Assimilation of NASA's Airborne Snow Observatory Snow Measurements for Improved Hydrological Modeling: A Case Study Enabled by the Coupled LIS/WRF‐Hydro System. Issue 3 (24th March 2022)

• Record Type: Journal Article
• Authors: Lahmers, Timothy M.; Kumar, Sujay V.; Rosen, Daniel; Dugger, Aubrey; Gochis, David J.; Santanello, Joseph A.; Gangodagamage, Chandana; Dunlap, Rocky
• Is Part Of: Water resources research. Volume 58:Issue 3(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-03-24
• Subjects: hydrologic modeling -- data assimilation -- snow hydrology; Hydrology -- Periodicals; 333.91
• DOI: 10.1029/2021WR029867
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: The NASA LIS/WRF‐Hydro system is a coupled modeling framework that combines the modeling and data assimilation (DA) capabilities of the NASA Land Information System (LIS) with the multi‐scale surface hydrological modeling capabilities of the WRF‐Hydro model, both of which are widely used in both operations and research. This coupled modeling framework builds on the linkage between land surface models (LSMs), which simulate surface boundary conditions in atmospheric models, and distributed hydrologic models, which simulate horizontal surface and sub‐surface flow, adding new land DA capabilities. In the present study, we employ this modeling framework in the Tuolumne River basin in central California. We demonstrate the added value of the assimilation of NASA Airborne Snow Observatory (ASO) snow water equivalent (SWE) estimates in the Tuolumne basin. This analysis is performed in both LIS as an LSM column model and LIS/WRF‐Hydro, with hydrologic routing. Results demonstrate that ASO DA in the basin reduced snow bias by as much as 30% from an open‐loop (OL) simulation compared to three independent datasets. It also reduces downstream streamflow runoff biases by as much as 40%, and improves streamflow skill scores in both wet and dry years. Analysis of soil moisture and evapotranspiration (ET) also reveals the impacts of hydrologic routing from WRF‐Hydro in the simulations, which would otherwise not be resolved in an LSM column model. By demonstrating the beneficial impact of SWE DA on the improving streamflow forecasts, the article outlines the importance of such observational inputs for reservoir operations and related water management applications. Plain Language Summary: Land surface models are useful because of their ability to resolve surface‐atmosphere feedbacks, including those with vegetation. Land surface models also have the capability to assimilate surface observations, usually measured through remote sensing techniques, into the model. Hydrologic models have the strength of resolving horizontal movements of water both on the surface and through the sub‐surface. In the present study, we combine the data‐assimilation capabilities of the NASA‐LIS land surface model with the WRF‐Hydro hydrologic model to combine the utility of both systems. We use this new system to demonstrate the impact of assimilating snow water equivalent, measured from an aircraft, on both the land surface and streamflow from the model in the Tuolumne River basin in Central California. Results show that assimilation of snow water equivalent into the coupled model corrects snow errors and improves the streamflow in both wet and dry years. We find that hydrologic processes that are now added to the land surface model impact simulated soil moisture and evapotranspiration. These findings are important because the ability for a model to better resolve streamflow, from snow assimilation, could be beneficial for water management. Key Points: Snow water equivalent data‐assimilation improves hydrologic response in the coupled LIS/WRF‐Hydro model for a case study in the Tuolumne River basin Horizontal surface routing increases soil moisture and evapotranspiration downstream near channels in LIS/WRF‐Hydro, compared to an LSM‐only simulation