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Several major flood events of recent years have encouraged research focused on a better understanding of climatological and hydrological causes of floods. In practice, these findings can be used in flood risk management, and in the light of ongoing climate change, also in preparing effective adaptation strategies. This study builds on the results of the Wetrax+ research project which combined a stochastic weather generator and a high-resolution fully-distributed rainfall-runoff model to produce a unique dataset of 10 000 years of hourly simulations of air temperatures, precipitations and river discharges in the Upper Danube River basin. As the generated dataset accounted for the expected changes in the frequencies and persistence of the identified weather patterns, it was used to assess the possible changes in the very extreme flows in the study basin. The length of the dataset maintained that numerous flood events that were larger than the most extreme observed floods occurred in the dataset and were available for analysis. The results indicated that on average the floods should occur sooner in the year in most of the Upper Danube sub-basins. Moreover, the frequency of floods associated with weather patterns related to heavy precipitation also increased. Despite the predictions about the future, changes in weather pattern frequencies cannot be taken for granted the results of the study can be useful in identifying the sources and causes of the most extreme floods helping those responsible to focus their mitigation efforts on certain sub-basins.

The latest advances and availability of satellite observations have great potential for improving hydrological model simulations of the water cycle. The recent study by Tong et al. (2021) showed that satellite observations of snow cover and soil moisture could improve river runoff simulations of conceptual hydrologic models with lumped model parameters. Still, the value and potential of spatial patterns of satellite observations for hydrologic model parametrization need to be better understood. This study aims to evaluate and compare different multiple-objective calibration strategies that use model inputs and satellite observations for the model calibration in lumped, spatially distributed and stepwise ways. We aim to test the potential of daily MODIS (Moderate Resolution Imaging Spectroradiometer) snow cover and ASCAT (Advanced Scatterometer) soil water index images observed over 204 Austrian catchments in 2000-2014. Results show that stepwise calibration strategies that first calibrate the snow model parameters to satellite snow cover data followed by calibrating the remaining model parameters outperform (particularly in lowlands) the classical calibration strategies estimating model parameters in one single calibration step. The use of distributed snow cover and soil moisture patterns in model calibration improves the snow and soil moisture simulation performance of the model. The use of MODIS snow cover data has a more significant contribution to the overall improvement in model performance than ASCAT soil moisture data. References:Tong, R., Parajka, J., Salentinig, A., Pfeil, I., Komma, J., Széles, B., Kubáň, M., Valent, P., Vreugdenhil, M., Wagner, W., and Blöschl, G.: The value of ASCAT soil moisture and MODIS snow cover data for calibrating a conceptual hydrologic model, Hydrol. Earth Syst. Sci., 25, 1389-1410, 10.5194/hess-25-1389-2021, 2021.

The Thaya is a trans-national river basin that is situated in the Czech Republic and Austria. Different human activities in the basin and multiple water uses increase the water demand. This increase, combined with the recent droughts events in 2017 and 2018, has recently resulted in reconsidering the water management strategies for future climates. This contribution aims to evaluate the effect of climate change on the water balance of the Thaya. The aim is to apply two different hydrological models in an identical setting (the same model inputs, scenarios, and regional and water use data) and to identify water availability and its change under various climate and water use scenarios. The assessments compare BILAN and TUWmodel hydrological models coupled with the WATERRES water use module and a large sample of climate projections (the CMIP5 and CMIP6 models), which represent various socioeconomic pathways combined with projections of possible changes in water use. The results will demonstrate an insight into how the water balance in different parts of the Thaya basin has changed in the past and what are the possible effects of climate change on these water resources in the future.

The alluvial backwater areas of the Danube are valuable ecological habitats containing important drinking water resources. Due to the river regulation and the construction of power plants, the river water levels and natural dynamics of the backwater areas continuously decline, threatening their typical characteristics. The aim of this study was to evaluate how an increased connectivity of the backwater branch located in a nature-protected riverine floodplain (enabled by diverting river water into the backwater system via a weir) affects the microbiological quality of groundwater resources. The defined quality criterion was that the diversion measures must not lead to an increased detection frequency of faecal indicators in groundwater. The microbiological water quality of the Danube, its backwater branch and the groundwater was analysed from 2010 to 2013. E. coli was selected as bacterial indicator for recent faecal pollution. C. perfringens (spores) was analysed as indicator for persistent faecal pollution and potentially occurring pathogenic protozoa. We simulated the microbial transport from the Danube and the backwater river into groundwater using a 3‑D unsaturated-saturated groundwater model coupled with 2‑D hydrodynamic flow simulations. Scenarios for no diversion measures were compared with scenarios for an additional discharge of 3, 20 and 80 m3/s from the Danube River into the backwater branch. While the additional discharge of 20 and 80 m3/s of Danube water into the floodplain strongly improved the ecological status according to ecological habitat models, the hydraulic transport simulations showed that this would result in a deterioration of the microbiological quality of groundwater resources. The presented approach shows how hydraulic transport modelling and microbiological analyses can be combined to support decision-making.

