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Hydrological Perspective of Climate Change Impact Assessment

Hydrological Perspective of Climate Change Impact Assessment. Distinguished Lecture - Hydrological Sciences Section. Professor Ke -Sheng Cheng Dept. of Bioenvironmental Systems Engineering National Taiwan University. Outline. The scale issue of climate change studies

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Hydrological Perspective of Climate Change Impact Assessment

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  1. Hydrological Perspective of Climate Change Impact Assessment Distinguished Lecture - Hydrological Sciences Section Professor Ke-Sheng Cheng Dept. of Bioenvironmental Systems Engineering National Taiwan University

  2. Outline • The scale issue of climate change studies • An example of climate change impact assessment focusing on changes in design storms. Department of Bioenvironmental Systems Engineering, National Taiwan University

  3. The scale issue • Climate changes have had profound impacts on climate and weather of our lives. • The impacts of climate change vary with the scales of interest. Department of Bioenvironmental Systems Engineering, National Taiwan University

  4. As scientists, we can assess the impacts of climate changes on all scales of variables of interest. However, practical actions for coping with climate changes are almost exclusively implemented in country and regional/local scales. • Although hydrologists and climatologists may conduct studies in similar scales, there are also scales which are of unique interests to hydrologists. Department of Bioenvironmental Systems Engineering, National Taiwan University

  5. Climatological Hydrological Scales for flood risk assessment Department of Bioenvironmental Systems Engineering, National Taiwan University

  6. Climatologists focus on climate-scale changes. • Changes in annual or long-term average rainfalls of global to regional scales. • Hydrologist are more concerned about the impacts of climate change on hydrological extremes such as floods and droughts. • Such hydrological extremes are results of extreme weather events. Department of Bioenvironmental Systems Engineering, National Taiwan University

  7. Studies related to climate changes usually involve multiple disciplines. • Terminologies commonly used by one discipline may not be familiar to other disciplines and, in some cases, terminologies actually cause misunderstandings or misinterpretations of the research results. • Effective and good communications are important in disseminating research outputs. Department of Bioenvironmental Systems Engineering, National Taiwan University

  8. Climatologists focus on climate-scale changes. • Changes in annual or long-term average rainfalls of global to regional scales. • Impact of Climate Change on River Discharge Projected by MultimodelEnsemble (Nohara et al., 2006, Journal of Hydrometeorology) • At the end of the twenty-first century, the annual mean precipitation, evaporation, and runoff increase in high latitudes of the Northern Hemisphere, southern to eastern Asia, and central Africa. Department of Bioenvironmental Systems Engineering, National Taiwan University

  9. Mean annual flow is the average daily flow for the individual year or multi-year period of interest. [http://streamflow.engr.oregonstate.edu/analysis/annual/] • Future changes in precipitation and impacts on extreme streamflow over Amazonian sub-basins (Guimberteau et al., 2013, Environ. Res. Lett.) • Hydrological annual extreme variations (i.e. low/high flows) associated with precipitation (and evapo-transpiration) changes are investigated over the Amazon River sub-basins. • Evaluating changes in mean annual flow (MAF), high flow (highest decile of MAF), low flow (lowest decile of MAF) over the 1980 – 2000 period and two periods of the 21st century. This study investigated changes in hydrological extremes which were associated with an annual resolution. Department of Bioenvironmental Systems Engineering, National Taiwan University

  10. Temperature dependence of global precipitation extremes (Liu et al., 2009, Geophysical Research Letters) • For Taiwan, the top 10% heaviest rain increases by about 140% for each degree increase in global temperature. • The top 10% bin rainfall intensity was defined as 13 mm/hrwhich was calculated based on long-term average daily rainfall intensities. • The above climatological rainfall extreme is much lower than the 79 mm design rainfalls (for 90-minute duration and 5-year return period) of the Taipei City. Department of Bioenvironmental Systems Engineering, National Taiwan University

  11. Example • Contours of the 100-year return period daily rainfall depth based on observed data and high-resolution downscaled rainfalls. (A) (B) Based on site observations Based on high-resolution downscaled rainfalls. Contours exhibit higher degree of spatial continuity. Department of Bioenvironmental Systems Engineering, National Taiwan University

