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Incorporating Catchment Attributes Grouping into Model Parameter Regionalization To Enhance Root Zone Soil Moisture Estimation

Author

Listed:
  • Hongxia Li

    (Sichuan University)

  • Yuting Zhao

    (Sichuan University)

  • Yongliang Qi

    (Sichuan University)

  • Yanjia Jiang

    (Sichuan University)

  • Elizabeth W. Boyer

    (Pennsylvania State University)

  • Carlos R. Mello

    (Universidade Federal de Lavras)

  • Li Guo

    (Sichuan University)

Abstract

Accurate prediction of root zone soil moisture (RZSM) is critical for advancing hydrological modeling and water cycle characterization. To improve RZSM estimation in ungauged regions and elucidate the role of catchment attributes in RZSM dynamics in time and space, this study proposed a novel regionalization framework that integrates catchment attribute classification with surface soil moisture (SSM) similarity metrics. We investigate the viability of extrapolating RZSM data from gauged to ungauged catchments, with emphasis on the adaptability of the Soil Moisture Analytical Relationship (SMAR) model and the influence of catchment attributes on prediction performance. The results show that the calibrated SMAR model effectively simulates RZSM patterns, achieving a mean root mean square error (RMSE) of 0.040 cm³/cm³ for the validation periods. Additionally, the results reveal significant disparities between SSM and RZSM dynamics across the catchment, underscoring the pronounced influence of catchment attributes on SSM-RZSM coupling. Notably, parameter regionalization strategies combining catchment attribute-based site grouping, including topographic wetness index (TWI), soil depth, and leaf area index (LAI), produced more accurate RZSM predictions (mean RMSE = 0.081 cm³/cm³) than results from relying solely on SSM similarity (mean RMSE = 0.145 cm³/cm³). The superior performance of TWI-based groupings highlights topography’s essential role in modulating nonlinear SSM-RZSM relationships. These insights underscore the interdependence between soil moisture dynamics and catchment attributes in headwater catchments, illustrating the value of catchment physiographic features in constraining predictive uncertainty for RZSM in ungauged regions.

Suggested Citation

  • Hongxia Li & Yuting Zhao & Yongliang Qi & Yanjia Jiang & Elizabeth W. Boyer & Carlos R. Mello & Li Guo, 2025. "Incorporating Catchment Attributes Grouping into Model Parameter Regionalization To Enhance Root Zone Soil Moisture Estimation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 39(9), pages 4317-4334, July.
    • Handle: RePEc:spr:waterr:v:39:y:2025:i:9:d:10.1007_s11269-025-04156-z
    DOI: 10.1007/s11269-025-04156-z
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    References listed on IDEAS

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    1. George Panagopoulos & George Bathrellos & Hariklia Skilodimou & Faini Martsouka, 2012. "Mapping Urban Water Demands Using Multi-Criteria Analysis and GIS," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(5), pages 1347-1363, March.
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    3. Hongxia Li & Yuanyuan Huang & Yongliang Qi & Yanjia Jiang & Xuan Tang & Elizabeth W. Boyer & Carlos R. Mello & Ping Lan & Li Guo, 2024. "Improved Streamflow Simulation by Assimilating In Situ Soil Moisture in Lumped and Distributed Approaches of a Hydrological Model in a Headwater Catchment," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(13), pages 4933-4953, October.
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