High-resolution water budget estimates benefit from modeling of human water management and satellite data assimilation (DA) in river basins with a large human footprint. Utilizing the Noah-MP land surface model, in combination with an irrigation module, Sentinel-1 backscatter and snow depth observations, we produce a set of 0.7-km$^2$ digital water budget replicas of the Po river basin (Italy) for 2015-2023. The results demonstrate that irrigation modeling consistently improves the seasonal soil moisture variation and summer streamflow at all gauges in the valley after withdrawal of irrigation water from the streamflow (12\% error reduction relative to observed low summer streamflow), even if the basin-wide irrigation amount is underestimated. Sentinel-1 backscatter DA for soil moisture updating strongly interacts with irrigation modeling: when both are activated, the soil moisture updates are limited, and the simulated irrigation amounts are reduced. Backscatter DA systematically reduces soil moisture in the spring, which improves downstream spring streamflow. Assimilating Sentinel-1 snow depth retrievals over the surrounding Alps and Apennines further improves spring streamflow in a complementary way (2\% error reduction relative to observed high spring streamflow). Despite the seasonal improvements, irrigation modeling and Sentinel-1 backscatter DA cannot significantly improve short-term or interannual variations in soil moisture, irrigation results in a systematically prolonged high vegetation productivity, and snow depth DA only impacts the deep snowpacks. These findings help to advance the design and production of digital water budget replicas for river basins.

Seasonal snow is an important water source and contributor to river discharge in mountainous regions. Therefore the amount of snow and its distribution are necessary inputs for hydrological modeling. However, the distribution of seasonal snow in mountains has long been uncertain, for lack of consistent, high resolution satellite retrievals over mountains. Recent research has shown the potential of the Sentinel-1 radar satellite to map snow depth at sub-kilometer resolution in mountainous regions. In this study we assimilate these new snow depth retrievals into the Noah-Multiparameterization land surface model using an ensemble Kalman filter for the western European Alps. The land surface model was coupled to the Hydrological Modeling and Analysis Platform to provide simulations of routed river discharge. The results show a reduction in the systematic underestimation of snow depth, going from 38 cm for the open loop (OL) to 11 cm for the data assimilation (DA) experiment. The mean absolute error similarly improves from 44 cm to 37 cm with DA, with an improvement at 59% of the in situ sites. The DA updates in snow depth results in enhanced snow water equivalent and discharge simulations. The systematic negative bias in the OL is mostly resolved, and the median temporal correlation between discharge simulations and measurements increases from 0.61 to 0.73 for the DA. Therefore, our study demonstrates the utility of the S1 snow depth retrievals to improve not only snow depth amounts, but also the snow melt contribution to river discharge, and hydrological modeling in general.