Improved Streamflow Simulation by Assimilating In Situ Soil Moisture in Lumped and Distributed Approaches of a Hydrological Model in a Headwater Catchment

• Published in: Water resources management Vol. 38; no. 13; pp. 4933 - 4953
• Main Authors: Li, Hongxia, Huang, Yuanyuan, Qi, Yongliang, Jiang, Yanjia, Tang, Xuan, Boyer, Elizabeth W., Mello, Carlos R., Lan, Ping, Guo, Li
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.10.2024; Springer Nature B.V
• Subjects: Atmospheric Sciences; Catchment area; Catchments; Civil Engineering; Cold season; Data collection; Deep layer; Earth and Environmental Science; Earth Sciences; Effectiveness; Environment; Geotechnical Engineering & Applied Earth Sciences; Headwaters; Hydrogeology; Hydrologic models; Hydrology; Hydrology/Water Resources; Kalman filters; Moisture content; Mountain regions; Mountainous areas; Predictions; Rainfall-runoff relationships; Remote sensing; Satellite observation; Seasonal variation; Seasonal variations; Soil; Soil improvement; Soil moisture; Spacecraft recovery; Stream discharge; Stream flow; Streamflow prediction; Rainfall-runoff models; Headwater catchment; Soil moisture; Data assimilation
• ISSN: 0920-4741; 1573-1650
• DOI: 10.1007/s11269-024-03895-9

Soil moisture data assimilation (SM-DA) is a valuable approach for enhancing streamflow prediction in rainfall-runoff models. However, most studies have focused on incorporating remotely sensed SM, and their results strongly depend on the quality of satellite products. Compared with remote sensing products, in situ observed SM data provide greater accuracy and more effectively capture temporal fluctuations in soil moisture levels. Therefore, the effectiveness of SM-DA in improving streamflow prediction remains site-specific and requires further validation. Here, we employed the Ensemble Kalman filter (EnKF) to integrate daily SM into lumped and distributed approaches of the Xinanjiang (XAJ) hydrological model to assess the importance of SM-DA in streamflow prediction. We observed a general improvement in streamflow prediction after conducting SM-DA. Specifically, the Nash-Sutcliffe efficiency increased from 0.61 to 0.65 for the lumped and from 0.62 to 0.70 for the distributed approaches. Moreover, the efficiency of SM-DA exhibits seasonal variation, with in situ SM proving particularly valuable for streamflow prediction during the wet-cold season compared to the dry-warm season. Notably, daily SM data from deep layers exhibit a stronger capability to improve streamflow prediction compared to surface SM. This indicates the significance of deep SM information for streamflow prediction in mountain areas. Overall, this study effectively demonstrates the efficacy of assimilating SM data to improve hydrological models in streamflow prediction. These findings contribute to our understanding of the connection between SM, streamflow, and hydrological connectivity in headwater catchments.