Comprehensive assessment of cascading dams-induced hydrological alterations in the lancang-mekong river using machine learning technique.

Abstract

The development of cascading hydropower dams in river basins has significantly altered natural flow regimes in recent decades. This study investigates hydrological alterations caused by cascading hydropower dams in the Lancang-Mekong River Basin (LMRB) by integrating the Indicators of Hydrologic Alteration (IHA) method with non-regulated flow predicted using the Random Forest (RF) machine learning (ML) technique. The analysis focuses on four hydrological stations: Chiang Saen, Mukdahan, Pakse, and Stung Treng across pre-impact (1961-1991), transition (1992-2008), and post-impact (2009-2021) periods. RF models predict streamflow altered primarily by human activities, particularly hydropower development using spatially averaged daily precipitation, cumulative precipitation, and temperature data. This approach isolates human-induced impacts, unlike previous studies that combined climate change and human activities. Results indicate that post-impact alterations were most pronounced at Chiang Saen (77.3%) and decreased downstream. Alterations increased from the transition to post-impact periods by 31.5%, 22.2%, 26.5%, and 17.6% at Chiang Saen, Mukdahan, Pakse, and Stung Treng, respectively. The median monthly flow, annual extreme conditions, and rate and frequency of flow change groups in the IHA were highly altered compared to natural conditions in the post-impact period. Human activities contributed over 50% of streamflow changes (2017-2021). This approach provides a more precise assessment of dam-induced hydrological alterations, aiding hydropower management by isolating human impacts and offering high-resolution predictions.