Zhang et al. (2026) A global dataset of reservoir in-situ water levels for hydrological and remote sensing applications

Identification

• Journal: Scientific Data
• Year: 2026
• Date: 2026-04-01
• Authors: Mingyang Zhang, Gang Zhao, Chunqiao Song, Zhongyao Liang, Xianhong Xie, Yao Li
• DOI: 10.1038/s41597-026-07091-9

Research Groups

• State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
• University of Chinese Academy of Sciences, Beijing, China
• Department of Global Ecology, Carnegie Institution for Science, Stanford, California, USA
• State Key Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, China
• Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, Xiamen University, Xiamen, China
• State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, China
• School of Geographical Sciences, Southwest University, Chongqing, China

Short Summary

This paper introduces the Global Reservoir Observed Water Levels (GROWL) dataset, a harmonized compilation of 4,134 global reservoir water level time series, to address the critical absence of a unified in-situ dataset for validating and inter-comparing remote sensing algorithms and hydrological models.

Objective

• To develop a unified, large-scale in-situ dataset of global reservoir water levels to overcome the current limitation in robust validation and inter-comparison of remote sensing algorithms and hydrological models.

Study Configuration

• Spatial Scale: Global
• Temporal Scale: Long-term, with most reservoirs having record lengths of 5–40 years (mean of 28 years). Data are provided at daily (77%) and monthly (23%) temporal resolutions.

Methodology and Data

• Models used: A harmonized workflow for unit standardization and multi-stage quality control was applied to compile the dataset. Altimeter-based water level data were incorporated and cross-validated against in-situ observations.
• Data sources: Publicly available in-situ water level and storage time series from global, national, and regional sources; satellite altimetry-derived records; and literature-based records.

Main Results

• The Global Reservoir Observed Water Levels (GROWL) dataset was successfully developed and made openly available.
• The dataset comprises 4,134 long-term time series.
• It includes 3,154 in-situ station records, 973 satellite altimetry–derived records, and 7 literature-based records.
• Approximately 77% of the records are provided at a daily temporal resolution, while 23% are at a monthly resolution.
• The majority of reservoirs in the dataset have record lengths ranging from 5 to 40 years, with an average record length of 28 years.

Contributions

• Provides a crucial and unified benchmark dataset for calibrating and validating satellite-based reservoir monitoring algorithms and hydrological models.
• Supports the development and application of reservoir-related deep learning algorithms.
• Fosters reproducible science and accelerates progress within the Earth observation and water resources communities by filling a significant data gap.

Funding

• Third Xinjiang Scientific Expedition Program (Grant Nos. 2021xjkk0803)
• Chinese Academy of Sciences Pioneer Initiative Talents Program

Citation

@article{Zhang2026global,
  author = {Zhang, Mingyang and Zhao, Gang and Song, Chunqiao and Liang, Zhongyao and Xie, Xianhong and Li, Yao},
  title = {A global dataset of reservoir in-situ water levels for hydrological and remote sensing applications},
  journal = {Scientific Data},
  year = {2026},
  doi = {10.1038/s41597-026-07091-9},
  url = {https://doi.org/10.1038/s41597-026-07091-9}
}