本研究採用極限學習機（extreme learning machine）輔以系集平均（ensemble mean）方法，建立臺灣南部主要集水區的連續型降雨逕流模式。為能推估氣候變遷情境下的長期連續日流量，連續型降雨逕流模式之輸入變量僅為日雨量及日溫度。本研究並探討變量的不同時間階數組合、資料預處理方法、極限學習機神經元個數、季節劃分等，建立最適合臺灣南部集水區的極限學習機降雨逕流模式。結果顯示極限學習機能有效推估集水區逕流量。本研究使用分位數增量映射（quantile delta mapping）與簡易尺度轉換（simple scaling）進行未來情境流量的偏差校正，分析氣候變遷情境（SSP2-4.5與SSP5-8.5）下未來短期（2021至2040年）與未來中期（2041至2060年）流量變化，並與基期（1995至2014年）之模擬結果比較，了解在氣候變遷情境下臺灣南部流量增減狀況。研究結果顯示臺灣南部主要集水區之氣候變遷情境總流量約為減少20%至增加30%之間，其中流量在枯水期減少，於豐水期增加，單日流量也將趨於極端。本研究以極限學習機建立之連續型降雨逕流模式，合理推估集水區逕流量之變化趨勢，並有效模擬氣候變遷下的流量變動。

This study utilized the extreme learning machine to develop a continuous rainfall–runoff model for simulating streamflow in southern Taiwan under climate change scenarios. Due to the focus on streamflow projections under these scenarios, the model incorporates rainfall and temperature as input variables but does not utilize previous runoff data. This presents a challenge in accurately simulating streamflow with this model structure. Historical streamflow simulations in the study area are deemed acceptable but tend to underestimate actual values. To address this, the study applied quantile delta mapping and simple scaling for bias correction of future scenario runoffs. Near-term (2021 to 2040) and mid-term (2041 to 2060) future streamflow were thus projected under climate change scenarios (SSP2-4.5 and SSP5-8.5). The projected results indicate that future scenario streamflow changes generally range from a decrease of 20% to an increase of 30% compared to the baseline streamflow. However, the likelihood of dry years occurring is greater than that of wet years, with higher runoff events primarily concentrated in a few wet years.

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