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Zou R.,Tetra Tech Inc. | Zou R.,Challenger Limited | Zhu X.,Kunming International Center for Pleantu Lakes | He B.,Kunming International Center for Pleantu Lakes | And 5 more authors.
Huanjing Kexue Xuebao/Acta Scientiae Circumstantiae | Year: 2011

Water quality modeling can provide an effective way to determine permissible waste load and predict consequences of different waste load allocation scenarios, while related uncertainty analysis can provide decision makers useful information regarding the potential risk in decision makings. For Lake Dianchi, the key management problem is to determine how the water quality would respond to the load reduction in the watershed. To support risk based decision making, an analytical response function was derived based on the numerical water quality model and was embedded into a Monte Carlo simulation as a surrogate of the time-consuming simulation model for computational efficiency. Two different levels of uncertainties, 5% and 10% stochastic variability, are considered in this analysis. The model results indicated that under the two uncertainty levels, the load reduction corresponding to Class III and V water quality targets are relatively safer with lower water quality violation risk. On the contrary, the load reduction corresponding to the Class IV water quality target is moderately risky that has higher probability of violating the corresponding water quality targets than the scenarios for Class III and V targets. The Monte Carlo simulation for Chlorophyll-a response showed that the reduction of nutrient can result in significant decrease in chlorophyll-a. However, there is still relatively higher risk of algal bloom even though the water quality target achieves the most aggressive Class III target in Lake Dianchi.

Zou R.,Tetra Technologies Incorporated Company 10306 Eaton Place | Zou R.,Challenger Limited | Dong Y.-X.,Kunming International Center for Pleantu Lakes | Yan X.-P.,Peking University | And 3 more authors.
Huanjing Kexue/Environmental Science | Year: 2011

A water quality model was developed through incorporating the water surface elevation and water quality data of Lake Chenghai into the CE-QUAL-W2 computational platform. The model integrates the water surface elevation and water quality into a holistic dynamic system based on the data of Lake Chenghai, and was calibrated against observed data using a multiple pattern inverse water quality modeling technology, which was driven by a robust genetic algorithm (GA). After the model was calibrated, it was used to produce robust predictions of the lake water quality in response to various water elevation controlled scenarios. The model established a basis for quantifying the water quality responses under uncertainty, and is valuable for supporting effective and reliable management decision making. The results of this research suggest that various water elevation control scenarios only result in insignificant water quality improvement in terms of TN, TP, and COD concentrations, therefore, it does not recommend to consider water elevation control to be the major water quality management option for Lake Chenghai.

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