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研究生:石棟鑫
研究生(外文):Dong-Sin Shih
論文名稱:結合高解析度降水於分佈型水文模式之降雨逕流模擬
論文名稱(外文):Rainfall-Runoff Simulations Using Distributed Watershed Models with High-Resolution Precipitation
指導教授:吳瑞賢吳瑞賢引用關係
指導教授(外文):Ray-Shyan Wu
學位類別:博士
校院名稱:國立中央大學
系所名稱:土木工程研究所
學門:工程學門
學類:土木工程學類
論文種類:學術論文
論文出版年:2006
畢業學年度:94
語文別:中文
論文頁數:140
中文關鍵詞:高解析度降水雷達降水分佈型水文模式漫地流地下水
外文關鍵詞:distributed-parameter modelsurface flowgroundwater flowradar-rainfall estimatesHigh-resolution precipitation
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台灣地區的水文災害通常是由極端降水所引起,極端降水會在短時間內產生高強度的降雨,有時更會持續性的降下豪雨,這都容易引起水災的發生,進而對人民的生命財產造成威脅。為了增進對極端水文事件的瞭解,本研究利用分佈型水文模式,結合高解析度的降水,進行降雨逕流的模擬研究。
一般而言,降雨資訊是由水文站量測而來,但是各量測點只能提供該點的降雨值,並不能表示其周遭位置的降雨量。而利用雷達推估降水則是已經證實可以提供較佳的空間降水資訊,但是對於降雨量的準確性仍有待努力。本研究以地面水文站的觀測值校正雷達降水資料,以台北市與石門水庫集水區為研究區域,2001年的納莉颱風(Typhoon Nari)為分析案例。研究結果發現,由五分山雷達站所提供的雷達回波資料,確實對降雨的描述與雨量站觀測值吻合,顯示五分山雷達站的確可以確實的掌握研究區域內的降水變化。另外,我們從雷達回波上,觀察到部分地區發生極端降雨,但是卻沒有適當的雨量站記錄。我們分析雷達降水跟雨量站觀測的結果發現,雷達降水的結果容易產生高估降雨量的情況,其中的差異性可以利用降雨空間分佈的研究進一步證實。
我們進一步將高解析度的降水資料應用於石門水庫集水區,進行集水區的降雨逕流模擬,降雨輸入是採用雷達與雨量站資料,集水區模式是二維的分佈型水文模式。經由模式的檢定與校正結果發現,利用二維的St. Venant 方程式應用在石門水庫集水區是可行的,模擬的水位值與實測值趨勢一致。此外,模擬結果發現,當網格大小取160公尺時,可以得到最佳的模擬結果,而其模擬時間還可以比120公尺的網格少百分之四十。案例研究中,我們發現以反距離權重法(inverse-distance weighting method)推估降雨量,可得到最佳的模擬結果,但以雨量站推估的降水皆差異不大,雷達推估降水的模擬部分以比值法(ratio method)最佳,而雷達降水的模擬誤差略高於雨量站估算的結果。
接著我們發展了一套以物理機制為基礎的分佈型水文模式,該模式結合地表水與地下水模組來探討水文循環過程。其中地表水是由漫地流與渠道流所組成,地下水模組則是包含了飽和含水層與非飽和含水層。該模式包含了降水、入滲、蒸發散、滲漏、地表漫地流與地下水流等水文過程,測試結果證實模式具有良好的應用性,並當模擬間距小於5秒時,建議採用空間解析度160公尺的網格模擬。本模式接著模擬不同的水文狀況,包括暴雨事件、暴雨的退水段、以及長時期的水文歷程,案例分析的結果顯示該模式具有良好的模擬結果。
Hydrological hazards often occur in conjunction with extreme precipitation events in Taiwan. The exceptional volume and intensity of the precipitation cause frequent torrential floods, sometimes with devastating effects on life and property. To improve our understanding of extreme events, the study modeled the rainfall-runoff processes using distributed watershed models with high-resolution precipitation input.
Precipitation data is generally collected from rain gauge stations. However, each measurement represents only the amount of rainfall at that particular spot, not precipitation in the surrounding area. Radar approaches are considered to offer a good spatial description of precipitation, but hardly predict precipitation quantities with acceptable accuracy. High resolution radar-rainfall estimates are compared with ground observations for an extreme precipitation event. The Taipei City area and the Shihmen reservoir watershed were chosen as the study sites, and the passage of Typhoon Nari (2001) through these areas was taken as the case study event. It was concluded that radar reflectivity from the Wufenshan radar station can be helpful for identifying precipitation variations during the passage of a land falling tropical cyclone. Spots with extreme rainfall can be identified when radar approaches are performed, but not based on gauge approaches. However, compared to the gauge approaches to the radar-rainfall estimates over the investigated domain tended to be overestimated. The divergence between radar-rainfall and gauge-rainfall can be identified via sub watershed investigations.
The watershed model with high resolution precipitation data was tested on a complex mountainous reservoir region, the Shihmen reservoir watershed. Radar-rainfall estimates were examined on this study. Numerical results generally revealed acceptable agreement between the observed and simulated reservoir stage hydrographs. The model calibration processes verified that the proposed model was effective for flood routing in the Shihmen reservoir watershed. Moreover, simulated results obtained using a grid size equal to 160m by 160m had the strongest agreement between simulated and measured data, and resulted in an execution time reduction of 40% than that of the case with 120m by 120m. Case study showed that inverse-distance weighting method carried the smallest error in estimation compared to all other spatial precipitation interpretations. The ratio approach produced the smallest residual error in simulation results among all other radar approaches. Precipitation is identified to be the main factor forcing model result.
A physical based distributed-parameter model combining surface runoff and groundwater flow is developed for investigating hydrological processes. Surface runoff is composed of both overland flow and river flow components, and the groundwater module considers the unsaturated zone and saturated zone in an unconfined aquifer system. An investigation of hydrological processes, including precipitation, infiltration, evaporation, percolation, surface runoff and groundwater flow are all considered in the proposed simulation model. Comparative analysis shows that the gradient method is superior to the GIS approach for describing the flow above riverbed. This study suggests using the Thiessen polygon method for precipitation interpolation. The best calibrations are obtained at a spatial resolution of 160m by 160m, when the simulated time step is less than five seconds. The proposed model shows good potential for storm based simulations, recession period description and long-term modeling. Therefore, the proposed model is confirmed to be suitable for mountainous watershed, such as Shihmen reservoir watershed.
TABLE OF CONTENTS
pages
中文摘要 I
ABSTRACT III
致謝 V
TABLE OF CONTENTS VI
LIST OF FIGURES XI
LISTS OF TABLES XIV
NOTATION XVII

