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研究生:郭俊超
研究生(外文):Kuo, Chun-Chao
論文名稱:結合季節雨量與水文模式於枯水期旬流量預測
論文名稱(外文):Ten-Day Streamflow Prediction of the Dry Season Integrated by Seasonal Rainfall and Hydrological Model
指導教授:游保杉游保杉引用關係
指導教授(外文):Yu, Pao-Shan
口試委員:張斐章鄭克聲李光敦李振誥徐國錦
口試委員(外文):Fi-John ChangKe-Sheng ChengKwan Tun LeeCheng-Haw LeeKuo-Chin Hsu
口試日期:2009-12-02
學位類別:博士
校院名稱:國立成功大學
系所名稱:水利及海洋工程學系
學門:工程學門
學類:河海工程學類
論文種類:學術論文
論文出版年:2009
畢業學年度:98
語文別:英文
論文頁數:106
中文關鍵詞:小波海溫季節雨量分解模式概念型降雨-逕流模式流域流量
外文關鍵詞:Waveletsea surface temperatureseasonal rainfalldisaggregation modelconceptual rainfall-runoff modelbasin streamflow
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台灣在豐枯水期降雨之分配較為懸殊,長期以來對枯水期之水資源供應是一大挑戰。再加上可能隨著全球暖化,近幾十年來觀測的資料中發現乾旱有趨嚴重的趨勢,不降雨日數亦有增加的現象,對現今水資源調度面臨更大的考驗。因此若能更建立一個枯水期流量預測模式,將對水資源管理有所幫助。因此本研究擬發展氣象-水文模式來預測石門及曾文集水區枯水期之季節旬流量,其中架構的第一部分先以小波理論探討枯水期兩季節(十一至一月及一月至三月)的特性,進而尋找適當的海溫預報子範圍,再採用以遺傳類神經網路模式(ANN-GA)預測枯水期季節雨量。架構的第二部分採用三個時間分解(disaggregation)模式,考慮傳統兩種廣為接受的分解模式及碎形架構的模式,分別為: Valencia and Schaake (VS)模式、Lane模式、及Canonical Random Cascade model (CRCM),來測試季節雨量降尺度至小尺度雨量之能力,並測試分解為不同尺度的結果。架構的第三部分由修正的HBV連續型降雨-逕流模式(MHBV)所組成,搭配自動化全域參數搜尋法及兩階段率定法,期望能針對台灣豐枯水期流量差異的情況下,來率定出適當的石門及曾文集水區參數。最後結合這三個部分來預測枯水期旬流量。
從季節雨量的的小波分析發現十一至一月份(NDJ)及一月至三月份(JFM)的雨量有2-4年之震盪週期,由季節雨量之去趨勢小波第一主成分(DCR-WPC1)與前一季去趨勢之小波尺度平均(DCR-SAWP)海溫,尋找出對應NDJ及JFM季節雨量之太平洋海溫的預報因子區域 (七至九月之海溫大致在5°N-30°N, 120°E-150°E之區域; 十至十二月海溫大致在0°N-60°N, 125°E-160°W之區域),進一步利用遺傳類神經網路來預測季節雨量。本文用前25年(1974-1998)做模式參數率定,後7年(1999-2005)做模式驗證。根據所採用的評鑑指標:相關係數、均方根誤差、及Hansen-Kuipers (HK) scores,預報結果在台灣北部及西部地區表現良好(整體結果ρ約0.3~0.7),但東南部地區表現的較差,可能的原因為前者的季節雨量對海溫的相關性較後者高。
在雨量分解部分,首先測試VS模式及Lane模式在旬及三日的分解成果,發現兩者差異不大,因此為了更接近水文模式之模擬尺度,後續採用三日的分解結果來應用。此兩模式進一步跟CRCM的結果進行比較,大致上VS模式與Lane模式較CRCM來的好。評估測試後選用Lane模式做為後續應用之模式。
在水文模式的模擬方面,兩階段率定應用在石門曾文集水區較過去一階段率定精度來的高。本架構在流量預測方面,首先將藉由率定好的遺傳類神經網路模式預測季節雨量,利用Lane模式分解成三日雨量,接著將分解後的三日雨量做為MHBV的輸入,如此可合理提供一季之旬流量預測,預測結果在兩流域相關係數為0.48至0.53之間。有助於此兩流域之水資源管理者進行更有效之規劃畫水庫操作。
A combined, climate-hydrologic model with three components to predict the streamflow of two river basins of Taiwan at one season (3-month) lead time for the November-January (NDJ) and January-March (JFM) seasons was developed. The first component consists of the wavelet-based, ANN-GA model (Artificial Neural Network calibrated by Genetic Algorithm) which predicts the seasonal rainfall by using selected sea surface temperature (SST) as predictors. For the second component, three disaggregation models, Valencia and Schaake (VS), Lane, and Canonical Random Cascade model (CRCM), were tested to compare the accuracy of seasonal rainfall disaggregated by these three models to 3-day time scale rainfall data. The third component consists of the continuous rainfall-runoff model modified from HBV (called the MHBV) and calibrated by a global optimization algorithm against the observed rainfall and streamflow data of the Shihmen and Tsengwen river basins of Taiwan.
