China Grid-level Natural Streamflow Estimates
based on Differentiable Parameter Learning (CHASE-dPL)
基于可微参数学习的
全国 1 km 分辨率天然径流估算数据集（CHASE-dPL）
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This dataset is derived from model simulations, for research use only.
本数据集为模型模拟值，仅限用于学术研究。
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1 km
{{ currentLang === 'en' ? 'Streamflow resolution' : '河道流量分辨率' }}
~1,200
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0.66
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0.81
{{ currentLang === 'en' ? 'Monthly median NSE' : '月尺度 NSE 中位数' }}
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{{ currentLang === 'en' ? 'CHASE-dPL: Gauge and River Network Data' : 'CHASE-dPL 站点与河网数据' }} Live
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- streamflow {{ t.var_flow }}
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- Due to non-negligible discrepancies between local gauges and CMFD2 precipitation forcing used for Taiwan, streamflow estimates are not reliable in Taiwan Province. This will be fixed in a future release.
- 由于在台湾省使用的 CMFD2 降水驱动数据与当地雨量站观测之间存在不可忽略的差异，当前版本台湾省的天然河道流量估算结果不可信。该问题将在后续版本中修正。
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- V1.0 (September 2026): First published.
- V1.0（2026 年 9 月）：首次发布。
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Dong, N., et al. (2026). Enhancing National High-Resolution Distributed Hydrological Modeling with Physics-Encoded Differentiable Regionalization. ESS Open Archive preprint.
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{{ t.contact }} Ningpeng Dong / 董宁澎 (dongnp@iwhr.com).