SMAP soil moisture data assimilation impacts on water quality and crop yield predictions in watershed modeling

Abstract

Model soil moisture predictions are routinely updated with independent observations using data assimilation techniques. While the impacts of these updates on modeled hydrology are well studied, impacts on predicted water quality and crop yield are unresolved. In this study, we evaluated data assimilation effects for a range of predictions in the Soil and Water Assessment Tool (SWAT). We used satellite surface soil moisture data products from the Soil Moisture Active/Passive (SMAP) mission to update SWAT soil moisture for two U.S. experimental watersheds. We addressed possible limitations to the vertical transfer of surface soil moisture updates to deeper layers in the model by additionally testing a modified soil percolation approach that relies on relative saturation rather than the original thresholding behavior of SWAT. Results at both watersheds demonstrated that data assimilation greatly impacted water quality and crop yield predictions. Modifying the soil percolation algorithm, however, did not improve assimilation results. Assimilation increased median soil moisture (+2% to +6 %) which in turn increased total streamflow (+0.43 to +1.70 m3/s daily). Critically, streamflow alone was not a sufficient predictor for the assimilation changes to water quality predictions as flow component contributions and seasonality differed between sites. A varied response in annual water quality predictions to soil moisture updates was identified (+1.70 to +123 kg/ha total nitrogen; −0.11 to −0.57 kg/ha total phosphorous; +8.1 to +50.0 kg/ha sediment) that was dependent upon the timing of the updates, site characteristics, and change to specific transport and transformation processes. Crop yield predictions were similarly impacted by data assimilation from changes to both water and nutrient availability that varied by crop type. The overall strong and diverse response of water quality and crop yield predictions to soil moisture updates provides evidence that assimilation can impact a range of model predictions. Efforts to improve soil moisture simulations, therefore, have potential for meaningful improvements in targeted, non-hydrologic predictions.