Analysis and attribution of the hydrological coherence of gridded precipitation and temperature datasets in the Italian Alpine Region

Large-scale hydrological modeling has gained a wealth of attention in the last decades, due to the importance of assessing the growing anthropogenic and climate change impacts on water resources. In the context of these studies, the Alpine Region has historically played a key role, being widely recognized as “Europe’s water tower” and given the complex combination of anthropogenic and climatic drivers influencing its hydrology. The application of hydrological modeling at the synoptic scale requires an accurate assessment of the climatic forcing, chiefly precipitation and temperature. Nowadays, a number of observation-derived gridded products providing precipitation and temperature over a regular grid are available to benchmark and support large-scale analyses. However, these products are often not tailored to potential hydrological applications and are based on data with different and often uncertain levels of accuracy and resolution. In this context, assessing the uncertainty due to the climatic forcing and its relationship with the hydrological response of different catchments becomes crucial in order to gain confidence in the simulations. In the present study, we analyze the ability of several gridded datasets (which are best suited to large-scale analyses) to reproduce observed streamflows of more than 200 reaches across the Italian Alps. The simulations have been conducted by feeding HYPERstreamHS, a distributed hydrological model specifically tailored for large-scale simulations, with the following gridded meteorological datasets: MESAN, COSMO reanalysis, APGD, MSWEP, E-OBS, MESCAN, and ERA5-Land. Hydrological coherence was first evaluated by means of the NSE and KGE efficiency indexes. Then, we attempted to break down the main drivers of hydrological coherence by classifying the analyzed catchments based on hydrological and geomorphological characteristics, and by analyzing the relative incidence on the uncertainty of temperature and precipitation, by means of ANOVA.