Linking Pan-European data to the local scale for decision making for global change and water scarcity within water resources planning and management

[EN] This study focuses on a novel type of methodology which connects Pan-European data to the local scale in the field of water resources management. This methodology is proposed to improve and facilitate the decision making within the planning and management of water resources, taking into account climate change and its expected impacts. Our main point of interest is focused on the assessment of the predictability of extreme events and their possible effects, specifically droughts and water scarcity. Consequently, the Júcar River Basin was selected as the case study, due to the ongoing water scarcity problems and the last drought episodes suffered in the Mediterranean region. In order to study these possible impacts, we developed a modeling chain divided into four steps, they are: i) data collection, ii) analysis of available data, iii) models calibration and iv) climate impact analysis. Over previous steps, we used climate data from 15 different regional climate models (RCMs) belonging to the three different Representative Concentration Pathways (RCPs) coming from a hydrological model across all of Europe called E-HYPE. The data were bias corrected and used to obtain statistical results of the availability of water resources for the future (horizon 2039) and in form of indicators. This was performed through a hydrological (EVALHID), stochastic (MASHWIN) and risk management (SIMRISK) models, all of which were specifically calibrated for this basin. The results show that the availability of water resources is much more enthusiastic than in the current situation, indicating the possibility that climate change, which was predicted to occur in the future has already happened in the Júcar River Basin. It seems that the so called Effect 80 , an important decrease in water resources for the last three decades, is not well contemplated in the initial data.The authors thank the anonymous reviewers for their valuable comments, suggestions and positive feedback. All remaining errors, however, are solely the responsibility of the authors. We would also like to express our gratitude to the Jucar River Basin Authority - Confederacion Hidrografica del Jucar (Spanish Ministry of Agriculture, Fishery, Food and Environment) for providing data to develop this study. The authors wish to thank the Spanish Ministry of Economyand Competitiveness for its financial support through the NUTEGES project (CGL2012-34978) and ERAS project (CTM2016-77804-P). We also value the support provided by the European Community's Seventh Framework Program in financing the projects ENHANCE (FP7-ENV-2012, 308438), AGUAMOD (Interreg V-B Sudoe 2016), SWICCA (ECMRWF-Copernicus-FA 2015/C3S_441-LOT1/SMHI) and IMPREX (H2020-WATER-2014-2015, 641811).Suárez-Almiñana, S.; Pedro Monzonís, M.; Paredes Arquiola, J.; Andreu Álvarez, J.; Solera Solera, A. (2017). Linking Pan-European data to the local scale for decision making for global change and water scarcity within water resources planning and management. The Science of The Total Environment. 603-604:126-139. https://doi.org/10.1016/j.scitotenv.2017.05.259S126139603-60

Flood-initiating catchment conditions: a spatio-temporal analysis of large-scale soil moisture patterns in the Elbe River basin

Floods are the result of a complex interaction between meteorological event characteristics and pre-event catchment conditions. While the large-scale meteorological conditions have been classified and successfully linked to floods, this is lacking for the large-scale pre-event catchment conditions. Therefore, we propose classifying soil moisture as a key variable of pre-event catchment conditions and investigating the link between soil moisture patterns and flood occurrence in the Elbe River basin. Soil moisture is simulated using a semi-distributed conceptual rainfall-runoff model over the period 1951–2003. Principal component analysis (PCA) and cluster analysis are applied successively to identify days of similar soil moisture patterns. The results show that PCA considerably reduced the dimensionality of the soil moisture data. The first principal component (PC) explains 75.71% of the soil moisture variability and represents the large-scale seasonal wetting and drying. The successive PCs express spatially heterogeneous catchment processes. By clustering the leading PCs, we identify large-scale soil moisture patterns which frequently occur before the onset of floods. In winter, floods are initiated by overall high soil moisture content, whereas in summer the flood-initiating soil moisture patterns are diverse and less stable in time

<i>HESS Opinions</i> "More efforts and scientific rigour are needed to attribute trends in flood time series"