CHAPTER 1. INTRODUCTION 1-1
1.1. Motivation 1-1
1.2. Literature reviews 1-3
1.2.1. Radar-rainfall estimates 1-3
1.2.2. Distributed-parameter models 1-5
1.2.3. Watershed models 1-7
1.3. Objectives and overall structure 1-8

CHAPTER 2. RELATED WORKS 2-1
2.1. Study areas 2-1
2.2. Selected land falling typhoons 2-2
2.3. Digital terrain model and land use 2-2
2.4. Error Evaluation 2-3

CHAPTER 3. A COMPARISON OF GAUGE AND RADAR-RAINFALL ESTIMATES IN A LAND FALLING TYPHOON IN TAIWAN 3-1
3.1. Precipitation inputs 3-1
3.1.1. Interpolation using rain gauges 3-2
3.1.2. Radar-rainfall estimates 3-3
3.1.3. Radar-gauge combinations 3-5
3.2. Discussion 3-6
3.2.1. Tracing spatial precipitation movements by the radar approach 3-6
3.2.2. Temporal variations of radar-rainfall estimates 3-7
3.2.3. Radar-rainfall spatial variations 3-8
3.2.4. Radar-rainfall amounts 3-10
3.3. Summary 3-13

CHAPTER 4. DISTRIBUTED FLOOD SIMULATIONS FOR THE SHIHMEN RESERVOIR WATERSHED WITH GAUGE OBSERVATIONS AND RADAR-RAINFALL ESTIMATES 4-1
4.1. Two-dimensional diffusive overland flow model. 4-1
4.1.1. Governing Equations. 4-2
4.1.2. Numerical approach 4-3
4.1.3. Infiltration model 4-4
4.2. Model calibrations 4-5
4.3. Sensitivity analysis 4-6
4.4. Case study 4-9
4.5. Summary 4-10