  12. Climate change impact assessment focusing on changes in design storms in Taiwan Cheng, K.S., Lin, G.F., Chen, M.J., Wu, Y.C, Wu, M.F. Hydrotech Research Institute, NTU Department of Bioenvironmental Systems Engineering, National Taiwan University

  13. Scale mismatch in climate projection and hydrological projection • In assessing the impact of climate change, hydrologists often are interested in changes in rainfall extremes, such as rainfall depths of high return periods (i.e., design storms such as rainfall depth of 24-hour, 100-year). • Such rainfall extremes are results of extreme weather events which are characteristic of relatively small spatial and temporal scales and cannot be resolved by GCMs. Department of Bioenvironmental Systems Engineering, National Taiwan University

  14. Projections in coarse spatial and time scales. (200 – 300 km; monthly) Projections in finer spatial scale. (5km; monthly) 24 GCMs RCM From GCM outputs to design storm depths – a problem of scale mismatch (both temporal and spatial) Design rainfall depths For example, 24-hr, 100-year rainfall depth Characteristics of extreme storm events Department of Bioenvironmental Systems Engineering, National Taiwan University

  15. Rainfall extremes represent quantities of high percentiles. • Predicting extreme values is far more difficult than predicting the means. • We may have reasonable confidence on climate projections (for example, long-term average seasonal rainfalls), whereas our confidence on extreme weather projections is generally low. Department of Bioenvironmental Systems Engineering, National Taiwan University

  16. Characteristics of storm events • Number of storm events • Duration of a storm event • Total rainfall depth • Time variation of rainfall intensities • These characteristics are random in nature and can be described by certain probability distributions. • Although the realized values of these storm characteristics of individual storm events represent weather observations, their probability distributions are climate (long term and ensemble) properties. Department of Bioenvironmental Systems Engineering, National Taiwan University

  17. A GCM – stochastic model integrated approach • Climatological projection by GCMs • Changes in the means of storm characteristics • For examples, • Average number of typhoons per year • Average duration of typhoons • Average event-total rainfall of typhoons • Hydrological projection by a stochastic storm rainfall simulation model • Generating realizations of storm rainfall process using storm characteristics which are representative of the projection period. • Preserving statistical properties of the all storm characteristics. Department of Bioenvironmental Systems Engineering, National Taiwan University

  18. Projections in coarse spatial and time scales. (200 – 300 km; monthly) Projections in finer spatial scale. (5km; monthly) 24 GCMs RCM Weather Generator (Richardson type) Conceptual flowchart Projections in finer time scale. (5km; daily) ANN Characteristics of storm events 1 Number of storm events • Onset of storm occurrences • Duration of a storm event • Total rainfall depth • Time variation of rainfall intensity Stochastic storm rainfall simulation Projections in point (spatial) and hourly (time) scales. Design rainfall depths For example, 24-hr, 100-year rainfall depth Characteristics of extreme storm events Department of Bioenvironmental Systems Engineering, National Taiwan University

  19. Climate change scenarios andGCM outputs • Emission scenario: A1B • Baseline period: 1980 – 1999 • Projection period • Near future: 2020 – 2039 • End of century: 2080 – 2099 • GCM model: 24 GCMs statistical downscaling • Hydrological scenario: changes in storm characteristics Department of Bioenvironmental Systems Engineering, National Taiwan University

  20. Changes in monthly rainfalls (Statistical downscaling, Ensemble average with standard deviation adjustment) Taipei area Near future (2080 – 2099) Near future (2020 – 2039) Department of Bioenvironmental Systems Engineering, National Taiwan University

  21. Annual counts of storm events estimated by ANN Frontal Maiyu Typhoon Convective North Center South Department of Bioenvironmental Systems Engineering, National Taiwan University

  22. Storm characteristics (average duration of typhoon) Gauge observations MRI (1979 - 2003) Source: NCDR, Taiwan Department of Bioenvironmental Systems Engineering, National Taiwan University MRI (2015 – 2039) MRI (2075 - 2099)

  23. Storm characteristics (average event-total rainfalls of typhoon) Gauge observations MRI (1979 - 2003) Source: NCDR, Taiwan Department of Bioenvironmental Systems Engineering, National Taiwan University MRI (2015 – 2039) MRI (2075 - 2099)