Increasing river floods and infrastructure development in many parts of the world have created an urgent need for accurate high-resolution flood hazard mapping for more efficient flood risk management. Mapping accuracy hinges on the quality of the underlying Digital Terrain Model (DTM) and other spatial datasets. This article presents a processing strategy to ensure consistent adaption of countrywide spatial datasets to the requirements of hydraulic modelling. The suggested methods are automatized to a large extent and include (i) automatic fitting of river axis positions to the DTM, (ii) detection of culverts and obstacles in the river channel (iii) Smooth elimination of obstacles by interpolation along the river axes (iv) geometric detection of water-land borders and the top edge of embankments for (v) integration of the submerged river bed geometry into the DTM. The processing chain is applied to a river network (33880 km) and a DTM from Airborne Laser Scanning (ALS) with 1 m spatial resolution covering the entire territory of Austria ( ∼ 84000 km 2 ). Thus, countrywide consistency of data and methods is achieved along with high local relevance. Semi-automatic validation and extensive manual checks demonstrate that processing significantly improves the DTM with respect to topographic and hydraulic consistency. However, some open issues of automatic processing remain, e.g. in case of long underground river reaches.

This paper deals with the simulation of inundated areas for a region of 84,000 km

The recent advances in remote sensing provide opportunities for estimating the parameters of conceptual hydrologic models more reliably. However, the question of whether and to what extent the use of satellite data in model calibration may assist in transferring model parameters to ungauged catchments has not been fully resolved. The aim of this study is to evaluate the efficiency of different methods for transferring model parameters obtained by multiple-objective calibrations to ungauged sites and to assess the model performance in terms of runoff, soil moisture, and snow cover predictions relative to existing regionalization approaches. The model parameters are calibrated to daily runoff, satellite soil moisture (Advanced Scatterometer – ASCAT), and snow cover (Moderate Resolution Imaging Spectroradiometer – MODIS) data. The assessment is based on 213 catchments situated in different physiographic and climate zones of Austria. For the transfer of model parameters, eight methods (global and local variants of arithmetic mean, regression, spatial proximity, and similarity) are examined in two periods, i.e., the period in which the model is calibrated (2000–2010) and an independent validation period (2010–2014). The predictive accuracy is evaluated by the leave-one-out cross-validation. The results show that the method by which the model is calibrated in the gauged catchment has a larger impact on runoff prediction accuracy in the ungauged catchments than the choice of the parameter transfer method. The best transfer methods are global and local similarity and the kriging approach. The performance of the transfer methods differs between lowland and alpine catchments. While the soil moisture and snow cover prediction efficiencies are higher in lowland catchments, the runoff prediction efficiency is higher in alpine catchments. A comparison of the model transfer methods, based on parameters calibrated to runoff, snow cover, and soil moisture with those based on parameters calibrated to runoff, only indicates that the former outperforms the latter in terms of simulating soil moisture and snow cover. The performance of simulating runoff is similar, and the accuracy depends mainly on the weight given to the runoff objective in the multiple-objective calibrations.

Background: Rivers are important sources for drinking water supply, however, they are often impacted by wastewater discharges from wastewater treatment plants (WWTP) and combined sewer overflows (CSO). Reduction of the faecal pollution burden is possible through enhanced wastewater treatment or prevention of CSOs. Few methodological efforts have been made so far to investigate how these measures would affect the long-term treatment requirements for microbiologically safe drinking water supply under future changes.Objectives: This study aimed to apply a new integrative approach to decipher the interplay between the effects of future changes and wastewater management measures on the required treatment of river water to produce safe drinking water. We investigated scenarios of climate change and population growth, in combination with different wastewater management scenarios (i.e., no upgrades and upgrades at WWTPs, CSOs, and both). To the best of our knowledge, this is the first study to investigate this interplay. We focussed on the viral index pathogens norovirus and enterovirus and made a cross-comparison with a bacterial and a protozoan reference pathogen (Campylobacter and Cryptosporidium).Methods: We significantly extended QMRAcatch (v1.0 Python), a probabilistic-deterministic model that combines virus fate and transport modelling in the river with quantitative microbial risk assessment (QMRA). To investigate the impact of climatic changes, we used a conceptual semi-distributed hydrological model and regional climate model outputs to simulate river discharges for the period 2035 – 2049. We assumed that population growth leads to a corresponding increase in WWTP discharges. QMRAcatch was successfully calibrated and validated based on a four-year dataset of a human-associated genetic MST marker and enterovirus. The study site was the Danube in Vienna, Austria.Results: In the reference scenario, approx. 98% of the enterovirus and norovirus loads at the study site (median: 1010 and 1013 N/d) originated from WWTP effluent, while the remainder was via CSO events. The required log reduction value (LRV) to produce safe drinking water was 6.3 and 8.4 log10 for enterovirus and norovirus. Future changes in population size, river flows and CSO events did not affect these treatment requirements, and neither did the prevention of CSOs. In contrast, in the scenario of enhanced wastewater treatment, which showed lower LRVs by 2.0 and 1.3 log10, climate-change-driven increases in CSO events had a considerable impact on the treatment requirements, as they affected the main pollution source. Preventing CSOs and installing enhanced treatment at the WWTPs together had the most significant positive effect with a reduction of LRVs by 3.9 and 3.8 log10 compared to the reference scenario.Conclusions: The integrative modelling approach was successfully realised. The simultaneous consideration of source apportionment and concentrations of the reference pathogens were found crucial to understand the interplay among the effects of climate change, population growth and pollution control measures. The approach was demonstrated for a study site representing a large river impacted by WWTP and CSO discharges, but is applicable at other sites to support long term water safety planning.

