A Hybrid Runoff Forecasting Framework Integrating Hydrological Physics and Data-Driven Models Article Swipe
Muzi Zhang
,
Tian Yao
,
Hongbin Gu
,
Weiwei Wang
,
Pan L
,
Huanghe Gu
,
Ying Pei
,
Baohong Lu
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/su172411120
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/su172411120
Runoff forecasting is essential for flood control, disaster mitigation, and sustainable water resources management. However, runoff processes are highly nonlinear and uncertain due to multiple interacting meteorological and underlying surface factors. Current models can be divided into process-driven and data-driven types. The former offers clear physical interpretability but involves complex calibration and simplifications, while the latter captures nonlinear relationships effectively but lacks physical consistency. To integrate their strengths, this study constructs process-based models and data-driven models, and proposes two hybrid strategies: (1) incorporating intermediate variables from physical models, such as soil moisture and runoff yield, as additional features for data-driven models, and (2) embedding physics-based constraints and synthetic data into loss functions. Using the Songxi River Basin as a case study, results show that both hybrid strategies significantly outperform standalone models. SHapley Additive exPlanations (SHAP)-based interpretability analysis further reveals the contribution mechanisms of key physical variables. This study demonstrates that coupling physical processes with data-driven learning effectively enhances runoff forecasting accuracy and offers a promising paradigm to support sustainable watershed management, climate-resilient water regulation, and flood risk reduction.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/su172411120
- https://www.mdpi.com/2071-1050/17/24/11120/pdf?version=1765465297
- OA Status
- gold
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4417244447Canonical identifier for this work in OpenAlex
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https://doi.org/10.3390/su172411120Digital Object Identifier
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A Hybrid Runoff Forecasting Framework Integrating Hydrological Physics and Data-Driven ModelsWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-12-11Full publication date if available
- Authors
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Muzi Zhang, Tian Yao, Hongbin Gu, Weiwei Wang, Pan L, Huanghe Gu, Ying Pei, Baohong LuList of authors in order
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https://doi.org/10.3390/su172411120Publisher landing page
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https://www.mdpi.com/2071-1050/17/24/11120/pdf?version=1765465297Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2071-1050/17/24/11120/pdf?version=1765465297Direct OA link when available
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0Total citation count in OpenAlex
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| abstract_inverted_index.constructs | 70 |
| abstract_inverted_index.functions. | 111 |
| abstract_inverted_index.mechanisms | 141 |
| abstract_inverted_index.outperform | 128 |
| abstract_inverted_index.reduction. | 177 |
| abstract_inverted_index.standalone | 129 |
| abstract_inverted_index.strategies | 126 |
| abstract_inverted_index.strengths, | 67 |
| abstract_inverted_index.underlying | 28 |
| abstract_inverted_index.variables. | 145 |
| abstract_inverted_index.calibration | 50 |
| abstract_inverted_index.constraints | 105 |
| abstract_inverted_index.data-driven | 39, 74, 99, 154 |
| abstract_inverted_index.effectively | 59, 156 |
| abstract_inverted_index.forecasting | 1, 159 |
| abstract_inverted_index.interacting | 25 |
| abstract_inverted_index.management, | 170 |
| abstract_inverted_index.management. | 13 |
| abstract_inverted_index.mitigation, | 8 |
| abstract_inverted_index.regulation, | 173 |
| abstract_inverted_index.strategies: | 80 |
| abstract_inverted_index.sustainable | 10, 168 |
| abstract_inverted_index.(SHAP)-based | 134 |
| abstract_inverted_index.consistency. | 63 |
| abstract_inverted_index.contribution | 140 |
| abstract_inverted_index.demonstrates | 148 |
| abstract_inverted_index.exPlanations | 133 |
| abstract_inverted_index.intermediate | 83 |
| abstract_inverted_index.incorporating | 82 |
| abstract_inverted_index.physics-based | 104 |
| abstract_inverted_index.process-based | 71 |
| abstract_inverted_index.relationships | 58 |
| abstract_inverted_index.significantly | 127 |
| abstract_inverted_index.meteorological | 26 |
| abstract_inverted_index.process-driven | 37 |
| abstract_inverted_index.interpretability | 46, 135 |
| abstract_inverted_index.simplifications, | 52 |
| abstract_inverted_index.climate-resilient | 171 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 8 |
| citation_normalized_percentile |