August 4, 2009, DPRI-Kyoto University, Uji

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1 McGill University Montreal, Quebec, Canada Brace Centre for Water Resources Management Global Environmental and Climate Change Centre Department of Civil Engineering and Applied Mechanics School of Environment SOME ADVANCES IN DOWNSCALING METHODS FOR CLIMATE-RELATED IMPACT AND ADAPTATION STUDIES Van-Thanh-Van Nguyen and Students 1

2 OUTLINE INTRODUCTION n Extreme Rainfall Estimation (IDF Relations) Issues? n Climate Variability and Climate Change Impacts? n Design Storm Concept Issues? OBJECTIVES METHODOLOGY n Downscaling Methods n The Spatial-Temporal Downscaling Method APPLICATIONS CONCLUSIONS 2

3 INTRODUCTION Information on rainfall characteristics is essential for planning, design, and management of various hydraulic structures (flood protection works, urban sewers, etc.) Rainfall records by raingages or radar are usually limited (< 5 years) and are not sufficient for assessing reliability of hydraulic structure design. Stochastic simulation of rainfall processes is needed to generate many long rainfall series. Several rainfall samples of adequate record length are needed to be able to determine how different system designs and operating policies i might perform. the variability and the range of future system performance are better understood, and better system designs and policies could be selected. Extreme storms and floods account for more losses than any other natural disaster (both in terms of loss of lives and economic costs). n Damages due to Saguenay flood in Quebec (Canada) in 1996: $8 million dollars. n Average annual flood damages in the U.S. are US$2.1 billion dollars. n Increased trends in number of natural disasters and related costs. Design Rainfall = maximum amount of precipitation at agivensitefor a specified duration and return period. 3

4 EXTREME RAINFALL ESTIMATION (IDF Relations) At-site or Regional Frequency Analysis of Extreme Rainfalls Intensity-Duration-Frequency (IDF) Relations DESIGN STORM ESTIMATION. Traditional IDF estimation methods: n Time scaling problem: no consideration of rainfall properties at different time scales; n Spatial scaling problem: results limited to data availability at a local site; n Climate change: no consideration. (WMO Guides to Hydrological Practices: 1 st Edition th Edition: Section 5.7, in press) 4

5 Extreme Rainfall Estimation Issues (1) The scale problem n The properties of a variable depend on the scale of measurement or observation. n Are there scale-invariance i properties? And how to determine these scaling properties? n Existing methods are limited to the specific time scale associated with the data used. n Existing methods cannot take into account the properties of the physical process over different scales. 5

6 Extreme Rainfall Estimation Issues (2) Climate Variability and Change will have important impacts on the hydrologic cycle, and in particular the precipitation process! How to quantify Climate Change? General Circulation Models (GCMs): A credible simulation of the average large-scale seasonal distribution of atmospheric pressure, temperature, and circulation. (AMIP 1 Project, 31 modeling groups) Climate change simulations from GCMs are inadequate for impact studies on regional scales: Spatial resolution ~ 5, km 2 Temporal resolution ~ daily, month, seasonal Reliability of some GCM output variables (such as cloudiness precipitation)? 6

7 n How to develop Climate Change scenarios for impacts studies in hydrology? Spatial scale ~ afewkm 2 to several 1 km 2 Temporal scale ~ minutes to years A scale mismatch between the information that GCM can confidently provide and scales required by impacts studies. Downscaling methods are necessary!!! GCM Climate Simulations Precipitation (Extremes) at a Local Site 7

8 DESIGN STORM CONCEPT A design storm describes completely the distribution of rainfall intensity during the storm duration for a given return period. Two main types of synthetic design storms: n Design Storms derived from the IDF relationships. n Design Storms resulted from analysing and synthesising the characteristics of historical storm data. Intensity Atypical design storm: I max n Maximum Intensity: I MAX n Time to peak: T p n Duration: T n Temporal pattern T p T Time 8

