Which rainfall score is more informative about the performance in river discharge simulation? A comprehensive assessment on 1318 basins over Europe

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 SCAT-terometer (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.