Distinct hydrologic response patterns and trends worldwide revealed by physics-embedded learning
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910916752179200 |
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| author | Ji, Haoyu Song, Yalan Bindas, Tadd Shen, Chaopeng Yang, Yuan Pan, Ming Liu, Jiangtao Rahmani, Farshid Abbas, Ather Beck, Hylke Lawson, Kathryn Wada, Yoshihide |
| author_facet | Ji, Haoyu Song, Yalan Bindas, Tadd Shen, Chaopeng Yang, Yuan Pan, Ming Liu, Jiangtao Rahmani, Farshid Abbas, Ather Beck, Hylke Lawson, Kathryn Wada, Yoshihide |
| contents | To track rapid changes within our water sector, Global Water Models (GWMs) need to realistically represent hydrologic systems' response patterns - such as baseflow fraction - but are hindered by their limited ability to learn from data. Here we introduce a high-resolution physics-embedded big-data-trained model as a breakthrough in reliably capturing characteristic hydrologic response patterns ('signatures') and their shifts. By realistically representing the long-term water balance, the model revealed widespread shifts - up to ~20% over 20 years - in fundamental green-blue-water partitioning and baseflow ratios worldwide. Shifts in these response patterns, previously considered static, contributed to increasing flood risks in northern mid-latitudes, heightening water supply stresses in southern subtropical regions, and declining freshwater inputs to many European estuaries, all with ecological implications. With more accurate simulations at monthly and daily scales than current operational systems, this next-generation model resolves large, nonlinear seasonal runoff responses to rainfall ('elasticity') and streamflow flashiness in semi-arid and arid regions. These metrics highlight regions with management challenges due to large water supply variability and high climate sensitivity, but also provide tools to forecast seasonal water availability. This capability newly enables global-scale models to deliver reliable and locally relevant insights for water management. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_10707 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Distinct hydrologic response patterns and trends worldwide revealed by physics-embedded learning Ji, Haoyu Song, Yalan Bindas, Tadd Shen, Chaopeng Yang, Yuan Pan, Ming Liu, Jiangtao Rahmani, Farshid Abbas, Ather Beck, Hylke Lawson, Kathryn Wada, Yoshihide Geophysics Machine Learning To track rapid changes within our water sector, Global Water Models (GWMs) need to realistically represent hydrologic systems' response patterns - such as baseflow fraction - but are hindered by their limited ability to learn from data. Here we introduce a high-resolution physics-embedded big-data-trained model as a breakthrough in reliably capturing characteristic hydrologic response patterns ('signatures') and their shifts. By realistically representing the long-term water balance, the model revealed widespread shifts - up to ~20% over 20 years - in fundamental green-blue-water partitioning and baseflow ratios worldwide. Shifts in these response patterns, previously considered static, contributed to increasing flood risks in northern mid-latitudes, heightening water supply stresses in southern subtropical regions, and declining freshwater inputs to many European estuaries, all with ecological implications. With more accurate simulations at monthly and daily scales than current operational systems, this next-generation model resolves large, nonlinear seasonal runoff responses to rainfall ('elasticity') and streamflow flashiness in semi-arid and arid regions. These metrics highlight regions with management challenges due to large water supply variability and high climate sensitivity, but also provide tools to forecast seasonal water availability. This capability newly enables global-scale models to deliver reliable and locally relevant insights for water management. |
| title | Distinct hydrologic response patterns and trends worldwide revealed by physics-embedded learning |
| topic | Geophysics Machine Learning |
| url | https://arxiv.org/abs/2504.10707 |