Evapotranspiration (ET) is the most significant water balance component and is also a very complex component to evaluate in spatio–temporal scales. Remotely-sensed data greatly increases the accuracy of basin wide ET estimation but only in periods with available satellite images. This paper describes an attempt to estimate daily ET regardless of the availability of the satellite images. The method is based on application of the interpolated evaporative fraction (Λ) from “historical” satellite images to periods with no satellite data available. Basin wide daily ET is obtained by combining interpolated Λ and standard PET methods on meteorological stations. The reliability of such approach was evaluated by comparing the obtained daily ET to the SEBAL ET estimates through the analysis of residuals (Δ), standard deviations of residuals (σ) and the Nash–Sutcliffe coefficient (NSE) over the basin. The SEBAL ET estimates were validated with the data from two lysimeters. The discrepancy of obtained ET versus the SEBAL ET estimates (Δ = 0.13 mm day⁻¹, σ = 0.64 mm day⁻¹, NSE = 0.07) indicated that the proposed concept has relatively high accuracy, which is notably higher than the Penman–Monteith interpolated ET estimates (Δ = 1.94 mm day⁻¹, σ = 1.03 mm day⁻¹, NSE = −4.71). It was shown that a total of five images can provide a reliable estimate of interpolated Λ and thus represent specific characteristics of a basin. As the presented concept requires minimum remote sensing data and ground based inputs, it could be applied to estimate basin wide daily ET in data scarce regions and in periods with no satellite images available.

Reliable gridded precipitation products (GPPs) are essential for effective hydrological simulations, particularly in mountainous regions with limited ground-based observations. This study evaluates the performance of two widely used GPPs, CHIRPS and ERA5, in estimating precipitation and supporting runoff generation using the Soil and Water Assessment Tool (SWAT) across three headwater catchments (Sill, Drava and Isel) in the Austrian Alps from 1991 to 2018. The region’s complex topography and climatic variability present a rigorous test for GPP application. The evaluation methods combined point-to-point comparisons with gauge observations and assessments of generated runoff and runoff trends at annual, seasonal and monthly scales. CHIRPS showed a lower precipitation error (RMAE = 25%) and generated more consistent runoff results (RMAE = 12%), particularly in smaller catchments, whereas ERA5 showed higher spatial consistency but higher overall precipitation bias (RMAE = 37%). Although both datasets successfully reproduced the seasonal runoff regime, CHIRPS outperformed ERA5 in trend detection and monthly runoff estimates. Both GPPs systematically overestimate annual and seasonal precipitation amounts, especially at lower elevations and during the cold season. The results highlight the critical influence of GPP spatial resolution and its alignment with catchment morphology on model performance. While both products are viable alternatives to observed precipitation, CHIRPS is recommended for hydrological modelling in smaller, topographically complex alpine catchments due to its higher spatial resolution. Despite its higher precipitation bias, ERA5’s superior correlation with observations suggests strong potential for improved model performance if bias correction techniques are applied. The findings emphasize the importance of selecting GPPs based on the scale and geomorphological and climatic conditions of the study area.

This study evaluates deterministic flood fatality models using a harmonised dataset of river and flash flood events in Europe (1980–2024). The objective is to quantify differences across data sources and critically assess the applicability of commonly used prediction models for hydrological floods, with particular emphasis on flash floods, which remain poorly represented in existing methodologies. The analysis integrates large-scale databases on flood fatalities (HANZE, EM-DAT) with detailed event-based studies containing hazard and other indicators, enabling a combined evaluation from different sources. Three model groups are assessed by comparing predicted and observed fatalities: Damage–Fatality, Depth–Fatality, and Depth–Velocity–Fatality approaches. Results confirm discrepancy between exposure and mortality: river floods dominate in terms of affected population (87%) and economic losses (71%), whereas flash floods account for nearly half of all fatalities despite affecting only 13% of people. All evaluated models show significant limitations for prediction of flash floods fatalities; single-parameter approaches perform poorly, while multi-parameter models remain highly sensitive to uncertain hydraulic inputs. The study demonstrates that current methods are not transferable to flash flood conditions and highlights the need for integrated, multi-variable approaches supported by consistent and high-quality datasets. The main contributions of the study are the first systematic validation of widely used models against historical river and flash flood events, revealing their uncertainties, and a comprehensive assessment of their robustness and sensitivity to key input indicators.