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<p>Reservoirs are fundamental to global water security, and their monitoring increasingly relies on satellite remote sensing. However, the absence of a unified, large-scale <i>in-situ</i> dataset has hindered robust validation and inter-comparison of remote sensing algorithms. To address this gap, we developed the Global Reservoir Observed Water Levels (GROWL) dataset by systematically compiling publicly available <i>in-situ</i> water level and storage time series from global, national, and regional sources. All records were processed through a harmonized workflow involving unit standardization and multi-stage quality control. To enhance spatial coverage, altimeter-based water level data were incorporated and cross-validated against <i>in-situ</i> observations. The final GROWL dataset comprises 4,134 long-term time series, including 3,154 <i>in-situ</i> station records, 973 satellite altimetry–derived records, and 7 literature-based records. Among these, 77% are provided at daily temporal resolution and 23% at monthly resolution. Most reservoirs have record lengths of 5–40 years (with a mean of 28 years). GROWL provides a crucial benchmark for calibrating and validating satellite-based reservoir monitoring algorithms and hydrological models, as well as for supporting reservoir-related deep learning algorithms, thereby fostering reproducible science and accelerating progress in the Earth observation and water resources communities.</p>

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A global dataset of reservoir in-situ water levels for hydrological and remote sensing applications

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月1日

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  • Mingyang Zhang,
  • Gang Zhao,
  • Chunqiao Song,
  • Zhongyao Liang,
  • Xianhong Xie,
  • Yao Li

摘要

Reservoirs are fundamental to global water security, and their monitoring increasingly relies on satellite remote sensing. However, the absence of a unified, large-scale in-situ dataset has hindered robust validation and inter-comparison of remote sensing algorithms. To address this gap, we developed the Global Reservoir Observed Water Levels (GROWL) dataset by systematically compiling publicly available in-situ water level and storage time series from global, national, and regional sources. All records were processed through a harmonized workflow involving unit standardization and multi-stage quality control. To enhance spatial coverage, altimeter-based water level data were incorporated and cross-validated against in-situ observations. The final GROWL dataset comprises 4,134 long-term time series, including 3,154 in-situ station records, 973 satellite altimetry–derived records, and 7 literature-based records. Among these, 77% are provided at daily temporal resolution and 23% at monthly resolution. Most reservoirs have record lengths of 5–40 years (with a mean of 28 years). GROWL provides a crucial benchmark for calibrating and validating satellite-based reservoir monitoring algorithms and hydrological models, as well as for supporting reservoir-related deep learning algorithms, thereby fostering reproducible science and accelerating progress in the Earth observation and water resources communities.