During the past years, we have been working on the eutrophication modeling and decision making support, including: 

a) Exploring Eutrophication Susceptibility to Nutrient Loading

The susceptibility of estuaries/lakes to nutrient loading is an important issue that cuts across a range of management needs. Under the support of U.S. National Oceanic and Atmospheric Administration (NOAA) and NSFC, we used a theory-driven but data-tested simple model to assist classifying estuaries according to their susceptibility to nutrients. The model performed remarkably well and the model analysis provides a first-order screening tool for estuarine susceptibility classification and can be viewed as a simple physical index for assessment of estuarine eutrophication vulnerability. We also conducted studies on two U.S. estuaries, Chesapeake Bay and Gulf of Mexico and a plateau lake in southwestern China, Lake Yilong, to explore the temporal increasing eutrophication susceptibility of the targeted estuaries/lake to watershed nutrient loading. The modeling results showed that, for the two estuaries, there was a two-stepwise increase in system sensitivity during the period of record. This change in sensitivity has greatly increased the nutrient reduction needed to achieve the established hypoxia goal. The modeling result of Lake Yilong proved that human disturbance is the key factor triggering the increase of eutrophication sensibility and the disastrous change of the lake status from grass-type clean water into the algae-type turbid water.

b) Developing Optimal Decision Making Modeling on Watershed Load Reduction under Uncertainty.

It is essential to identify optimal nutrient management strategies for water quality restoration. Previous optimization-based watershed decision making approaches suffer two major limitations on uncertainties and absence of adaptive management. We have presented a guided adaptive optimal (GAO) decision making approach to overcome the limitations of the previous methods for more efficient and reliable decision making at the watershed scale. In the following studies, we have proposed a nonlinearity interval mapping scheme (NIMS) to overcome the computational barrier of applying the simulation-optimization approach for a waste load allocation analysis. The case studies showed that the computational efficiency of NIMS is 20%~50% higher than the current popularly used algorithms (such as genetic algorithm). NIMS is possible for extensive watershed and decision control as it can quantize systematic mechanism response process of watershed and the uncertainty and risks in decision making with high accuracy, high computational efficiency and robustness.

c) Decision Supporting for Watershed Nutrient Load Reduction on Eutrophication Control of Lake Dianchi. 

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. Without a quantitative understanding between the load reduction and the response in lake water quality, it is highly possible that the following 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. The results show that the algal bloom in Lake Dianchi responds to load reduction in a complex and nonlinear way. Therefore, significant watershed pollutant reduction would be required even to achieve the lowest level of water quality targets (Class V), suggesting that a phased approach should be adopted to set a site-specific water quality target. Complete compliance at the highest target level (Class III) would require approximately 80% loading reduction, which seems infeasible based on available management technologies.