A water quality prediction approach for the Downstream and Delta of Dongjiang River Basin under the joint effects of water intakes, pollution sources, and climate change

• Published in: Journal of hydrology (Amsterdam) 2024-08, Vol.640, p.131686, Article 131686
• Main Authors: Huang, Yaping, Cai, Yanpeng, He, Yanhu, Dai, Chao, Wan, Hang, Guo, Hongjiang
• Format: Article
• Language: English
• Subjects: Climate change; General likelihood uncertainty estimation; Uncertainty; Water intakes; Water Quality Analysis Simulation Program (WASP); Water quality prediction
• ISSN: 0022-1694
• DOI: 10.1016/j.jhydrol.2024.131686

•Developing novel multiple parameters and pollution sources estimation method (MPSIE);•A new multi-scenario water quality prediction method (MEWQP) based on MPSIE;•Quantifying joint impacts of water intakes, pollution sources, and climate change;•Without intake can improve water quality and offset the impact of climate change;•An optimized scheme of water intake could improve dissolved oxygen by 6%. Water quality prediction is an important means for scientific water environment management and early warning measures. However, its reliability is often restricted by the uncertainties of parameters and pollution sources. Previous methods for resolving these uncertainties still have shortcomings due to low computational efficiency and incomplete coverage of mechanisms. With insufficient data, they also overlooked revealing the comprehensive impact of water intakes, pollution sources, and climate change on water quality in river networks. Insight understanding migration law and the main driving mechanisms under these factors is crucial for water resources and sustainable socioeconomic development. Here, we developed an efficient multi-scenario ensemble water quality prediction (MEWQP) approach to address the deficiency. The Downstream and Delta of Dongjiang River Basin was the demonstrative case. Firstly, we developed multiple parameters and pollution sources inverse estimation method by incorporating the Water Quality Analysis Simulation Program (WASP), general likelihood uncertainty estimation, and inverse methods to improve accuracy. Secondly, the Markov-Chain was used to predict flows affected by climate change. Thirdly, 60 scenarios were analyzed to quantify the impacts of multiple factors on water quality including 5-day biochemical oxygen demand (BOD5), ammonia nitrogen (NH3-N), and dissolved oxygen (DO), and identify an optimized scheme of water intakes. Results show that: (1) WASP has good performance for BOD5, NH3-N, and DO with Mean Absolute Relative Error of 5.87%, 9.19%, and 15.25%, respectively; (2) pollution sources have the greatest impact, followed by extreme climate and intakes. Their combined impacts are greater than individuals; (3) under non-water intakes DO can be improved by 31%, also offset the impact of climate change by 16%; (4) the optimized scheme of intakes could improve DO by 6%. Therefore, decision-makers should simultaneously adjust water intake and reduce pollution sources to obtain high-quality water resources.