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  4. 最佳管理作業(BMPs)最佳化配置之研究 —應用於翡翠水庫集水區
 
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最佳管理作業(BMPs)最佳化配置之研究 —應用於翡翠水庫集水區

Date Issued
2004
Date
2004
Author(s)
仇士愷
DOI
zh-TW
URI
http://ntur.lib.ntu.edu.tw//handle/246246/50311
Abstract
The nonpoint source pollution control is in the beginning stage in Taiwan. The techniques of nonpoint source pollution control, that is Best Management Practices (BMPs), already have several related research. Although the soil and water conservation law provides regulations that the land developer should capture the exceeding stormwater runoff and sediment due to any significant development in the watershed. Most of stormwater controls have been designed at the local, or on-site level, but not at the regional or watershed level in a comprehensive and systematic manner. The purpose of this study is to establish an optimization model for the optimal placement of structural Best Management Practices (BMPs) at the watershed scale. The complete model consists of four interacting components, i.e. a watershed water quality simulation model (Virginia Stormwater model, VAST), a river water quality model (first order decay reaction), a reservoir water quality model (adopted Vollenweider model) and an optimization model base on the dynamic programming (DP). This research takes Fei-Tsui Reservoir for example. In the optimization model, the objective function is to minimize the total cost of BMPs and constraints consider water quality standards for total phosphorus (TP) and total suspended solid (TSS) concentrations. This study simulates the discharge and pollutant loading in 2003. The river water quality model simulates the change of pollutant concentration between subbasins. Also, the reservoir water quality model is embedded to simulate the pollutant concentration variation after the pollutant flow into Fei-Tsui Reservoir. The result can provide the decision makers with a clear and useful way for nonpoint pollution control and management at the watershed scale.
Subjects
非點源污染
水質
最佳管理作業
nps
water quality
BMPs
Type
thesis
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ntu-93-R91521316-1.pdf

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