From the scale average wavelet power (SAWP) computed for the seasonal rainfall, it seems that the data exhibit interannual oscillations at 2-4-year period. On the basis of correlation fields between DCR-WPC1 of seasonal rainfall and DCR-SAWP of SST of Pacific Ocean at one-season lead time, SST of some domains of the western Pacific Ocean (July-September SST around 5°N-30°N, 120°E-150°E; October-December SST around 0°N-60°N, 125°E-160°W) were selected as predictors to predict seasonal NDJ and JFM rainfall of Taiwan at one season lead time respectively, using an Artificial Neural Network calibrated by Genetic Algorithm (ANN-GA). The ANN-GA was first calibrated using the 1974-1998 data and independently validated using 1999-2005 data. In terms of summary statistics such as the correlation coefficient, root-mean-square error (RMSE), and Hansen-Kuipers (HK) scores, the prediction of seasonal rainfall of northern and western Taiwan using ANN-GA are generally good for both calibration and validation stages (correlation coefficient ranged from 0.3 to 0.7), but not so for southeastern Taiwan because the seasonal rainfall of the former are much more significantly correlated to the SST of selected sectors of the Pacific Ocean than the latter.
In the part of rainfall disaggregation, the VS and Lane models were tested for disaggregating seasonal rainfall to 10-day and 3-day rainfall. The results revealed no obvious differences between them. In order to be closer to the time scale of the hydrological model, 3-day disaggregated rainfall was used for later application. The disaggregated results of these two models were further compared with the one of CRCM. Overall, the performances of the VS and the Lane models are better than the one of CRCM. The Lane model is chosen for further application after evaluation.
In the aspect of streamflow simulation, the analytical results of the two-stage calibration are better than the ones of the one-stage calibration. The proposed framework was tested, first by disaggregating the predicted seasonal rainfall of ANN-GA to rainfall of 3-day time step using the Lane model; then the disaggregated rainfall data was used to drive the calibrated MHBV to predict the streamflow for both river basins at 3-day time step up to a season’s lead time. Overall, the correlation of 10-day streamflow prediction is between 0.48 and 0.53. That will be useful for the seasonal planning and management of water resources of these two river basins of Taiwan.
Chapter 1 Introduction 1-1
1.1. Research motivation and objectives 1-1
1.2. Literature review 1-5
1.3. Data and study area 1-9
1.4. Dissertation structure 1-12
Chapter 2 Wavelet analysis on the variability, teleconnectivity and predictability of the seasonal rainfall of Taiwan 2-1
2.1. Introduction 2-1
2.2. Research methodology 2-2
2.2.1. Wavelet analysis 2-2
2.2.2. Principal Component Analysis 2-5
2.2.3. Artificial Neural Network-Genetic Algorithm (ANN-GA) 2-5
2.3. Results and discussion 2-8
2.3.1. Dominant mode of seasonal rainfall 2-8
2.3.2. Seasonal rainfall variability 2-9
2.3.3. Connectivity between DCR-WPC1 of seasonal rainfall and DCR-SAWP of Pacific Ocean SST 2-11
2.3.4. Wavelet coherence and phase differences 2-13
2.3.5. Prediction of seasonal rainfall 2-15
2.3.6. Composite analysis 2-18
2.4. Summary and conclusions 2-22
Chapter 3 Seasonal rainfall disaggregation of the dry season 3-1
3.1. Introduction 3-1
3.2. Research methodology 3-3
3.2.1. Valencia and Schaake (VS) model 3-3
3.2.2. Lane model 3-4
3.2.3. Canonical Random Cascade Model (CRCM) 3-5
3.3. Results and discussion 3-8
3.3.1. Comparison of different disaggregation time scales 3-8
3.3.2. Dissaggregations by VS, Lane, and CRCM models 3-10
3.4. Summary and conclusions 3-16
Chapter 4 Construction of continuous rainfall-runoff model for dry season 4-1
4.1. Introduction 4-1
4.2. Research methodology 4-3
4.2.1. Modified HBV model 4-3
4.2.2. Automatic parameter calibration 4-6
4.2.3. Two-stage calibration 4-10
4.3. Results and discussion 4-11
4.3.1. Calibration of MHBV 4-11
4.3.2. Validation of MHBV 4-15
4.3.3. Simulation results of MHBV using average air temperature data 4-24
4.4. Summary and conclusions 4-25
Chapter 5 Ten-day streamflow prediction of the dry season 5-1
5.1. Introduction 5-1
5.2. Seasonal rainfall prediction of Shihmen and Tsengwen for NDJ and JFM seasons 5-2
5.3. Disaggregation of predicted seasonal (NDJ and JFM) rainfall for Shihmen and Tsengwen 5-3
5.4. Prediction results of 10-day streamflow 5-5
5.5. Summary and conclusions 5-9
Chapter 6 Conclusions and recommendations 6-1
6.1. Conclusions 6-1
6.2. Recommendations 6-2
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