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Applied Hydrology Climate Change and Hydrology

Applied Hydrology Climate Change and Hydrology. Professor Ke-Sheng Cheng Department of Bioenvironmental Systems Engineering National Taiwan University. Effect of climate change on storm characteristics. Storm types Convective storms Typhoons MCS (Mei-yu) Frontal systems

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Applied Hydrology Climate Change and Hydrology

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  1. Applied HydrologyClimate Change and Hydrology Professor Ke-Sheng Cheng Department of Bioenvironmental Systems Engineering National Taiwan University

  2. Effect of climate change on storm characteristics • Storm types • Convective storms • Typhoons • MCS (Mei-yu) • Frontal systems • Assessed based on MRI high-resolution outpots (dynamic downscaling) Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Eng, NTU

  3. 探討各降雨類型之統計特性且評估氣候變遷下之差異探討各降雨類型之統計特性且評估氣候變遷下之差異 TCCIP Team 3 蘇元風博士

  4. 緣起 • 過去探討氣候變遷對降雨特性影響之相關研究，多以年降雨量、季節降雨量或月降雨量為研究對象。 • 許多水資源工程規劃、設計，或是水庫供水調度而言，事件降雨特性至關重要。 • 逕流演算 • 入庫流量預報 • 水利工程設施規劃 動力降尺度 (例MRI) 時雨量資料

  5. 降雨事件之門檻與統計參數 • 門檻 • 時雨量值(例：2mm/hr) • 降雨延時(例：12 hours) • 統計參數 • 平均次數 • 總降雨量 • 降雨延時 • 降雨事件間隔時距

  6. 事件降雨特性 序率暴雨模擬模式 • 後續水文需求： • 頻率分析 • 逕流演算 • 入庫流量預報 • 水利工程設計規劃 • … • 水利署相關計畫之使用 • 氣候變遷下台灣地區地下水資源補注之影響評估(台大) • 強化台灣西北及東北地區因應氣候變遷海岸災害調適能力研究計畫(1/2)(成大) • 台灣地區各水資源分區因應氣候變遷水資源管理調適能力綜合研究 (台大) • 強化中部水資源分區因應氣候變遷水資源管理調適能力研究(交大) • 氣候變遷對中部地區水旱災災害防救衝擊評估及調適策略擬定(1/2)(成大)

  7. MRI-WRF-5km時雨量 (基期1979-2003) 測站觀測時雨量 (基期1979-2003) MRI-WRF-5km資料是否能重現觀測資料之統計特性？ 降雨門檻 降雨門檻 事件降雨特性參數 事件降雨特性參數 比較 事件降雨特性參數改變率 (近未來) MRI-WRF-5km時雨量 (近未來2015-2039) MRI-WRF-5km時雨量 (世紀末2075-2099) 事件降雨特性參數改變率 (世紀末)

  8. 暴雨事件切割門檻(測站資料) • 採用降雨事件間距門檻值，濾除較小的降雨事件 • 降雨延時為1 小時或時雨量低於0.5mm的小事件移除。 • 降雨類型切割

  9. 資料說明 • 測站資料 • 時間：1979-2003時雨量 • 站數：84站 • MRI-WRF-5km • 空間解析度: 5km • 1979-2003 • 2015-2039 • 2075-2099 時雨量

