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Article

Assessing Hydropower Impacts on Flood and Drought Hazards in the Lancang–Mekong River Using CNN-LSTM Machine Learning

1
China Renewable Energy Engineering Institute, Beijing 100120, China
2
Ecosystem Study Commission for International Rivers, China Society for Hydropower Engineering, Beijing 100120, China
3
College of Water Science, Beijing Normal University, Beijing 100875, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Water 2025, 17(15), 2352; https://doi.org/10.3390/w17152352 (registering DOI)
Submission received: 24 June 2025 / Revised: 23 July 2025 / Accepted: 6 August 2025 / Published: 7 August 2025
(This article belongs to the Section Water and Climate Change)

Abstract

The efficient and rational development of hydropower in the Lancang–Mekong River Basin can promote green energy transition, reduce carbon emissions, prevent and mitigate flood and drought disasters, and ensure the sustainable development of the entire basin. In this study, based on publicly available hydrometeorological observation data and satellite remote sensing monitoring data from 2001 to 2020, a machine learning model of the Lancang–Mekong Basin was developed to reconstruct the basin’s hydrological processes, and identify the occurrence patterns and influencing mechanisms of water-related hazards. The results show that, against the background of climate change, the Lancang–Mekong Basin is affected by the increasing frequency and intensity of extreme precipitation events. In particular, Rx1day, Rx5day, R10mm, and R95p (extreme precipitation indicators determined by the World Meteorological Organization’s Expert Group on Climate Change Monitoring and Extreme Climate Events) in the northwestern part of the Mekong River Basin show upward trends, with the average maximum daily rainfall increasing by 1.8 mm/year and the total extreme precipitation increasing by 18 mm/year on average. The risks of flood and drought disasters will continue to rise. The flood peak period is mainly concentrated in August and September, with the annual maximum flood peak ranging from 5600 to 8500 m3/s. The Stung Treng Station exhibits longer drought duration, greater severity, and higher peak intensity than the Chiang Saen and Pakse Stations. At the Pakse Station, climate change and hydropower development have altered the non-drought proportion by −12.50% and +15.90%, respectively. For the Chiang Saen Station, the fragmentation degree of the drought index time series under the baseline, naturalized, and hydropower development scenarios is 0.901, 1.16, and 0.775, respectively. These results indicate that hydropower development has effectively reduced the frequency of rapid drought–flood transitions within the basin, thereby alleviating pressure on drought management efforts. The regulatory role of the cascade reservoirs in the Lancang River can mitigate risks posed by climate change, weaken adverse effects, reduce flood peak flows, alleviate hydrological droughts in the dry season, and decrease flash drought–flood transitions in the basin. The research findings can enable basin managers to proactively address climate change, develop science-based technical pathways for hydropower dispatch, and formulate adaptive disaster prevention and mitigation strategies.
Keywords: hydropower development; machine learning; flood and drought disasters; runoff simulation hydropower development; machine learning; flood and drought disasters; runoff simulation

Share and Cite

MDPI and ACS Style

Zhang, M.; Chi, B.; Gu, H.; Zhou, J.; Chen, H.; Wang, W.; Wang, Y.; Chen, J.; Yang, X.; Zhang, X. Assessing Hydropower Impacts on Flood and Drought Hazards in the Lancang–Mekong River Using CNN-LSTM Machine Learning. Water 2025, 17, 2352. https://doi.org/10.3390/w17152352

AMA Style

Zhang M, Chi B, Gu H, Zhou J, Chen H, Wang W, Wang Y, Chen J, Yang X, Zhang X. Assessing Hydropower Impacts on Flood and Drought Hazards in the Lancang–Mekong River Using CNN-LSTM Machine Learning. Water. 2025; 17(15):2352. https://doi.org/10.3390/w17152352

Chicago/Turabian Style

Zhang, Muzi, Boying Chi, Hongbin Gu, Jian Zhou, Honggang Chen, Weiwei Wang, Yicheng Wang, Juanjuan Chen, Xueqian Yang, and Xuan Zhang. 2025. "Assessing Hydropower Impacts on Flood and Drought Hazards in the Lancang–Mekong River Using CNN-LSTM Machine Learning" Water 17, no. 15: 2352. https://doi.org/10.3390/w17152352

APA Style

Zhang, M., Chi, B., Gu, H., Zhou, J., Chen, H., Wang, W., Wang, Y., Chen, J., Yang, X., & Zhang, X. (2025). Assessing Hydropower Impacts on Flood and Drought Hazards in the Lancang–Mekong River Using CNN-LSTM Machine Learning. Water, 17(15), 2352. https://doi.org/10.3390/w17152352

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