應用模糊理論分析降雨空間變異性於非點源污染之模擬

Abstract

在流域中河川流量、土壤沖刷和河川水質等都和降雨量有密切之關係，所以降雨資料是水資源管理之重要的輸入因子。本研究使用模糊理論結合熵理論( Entropy theory )分析降雨資料，以雨量站之水平距離和高程差值做為流域各點之降雨量權重值，將所模擬的降雨資料和降雨站實際值進行比較，並藉由模擬之降雨補遺資料載入 BASINS（Better Assessment Science Integrating point & Non-point Sources）系統中以模擬流量及非點源污染量。由於推估降雨量都是由降雨站所獲得的資料來估算其降雨量，往往因為降雨站的數量及位置是影響到推估的準確性，因此降雨資料是推估雨量的重要參數，而降雨具有不確定性，所以正確評估出降雨的資料是必要的。但是在有限的資料來推估降雨的空間變異性，必須運用模式模擬，所以本研究運用模糊理論來解決降雨的不確定性，以降低降雨推估的誤差，將能更有效率地規劃水資源管理策略。正確降雨量資料可以提供完整的水文分析資料，對地區水資源決策與污染規劃設計上有很大的助益。

Rainfall plays an important role in watershed management; it affects the stream flow, soil erosion and water quality in the watershed. The objective of this study was to improve the use of a fuzzy model to supplement rainfall data when the watershed’s meteorological station is either far away or suffers a loss of rainfall data. The fuzzy model can also supplement rainfall data when the station is nearby. The accuracy of rainfall data in the EPA’s BASINS (Better Assessment Science Integrating Point and Nonpoint Sources) decision support tool is affected by the sparse meteorological data contained in BASINS. This study also assessed the improvement in stream flow prediction with the BASINS. This study demonstrates that using a fuzzy model to supplement rainfall data has the potential to improve stream flow predictions, thus aiding water quality assessment in the nonpoint water quality assessment decision tool.