對於由多個淹水因素引發的淹水事件，淹水因素之間的相依性對洪水風險有極大影響。本研究以統計模型結合淹水數值模擬探究淹水風險，分析不同情境對於淹水風險的影響以及傳統與非傳統淹水頻率分析差異。使用雙、三變量聯結函數來建構大安溪沿海地區31場歷史事件中，雨量、潮位、流量的聯合機率模型，利用最大概似法估計模型參數，並藉由赤池訊息準則與貝葉斯信息準則選取最佳聯合機率模型。本研究共提出四種淹水情境統計模型，分別為(1) 雨量機率分布(Scenario-R)、(2)雨量、潮位聯合機率分布(Scenario-RT)、(3) 雨量、流量聯合機率分布(Scenario-RQ)、(4)雨量、潮位、流量聯合機率分布(Scenario-RTQ)，為了估算4種情境的淹水風險，根據各情境最佳機率分布，採用蒙地卡羅法(Monte Carlo Simulation Method)繁衍1000場淹水變量，作為淹水模擬之輸入條件，根據淹水模擬結果(平均淹水深度)，得知輸入情境為Scenario-RQ與Scenario-RTQ時，淹水深度與歷史淹水深度經驗累積分布(ECDF)具有同質性，其中Scenario-RQ之RMSE為0.0095 m為最合適淹水情境。選擇Scenario-RQ聯合機率分布對研究區域進行傳統淹水聯合重現期分析，另外使用Scenario-RQ模擬的平均淹水深度ECDF計算淹水重現期，兩者進行比較，顯示後者估算的重現期介於傳統淹水重現期交集與聯集情況，代表以聯合重現期分析淹水風險容易偏估。故本研究提出以淹水深度量化淹水發生機率方法，能較準確評估淹水風險，可應用於不同地區，加強複合淹水影響下的淹水風險與災害管理。

For flooding triggered by multiple factors, flood risks are greatly influenced by the correlations among these triggering factors. In this study, copulas were used to describe the joint probability of rainfall (R), storm surge (T), and river discharge (Q) based on 31 historical events in the coastal area of the Daan River, Taiwan. The model parameters were estimated using the maximum likelihood method, and the best-fit marginal and copula functions were selected using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). In this study, the flooding scenarios were analyzed based on 4 statistical models considering different triggering factors: (1) Scenario-R (considering only R), (2) Scenario-RT(considering R & T), (3) Scenario-RQ (considering R & Q) (4) (considering R & T & Q). In order to estimate the flood risk, the Monte Carlo Simulation Method is used to generate 1000 sets of flood variables as input conditions for flood simulation. Based on the results of the flood simulation (average flood depth). The results show that the root mean square error (RMSE) of Scenario-RQ is 0.0095 m, making it the most suitable flooding scenario. The return period of the average flood depth for the Scenario-RQ falls between the joint return periods when R and Q were simultaneously and individually exceeded. This suggests that flood risk will be misestimated by traditional joint probability. The method proposed in this study assess flood risk better and can be applied to different regions to enhance flood risk under compound flood impacts.

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