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  • 學位論文

系集演算應用於河道輸砂數值模擬之研究

Numerical Simulation of Sediment Transport along Channel with Ensemble Method

指導教授 : 李鴻源

摘要


河道演變與泥砂運動、水動力等機制相關,目前相關研究主要是透過數值模式中的經驗公式搭配動量方程式將複雜水砂運動過程簡化後進行分析。因此,模式中的參數優選便顯得十分重要,不適當的參數在模擬過程將導致模擬結果產生誤差,而長時間的模擬將使誤差不斷累積。過去已有許多研究探討數值模式參數優選之議題,但多為循序檢定單一參數,選定各個優化參數後,其是否為最適合之參數組合鮮少討論。本研究擬透過廣泛運用於颱風路徑預測之系集(Ensemble)概念,用單一模式結合多重參數來進行河道演變的預測,進而提出模擬河段的不同參數組合之適用範圍。本研究以大漢溪流域與北港溪流域為研究範圍,透過美國墾務局所發展之水理輸砂模式(SRH-2D)進行河道模擬。在泥砂運移相關參數中隨機產生輸入值,並篩選較佳參數設定做為系集成員。前者以泥砂濃度作為率定標的,研究成果指出當系集成員數(Ensemble size)為20組時模擬的結果與計算效率最佳,而系集成員數增加至100組時的模擬結果,相對於20組並無明顯優化現象,但卻耗費較長之計算時間。結果顯示20組系集成員所演算之洪峰濃度與發生時間皆落於合理範圍內,顯示此系集演算方法可提供合理的輸砂模擬。後者應用同一概念以底床高程模擬結果為率定標的,結果顯示較佳系集成員數亦為20組,且模擬底床高程變化可適切反應實測底床之變化趨勢。綜上所述,本研究表明系集演算結合單一模式多重參數之率定方法將來應可用來預測河道演變之趨勢,同時不論泥砂濃度或是河道高程的預測,20組系集成員數便可以有效降低模式的不確定性。最後,本研究的模擬流程可供使用者一個簡易且規範化之方法進行水理輸砂的數值演算。

並列摘要


The changing of river channel is related to the mechanisms of sediment transport and hydrodynamic. The numerical modeling with the empirical equations and the simplified momentum equation is the common means to analyze the complicated sediment transport processing in river channels. The optimization of parameters is very important to obtain the accurate results. Improper parameters will cause errors during the simulation process, and will cause the errors accumulation with long-time simulation. Therefore, most of related studies focused on the parameter optimization, and calibrated the specific parameter step by step. They rarely discussed on the optimized parameter combination for the model. This study adopted the ensemble method which is widely used in typhoon route forecasting to simulate the river channel changing with a single model combined with multiple parameters. The optimized parameter combinations for a given river reach was also discussed.Dahan and Beigang river basins were used as study cases to model river morphology through the SRH-2D, which was developed by the U.S. Bureau of Reclamation. The input parameters related to the sediment transport module were randomly selected within a reasonable range. The parameter sets with proper results were selected as ensemble members. The Dahan case used the concentration of sedimentation to do calibration, while the Beigang one chose bed elevation. Compared with single ensemble member, both show that the 20 ensemble members are good enough to obtain the results and save simulation time. However, there were no significant improvement but longer simulation time when the ensemble members increased to 100. The Dahan case showed that the peak concentration and the occurrence time can be predicted by the ensemble size of 20, and can provide a reasonable sediment transport result. The Beigang case considered the bed elevation as the compared target. The result showed this method can quantitatively simulate the bed elevation changing. With both cases, this study showed the ensemble method can be adopted to simulate the river channel. The ensemble size of 20 can effectively obtain the result and reduce the uncertainty for sediment transport simulation. Finally, this study provided another means to do numerical modeling of sedimentation.

參考文獻


1. Abrahart, R. J., & See, L. (2002). Multi-model data fusion for river flow forecasting: an evaluation of six alternative methods based on two contrasting catchments. Hydrology and Earth System Sciences Discussions, 6(4), 655-670.
2. Afan, H. A., El-Shafie, A., Yaseen, Z. M., Hameed, M. M., Mohtar, W. H. M. W., & Hussain, A. (2015). ANN based sediment prediction model utilizing different input scenarios. Water Resources Management, 29(4), 1231-1245.
3. Ajami, N. K., Duan, Q., & Sorooshian, S. (2007). An integrated hydrologic Bayesian multimodel combination framework: Confronting input, parameter, and model structural uncertainty in hydrologic prediction. Water Resources Research, 43(1).
4. Ajami, N. K., Duan, Q., Gao, X., & Sorooshian, S. (2006). Multimodel combination techniques for analysis of hydrological simulations: Application to distributed model intercomparison project results. Journal of Hydrometeorology, 7(4), 755-768.
5. Ajami, N. K., Duan, Q., Moradkhni, H., & Sorooshian, S. (2005). 1.3 RECURSIVE BAYESIAN MODEL COMBINATION FOR STREAMFLOW FORECASTING.

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