Recent advances in soil moisture remote sensing have produced satellite data sets with improved soil moisture mapping under vegetation and with higher spatial and temporal resolutions. In this study, we evaluate the potential of a new, experimental version of the Advanced Scatterometer (ASCAT) soil water index data set for multiple objective calibrations of a conceptual hydrologic model. The analysis is performed in 213 catchments in Austria for the period 2000–2014. An HBV (Hydrologiska Byråns Vattenbalansavdelning)-type hydrologic model is calibrated based on runoff data, ASCAT soil moisture data, and Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover data for various calibration variants. Results show that the inclusion of soil moisture data in the calibration mainly improves the soil moisture simulations, the inclusion of snow data mainly improves the snow simulations, and the inclusion of both of them improves both soil moisture and snow simulations to almost the same extent. The snow data are more efficient at improving snow simulations than the soil moisture data are at improving soil moisture simulations. The improvements of both runoff and soil moisture model efficiencies are larger in low elevation and agricultural catchments than in others. The calibrated snow-related parameters are strongly affected by including snow data and, to a lesser extent, by soil moisture data. In contrast, the soil-related parameters are only affected by the inclusion of soil moisture data. The results indicate that the use of multiple remote sensing products in hydrological modeling can improve the representation of hydrological fluxes and prediction of runoff hydrographs at the catchment scale.

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.