CHAPTER 5. COUPLED SURFACE AND GROUNDWATER MODELS FOR INVESTIGATING HYDROLOGICAL PROCESSES 5-1
5.1. Model development 5-1
5.1.1. Surface flow 5-2
5.1.1.1. Channel flow 5-2
5.1.1.2. Overland flow 5-4
5.1.2. Groundwater model 5-6
5.1.2.1. Unsaturated groundwater module 5-6
5.1.2.2. Saturated groundwater module 5-7
5.1.3. Model linkage 5-8
5.1.3.1. Simulation procedure for the surface flow 5-8
5.1.3.2. Simulation procedure for the groundwater module 5-9
5.1.3.3. Module combination 5-9
5.2. Model configurations setup 5-10
5.2.1. Study area 5-10
5.2.2. Reservoir boundary determination 5-11
5.2.3. Precipitation input 5-11
5.2.4. Channel flow setup 5-13
5.2.5. Temporal resolution 5-14
5.2.6. Spatial resolution 5-15
5.3. Model calibrations 5-16
5.3.1. Calibration of the Manning’s roughness coefficient for the channel (nc) 5-16
5.3.2. Calibration of the Manning’s roughness coefficient for surface land (n(j,k)) 5-17
5.3.3. Calibration of the wetting front soil suction head ( ) 5-18
5.3.4. Calibration of the equilibrium capacity (fc) 5-19
5.3.5. Calibration of the constant decay rate (k) 5-19
5.3.6. Calibration of the thickness of the riverbed mud (mt) 5-20
5.4. Case study 5-20
5.4.1. Storm based simulation 5-21
5.4.2. Recession period simulations 5-22
5.4.3. Long-term simulations 5-23
5.5. Summary 5-24

CHAPTER 6. CONCLUSIONS 6-1

BIBLIOGRAPHY R-1

APPENDIX A-1


LIST OF FIGURES
Pages
Fig. 2-1. Study site (Shihmen reservoir watershed and Taipei City). 2-7
Fig. 3-1. Rain gauge locations in the study site (Shihmen reservoir watershed and Taipei City). 3-16
Fig. 3-2. Radar reflectivity in north Taiwan from 09/16/2000 to 09/17/0130. 3-17
Fig. 3-3. Radar reflectivity in North Taiwan from 09/17/1300 to 09/17/1830. 3-18
Fig. 3-4. Hourly radar-rainfall estimates and gauge observations for the Taipei City from 09/16/2000 to 09/17/1200. 3-19
Fig. 3-5. Hourly radar-rainfall estimates and gauge observations for the Shihmen reservoir watershed from 09/16/2000 to 09/17/2000. 3-20
Fig. 3-6. Regional precipitation estimated from gauge-rainfall between 09/17/0000 to 09/17/0100: (a) Thiessen polygon method, (b) Inverse-distance weighting method (first-order), (c) Inverse-distance weighting method (second-order), (d) Kriging method. 3-21
Fig. 3-7. Regional radar-rainfall between 09/17/0000 to 09/17/0100: (a) Linear regression, (b) Quadratic regression, (c) Ratio, (d) Objective analysis. 3-22
Fig. 3-8. Subwatersheds in the Shihmen reservoir watershed and the gauge measured flow. 3-23
Fig. 3-9. Accumulation of precipitation and discharge in sub watersheds of the Shihmen reservoir watershed in 2001. 3-24
Fig. 4-1 Simulated results for model calibration. 4-19
Fig. 4-2 Simulated reservoir stage for the case study (gauge interpolation approaches). 4-20
Fig. 4-3 Simulated reservoir stage for the case study (radar-rainfall estimates). 4-21
Fig. 5-1. Model construction. 5-36
Fig. 5-2. River basin in the Shihmen reservoir watershed. 5-37
Fig. 5-3. Computational results for rating curve and simulations. 5-37
Fig. 5-4. Simulated inflows for the various precipitation methods. 5-38
Fig. 5-5. Simulated inflows for the various riverbed methods. 5-38
Fig. 5-6. Simulated inflows for the various computational time steps. 5-39
Fig. 5-7. Simulated inflows for the various spatial resolutions. 5-39
Fig. 5-8. Simulated inflows for the various Manning’s N (rivers). 5-40
Fig. 5-9. Simulated inflows for the various Manning’s N (forests). 5-40
Fig. 5-10. Simulated inflows for the various wetting front suction heads ( ).
5-41
Fig. 5-11. Simulated inflows for the various equilibrium infiltration capacities ( ).
5-41
Fig. 5-12. Simulated inflows for the various infiltration decay parameters ( ).
5-42
Fig. 5-13. Simulated inflows for the various mud thicknesses ( ).
5-42
Fig. 5-14. Storm based simulation (Typhoon Wayne). 5-43
Fig. 5-15. Storm based simulation (Typhoon Nari). 5-43
Fig. 5-16. Recession simulation (Typhoon Wayne). 5-44
Fig. 5-17. Recession simulation (Typhoon Nari). 5-44
Fig. 5-18. Long-term simulation (1994/07/09~1994/08/09). 5-45



