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Predicting lake water quality responses to load reduction: a three-dimensional modeling approach for total maximum daily load
Wang, Z.; Zou, R.; Zhu, X.; He, B.; Yuan, G.; Zhao, L. & Liu, Y.
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.
Keywords
Algae bloom; Water quality modeling; Scenario analysis; Total maximum daily load; Lake Dianchi
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