臺灣博碩士論文加值系統

台灣由於降雨時空分布不均且受到地形條件影響，容易產生洪水，如今隨著都市發展與氣候異常，極端降雨事件發生機率增加，導致洪水災害越發頻繁；近年來，結合大氣模式與水文模式進行洪水預報的技術逐漸成熟，但若要建立大氣水文整合模擬之洪水預報系統，各模式的誤差會對預報結果產生一定的影響，而如何有效地評估出各模式對最後預報誤差的影響，為本研究主要的課題。本研究結合WRF大氣數值模式與WASH123D集水區數值模式，以高屏溪流域進行數值模擬，並利用HEC-HMS降雨逕流模式計算逕流量，匯入WASH123D進行一維河川水流、二維地表漫地流水文模擬，並利用近年來的水文事件進行水文模式檢定與驗證，再將各模式之輸出結果與觀測值作比對得到各別模式之誤差；最後再進行模式間的聯合模擬，藉由得到的結果與觀測值所得之誤差，分析並探討聯合模擬所產生之誤差與個別模式所產生誤差之關聯。
模式檢定結果顯示，HEC-HMS與WASH123D分別於逕流演算與河道模擬部分之相關係數皆高於0.7，顯示以觀測雨量與流量驅動模式可以得到合理之模擬結果；更進一步分析模擬之均方根誤差，本研究發現河道模擬之整體誤差(RMSE)約為0.43 m，洪峰誤差為0.31 m，誤差約在4.0%以內；而逕流的部分則是整體誤差約為108 cms，洪峰誤差為59 cms，誤差則達到40.0%；若更進一步結合雨量站資料，將兩模式聯合模擬，討論水文模式所產生之誤差，結果顯示其整體相關係數仍高於0.7，表示仍能得到合理之結果，誤差為0.57 m，高於原本的河道模擬誤差約26%，洪峰誤差為0.32 m，相差不大；最後結合WRF之預報雨量，發現若雨量誤差區間越大，其水位模擬誤差範圍則會更大。

Due to the uneven rainfall distribution and topographic effects, flash floods are commonly happened in Taiwan. In recent years, the situation has getting severe because the influences of urbanization and the global climate change are more serious. Using integrated hydro-meteorological models to implement flood forecast is an under- developing technology to prevent relevant disasters. However, each modeling has their individual errors for a hydro-meteorological system, that have to be clarified before any application. To conduct this issue is the major concern of this research. The study used a flood forecast system in which utilizing WRF weather model to generate regional precipitations, the HEC-HMS to obtain mountainous runoffs, and the WASH123D watershed model to conduct one-dimensional (1-D) flood routing and two-dimensional (2-D) surface inundations. Pingtung Plain located in southern Taiwan is selected as study site, and three rainfall events are examined for model calibrations and validations. Simulation errors are discussed at first for individual model, and coupling simulation than conduced to study their interactive errors.
Results indicated that individual simulation errors of HEC-HMS and WASH123D model both are higher than 0.7 in efficiency coefficient (CE). That means above two models using observed rainfall data to drive hydrological routing can obtain reasonable and good responses. In further to check their root mean square errors (RMSE), average error is 0.43 m and peak error is 0.31 m for WASH123D simulations, in which less than 4.0 percent as compared to observed channel stages. The RMSE of HEC-HMS simulation is 108 cms and peak error is 59 cms, in which 40.0 percent is identified of discharges. For HEC-HMS coupled WASH123D simulations, the CE error is still higher than 0.7, that means a reasonable and good simulated trend are obtained. And the peak error is 0.32 m in which is not obvious different as compared to individual simulation. However, RMSE is 0.57 m in which 26 % higher than only WASH123D modeling. Finally, a hydro-meteorological flooding warning system using WRF to produce rainfall is studied, simulations revealed a divergence result with relevant to rainfall forecasts.

摘要 i
Abstract ii
目錄 iv
圖目錄 vii
表目錄 x
符號表 xiv
第一章 緒論 1
1-1 前言 1
1-2 文獻回顧 3
1-3研究架構 5
第二章 理論分析 7
2-1 大氣模式 7
2-2 WASH123D水文模式 8
2-2-1 一維河道演算 8
2-2-2 二維漫地流演算 9
2-2-3 一維河道與二維漫地流耦合 12
2-3 HEC-HMS降雨逕流模式 14
2-3-1 HEC-HMS介紹 15
2-3-2 HEC-HM運算模組 16
2-4 評估標準 17
第三章 研究區域概述與模式建立 19
3-1 研究區域 19
3-1-1 屏東平原概述 19
3-1-2 區域水文氣象資料 20
3-2 降雨逕流模式之建立 22
3-3 集水區水文數值模式建立 23
3-3-1 一維河道模擬網格建立 23
3-2-2 二維河道模擬網格建立 24
第四章 結果與討論 27
4-1 各模式檢定驗證及其誤差分析 27
4-1-1 大氣數值模式 (WRF)模擬結果與誤差分析 28
4-1-2 降雨逕流模式(HEC-HMS)模擬結果與誤差分析 32
4-1-3 集水區水文數值模式(WASH123D)模擬結果與誤差分析 48
4-2 聯合模擬之結果與誤差分析 58
4-2-1 降雨逕流模式(WRF)與集水區水文數值模式(WASH123D)聯合模擬 58
4-2-2 大氣數值模式(WRF)與水文模式(HEC-HMS+WASH123D)聯合模擬 68
第五章 結論與建議 77
5-1 結論 77
5-2建議 78
參考文獻 79
圖附錄 83
表附錄 100

