Predicting lake water quality responses to load reduction: a three-dimensional modeling approach for total maximum daily load

Abstract

Water quality restoration efforts often suffer the risk of ineffectiveness and failure due to lack of quantitative decision supports. During the past two decades, the restoration of one of China’s most heavily polluted lakes, Lake Dianchi, has experienced costly decision ineffectiveness with no detectable water quality improvement. The governments are planning to invest tremendous amount of funds in the next 5 years to continue the lake restoration process; however, without a quantitative understanding between the load reduction and the response in lake water quality, it is highly possible that these planned efforts would suffer the similar ineffectiveness as before. To provide scientifically sound decision support for guiding future load reduction efforts in Lake Dianchi Watershed, a sophisticated quantitative cause-and-effect response system was developed using a three-dimensional modeling approach. It incorporates the complex three dimensional hydrodynamics, fate and transport of nutrients, as well as nutrient-algae interactions into one holistic framework. The model results show that the model performs well in reproducing the observed spatial pattern and temporal trends in water quality. The model was then applied to three total maximum daily load scenarios and two refined restoration scheme scenarios to quantify phytoplankton responses to various external load reduction intensities. The results show that the algal bloom in Lake Dianchi responds to load reduction in a complex and nonlinear way, therefore, it is necessary to apply the developed system for future load reduction and lake restoration schemes for more informed decision making and effective management.