The global availability of satellite rainfall products (SRPs) at an increasingly high temporal and spatial resolution has made their exploitation in hydrological applications possible, especially in data-scarce regions. In this context, understanding how uncertainties transfer from SRPs to river discharge simulations, through the hydrological model, is a main research question. SRPs' accuracy is normally characterized by comparing them with ground observations via the calculation of categorical (e.g. threat score, false alarm ratio and probability of detection) and/or continuous (e.g. bias, root mean square error, Nash–Sutcliffe index, Kling–Gupta efficiency index and correlation coefficient) performance scores. However, whether these scores are informative about the associated performance in river discharge simulations (when the SRP is used as input to a hydrological model) is an under-discussed research topic. This study aims to relate the accuracy of different SRPs both in terms of rainfall and in terms of river discharge simulation. That is, the following research questions are addressed: is there any performance score that can be used to select the best performing rainfall product for river discharge simulation? Are multiple scores needed? And, which are these scores? To answer these questions, three SRPs, namely the Tropical Rainfall Measurement Mission (TRRM) Multi-satellite Precipitation Analysis (TMPA), the Climate Prediction Center MORPHing (CMORPH) algorithm and the SM2RAIN algorithm applied to the Advanced SCATterometer (ASCAT) soil moisture product (SM2RAIN–ASCAT) have been used as input into a lumped hydrologic model, “Modello Idrologico Semi-Distribuito in continuo” (MISDc), for 1318 basins over Europe with different physiographic characteristics. Results suggest that, among the continuous scores, the correlation coefficient and Kling–Gupta efficiency index are not reliable indices to select the best performing rainfall product for hydrological modelling, whereas bias and root mean square error seem more appropriate. In particular, by constraining the relative bias to absolute values lower than 0.2 and the relative root mean square error to values lower than 2, good hydrological performances (Kling–Gupta efficiency index on river discharge greater than 0.5) are ensured for almost 75 % of the basins fulfilling these criteria. Conversely, the categorical scores have not provided suitable information for addressing the SRP selection for hydrological modelling.

In this study, we perform a data assimilation (DA) experiment on a very large number (>700) of small‐ and medium‐scale (150–10,000 km²) European catchments to assess the impact of satellite soil moisture (SM) DA on streamflow simulations for different climatic and hydrologic conditions. In the experiment, Climate Change Initiative SM active, passive and combined products are assimilated over a time period 2003–2016 via an Ensemble Kalman Filter (EnKF). The results show that, on average, the assimilation of the three products provides relatively small improvements as compared to analogous open loop (OL) results (i.e., with an increase on median Kling‐Gupta Efficiency equal to 0.0048, 0.0033, and 0.0022 [−] for the active, the passive, and the combined products, respectively). OL performance itself is found to be a significant driver of the assimilation results: greater improvements are observed in catchments with poor OL streamflow predictions and inaccurate precipitation estimates. The remotely sensed product accuracy also emerges as relevant for assimilation efficiency, while factors affecting SM retrievals such as vegetation density, topographic complexity and basin area are found to have only a limited impact on the spatial pattern of performance. Small and detrimental effects of SM assimilation are observed over fully humid catchments and at high latitudes where pre‐storm soil moisture has reduced control on runoff generation as well as in basins where the hydrological model contains structural limitations.

Soil moisture is a highly suitable indicator for assessing agricultural drought, as plants start to wilt when there is not sufficient soil water to meet evapotranspiration demand. In this study, we provide insights on information obtained from satellite surface soil moisture observations (as compared to modeled soil moisture and observed ground precipitation) on water stress and its impact on crop production variability in India. The analysis involved generating a Standardized Soil Moisture Index using (1) satellite soil moisture observations from the European Space Agency Climate Change Initiative and (2) the Modern‐Era Retrospective Analysis for Research and Applications, version 2 soil moisture data set as well as producing a Standardized Precipitation Index using ground‐based rainfall observations from the Indian Meteorological Department. Spanning the period from 1998 to 2015, the study covers Maharashtra and Karnataka states. These states were recently hit by a severe drought, resulting in significant crop failure and human losses. Results show that soil moisture is an important limiting factor for crop production. As such, it is more suitable for representing agricultural drought than precipitation during drought conditions, as it correlates more closely with reduced crop yields. Additionally, using the satellite‐based Standardized Soil Moisture Index seemed to explain crop yield reductions better than when we applied the model‐based Standardized Soil Moisture Index from Modern‐Era Retrospective Analysis for Research and Applications, version 2, particularly for irrigated crops (i.e., wheat). This preliminary study can underpin future crop‐forecasting tools assimilating satellite soil moisture data. In practice, satellite soil moisture may help to improve the efficiency of agricultural and irrigation management practices.

In a changing climate, assessing the effects that the variation of the expected rainfalls can cause to slope stability is of primary importance. Precipitations are expected to increase, and, in particular, there will be more events characterised by extreme rainfalls, which legitimates the possibility of an increase in landslide activity. A probabilistic physically based model, which takes into account the uncertainty in soil characterisation, has been applied to a study area in central Italy, forced with different scenarios of expected rainfalls. The results of the prediction are compared in terms of variation of percentage of unstable territory. It is observed that the projection of the expected rainfall produces a general increase of the number of potentially unstable zones. Although many uncertainties in the analyses of the climatic trends and in their related effects at the ground still exist, the presented approach shows that physically based methods can be used to support quantitative projections of the expected impacts.