1.林耕百，2015，應用集水區耦合型模式於地下水補注之研究-以屏東平原為例，碩士勒文，國立中興大學土木工程學系。
2.林拓，2014，結合數值模式與模糊理論進行洪水預報最佳化之研究，碩士論文，國立中興大學土木工程學系。
3.李光敦，2005，水文學，五南圖書。
4.洪逸鈞，2014，結合水文及數值模式應用於河川水位預報—以高屏溪為例，碩士論文，國立中央大學土木工程學系。
5.施上栗、李鴻源、胡通哲，2008，應用數值模式評估丁壩工於蘭陽溪水鳥保護區魚類棲地改善效益，中國土木水利工程學刊，第二十卷，第三期，pp.331-332。
6.陳明遠，2014，高屏溪流域降雨逕流模式參數之研究，碩士論文，國立屏東科技大學土木工程學系。
7.黃成甲、葉森海、許銘熙、葉克家，2013，二維淹水分散計算系統簡介，災害防救電子報，第九十三期，pp.1-13。
8.蔡忠遠，2015，探討土地利用和氣候變遷下鳳山溪流域地下水位及流量的影響，碩士論文，國立中央大學土木工程學系。
9.葉克家、陳弘凷、王書益、陳春宏、廖仲達，2009，美國國家計算水科學及工程中心河道變遷模式之引進及應用研究，第十三屆海峽兩岸水利科技交流研討會。
10.蘇奕叡，2014，颱風路徑、降雨及水位支系集模擬研究：以凡那比(2010)颱風個案為例，碩士論文，國立中央大學大氣物理研究所。
11.羅冠名，2012，應用NETSTARS於八掌溪結合橋墩沖刷之研究，碩士倫文，成大水利與海洋工程學系。
12.Asaad Y. Shamseldin., 1997. “Application of a neural network technique to rainfall-runoff modeling.” Journal of Hydrology vol.199, pp. 272-294.
13.Chih Ted Yamg and Francisco J. M. SIMÕES., 2008. “GSTARS computer models and their applications, part I: theoretical development.” International Journal of Sediment Research, pp. 197-211.
14.Hsu Tai-Wen, Shih Dong-Sin, Li Chi-Yu, Lan Yuan-Jyh, and Lin Yu-Chen.,2017. ”A Study on Coastal Flooding and Risk Assessment under Climate Change in the Mid-Western Coast of Taiwan. ” Water. Vol.9(6), 390. SCI, 34/88, Water Resources.
15.Jyh-Shing Roger Jang., 1993. “ANFIS：Adaptive-Network-Based Fuzzy Inference System.” IEEE Transactions on system, Man, and Cybernetics, vol.23, No.3 .
16.Lan Yu, Lloyd Hock Chye Chua, Dong-Sin Shih*. (2014, Aug). AN ENSEMBLE APPROACH FOR TYPHOON RUNOFF SIMULATION WITH PERTURBED RAINFALL FORECASTS IN TAIWAN. 11th International Conference on Hydroinformatics, New York City, USA.
17.Lan Yu, Lloyd H C Chua, Dong-Sin Shih, Soon Keat Tan., 2015. “Development of a Modified Ensemble Model Approach for Flood Forecasts.”
18.Mahmut Firat, Mahmud GぴungぴorRiver., 2007. “Flow estimation using adaptive neuro fuzzy inference system.” Mathematics and Computers in Simulation vol.75, pp.87-96.
19.Rawls, W.J., D.L. Brakensiek, and N. Miller,. 1983. “Green–Ampt infiltration parameters from soils data.” J. Hydraul. Eng. 109:62–70.
20.Shen, H. and L. Chang., 2012. “On-line multistep-ahead inundation depth forecasts by recurrent NARX networks.”Hydrol. Earth Syst. Sci. Discuss 9: 11999-12028.
21.Shih Dong-Sin, and Gour-Tsyh Yeh., 2011, Feb. “Identified Model Parameterization, Calibration and Validation of the Physically Distributed Hydrological Model, WASH123D in Taiwan.” Journal of Hydrologic Engineering （ASCE）, Vol.16（2）, pp.126-136.（SCI）.
22.Shih Dong-Sin, Tai-Wen Hsu, Kuo-Chyang Chang, and Hsiang-Lan Juan., 2012,
Oct. “Implementing Coastal Inundation Data with an Integrated Wind Wave Model and Hydrological Watershed Simulations.”Terrestrial Atmospheric and Oceanic Sciences, Vol.23（5）, pp.513-525.（SCI）.
23.Shih Dong-Sin, Cheng-Hsin Chen, Gour-Tsyh Yeh., 2014. “Improving our
understanding of flood forecasting using earlier hydro-meteorological
intelligence.”Journal of Hydrology vol.512, pp.470–481.
24.Yeh Gour-Tsyh, Guobiao Huang, Fan Zhang, Hwai-Ping Cheng, and Hsin-Chi Lin., 2005. “WASH123D：A Numerical Model of Flow, Thermal Transport, and Salinity, Sediment, and Water Quality Transport in WAterSHed System of 1-D Stream-River Network, 2-D Overland Regime, and 3-D Subsurface Media.”
25.Yeh Gour-Tsyh, Dong-Sin Shih, Jing-Ru C. Cheng., 2011. “An Integrated Media,Integrated Processes Watershed Model.” Computers & Fluids, Vol.45, pp.2-13.