title: Quantitative evaluation of lake eutrophication responses under alternative water diversion scenarios: a water quality modeling based statistical analysis approach.
abstract: China is confronting the challenge of accelerated lake eutrophication, where Lake Dianchi is considered as the most serious one. Eutrophication control for Lake Dianchi began in the mid-1980s. However, decision makers have been puzzled by the lack of visible water quality response to past efforts given the tremendous investment. Therefore, decision makers desperately need a scientifically sound way to quantitatively evaluate the response of lake water quality to proposed management measures and engineering works. We used a water quality modeling based scenario analysis approach to quantitatively evaluate the eutrophication responses of Lake Dianchi to an under-construction water diversion project. The primary analytic framework was built on a three-dimensional hydrodynamic, nutrient fate and transport, as well as algae dynamics model, which has previously been calibrated and validated using historical data. We designed 16 scenarios to analyze the water quality effects of three driving forces, including watershed nutrient loading, variations in diverted inflow water, and lake water level. A two-step statistical analysis consisting of an orthogonal test analysis and linear regression was then conducted to distinguish the contributions of various driving forces to lake water quality. The analysis results show that (a) the different ways of managing the diversion projects would result in different water quality response in Lake Dianchi, though the differences do not appear to be significant; (b) the maximum reduction in annual average and peak Chl-a concentration from the various ways of diversion project operation are respectively 11% and 5%; (c) a combined 66% watershed load reduction and water diversion can eliminate the lake hypoxia volume percentage from the existing 6.82% to 3.00%; and (d) the water diversion will decrease the occurrence of algal blooms, and the effect of algae reduction can be enhanced if diverted water are seasonally allocated such that wet season has more flows.
authors.affiliation: College of Environmental Science and Engineering, The Key Laboratory of Water and Sediment Sciences Ministry of Education, Peking University, Beijing 100871, China.
authors.forename: Yong
authors.initials: Y
authors.lastname: Liu
authors.forename: Yilin
authors.initials: Y
authors.lastname: Wang
authors.forename: Hu
authors.initials: H
authors.lastname: Sheng
authors.forename: Feifei
authors.initials: F
authors.lastname: Dong
authors.forename: Rui
authors.initials: R
authors.lastname: Zou
authors.forename: Lei
authors.initials: L
authors.lastname: Zhao
authors.forename: Huaicheng
authors.initials: H
authors.lastname: Guo
authors.forename: Xiang
authors.initials: X
authors.lastname: Zhu
authors.forename: Bin
authors.initials: B
authors.lastname: He
mesh_terms: D002681:China; D005068:Eutrophication / Q000502:physiology*; D060106:Lakes / Q000737:chemistry / Q000382:microbiology*; D016014:Linear Models; D008954:Models, Biological; D014872:Water Movements; D014876:Water Pollution / Q000517:prevention & control*; D060753:Water Quality / Q000592:standards*
keywords: Eutrophication response; Lake Dianchi; Linear regression; Orthogonal test; Scenario analysis; Water diversion