Abstract
Limited streamflow observations have constrained long-term hydrological assessment and water resources management in the Amur River Basin, the largest transboundary river in Northeast Asia. To overcome this limitation, we reconstructed two monthly streamflow datasets—representing naturalized and human-influenced conditions—at 6 arcmin (~0.1°) spatial resolution for the period 1902–2022 using the Common Land Model coupled with the CaMa-Flood river routing model. Human-influenced streamflow considers major anthropogenic processes, including land use and land cover change, water withdrawals, and reservoir regulation. Evaluation against data from five major gauge stations shows satisfactory performance, with Nash-Sutcliffe Efficiency (NSE) and Kling-Gupta Efficiency (KGE) generally exceeding 0.50. These datasets not only capture the long-term hydroclimatic variability across the basin, but also reveal the cumulative influence of anthropogenic processes on streamflow magnitude and seasonality. These century-scale, internally consistent datasets provide new opportunities for characterizing the spatial and temporal variability of streamflow, advancing streamflow variability attribution, supporting basin-wide water resources planning, and promoting sustainable management in Northeast Asia.
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Data availability
The reconstructed monthly streamflow datasets for the Amur River are accessible without restrictions in the repository Zenodo under the following link: https://doi.org/10.5281/zenodo.1717281944.
Code availability
Publicly available source codes and manuals for CoLM 2024 and CaMa-Flood v4.20 were downloaded from https://github.com/CoLM-SYSU/CoLM202X and https://hydro.iis.u-tokyo.ac.jp/~yamadai/cama-flood/. Data processing and plotting were performed using Excel and Python. The Python code used for data processing and the water withdrawal script can be accessed through the GitHub repository (https://github.com/DLUT-yfeng/Streamflow_Amur.git).
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Acknowledgements
This research has been supported by the Ministry of Science and Technology of the People’s Republic of China (No. 2023YFF0804900) and the National Natural Science Foundation of China (No. 52479005).
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Yunfei Feng: conceptualization, methodology, data analysis, model implementation, writing–original draft. Yu Li: conceptualization, data analysis, funding acquisition, writing–review and editing. Bingyao Zhang: conceptualization, investigation, resources. Shupeng Zhang: data preprocessing, model implementation. Tongtiegang Zhao: conceptualization, writing–review and editing. Chi Zhang: conceptualization, funding acquisition, writing–review and editing.
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Feng, Y., Li, Y., Zhang, B. et al. Naturalized and human-influenced streamflow of the Amur River for century-scale hydrological assessment. Sci Data (2026). https://doi.org/10.1038/s41597-026-06685-7
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DOI: https://doi.org/10.1038/s41597-026-06685-7