Alluvial wetlands are important natural habitats and contain valuable drinking water resources. The transport of pathogens via the inflows of river water or the release and runoff from animal faecal deposits into the backwater bodies can pose health risks. The aim of this study was to develop a combined modelling approach for predicting the concentrations and loads of protozoan reference pathogens during floods and rainfall events in an alluvial wetland river. The probabilistic-deterministic model QMRAcatch (v 1.1 python backwater) was newly adapted to account for short-time variations in the flow and microbial transport of alluvial wetlands. The wetland discharge rates, together with the inundated volumes and areas served as input to the model. The latter were determined by means of regression analysis based on results of a 2D hydrodynamic flow model during a flood event. To evaluate the model performance of QMRAcatch, we used concentrations of human, ruminant, pig and bird associated microbial faecal source tracking (MST) markers and E. coli measured in the Danube and in the wetland river from 2010 to 2015. The microbial die-off / degradation was identified to be the most relevant optimization parameter. To obtain this parameter, we conducted a literature survey on the degradation of MST markers in water environments, determined confidence limits of the temperature-dependent rate coefficients, and adjusted them within these limits during the optimization. Scenarios of the different transport pathways of Cryptosporidium and Giardia into the wetland bodies during floods and rainfall events were then simulated. The scenarios showed that the highest loads of Cryptosporidium and Giardia were transported via the main river into the wetland during high flows, followed by the rainfall-induced release from animal faecal deposits, and the resuspension in flooded areas. The combined modelling approach is useful to support the drinking water safety management of alluvial wetlands.Funding source: This work was supported by the Vienna Science and Technology Fund (WWTF) [grant number ESR17-070] and by the European Union and Vienna Water [programme number LE07-13, project name ‘Groundwater Resource Systems Vienna’].

This paper analyses the spatial patterns and process controls of the mean annual flood (MAF), coefficient of variation (CV) and skewness (CS) of flood discharges in Europe. The data consist of maximum annual flood discharge series observed in 2370 catchments in Europe covering the period 1960–2010. On average, in Europe, the estimated moments MAF, CV and CS are 0.17 m3 s−1 km−2, 0.52 and 1.28, respectively. The results indicate that MAF is largest along the Atlantic coast, the high rainfall areas of the Mediterranean coast and in the mountain ranges of Europe, while it is smallest in the sheltered parts of the East European plain. CV is largest in Southern and Eastern Europe, while it is smallest in the regions of strong Atlantic influence. The pattern of CS is similar to that of CV, albeit a little more erratic. In the Mediterranean, MAF, CV and CS decrease strongly with catchment area suggesting that floods in small catchments are relatively very large, while in Eastern Europe the dependence is much weaker. The Process controls on the flood moments in five hydroclimatic regions are identified by correlation and multiple linear regression analyses with a range of covariates. Precipitation-related variables are found to be the main controls of the spatial patterns of MAF in most of Europe except for the regions with snow influence where air temperature is more important. Catchment area is another relevant variable. Aridity is by far the most important control of the spatial pattern of CV in all of Europe. Precipitation-related variables are relevant in Southern Europe. Overall, the findings suggest that, at the continental scale, climate variables dominate over land surface characteristics, such as land use and soil type, in controlling the spatial patterns of flood moments. Finally, the estimation accuracy of the multiple linear regression model for estimating flood moments in ungauged basins is evaluated as a baseline for more accurate local studies.

Karst aquifers are an important water resource, but are particularly vulnerable to pollution due to the typically short residence times. As the rainwater runs off on the surface it may collect contamination from faeces and other sources, before infiltrating the surface. It is therefore important to understand the spatial distribution of the frequency of surface runoff in karst areas. This paper proposes a new field-mapping method for the ability of the landscape to produce and convey surface runoff. The mapping method is based on (i) prior spatial information (e.g. geological map, terrain model), (ii) a visual assessment from a distance at the landscape scale (e.g. traces of surface runoff) and (iii) local data collection in the field (e.g. soil moisture, grain size distribution). The focus on variables that can be assessed from a distance in the landscape makes the method suitable for mapping larger areas than traditional field mapping. The mapping method is developed and tested for the 60 km2 Hochschwab area in Austria. The field mapping is used to specify a surface runoff propensity index which is tested against the spatial distribution of observed sink holes in the area. The mapping indicates that, in the study region, runoff occurs much more frequently in the poorly karstified dolomitic areas than in the limestone areas that are highly karstified. On dolomites, low permeable soils or debris prevail, often resulting in a permanent surface drainage network. On karstified limestone, sometimes overlaid by debris, surface runoff only occurs through infiltration excess at high rainfall intensities. Overall the analyses suggest that the mapping method is suitable for efficiently and reliably identifying spatial patterns of the ability of the landscape to produce and convey surface runoff in karst areas.

