Eutrophication Prediction Using a Markov Chain Model: Application to Lakes in the Yangtze River Basin, China

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• 作者：Jiacong Huang ; Junfeng Gao ; Yinjun Zhang
• 关键词：Eutrophication prediction ; Markov chain ; Eutrophication assessment ; Yangtze River Basin
• 刊名：Environmental Modeling & Assessment
• 出版年：2016
• 出版时间：April 2016
• 年：2016
• 卷：21
• 期：2
• 页码：233-246
• 全文大小：1,829 KB
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• 作者单位：Jiacong Huang (1)
  Junfeng Gao (1)
  Yinjun Zhang (2)
  
  1. Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, 73 East Beijing Road, Nanjing, 210008, China
  2. China National Environmental Monitoring Centre, 8 (B) Dayangfang Beiyuan Road, Chaoyang District, Beijing, 100012, China
  
• 刊物类别：Earth and Environmental Science
• 刊物主题：Environment
  Environment
  Mathematical Modeling and IndustrialMathematics
  Applications of Mathematics
  
• 出版者：Springer Netherlands
• ISSN：1573-2967

文摘

Lake eutrophication is harmful and difficult to predict due to its complex evolution. As an alternative to existing mechanistic models, a Markov chain model was developed to predict the development of lake eutrophication based on an 11-year dataset in 41 lakes of the Yangtze River Basin. This model was validated using a real-time update strategy and was demonstrated to be reliable. Based on the dataset, the lake eutrophication dynamics from 2000 to 2010 were analyzed. Lakes with different trophic states from 2011 to 2050 and their responses to different water management practices were simulated based on the developed model. The simulation results show that lake eutrophication would worsen from 2011 to 2040; however, eutrophication could be significantly alleviated by changing 100 km2 of hypereutrophic lakes into eutrophic lakes per year from 2010 to 2020. The nutrient conditions in most of the lakes in the Yangtze River Basin show that phosphorus control would be more efficient than nitrogen control in eutrophication management practices. This case study demonstrates the utility of Markov chain models in using prior information to predict the long-term evolution of lake eutrophication at large spatial scales. The Markov chain technique can be easily adapted to predict evolutionary processes in other disciplines.