LISTS OF TABLES
pages
Table 2-1. Precipitation events. 2-5
Table 2-2. Shihmen reservoir watershed (763km2) land use. 2-6
Table 3-1. Precipitation accumulations in the Shihmen reservoir watershed estimated with the various approaches from 09/16/2000 to 09/17/2000. 3-14
Table 3-2. Precipitation and discharge accumulations in the sub watersheds of the Shihmen reservoir watershed in 2001. 3-14
Table 3-3. Precipitation accumulations based on various approaches for the sub watersheds of the Shihmen reservoir watershed from 09/16/2000 to 09/17/2000. 3-15
Table 4-1. List of simulated typhoon events. 4-12
Table 4-2. Shihmen reservoir watershed (763km2) land use. 4-13
Table 4-3. Residual statistics for model calibrations. 4-14
Table 4-4. Simulated results obtained by a 120m by 120m resolution with variable Manning’s roughness coefficient for the forested land use and initial infiltration capacity. 4-15
Table 4-5. Simulated results obtained with a 120m by 120m resolution with variable infiltration decay parameter. 4-15
Table 4-6. Simulated results obtained with a 160m by 160m resolution with variable Manning’s roughness coefficient for the forested land use and initial infiltration capacity. 4-16
Table 4-7. Simulated results obtained with a 160m by 160m resolution with variable constant infiltration decay parameter. 4-16
Table 4-8. Simulated results obtained with a 240m by 240m resolution with variable Manning’s roughness coefficient for the forested land use and initial infiltration capacity. 4-17
Table 4-9. Simulated results obtained with a 240m by 240m resolution with variable constant infiltration decay parameter. 4-17
Table 4-10. Comparative simulated results for various spatial resolutions. 4-18
Table 4-11. Residual errors obtained with variable precipitation algorithms. 4-18
Table 5-1. Precipitation events. 5-27
Table 5-2. Shihmen reservoir watershed (763km2) land use. 5-27
Table 5-3. Errors in peak flow obtained with variable precipitation algorithms. 5-28
Table 5-4. Stream length and average gradient. 5-28
Table 5-5. Errors in peak obtained with various riverbed generations. 5-29
Table 5-6. Errors in peak flow obtained with a variable computational time step. 5-29
Table 5-7. Residual errors obtained with a variable spatial resolution. 5-30
Table 5-8. Residual errors obtained with variable Manning’s roughness (rivers). 5-30
Table 5-9. Residual errors obtained with variable Manning’s roughness (forest). 5-31
Table 5-10. Wetting front soil suction head. 5-31
Table 5-11. Residual errors obtained with variable wetting front soil suction head. 5-32
Table 5-12. Equilibrium infiltration rate. 5-32
Table 5-13. Residual errors obtained with variable equilibrium infiltration capacity. 5-33
Table 5-14. Residual errors obtained with variable infiltration decay parameters. 5-33
Table 5-15. Simulation of thickness of riverbed mud. 5-34
Table 5-16. Case study (Typhoon Wayne). 5-34
Table 5-17. Case study (Typhoon Nari). 5-35
Table 5-18. Recession and long-term simulations. 5-35
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