Contributions of Irrigation Modeling, Soil Moisture and Snow Data Assimilation to High‐Resolution Water Budget Estimates Over the Po Basin: Progress Towards Digital Replicas

Abstract

Abstract 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 with dynamic vegetation growth and river routing, in combination with an irrigation module, Sentinel‐1 backscatter and snow depth retrievals, we produce a set of 0.7‐km2 water budget estimates of the Po river basin (Italy) for 2015–2023. The results demonstrate that irrigation modeling improves the seasonal soil moisture variation and summer streamflow at all gauges in the valley after withdrawal of irrigation water from the streamflow in postprocessing (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 modeling causes a systematically prolonged high vegetation productivity, and snow depth DA only impacts the deep snowpacks. This study helps advancing the design of digital water budget replicas for river basins.

Keywords

• digital twin
• data assimilation
• Sentinel‐1
• soil moisture
• snow
• streamflow