The dataset presents quantification of fluvial flood risk of company assets by integrating flood hazard scenarios with flexible state-of-the-art Bayesian Network-based flood loss model and object-specific exposure data. The dataset consists of spatially explicit loss in terms of Expected Annual Damage (EAD) of companies (commercial and industrial sectors) for each NUTS3 region corresponding to a baseline and potential future scenarios shaped by climate change, exposure dynamics, and their combined effects. Additionally, the dataset includes simulation results under an intervention scenario "measures for all" where each company has atleast implemented one of the possible precautionary measures against flooding. In addition to the NUTS3-level EAD values, the dataset also includes loss estimates for each tile corresponding to the baseline (1995) scenario. More information on input variables, usage and calculation of EAD can be found in Devadas et al. (2025) [DOI will be updated after publication].

This repository hosts the data used in the manuscript "Potential legacy of SWOT mission for the estimation of Flow-Duration Curves” by Alessio Domeneghetti, Serena Ceola, Alessio Pugliese, Simone Persiano, Irene Palazzoli, Attilio Castellarin, Alberto Marinelli, Armando BrathThe study investigates the potential of the Surface Water and Ocean Topography (SWOT) mission for the estimation of Flow-Duration Curves (FDCs) globally. SWOT-like river flow data is derived from the Global Runoff Data Centre (GRDC) dataset by selecting river gauging stations with a cross section wider than 100 m and with more than 10-year long daily river flow time series. Overall, 1200 river gauging stations are considered, from which 24 alternative SWOT-like river flow datasets can be derived by assuming different satellite revisiting times, biases and random errors, as detailed in the manuscript.The dataset includes detailed features on the selected 1200 river gauging stations. GRDC river flow daily time series for each station is provided as .csv. A .shp file provides the geographical location of each GRDC gauging station, including GRDC number, river, gauging station municipality, country, latitude and longitude, contributing area, altitude and Köppen-Geiger climate classification.

This repository hosts the data used in the manuscript "Potential legacy of SWOT mission for the estimation of Flow-Duration Curves” by Alessio Domeneghetti, Serena Ceola, Alessio Pugliese, Simone Persiano, Irene Palazzoli, Attilio Castellarin, Alberto Marinelli, Armando BrathThe study investigates the potential of the Surface Water and Ocean Topography (SWOT) mission for the estimation of Flow-Duration Curves (FDCs) globally. SWOT-like river flow data is derived from the Global Runoff Data Centre (GRDC) dataset by selecting river gauging stations with a cross section wider than 100 m and with more than 10-year long daily river flow time series. Overall, 1200 river gauging stations are considered, from which 24 alternative SWOT-like river flow datasets can be derived by assuming different satellite revisiting times, biases and random errors, as detailed in the manuscript.The dataset includes detailed features on the selected 1200 river gauging stations. GRDC river flow daily time series for each station is provided as .csv. A .shp file provides the geographical location of each GRDC gauging station, including GRDC number, river, gauging station municipality, country, latitude and longitude, contributing area, altitude and Köppen-Geiger climate classification.

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.

This repository contains the R code for INSYDE, a synthetic, probabilistic flood damage model based on explicit cost analysis. Correspondence to: Rui Figueiredo ([email protected])

This repository contains the R code for INSYDE, a synthetic, probabilistic flood damage model based on explicit cost analysis. Correspondence to: Rui Figueiredo ([email protected])