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Bridging the gap: An interpretable coupled model (SWAT-ELM-SHAP) for blue-green water simulation in data-scarce basins

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  • Guo, Zhonghui
  • Feng, Chang
  • Yang, Liu
  • Liu, Qing

Abstract

Blue water (BW) and green water (GW) are crucial components of the hydrological cycle, but their accurate simulation and interpretation remain challenging in data-scarce basins. We propose the SWAT-ELM-SHAP model, coupling the Soil and Water Assessment Tool (SWAT), Ensemble Learning Model (ELM), and Shapley Additive Explanations (SHAP) method. This novel approach bridges the gap between a physically-based hydrological model, a data-driven machine learning (ML) model, and a holistically-interpreted SHAP method, offering accurate blue-green water simulation and holistic result interpretation for improved water resources management in data-scarce basins. We took the transfer simulation of blue-green water from the Xiangjiang River Basin (source basin) to the Zishui River Basin (target basin) as a case study to test and evaluate the feasibility of the coupled model during 1991–2022. The model performance results indicate that the simulation accuracy of our new coupled model is improved in data-scarce basins. In combination with hydrological response features generated by SWAT and meteorological features as the ELM input, our model enhances the daily blue-green water simulation. The Nash-Sutcliffe Efficiency coefficient (NSE) for BW, Green water flow (GWF), and Green water storage (GWS) consistently exceeds 0.77 during the calibration period (1991–2010) and exceeds 0.8 during the testing period (2011–2022). The interpretation results of coupled model demonstrate that SHAP holistic interpretation provides good interpretability for blue-green water simulation results in data-scarce basins. In general, the SWAT-ELM-SHAP offers a referenced approach that can reliably and efficiently simulate blue-green water in data-scarce basins, but more importantly, can further our understanding of the potential causal relationships, influence mechanisms, and variation mechanisms of blue-green water under changing environmental conditions.

Suggested Citation

  • Guo, Zhonghui & Feng, Chang & Yang, Liu & Liu, Qing, 2024. "Bridging the gap: An interpretable coupled model (SWAT-ELM-SHAP) for blue-green water simulation in data-scarce basins," Agricultural Water Management, Elsevier, vol. 306(C).
  • Handle: RePEc:eee:agiwat:v:306:y:2024:i:c:s0378377424004931
    DOI: 10.1016/j.agwat.2024.109157
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    References listed on IDEAS

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    1. Jeyrani, F. & Morid, S. & Srinivasan, R., 2021. "Assessing basin blue–green available water components under different management and climate scenarios using SWAT," Agricultural Water Management, Elsevier, vol. 256(C).
    2. Yash Agrawal & Manoranjan Kumar & Supriya Ananthakrishnan & Gopalakrishnan Kumarapuram, 2022. "Evapotranspiration Modeling Using Different Tree Based Ensembled Machine Learning Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(3), pages 1025-1042, February.
    3. Feng, Yu & Cui, Ningbo & Gong, Daozhi & Zhang, Qingwen & Zhao, Lu, 2017. "Evaluation of random forests and generalized regression neural networks for daily reference evapotranspiration modelling," Agricultural Water Management, Elsevier, vol. 193(C), pages 163-173.
    4. Patrick W. Keys & Malin Falkenmark, 2018. "Green water and African sustainability," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 10(3), pages 537-548, June.
    5. Aouissi, Jalel & Benabdallah, Sihem & Lili Chabaâne, Zohra & Cudennec, Christophe, 2016. "Evaluation of potential evapotranspiration assessment methods for hydrological modelling with SWAT—Application in data-scarce rural Tunisia," Agricultural Water Management, Elsevier, vol. 174(C), pages 39-51.
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