  24. Stochastic storm rainfall process • Storm characteristics • Duration • Event-total depth • Inter-arrival(or inter-event) time • Time variation of rain-rates Rainrate Inter-arrival time Inter-arrival time Total depth Time(hr) Duration Duration Duration Duration Department of Bioenvironmental Systems Engineering, National Taiwan University

  25. Season-specific storm characteristics Convective, Typhoon Rainfalls (mm) Frontal Mei-Yu Frontal Nov - Dec Jan- April July - October May - June Department of Bioenvironmental Systems Engineering, National Taiwan University

  26. Stochastic Storm Rainfall Simulation Model (SSRSM) • Simulating occurrences of storms and their rainfall rates • Preserving seasonal variation and temporal autocorrelation of rainfall process. • Duration and event-total depth • Inter-event times • Percentage of total rainfalls in individual intervals (Storm hyetographs) Department of Bioenvironmental Systems Engineering, National Taiwan University

  27. Simulating occurrences of storm events of various storm types • Number of events per year • Poisson distribution for typhoon and Mei-Yu • Inter-event time • Gamma or log-normal distributions Department of Bioenvironmental Systems Engineering, National Taiwan University

  28. Simulating joint distribution of duration and event-total depth • Bivariate gamma distribution (e.g. typhoons) • Log-normal-Gamma bivariate • Non-Gaussian bivariate distribution was transformed to a corresponding bivariate standard normal distribution with desired correlation matrix. Department of Bioenvironmental Systems Engineering, National Taiwan University

  29. Bivariate gamma (X,Y) Department of Bioenvironmental Systems Engineering, National Taiwan University

  30. Department of Bioenvironmental Systems Engineering, National Taiwan University

  31. Simulating percentages of total rainfalls in individual intervals (Simulation of storm hyetographs) • Based on the simple scaling property • Durations of all events of the same storm types are divided into a fixed number of intervals (e.g. 24 intervals). • For a specific interval, rainfall percentages of different events are identically and independently distributed (IID). • Rainfall percentages of adjacent intervals are correlated. • The simple scaling leads to the Horner equation fitting of the IDF curves. Department of Bioenvironmental Systems Engineering, National Taiwan University

  32. Simple scaling (Random fractal) Department of Bioenvironmental Systems Engineering, National Taiwan University

  33. Department of Bioenvironmental Systems Engineering, National Taiwan University

  34. Modeling the storm hyetograph Probability density of x(15) Department of Bioenvironmental Systems Engineering, National Taiwan University

  35. Taking all the above properties into account, we propose to model the dimensionless hyetograph by a truncated gamma Markov process. Department of Bioenvironmental Systems Engineering, National Taiwan University

  36. Truncated gamma density (parameters estimation, including the truncation level) Department of Bioenvironmental Systems Engineering, National Taiwan University

  37. Effect of modeling truncated data with an untruncated density Department of Bioenvironmental Systems Engineering, National Taiwan University

  38. Parameters estimation Truncated gamma distribution Department of Bioenvironmental Systems Engineering, National Taiwan University

  39. Department of Bioenvironmental Systems Engineering, National Taiwan University

  40. Department of Bioenvironmental Systems Engineering, National Taiwan University

  41. Stochastic simulation Department of Bioenvironmental Systems Engineering, National Taiwan University

  42. Department of Bioenvironmental Systems Engineering, National Taiwan University

  43. Department of Bioenvironmental Systems Engineering, National Taiwan University

  44. Department of Bioenvironmental Systems Engineering, National Taiwan University

  45. Department of Bioenvironmental Systems Engineering, National Taiwan University

  46. Department of Bioenvironmental Systems Engineering, National Taiwan University

  47. Example 1 Department of Bioenvironmental Systems Engineering, National Taiwan University

  48. Example 2 Department of Bioenvironmental Systems Engineering, National Taiwan University

  49. Department of Bioenvironmental Systems Engineering, National Taiwan University

  50. CHECK • Validation by stochastic simulation Department of Bioenvironmental Systems Engineering, National Taiwan University

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