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

Hongxia Li, Yuanyuan Huang, Yongliang Qi, Yanjia Jiang, Xuan Tang, Elizabeth W. Boyer, Carlos R. Mello, Ping Lan and Li Guo ()
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Hongxia Li: Sichuan University
Yuanyuan Huang: Sichuan University
Yongliang Qi: Sichuan University
Yanjia Jiang: Sichuan University
Xuan Tang: Sichuan University
Elizabeth W. Boyer: Pennsylvania State University
Carlos R. Mello: Universidade Federal de Lavras, CP 3037
Ping Lan: Sichuan University
Li Guo: Sichuan University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2024, vol. 38, issue 13, No 1, 4933-4953

Abstract: Abstract 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.

Keywords: Data assimilation; Headwater catchment; Rainfall-runoff models; Soil moisture; Streamflow prediction (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11269-024-03895-9

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