ALBERT

Description: The increase in frequency and intensity of extreme precipitation events caused by the changing climate (e.g., cloudbursts, rainstorms, heavy rainfall, hail, heavy snow), combined with the high population density and concentration of assets, makes urban areas particularly vulnerable to pluvial flooding. Hence, assessing their vulnerability under current and future climate scenarios is of paramount importance. Detailed hydrologic-hydraulic numerical modeling is resource intensive and therefore scarcely suitable for performing consistent hazard assessments across large urban settlements. Given the steadily increasing availability of LiDAR (Light Detection And Ranging) high-resolution DEMs (Digital Elevation Models), several studies highlighted the potential of fast-processing DEM-based methods, such as the Hierarchical Filling-&-Spilling or Puddle-to-Puddle Dynamic Filling-&-Spilling Algorithms (abbreviated herein as HFSAs). We develop a fast-processing HFSA, named Safer_RAIN, that enables mapping of pluvial flooding in large urban areas by accounting for spatially distributed rainfall input and infiltration processes through a pixel-based Green-Ampt model. We present the first applications of the algorithm to two case studies in Northern Italy. Safer_RAIN output is compared against ground evidence and detailed output from a two-dimensional (2D) hydrologic and hydraulic numerical model (overall index of agreement between Safer_RAIN and 2D benchmark model: sensitivity and specificity up to 71% and 99%, respectively), highlighting potential and limitations of the proposed algorithm for identifying pluvial flood-hazard hotspots across large urban environments.

Description: The growing concern about the possible effects of climate change on flood frequency regime is leading Authorities to review previously proposed reference procedures for design-flood estimation, such as national flood frequency models. Our study focuses on Triveneto, a broad geographical region in North-eastern Italy. A reference procedure for design flood estimation in Triveneto is available from the Italian NCR research project "VA.PI.", which considered Triveneto as a single homogeneous region and developed a regional model using annual maximum series (AMS) of peak discharges that were collected up to the 1980s by the former Italian Hydrometeorological Service. We consider a very detailed AMS database that we recently compiled for 76 catchments located in Triveneto. All 76 study catchments are characterized in terms of several geomorphologic and climatic descriptors. The objective of our study is threefold: (1) to inspect climatic and scale controls on flood frequency regime; (2) to verify the possible presence of changes in flood frequency regime by looking at changes in time of regional L-moments of annual maximum floods; (3) to develop an updated reference procedure for design flood estimation in Triveneto by using a focused-pooling approach (i.e. Region of Influence, RoI). Our study leads to the following conclusions: (1) climatic and scale controls on flood frequency regime in Triveneto are similar to the controls that were recently found in Europe; (2) a single year characterized by extreme floods can have a remarkable influence on regional flood frequency models and analyses for detecting possible changes in flood frequency regime; (3) no significant change was detected in the flood frequency regime, yet an update of the existing reference procedure for design flood estimation is highly recommended and we propose the RoI approach for properly representing climate and scale controls on flood frequency in Triveneto, which cannot be regarded as a single homogeneous region.

Description: Our study develops and tests a geostatistical technique for locally enhancing macro-scale rainfall-runoff simulations on the basis of observed streamflow data that were not used in calibration. We consider Tyrol (Austria and Italy) and two different types of daily streamflow data: macro-scale rainfall-runoff simulations at 11 prediction nodes and observations at 46 gauged catchments. The technique consists of three main steps: (1) period-of-record flow-duration curves (FDCs) are geostatistically predicted at target ungauged basins, for which macro-scale model runs are available; (2) residuals between geostatistically predicted FDCs and FDCs constructed from simulated streamflow series are computed; (3) the relationship between duration and residuals is used for enhancing simulated time series at target basins. We apply the technique in cross-validation to 11 gauged catchments, for which simulated and observed streamflow series are available over the period 1980–2010. Our results show that (1) the procedure can significantly enhance macro-scale simulations (regional NSE increases from nearly zero to ≈ 0.7) and (2) improvements are significant for low gauging network densities (i.e. 1 gauge per 2000 km2).

Description: Our study develops and tests a geostatistical technique for locally enhancing macro-scale rainfall–runoff simulations on the basis of observed streamflow data that were not used in calibration. We consider Tyrol (Austria and Italy) and two different types of daily streamflow data: macro-scale rainfall–runoff simulations at 11 prediction nodes and observations at 46 gauged catchments. The technique consists of three main steps: (1) period-of-record flow–duration curves (FDCs) are geostatistically predicted at target ungauged basins, for which macro-scale model runs are available; (2) residuals between geostatistically predicted FDCs and FDCs constructed from simulated streamflow series are computed; (3) the relationship between duration and residuals is used for enhancing simulated time series at target basins. We apply the technique in cross-validation to 11 gauged catchments, for which simulated and observed streamflow series are available over the period 1980–2010. Our results show that (1) the procedure can significantly enhance macro-scale simulations (regional LNSE increases from nearly zero to ≈0.7) and (2) improvements are significant for low gauging network densities (i.e. 1 gauge per 2000 km2).

Description: Considering the entire fluvial network of Tyrol and South Tyrol, 81 sampling sites were selected according to the following requirements: (1) degree of hydrological disturbance (undisturbed, residual water, temporal residual water or hydropeaking); (2) type of water regime (e.g. groundwater, glacial, nivo-pluvial); (3) degree of morphological disturbance (either near natural conditions or anthropogenic changed conditions); (4) dominant land use type in the catchment with a total area of 4 ha (either grassland, forest, vineyard/orchard or urban settlement); (5) hormone pollution (based on the location of the sample point - before or after sewage treatment plant, or pesticide suspicion). Hydrological and morphological indicators as well as derived indices are based on a standardized methodical approach: (1) survey of morphological and hydrological stream characteristics using standardised field manual and entry form. The hydrological indicators were identified through flow-duration curves, which represent quantitative characterisation of the streamflow regime and harmonised data provided by local agencies and on-site probes; (2) standardized index calculations, starting with the hydrological index IH and morphological index IM, which were summed up to the hydromorphological index IHM. Further details regarding the methodical approach and its application are explained in the publication resulting from the analysis of this hydromorphological data (see Supplement to).

Description: The dataset consists of a GIS vector layer of the contours of 24,148 elementary catchments in Europe and the associated representation of the streamflow regime in terms of empirical flow–duration curves (FDCs). FDCs are estimated by means of the geostatistical procedure termed total negative deviation top-kriging (TNDTK), starting from the empirical FDCs available for 2484 discharge measurement stations across Europe. Together with the estimated FDCs' percentiles, for each catchment, indicators of the accuracy and reliability of the performed large-scale geostatistical prediction are provided. The file is stored using the ESRI Shapefile format in the ETRS89 (European Terrestrial Reference System 1989) – LAEA (Lambert Azimuthal Equal Area) datum and geographic coordinate system.