Hydrological monitoring systems relying on radar data and distributed hydrological models are now feasible at large-scale and represent effective early warning systems for flash floods. Here we describe a system that allows hydrological occurrences in terms of streamflow at a national scale to be monitored. We then evaluate its operational application in Italy, a country characterized by various climatic conditions and topographic features. The proposed system exploits a modified conditional merging (MCM) algorithm to generate rainfall estimates by blending data from national radar and rain-gauge networks. Then, we use the merged rainfall fields as input for the distributed and continuous hydrological model, Continuum, to obtain real-time streamflow predictions. We assess its performance in terms of rainfall estimates from MCM, using cross-validation and comparison with a conditional merging technique at an event-scale. We also assess its performance against rainfall fields from ground-based data at catchment-scale. We further evaluate the performance of the hydrological system in terms of streamflow against observed data (relative error on high flows less than 25% and Nash–Sutcliffe Efficiency greater than 0.5 for 72% and 46% of the calibrated study sections, respectively). These results, therefore, confirm the suitability of such an approach, even at national scale, over a wide range of catchment types, climates, and hydrometeorological regimes, and for operational purposes.

Drought risk will increase in the next decades due to anthropogenic warming, especially in the Mediterranean region. Therefore, robust hydrological models during droughts are essential tools for disaster risk reduction and climate change adaptation strategies. Yet, many studies showed drops in model performance when simulating periods with different climatic conditions from those of the calibration period, which poses challenges in properly simulating discharge (Q) during droughts. Some works also revealed that these issues may be related to the simulation of evapotranspiration (ET) and changes in terrestrial water storage (TWS) in the catchment, which has been shown to be highly sensitive to the calibration period too. Here, we analyzed how the simulation of Q, ET, and TWS differs according to the selected calibration period and during droughts, thus expanding on previous work on this matter that has mostly focused on Q. We compared two parameterizations of the distributed hydrological model Continuum for the Po river basin over 2009 – 2019. The northern Italian study area is characterized by a transition from continental to Mediterranean climates and experienced two major drought events during the study period (2012 and 2017). The two model parameterizations result from an iterative semi-automated calibration against Q data during a wet period for the first model variant (2018-2019), and during a dry period for the second model variant (2016-2017). We then evaluated the modelling skills in simulating Q, ET, and TWS for the whole river basin and 43 subcatchments in terms of both temporal and spatial variability, using ground-based and satellite-derived data as benchmark. Calibrating during a dry period improved the simulation of Q during low-flow conditions, as expected, though at the expense of model internal consistency, ET, and TWS representation. We also detected a general deterioration of modelling skills in reproducing Q and ET temporal dynamics, as well as ET and TWS spatial patterns, during droughts for both the model variants. Results call for (i) comprehensive evaluation of the output and states of hydrological models across the whole water balance, rather than only Q, to verify their internal consistency and (ii) the development of alternative calibration procedures to improve the distributed modelling of Q, ET, and TWS during dry periods. This is highly needed to properly predict water availability in the different compartments of the hydrological cycle in a changing climate.

Understanding how deficit of precipitation impacts the hydrological cycle is of growing interest and is essential for water resource management. It has been recently observed that the relationship between precipitation and runoff during droughts is subjected to a shift in the sense that the predicted runoff is much less than the one expected due to the deficit in precipitation. Unraveling why this occurs requires an accurate knowledge of all the components of the water balance equation. However, large-scale and consistent samples of precipitation, runoff, evapotranspiration, ET and change in storage have always been challenging to collect. Here, we hypothesized that blending ground-based and remote-sensing data products could fill this gap. We present a countrywide dataset of catchment-scale water balance, covering the last 10 water years in Italy. Italy shows a broad variety of climatic and topographic features and faced several droughts over recent years. We use ground-based daily runoff data, interpolated precipitation maps, and a remote-sensed daily evapotranspiration dataset from the LSASAF ET product. The ET dataset is additionally compared with flux towers data across the country, obtaining root mean square errors on the order of 30 mm/month. Lastly, changes in storage are estimated to close the water balance. More than 100 catchments - including the major Italian basins - are selected, according to data availability and reliability. These catchments cover a wide range of size, morphologic and climatic characteristics. This dataset is a strategic source of information to analyze catchment-scale runoff, ET and storage response to climatic variability across climates and landscapes.