Remote sensing products have been widely applied in hydrological modeling for more realistic representations of hydrological processes. In this study, in addition to gauged discharge, the combined MODIS snow cover maps and ERS scatterometer based soil moisture products were added to constrain a semi-distributed conceptual hydrological model. The latest version of MODIS snow cover images provides a daily Normalized Difference Snow Index (NDSI) in a 500-meter resolution. We derived the snow cover maps by using a new NDSI thresholding method from the MODIS Aqua (MYD10A1) and Terra (MOD10A1) daily snow cover products. Furthermore, the newest ERS soil moisture product also provided a finer spatial resolution of 500-meter over Austria. The semi-distributed TUW-model was tested in 213 catchments using both single and multiple object calibration methods. We found that the semi-distributed TUW-model performed well in discharge modeling. Moreover, applying the MODIS snow cover maps improved the accuracy in the snow-melt season, while the soil moisture product helped the discharge simulation in the no-snow period.

A novel concept for fecal pollution analysis was applied at alluvial water resources to substantially extend the information provided by fecal indicator bacteria (FIB). FIB data were linked to river connectivity and genetic microbial source tracking (MST). The concept was demonstrated at the Danube River and its associated backwater area downstream of the city of Vienna, using a comprehensive 3-year data set (10 selected sites, n = 317 samples). Enumeration of Escherichia coli (ISO 16649-2), intestinal enterococci (ISO 7899-2) and Clostridium perfringens (ISO 14189) revealed a patchy distribution for the investigation area. Based on these parameters alone a clear interpretation of the observed fecal contamination patterns was not possible. Comparison of FIB concentrations to river connectivity allowed defining sites with dominating versus rare fecal pollution influence from the River Danube. A strong connectivity gradient at the selected backwater sites became obvious by 2D hydrodynamic surface water modeling, ranging from 278 days (25%) down to 5 days (

There are concerns that recent climate change is altering the frequency and magnitude of river floods in an unprecedented way. Historical studies have identified flood-rich periods in the past half millennium in various regions of Europe. However, because of the low temporal resolution of existing datasets and the relatively low number of series, it has remained unclear whether Europe is currently in a flood-rich period from a long-term perspective. Here we analyse how recent decades compare with the flood history of Europe, using a new database composed of more than 100 high-resolution (sub-annual) historical flood series based on documentary evidence covering all major regions of Europe. We show that the past three decades were among the most flood-rich periods in Europe in the past 500 years, and that this period differs from other flood-rich periods in terms of its extent, air temperatures and flood seasonality. We identified nine flood-rich periods and associated regions. Among the periods richest in floods are 1560–1580 (western and central Europe), 1760–1800 (most of Europe), 1840–1870 (western and southern Europe) and 1990–2016 (western and central Europe). In most parts of Europe, previous flood-rich periods occurred during cooler-than-usual phases, but the current flood-rich period has been much warmer. Flood seasonality is also more pronounced in the recent period. For example, during previous flood and interflood periods, 41 per cent and 42 per cent of central European floods occurred in summer, respectively, compared with 55 per cent of floods in the recent period. The exceptional nature of the present-day flood-rich period calls for process-based tools for flood-risk assessment that capture the physical mechanisms involved, and management strategies that can incorporate the recent changes in risk., This work was supported by the ERC Advanced Grant ‘FloodChange’ project (no. 291152), the Horizon 2020 ETN ‘System Risk’ project (no. 676027), the DFG project FOR 2416, the FWF projects I 3174 and W1219-N22, the Spanish Agency of Science and FEDER/UE projects CGL2016-75475/R, CGL2017-86839-C3-1-R, CGL2016-75996-R and CTM2017-83655-C2-2-R, the ICREA Academia programme, and project CZ.02.1.01/0.0/0.0/16_019/0000797, Ministry of Education, Youth and Sports of the Czech Republic.

Journal of hydraulic engineering 146(1), 05019005 (2020). doi:10.1061/(ASCE)HY.1943-7900.0001657, Published by American Society of Civil Engineers, ASCE, New York, NY

Summary The purpose of this paper is to contribute to the understanding of the importance of the initial soil moisture state for flash flood magnitudes. Four extreme events that occurred in different case study regions were analysed, one winter and one autumn flash flood in the Giofiros and Almirida catchments in Crete, and two summer floods in the Rastenberg catchment in Austria. The hydrological processes were simulated by the spatially distributed flash flood model Kampus. For the Crete cases Kampus model was calibrated against remotely sensed soil moisture while for the Austrian case the model was calibrated against observed runoff. Kampus model was then used to estimate the sensitivity of the stream flow peak to initial soil moisture. The largest of the events analysed (in terms of specific peak discharge) was found to have a sensitivity of less than 0.2% flood peak change per % soil moisture change while the smallest event had a sensitivity of more than 3% flood peak change per % soil moisture change. This suggests that initial soil moisture effects on the flash flood response probably depend on event magnitude rather than on the climate or region. Moreover, the Austrian catchment was found to exhibit a more nonlinear relationship between antecedent soil moisture and the peak discharge than the Cretan catchments which was explained by differences in the soil type.

Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover maps have been successfully applied in regional snow cover mapping and hydrological modelling in many regions. The new MODIS products (i.e. Collection 6) provide information about the Normalized Difference Snow Index (NDSI) instead of simple binary information about snow cover of the former products. The objective of this study is to investigate the sensitivity and accuracy of different NDSI thresholds used for snow cover mapping and to compare the results with former snow cover classification based on a fixed NDSI threshold (NDSI = 0.4). The accuracy is tested for both Aqua (MYD10A1) and Terra (MOD10A1) daily snow cover products by using daily snow depth observations at 665 climate stations in Austria in the period 2002–2014. The results show the overall classification accuracy over 665 climate stations to be larger than 97% (97.4% for Terra and 97.6% for Aqua) in Austria. The best NDSI thresholds fitted to individual stations can differ from a fixed threshold (NDSI = 0.4) and are sensitive to the snow depth threshold indicating snow cover at the ground. The NDSI thresholds vary seasonally, decrease with increasing elevation and are lower in forested than open land cover settings. We found that the NDSI thresholds fitted to different elevation and land cover classes improve regional snow cover mapping by 3–10% in forested regions above 900 m a.s.l. in January to March.

This study compares ERS scatterometer top soil moisture observations with simulations of a dual layer conceptual hydrologic model. The comparison is performed for 148 Austrian catchments in the period 1991–2000. On average, about 5 to 7 scatterometer images per month with a mean spatial coverage of about 37% are available. The results indicate that the agreement between the two top soil moisture estimates changes with the season and the weight given to the scatterometer in hydrologic model calibration. The hydrologic model generally simulates larger top soil moisture values than are observed by the scatterometer. The differences tend to be smaller for lower altitudes and the winter season. The average correlation between the two estimates is more than 0.5 in the period from July to October, and about 0.2 in the winter months, depending on the period and calibration setting. Using both ERS scatterometer based soil moisture and runoff for model calibration provides more robust model parameters than using either of these two sources of information.

Schneedaten mit hydrologischen Simulationen zu kombinieren ist ein zeitgemaser und effizienter Ansatz, die Schneerucklage und die Schneeschmelze in Einzugsgebieten zu bestimmen. Diese Kombination bietet den Vorteil, mehre Datenarten fur Vorhersagen bzw. Simulationen in konsistenter Weise berucksichtigen zu konnen, wie z. B. Niederschlag, Schneekenngrosen und Abfluss. Zum einen kommen als Schneebeobachtungen die von Hydrographischen Diensten gemessenen Daten infrage (besonders Schneehohendaten). Zum anderen kommen Satellitendaten infrage, insbesondere von optischen Sensoren. Anhand von Schneesimulationen in Osterreich wird gezeigt, dass die Schneeinformation eine wesentliche Verbesserung der hydrologischen Simulationen und eine verbesserte Einschatzung deren Zuverlassigkeit gegenuber den herkommlichen Punktdaten von Niederschlag, Lufttemperatur etc. und Abflussdaten alleine ermoglicht.

Many physical, chemical and biological processes taking place at the land surface are strongly influenced by the amount of water stored within the upper soil layers. Therefore, many scientific disciplines require soil moisture observations for developing, evaluating and improving their models. One of these disciplines is meteorology where soil moisture is important due to its control on the exchange of heat and water between the soil and the lower atmosphere. Soil moisture observations may thus help to improve the forecasts of air temperature, air humidity and precipitation. However, until recently, soil moisture observations had only been available over a limited number of regional soil moisture networks. This has hampered scientific progress as regards the characterisation of land surface processes not just in meteorology but many other scientific disciplines as well. Fortunately, in recent years, satellite soil moisture data have increasingly become available. One of the freely available global soil moisture data sets is derived from the backscatter measurements acquired by the Advanced Scatterometer (ASCAT) that is a C-band active microwave remote sensing instrument flown on board of the Meteorological Operational (METOP) satellite series. ASCAT was designed to observe wind speed and direction over the oceans and was initially not foreseen for monitoring soil moisture over land. Yet, as argued in this review paper, the characteristics of the ASCAT instrument, most importantly its wavelength (5.7 cm), its high radiometric accuracy, and its multiple-viewing capabilities make it an attractive sensor for measuring soil moisture. Moreover, given the operational status of ASCAT, and its promising long-term prospects, many geoscientific applications might benefit from using ASCAT soil moisture data. Nonetheless, the ASCAT soil moisture product is relatively complex, requiring a good understanding of its properties before it can be successfully used in applications. To provide a comprehensive overview of the major characteristics and caveats of the ASCAT soil moisture product, this paper describes the ASCAT instrument and the soil moisture processor and near-real-time distribution service implemented by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). A review of the most recent validation studies shows that the quality of ASCAT soil moisture product is - with the exception of arid environments -comparable to, and over some regions (e.g. Europe) even better than currently available soil moisture data derived from passive microwave sensors. Further, a review of applications studies shows that the use of the ASCAT soil moisture product is particularly advanced in the fields of numerical weather prediction and hydrologic modelling. But also in other application areas such as yield monitoring, epidemiologic modelling, or societal risks assessment some first progress can be noted. Considering the generally positive evaluation results, it is expected that the ASCAT soil moisture product will increasingly be used by a growing number of rather diverse land applications.