  10. 事件數 Gauges MRI-WRF 基期 近未來 世紀末

  11. 平均延時 Gauges MRI-WRF 基期 近未來 世紀末

  12. 平均總降雨量 Gauges MRI-WRF 基期 近未來 世紀末

  13. 事件間隔

  14. 事件數 降雨延時>4hrs 時雨量>0.5mm 降雨延時>4hrs 時雨量>2mm Gauges MRI-WRF 基期 近未來 世紀末

  15. 平均延時 降雨延時>4hrs 時雨量>0.5mm 降雨延時>4hrs 時雨量>2mm Gauges MRI-WRF 基期 近未來 世紀末

  16. 平均總降雨量 降雨延時>4hrs 時雨量>0.5mm 降雨延時>4hrs 時雨量>2mm Gauges MRI-WRF 基期 近未來 世紀末

  17. 事件間隔 降雨延時>4hrs 時雨量>0.5mm 降雨延時>4hrs 時雨量>2mm

  18. 事件數 降雨延時>3hrs 時雨量>0.5mm 降雨延時>3hrs 時雨量>2mm Gauges MRI-WRF 基期 近未來 世紀末

  19. 平均延時 降雨延時>3hrs 時雨量>0.5mm 降雨延時>3hrs 時雨量>2mm Gauges MRI-WRF 基期 近未來 世紀末

  20. 平均總降雨量 降雨延時>3hrs 時雨量>0.5mm 降雨延時>3hrs 時雨量>2mm Gauges MRI-WRF 基期 近未來 世紀末

  21. 事件間隔 降雨延時>3hrs 時雨量>0.5mm 降雨延時>3hrs 時雨量>2mm

  22. 事件數 Gauges MRI-WRF 基期 近未來 世紀末

  23. 平均延時 Gauges MRI-WRF 基期 近未來 世紀末

  24. 平均總降雨量 Gauges MRI-WRF 基期 近未來 世紀末

  25. 事件間隔

  26. 結論 MRI-WRF-5km所得到的降雨參數大致上能夠反映出測站資料所得降雨參數之空間分布特性，尤其是颱風與鋒面兩類降雨類型。

  27. Stochastic storm rainfall simulation model (SSRSM) • Occurrences of storm events and time distribution of the event-total rainfalls are random in nature. • Physical parameters based • # of events in a certain period • Duration • Event-total depths • Time distribution (hyetograph) • Rainfall intermittence Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Eng, NTU

  28. Modeling occuerrences of storms • Number of storm events in a certain period • Occurrences of rare events like typhoons can be modeled by the Poisson process. • Inter-event-time has an exponential distribution. • Occurrences of other types of storms which are more frequently occurred may not be well characterized by the Poisson process. Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Eng, NTU

  29. Duration and total depth • Generally speaking, storms of longer durations draw higher amount of total rainfalls. • Event-total rainfall (D) and duration (tr) are correlated and can be modeled by a joint distribution. • (D, tr) of typhoons are modeled by a bivariate gamma distribution. • Bivariate distribution of different families of marginal densities may be possible. Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Eng, NTU

  30. Simulation of bivariate gamma distribution – A frequency factor based approach • Transforming a bivariate gamma distribution to a corresponding bivariate standard normal distribution. • Conversion of BVG correlation and BVN correlation. Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Eng, NTU

  31. Gamma density

  32. Rationale of BVG simulation using frequency factor • From the view point of random number generation, the frequency factor can be considered as a random variable K, and KT is a value of K with exceedence probability 1/T. • Frequency factor of the Pearson type III distribution can be approximated by Standard normal deviate [A]

  33. Assume two gamma random variables X and Y are jointly distributed. • The two random variables are respectively associated with their frequency factors KX and KY . • Equation (A) indicates that the frequency factor KX of a random variable X with gamma density is approximated by a function of the standard normal deviate and the coefficient of skewness of the gamma density.

  34. Flowchart of BVG simulation (1/2)

  35. Flowchart of BVG simulation (2/2)

  36. [B]

  37. Time distribution of event-total rainfall • The duration is divided into n intervals of equal length. Each interval is associated with a rainfall percentage. • Based on the simple scaling assumption, rainfall percentages of the i-th interval (i = 1, …, n) of all events (of the same storm type) form a random sample of a common distribution. • Rainfall percentages of individual intervals form a random process. • Gamma-Markov process Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Eng, NTU

  38. Modeling the dimensionless hyetograph • Rainfall percentages can only assume values between 0 and 100. • The sum of all rainfall percentages should equal 100%. • Constrained gamma-Markov simulation • Gamma distribution will generate random numbers exceeding 100%. • Truncated gamma distribution (truncated from above) • The truncation threshold (cut off value) is significantly lower than 100%. Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Eng, NTU

  39. Observations of rainfall percentages are samples of truncated gamma distributions. • Determining parameters of the truncated gamma distributions. • Scale parameter, shape parameter and the truncation threshold. • Gamma-Markov simulation is based on simulation of a bivariate truncated-gamma distribution. • Determing the correlation coefficient of the parent bivariate gamma distribution. Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Eng, NTU

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