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

Authors: PIGNOTTI, GARETT - Purdue University; CRAWFORD, MELBA - Purdue University; HAN, EUNJIN - Columbia University; Williams, Mark; CHAUBEY, INDRAJEET - University Of Connecticut

Submitted to: Journal of Hydrology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/12/2023
Publication Date: 1/17/2023
Citation: Pignotti, G., Crawford, M., Han, E., Williams, M.R., Chaubey, I. 2023. SMAP soil moisture data assimilation on water quality and crop yield predictions in watershed modeling. Journal of Hydrology. 617(C). Article: 129122. https://doi.org/10.1016/j.jhydrol.2023.129122.
DOI: https://doi.org/10.1016/j.jhydrol.2023.129122

Interpretive Summary: Soil moisture can have a large impact on both water quality , water quantity, and crop yield, but accurately measuring and modeling soil moisture is challenging. In this study, we used satellite surface soil moisture data products from the Soil Moisture Active/Passive (SMAP) mission to update soil moisture in the Soil and Water Assessment Tool (SWAT) for two watersheds in the U.S. (Indiana and Georgia) and test different approaches on water movement through the soil profile within the model. Utilizing the satellite soil moisture data increased soil moisture ((+2% to +6%) which in turn increased total streamflow (+0.43 to +1.70 m3/s daily) in the model. Water quality (e.g., nitrogen, phosphorus, and sediment loads) and crop yield predictions were also impacted by the use of the soil moisture data, but varied considerably depending on site characteristics, crop type, and other factors. The variable response of water quality and crop yield predictions to soil moisture updates provides evidence that data assimilation can impact a range of model outputs; thus, improved soil moisture data collection and simulation have the potential for meaningful improvements in targeted, non-hydrologic predictions.

Technical Abstract: Model simulations are routinely used to evaluate and predict impacts to ecosystems from land management actions. These predictions rely on capability to accurately measure and estimate key environmental variables, such as soil moisture. Soil moisture is impacted by complex processes including both the physics of the system and dynamic environmental processes. Predictions, which are impacted by uncertainties in both models and measurements, are often updated via data assimilation, where independent measurements are optimally combined with model estimates. The impact of these soil moisture updates on modeled water quality and crop yield predictions is, however, not well understood. 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, although it outperformed the original model for baseline simulations. 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.