Summary Design floods for a given location at a stream can be estimated by a number of approaches including flood frequency statistics and the design storm method. If applied to the same catchment the two methods often yield quite different results. The aim of this paper is to contribute to understanding the reasons for these differences. A case study is performed for 10 alpine catchments in Tyrol, Austria, where the 100-year floods are estimated by (a) flood frequency statistics and (b) an event based runoff model. To identify the sources of the differences of the two methods, the 100-year floods are also estimated by (c) Monte Carlo simulations using a continuous runoff model. The results show that, in most catchments, the event based model gives larger flood estimates than flood frequency statistics. The reasons for the differences depend on the catchment characteristics and different rainfall inputs that were applied. For catchments with a high storage capacity the Monte Carlo simulations indicate a step change in the flood frequency curve when a storage threshold is exceeded which is not captured by flood frequency statistics. Flood frequency statistics therefore tends to underestimate the floods in these catchments. For catchments with a low storage capacity or significant surface runoff, no step change occurs, but in three catchments the design storms used were larger than those read from the IDF (intensity duration frequency) curve leading to an overestimation of the design floods. Finally, also the correct representation of flood dominating runoff components was shown to influence design flood results. Geologic information on the catchments was essential for identifying the reasons for the mismatch of the flood estimates.

A generalised framework of space-time variability in flood response is used to characterise five flood events of different type in the Kamp area in Austria: one long-rain event, two short-rain events, one rain-on-snow event and one snowmelt event. Specifically, the framework quantifies the contributions of the space-time variability of rainfall/snowmelt, runoff coefficient, hillslope and channel routing to the flood runoff volume and the delay and spread of the resulting hydrograph. The results indicate that the components obtained by the framework clearly reflect the individual processes which characterise the event types. For the short-rain events, temporal, spatial and movement components can all be important in runoff generation and routing, which would be expected because of their local nature in time and, particularly, in space. For the long-rain event, the temporal components tend to be more important for runoff generation, because of the more uniform spatial coverage of rainfall, while for routing the spatial distribution of the produced runoff, which is not uniform, is also important. For the rain-on-snow and snowmelt events, the spatio-temporal variability terms typically do not play much role in runoff generation and the spread of the hydrograph is mainly due to the duration of the event. As an outcome of the framework, a dimensionless response number is proposed that represents the joint effect of runoff coefficient and hydrograph peakedness and captures the absolute magnitudes of the observed flood peaks.

Die Bodenfeuchtigkeit spielt eine zentrale Rolle im hydrologischen Kreislauf auf den verschiedensten Masstabsskalen. Fernerkundungsmethoden, wie etwa die Aufnahme mit Mikrowellen, erlauben eine flachendeckende Beschreibung der Bodenfeuchte an der Landoberflache. Diese kann als Input fur hydrologische Modelle und fur Wettervorhersagemodelle, sowie als Information fur den Katastrophenschutz dienen. Dieser Beitrag gibt eine Einfuhrung in die aktuellen Entwicklungen von Bodenfeuchtigkeitsprodukten. Anhand von Fallstudien in Osterreich wird der Nutzen fur hydrologisch-wasserwirtschaftliche Anwendungen aufgezeigt.

Summary The aim of this paper is to examine the benefits of updating soil moisture of a distributed rainfall runoff model in forecasting large floods. The updating method uses Ensemble Kalman Filter concepts and involves an iterative similarity approach that avoids calculation of the Jacobian that relates the states and the observations. The soil moisture is updated based on observed runoff in a real-time mode, and is then used as an initial condition for the flood forecasts. The case study is set in the 622 km 2 Kamp catchment, Austria. The results indicate that the updating procedure indeed improves the forecasts substantially. The mean absolute normalised error of the peak flows of six large floods decreases from 25% to 12% (3 h lead time), and from 25% to 19% (48 h lead time). The Nash-Sutcliffe efficiency of forecasting runoff for these flood events increases from 0.79 to 0.92 (3 h lead time), and from 0.79 to 0.88 (48 h lead time). The flood forecasting system has been in operational use since early 2006. a 2008 Elsevier B.V. All rights reserved.

This paper proposes a framework for identifying the parameters of a lumped routing model in small to medium sized catchments where lateral inflows can be large but poorly defined. In a first step, a priori estimates of the parameters are made based on topography, aerial photographs, flood marks and field surveys. In a second step, runoff data are analysed of reservoir release events and convective events where no rainfall in the direct catchments occurred. In a third step the routing model is calibrated to the results of hydrodynamic models for scenarios of different magnitudes. In a fourth step, these pieces of information are combined, allowing for soft expert judgement to be incorporated. In a fifth step, the routing parameters are fine tuned to observed flood events where lateral inflows are estimated by a rainfall—runoff model. The framework is illustrated by the Kamp flood forecasting system in Austria that has been in operational use since 2006.

There has been a surprisingly large number of major floods in the last years around the world, which suggests that floods may have increased and will continue to increase in the next decades. However, the realism of such changes is still hotly discussed in the literature. This overview article examines whether floods have changed in the past and explores the driving processes of such changes in the atmosphere, the catchments and the river system based on examples from Europe. Methods are reviewed for assessing whether floods may increase in the future. Accounting for feedbacks within the human‐water system is important when assessing flood changes over lead times of decades or centuries. It is argued that an integrated flood risk management approach is needed for dealing with future flood risk with a focus on reducing the vulnerability of the societal system. WIREs Water 2015, 2:329–344. doi: 10.1002/wat2.1079 For further resources related to this article, please visit the WIREs website.