The question whether the magnitude and frequency of floods have changed due to climate change or other drivers of change is of high interest. The number of flood trend studies is rapidly rising. When changes are detected, many studies link the identified change to the underlying causes, i.e. they attribute the changes in flood behaviour to certain drivers of change. We propose a hypothesis testing framework for trend attribution which consists of essential ingredients for a sound attribution: evidence of consistency, evidence of inconsistency, and provision of confidence statement. Further, we evaluate the current state-of-the-art of flood trend attribution. We assess how selected recent studies approach the attribution problem, and to which extent their attribution statements seem defendable. In our opinion, the current state of flood trend attribution is poor. Attribution statements are mostly based on qualitative reasoning or even speculation. Typically, the focus of flood trend studies is the detection of change, i.e. the statistical analysis of time series, and attribution is regarded as an appendix: (1) flood time series are analysed by means of trend tests, (2) if a significant change is detected, a hypothesis on the cause of change is given, and (3) explanations or published studies are sought which support the hypothesis. We believe that we need a change in perspective and more scientific rigour: detection should be seen as an integral part of the more challenging attribution problem, and detection and attribution should be placed in a sound hypothesis testing framework

Understanding hydrologic variability across Europe through catchment classification

This study contributes to better understanding the physical controls on spatial patterns of pan-European flow signatures – taking advantage of large open datasets for catchment classification and comparative hydrology. Similarities in 16 flow signatures and 35 catchment descriptors were explored for 35 215 catchments and 1366 river gauges across Europe. Correlation analyses and stepwise regressions were used to identify the best explanatory variables for each signature. Catchments were clustered and analyzed for similarities in flow signature values, physiography and the combination of the two. We found the following. (i) A 15 to 33 % (depending on the classification used) improvement in regression model skills when combined with catchment classification versus simply using all catchments at once. (ii) Twelve out of 16 flow signatures were mainly controlled by climatic characteristics, especially those related to average and high flows. For the baseflow index, geology was more important and topography was the main control for the flashiness of flow. For most of the flow signatures, the second most important descriptor is generally land cover (mean flow, high flows, runoff coefficient, ET, variability of reversals). (iii) Using a classification and regression tree (CART), we further show that Europe can be divided into 10 classes with both similar flow signatures and physiography. The most dominant separation found was between energy-limited and moisture-limited catchments. The CART analyses also separated different explanatory variables for the same class of catchments. For example, the damped peak response for one class was explained by the presence of large water bodies for some catchments, while large flatland areas explained it for other catchments in the same class. In conclusion, we find that this type of comparative hydrology is a helpful tool for understanding hydrological variability, but is constrained by unknown human impacts on the water cycle and by relatively crude explanatory variables

CD_soiltype_35408_E-HYPE

Soil types in percentage of upstream area for all E-HYPE sub-basins outlets. 8 different types are used: coarse soil, medium soil, fine soil, peat, no texture, shallow and moraine. This data can be linked to the shapefile "MULTIHARO_TotalDomain_WGS84_20140428_2" available as a separate download. To do so, link the "SUBID_OUT" filed of the shapefile with the "SUBID" field of this dataset

4 th International Symposium on Flood Defence: Managing Flood Risk, Reliability and Vulnerability PROBABILISTIC ANALYSIS OF HYDROLOGICAL LOADS TO OPTIMIZE THE DESIGN OF FLOOD CONTROL SYSTEMS

ABSTRACT: Recent severe flood events prompt for considering a broad range of different hydrological scenarios in the design of flood protection structures such as flood control reservoirs and polders, instead of using the traditional approach of considering one single design flood of a predefined return period only. In this paper a method to categorize generated hydrological loads is presented, which is then applied for the analysis, optimization and extension of the flood control system of the Unstrut watershed in Mid-East Germany. The spatial structure of the flood events is analyzed by the joint probability of the inflow peaks of two dams located downstream of the northern and southern main tributaries. For the design of flood detention structures it is important to consider the flood volume apart from the flood peak. Therefore the joint probability of corresponding flood peaks and volumes are used to categorize the flood events. Hereby the copula-method is used for the construction of the bivariate distribution function

CD_climate_35408_E-HYPE

Mean annual precipitation, seasonality index of precipitation, mean annual temperature, aridity index calculated for upstream area for all E-HYPE sub-basins outlets. For definitions used for aridity index and seasonality index see reference paper (Kuentz et al., 2016). Reanalysis temperature and precipitation data for subbasins was taken from the nearest meteorological grid point to each subbasin centroid. Daily time-series over 1979-2010 were used to calculate the different indices. Then, an average value for the whole upstream area of each subbasin outlet was calculated as the mean of all upstream subbasins weighted by their area. This data can be linked to the shapefile "MULTIHARO_TotalDomain_WGS84_20140428_2" available as a separate download. To do so, link the "SUBID_OUT" filed of the shapefile with the "SUBID" field of this dataset