as a compromise between single best estimate synthetic design hydrographs and continuous simulation studies.

Accurate estimates of flood peaks, corresponding volumes and hydrographs are required to design safe and cost-effective hydraulic structures. In this paper, we propose a statistical approach for the estimation of the design variables peak and volume by constructing synthetic design hydrographs for different flood types such as flash-floods, short-rain floods, long-rain floods, and rain-on-snow floods. Our approach relies on the fitting of probability density functions to observed flood hydrographs of a certain flood type and accounts for the dependence between peak discharge and flood volume. It makes use of the statistical information contained in the data and retains the process information of the flood type. The method was tested based on data from 39 meso-scale catchments in Switzerland and provides catchment specific and flood type specific synthetic design hydrographs for all of these catchments. We demonstrate that flood type specific synthetic design hydrographs are meaningful in flood risk management when combined with knowledge on the seasonality and the frequency of different flood types.

The 2018 drought event had severe ecological, economic, and social impacts. How extreme was it in Switzerland? We addressed this question by looking at different types of drought, including meteorological, hydrological, agricultural, and groundwater drought, and at the two characteristics deficit and deficit duration. The analysis consisted of three main steps: (1) event identification using a threshold-level approach, (2) drought frequency analysis, and (3) comparison of the 2018 event to the severe 2003 and 2015 events. In Step 2 the variables precipitation, discharge, soil moisture, and low-flow storage were first considered separately in a univariate frequency analysis; pairs of variables were then investigated jointly in a bivariate frequency analysis using a copula model for expressing the dependence between the two variables under consideration. Our results show that the 2018 event was especially severe in north-eastern Switzerland in terms of soil moisture, with return periods locally exceeding 100 years. Slightly longer return periods were estimated when discharge and soil moisture deficits were considered together. The return period estimates depended on the region, variable, and return period considered. A single answer to the question of how extreme the 2018 drought event was in Switzerland is therefore not possible – it rather depends on the processes one is interested in.

International audience

Flood estimates needed for designing efficient and cost-effective flood protection structures are usually derived using observed peak discharges. This approach neglects, firstly, that floods are characterized not only by peak discharge but also by flood volume, and, secondly, that these characteristics are subject to modifications under climate and land use changes. Bivariate flood frequency analysis based on simulated discharge time series makes it possible to consider both flood peak and flood volume in design flood estimation. Further, this approach considers changes in discharge characteristics by using discharge series generated from climate time series used as an input for a hydrological model. Such series are usually not available at an hourly resolution but at a certain aggregation level (e.g. 24 h) and might not perfectly represent observed precipitation distributions. In this study, we therefore investigate how the aggregation and distribution of precipitation series and discharge distribution affect flood peaks and volumes and their dependence. We propose a framework for assessing the uncertainty in bivariate design flood estimates that is caused by different factors in the modeling chain, which consists of precipitation-discharge modeling, flood event sampling, and bivariate flood frequency analysis. The uncertainty sources addressed are precipitation aggregation and distribution, parameter and model uncertainty, and discharge resolution. Our results show that all of these uncertainty sources are relevant for design flood estimation and that the importance of the individual uncertainty sources is catchment dependent. Our results also demonstrate that substantial uncertainty is introduced already in the first step of the model chain because commonly used calibration procedures do not take into account the reproduction of flood volumes. Researchers should be aware of such deficiencies when performing bivariate flood frequency analysis on modeled discharge time series and should aim to tailor model calibration procedures to the problem at hand.

Stochastically generated streamflow time series are widely used in water resource planning and management. Such series represent sets of plausible yet unobserved streamflow realizations which should reproduce the main characteristics of observed data. These characteristics include the distribution of daily streamflow values and their temporal correlation as expressed by short- and long-range dependence. Existing streamflow generation approaches have mainly focused on the time domain, even though simulation in the frequency domain provides good properties. These properties comprise the simulation of both short- and long-range dependence as well as extension to multiple sites. Simulation in the frequency domain is based on the randomization of the phases of the Fourier transformation. We here combine phase randomization simulation with a flexible, four-parameter kappa distribution, which allows for the extrapolation to as yet unobserved low and high flows. The simulation approach consists of seven steps: (1) fitting the theoretical kappa distribution, (2) normalization and deseasonalization of the marginal distribution, (3) Fourier transformation, (4) random phase generation, (5) inverse Fourier transformation, (6) back transformation, and (7) simulation. The simulation approach is applicable to both individual and multiple sites. It was applied to and validated on a set of four catchments in Switzerland. Our results show that the stochastic streamflow generator based on phase randomization produces realistic streamflow time series with respect to distributional properties and temporal correlation. However, cross-correlation among sites was in some cases found to be underestimated. The approach can be recommended as a flexible tool for various applications such as the dimensioning of reservoirs or the assessment of drought persistence. Highlights. Stochastic simulation of streamflow time series for individual and multiple sites by combining phase randomization and the kappa distribution. Simulated time series reproduce temporal correlation, seasonal distributions, and extremes of observed time series. Simulation procedure suitable for use in water resource planning and management.

International audience; Flood hydrograph shapes contain valuable information on the flood-generation mechanisms of a catchment. To make good use of this information, we express flood hydrograph shapes as continuous functions using a functional data approach. We propose a clustering approach based on functional data for flood hydrograph shapes to identify a set of representative hydrograph shapes on a catchment scale and use these catchment-specific sets of representative hydrographs to establish regions of catchments with similar flood reactivity on a regional scale. We applied this approach to flood samples of 163 medium-size Swiss catchments. The results indicate that three representative hydrograph shapes sufficiently describe the hydrograph shape variability within a catchment and therefore can be used as a proxy for the flood behavior of a catchment. These catchment-specific sets of three hydrographs were used to group the catchments into three reactivity regions of similar flood behavior. These regions were not only characterized by similar hydrograph shapes and reactivity but also by event magnitudes and triggering event conditions. We envision these regions to be useful in regionalization studies, regional flood frequency analyses, and to allow for the construction of synthetic design hydrographs in ungauged catchments. The clustering approach based on functional data which establish these regions is very flexible and has the potential to be extended to other geographical regions or toward the use in climate impact studies.

Climate impact studies regarding floods usually focus on peak discharges and a bivariate assessment of peak discharges and hydrograph volumes is not commonly included. A joint consideration of peak discharges and hydrograph volumes, however, is crucial when assessing flood risks for current and future climate conditions. Here, we present a methodology to develop synthetic design hydrographs for future climate conditions that jointly consider peak discharges and hydrograph volumes. First, change factors are derived based on a regional climate model and are applied to observed precipitation and temperature time series. Second, the modified time series are fed into a calibrated hydrological model to simulate runoff time series for future conditions. Third, these time series are used to construct synthetic design hydrographs. The bivariate flood frequency analysis used in the construction of synthetic design hydrographs takes into account the dependence between peak discharges and hydrograph volumes, and represents the shape of the hydrograph. The latter is modeled using a probability density function while the dependence between the design variables peak discharge and hydrograph volume is modeled using a copula. We applied this approach to a set of eight mountainous catchments in Switzerland to construct catchment-specific and season-specific design hydrographs for a control and three scenario climates. Our work demonstrates that projected climate changes have an impact not only on peak discharges but also on hydrograph volumes and on hydrograph shapes both at an annual and at a seasonal scale. These changes are not necessarily proportional which implies that climate impact assessments on future floods should consider more flood characteristics than just flood peaks.

Estimates of flood event magnitudes with a certain return period are required for the design of hydraulic structures. While the return period is clearly defined in a univariate context, its definit ...