The June 2013 flood in the Upper Danube Basin was one of the largest floods in the past two centuries. An atmospheric blocking situation produced precipitation exceeding 300 mm over four days at the northern rim of the Alps. The high precipitation, along with high antecedent soil moisture, gave rise to extreme flood discharges in a number of tributaries including the Tiroler Ache, Saalach, Salzach and Inn. Runoff coefficients ranged from 0.2 in the Bavarian lowlands to 0.6 in the Alpine areas in Austria. Snowfall at high altitudes (above about 1600 m a.s.l.) reduced the runoff volume produced. Precipitation was distributed over two blocks separated by a few hours, which resulted in a single peak, long-duration flood wave at the Inn and Danube. At the confluence of the Bavarian Danube and the Inn, the small time lag between the two flood waves exacerbated the downstream flood at the Danube. Because of the long duration and less inundation, there was less flood peak attenuation along the Austrian Danube reach than for the August 2002 flood. Maximum flood discharges of the Danube at Vienna were about 11 000 m3 s−1, as compared to 10 300, 9600 and 10 500 m3 s−1 in 2002, 1954 and 1899, respectively. This paper reviews the meteorological and hydrological characteristics of the event as compared to the 2002, 1954 and 1899 floods, and discusses the implications for hydrological research and flood risk management.

[1] Flood forecasts are generally associated with errors, which can be attributed to uncertainties in the meteorological forecasts and the hydrologic simulations, and ensemble spreads are usually considered capable of representing them. To quantify these two components of the total forecast errors and to compare these to ensemble spreads, an extended data set is used. Four years of operational flood forecasts at hourly time step with lead times up to 48 h are evaluated for 43 catchments in Austria and Germany. Catchment sizes range from 70 to 25,600 km2, elevations from 200 to 3800 m, and mean annual precipitation from 700 to 2000 mm. A combination of ECMWF and ALADIN ensemble forecasts are used as input in a semidistributed conceptual water balance model on an hourly time step. The results indicate that, for short lead times, the ratio of hydrological simulation error to precipitation forecast error is 1.2 to 2.7 with increasing catchment size from 100 to 10,000 km2. For long lead times the ratio of hydrological simulation error to precipitation forecast error decreases from 1.1 to 0.9 with increasing catchment size. Clear scaling relationships of the forecast error components with catchment area are found. A similar scaling is also found for ensemble spreads, which are shown to represent quantitatively the total forecast error when forecasting floods.

[1] Empirical distribution functions of flood peaks in small catchments sometimes show discontinuities in the slope; that is, the largest flood peaks are significantly larger than the rest of the record. The aim of this paper is to understand whether these discontinuities, or step changes, can be a consistent effect of hydrological processes. We conducted field surveys in two Austrian alpine catchments 73 km2in size to map the spatial patterns of surface runoff generation and hydrogeologic storage. On the basis of this information, we selected the parameters of a distributed continuous runoff model, which is designed to simulate well the point when the storage capacity of the catchment is exhausted. Then we calibrated a stochastic rainfall model and performed Monte Carlo simulations of runoff to generate flood frequency curves for the two catchments. The curves exhibit a step change around a return period of 30 years. An analysis of the storage capacities suggests that this step change is due to a threshold of storage capacity being exceeded, which causes fast surface runoff in large parts of the catchments. The threshold occurs when the storage within the catchment is spatially rather uniform. To identify step changes, reliable estimates of the catchment storage capacity are needed on the basis of detailed hydrogeological information. The occurrence of a step change is of importance for estimating low-probability floods since the flood estimates with the step change accounted for can be significantly different from those based on commonly used distribution functions. We therefore suggest that step changes in the flood frequency curve of small catchments can be real and their possible presence should be taken into account in design flood estimation.

In this paper, we analyse the effectiveness of flood management measures based on the concept known as retaining water in the landscape . The investigated measures include afforestation, micro-ponds and small-reservoirs. A comparative and model-based methodological approach has been developed and applied for three meso-scale catchments located in different European hydro-climatological regions: Poyo (184 km2) in the Spanish Mediterranean, Upper Iller (954 km2) in the German Alps and Kamp (621 km2) in Northeast-Austria representing the Continental hydroclimate. This comparative analysis has found general similarities in spite of the particular differences among studied areas. In general terms, the flood reduction through the concept of retaining water in the landscape depends on the following factors: the storage capacity increase in the catchment resulting from such measures, the characteristics of the rainfall event, the antecedent soil moisture condition and the spatial distribution of such flood management measures in the catchment. In general, our study has shown that, this concept is effective for small and medium events, but almost negligible for the largest and less frequent floods: this holds true for all different hydro-climatic regions, and with different landuse, soils and morphological settings., Most of this research was done within the ERANET-Crue project "Room for the River", which was sponsored by: Lebensministerium (Austria), Bundesministerium fur Bildung und Forschung (Germany) and Ministerio de Ciencia y Educacion (Spain). We would like to thank each basin authority and national weather agencies for providing the hydro-meteorological data. The authors would like to thank to the Editor Andreas Gunther and two anonymous referees for their valuable comments.

Im Bereich des Flussbaus werden die Hochwasserdurchflusse einer bestimmten Jahrlichkeit in der Regel durch hochwasserstatistische Methoden ermittelt, im Bereich der Wildbach- und Lawinenverbauung hingegen durch Niederschlags- Abflussmodelle. Da die beiden Methoden oft stark unterschiedliche Ergebnisse liefern, ist eine Harmonisierung wunschenswert. Aus diesem Grund initiierten der Hydrographische Dienst Tirol und der Forsttechnische Dienst fur Wildbach- und Lawinenverbauung Sektion Tirol das HOWATI Projekt mit dem Ziel, die Ursachen fur die Unterschiede der Methoden zu verstehen. Zehn Leiteinzugsgebiete wurden ausgewahlt und in Feldbegehungen das oberflachliche Abflussverhalten und die hydrogeologischen Situation genau aufgenommen. Die Hochwasserdurchflusse wurden sodann mit dem Niederschlags-Abflussmodell Zemokost und mit statistischer Langzeitsimulation (Monte Carlo) berechnet. Die Unter-schiede der Methoden lassen sich vorwiegend damit erklaren, dass die Wahl eines plausiblen Niederschlagsinputs entscheidend ist, um die Hochwasserwerte mit dem Abflussmodell nicht zu uberschatzen und dass in Gebieten mit groser Speicherfahigkeit die statistische Verteilungsfunktion einen Knick haben kann, der zu wesentlich hoheren Hochwasserwerten fuhrt als die ubliche Pegelstatistik. Die Erkenntnisse aus den Leiteinzugsgebieten wurden in eine flachendeckende Auswertung fur ganz Tirol ubernommen.

Quantifying the uncertainty of flood forecasts by ensemble methods is becoming increasingly important for operational purposes. The aim of this paper is to examine how the ensemble distribution of precipitation forecasts propagates in the catchment system, and to interpret the flood forecast probabilities relative to the forecast errors. We use the 622 km2 Kamp catchment in Austria as an example where a comprehensive data set, including a 500 yr and a 1000 yr flood, is available. A spatially-distributed continuous rainfall-runoff model is used along with ensemble and deterministic precipitation forecasts that combine rain gauge data, radar data and the forecast fields of the ALADIN and ECMWF numerical weather prediction models. The analyses indicate that, for long lead times, the variability of the precipitation ensemble is amplified as it propagates through the catchment system as a result of non-linear catchment response. In contrast, for lead times shorter than the catchment lag time (e.g. 12 h and less), the variability of the precipitation ensemble is decreased as the forecasts are mainly controlled by observed upstream runoff and observed precipitation. Assuming that all ensemble members are equally likely, the statistical analyses for five flood events at the Kamp showed that the ensemble spread of the flood forecasts is always narrower than the distribution of the forecast errors. This is because the ensemble forecasts focus on the uncertainty in forecast precipitation as the dominant source of uncertainty, and other sources of uncertainty are not accounted for. However, a number of analyses, including Relative Operating Characteristic diagrams, indicate that the ensemble spread is a useful indicator to assess potential forecast errors for lead times larger than 12 h.

Many recent studies have sought to characterize variations of the annual maximum flood discharge series over time and across space in Europe, including some that have elucidated different process controls on different statistical properties of these series. To further support these studies, we conduct a pan-European assessment of process controls on key properties of this series, including the mean annual flood (MAF) and coefficients of variation (CV) and skewness (CS) of flood discharges. These annual maximum flood discharge series consist of instantaneous peaks and daily means observed in 2370 catchments in Europe without strong human modifications covering the period 1960–2010. We explore how the estimated moments MAF, CV and CS vary due to catchment size, climate and other controls across Europe, where their averages are 0.17 m3 s−1 km−2, 0.52 and 1.28, respectively. The results indicate that MAF is largest along the Atlantic coast, in the high-rainfall areas of the Mediterranean coast and in mountainous regions, while it is smallest in the sheltered parts of the East European Plain. The CV is largest in southern and eastern Europe, while it is smallest in the regions subject to strong Atlantic influence. The pattern of the CS is similar, albeit more erratic, in line with the greater sampling variability of CS. In the Mediterranean, MAF, CV and CS decrease strongly with catchment area, suggesting that floods in small catchments are relatively very large, while in eastern Europe this dependence is much weaker, mainly due to more synchronized timing of snowmelt over large areas. The process controls on the flood moments in five predetermined hydroclimatic regions are identified through correlation and multiple linear regression analyses with a range of covariates, and the interpretation is aided by a seasonality analysis. Precipitation-related covariates are found to be the main controls of the spatial patterns of MAF in most of Europe except for regions in which snowmelt contributes to MAF, where air temperature is more important. The Aridity Index is, by far, the most important control on the spatial pattern of CV in all of Europe. Overall, the findings suggest that, at the continental scale, climate variables dominate over land surface characteristics, such as land use and soil type, in controlling the spatial patterns of flood moments. Finally, to provide a performance baseline for more local studies, we assess the estimation accuracy of regional multiple linear regression models for estimating flood moments in ungauged basins.