9 Design Storm Estimation Issues Different synthetic ti design storm models available in various countries: n US Chicago storm model (Keifer and Chu, 1957) n US Normalized storm pattern by Huff (1967) n Czechoslovakian storm pattern by Sifalda (1973) n Australian design storm by Pilgrim and Cordery (1975) n UK Mean symmetric pattern (Flood Studies Report, 1975) n French storm model by Desbordes (1978) n US storm pattern by Yen and Chow (198) n Canadian Atmospheric Environment Service (198) n US balanced storm model (Army Corps of Engineer, 1982) n Canadian temporal rainfall patterns (Nguyen, 1981, ) n Canadian storm model by Watt et al. (1986) No general agreement as to which temporal storm pattern should be used for a particular site How to choose? How to compare? 9

10 Design Storm Comparison (Peyron et al., 25) For runoff peak flows: n the Canadian AES design storm n the Desbordes model (with a peak intensity duration of 3 minutes) For runoff volumes: n the Canadian pattern proposed by Watt et al. None of the eight design storms was able to provide accurate estimation of both runoff parameters. 1

11 The 1-hr optimal storm pattern for southern Quebec (Canada) Intensity 1.4 I 15min Total Volume = 1.3 V 1hr 2 15 PROPOSED DESIGN STORM Return Period: 2 years 5 years 1 years 5 years 1 years.8 I 15min Intensity (m mm/hr) min 25 min Time min Time (min) 6 min 11

12 Assessment of the Proposed Optimal Storm Pattern Runoff peak flows Basin shape Imperviousness Desbordes AES (%) (3 min) Watt Proposed Square Rectangular L/W= Rectangular L/W= Rectangular (Residential) Runoff volumes Basin shape Imperviousness Desbordes AES (%) (3 min) Watt Proposed Square Rectangular L/W= Rectangular L/W= Rectangular (Residential)

13 OBJECTIVES To assess the performance of statistical downscaling methods to find the best method in the simulation of daily precipitation time series for climate change impact studies. To develop an approach that t could link daily simulated climate variables from GCMs to sub-daily precipitation characteristics at a regional or local scale (the spatial-temporal temporal downscaling method). To assess the climate change impacts on extreme rainfalls, design storms, and the resulting runoff characteristics. 13

14 METHODOOGY DOWNSCALING METHODS Scenarios 14

15 (SPATIAL) DYNAMIC DOWNSCALING METHODS Coarse GCM + High resolution AGCM Variable resolution GCM (high resolution over the area of interest) GCM + RCM or LAM (Nested Modeling Approach) n More accurate downscaled d results as compared to the use of GCM outputs alone. n Spatial scales for RCM results ~ 2 to 5 km still larges for many hydrologic models. n Considerable computing resource requirement. 15

16 (SPATIAL) STATISTICAL DOWNSCALING METHODS Weather Typing or Classification n Generation daily weather series at a local site. n Classification schemes are somewhat subjective. Stochastic Weather Generators n Generation of realistic statistical properties of daily weather series at a local site. n Inexpensive computing resources n Climate change scenarios based on results predicted by GCM (unreliable for precipitation) Regression-Based Approaches n Generation daily weather series at a local site. n Results limited to local climatic conditions. n Long series of historical data needed. n Large-scale and local-scale parameter relations remain valid for future climate conditions. n Simple computational requirements. 16

17 SUMMARY Downscaling is necessary!!! GCM Climate Predictors Is it feasible? Local Daily Precipitation Series Is it feasible? Daily Extreme Precipitations Is it feasible? Sub-Daily Extreme Precipitations 17

18 The Spatial-Temporal Downscaling Approach GCMs: HadCM3 and CGCM2. NCEP Re-analysis data. Spatial downscaling method: the statistical downscaling model SDSM (Wilby et al., 22). Temporal downscaling method: the scaling GEV model (Nguyen et al. 22). 18

19 APPLICATIONS LARS-WG Stochastic ti Weather Generator (Semenov et al., 1998) n Generation of synthetic series of daily weather data at a local site (daily precipitation, maximum and minimum temperature, and daily solar radiation) n Procedure: 4Use semi-empiricalempirical probability distributions to describe the state of a day (wet or dry). 4Use semi-empirical empirical distributions for precipitation amounts (parameters estimated for each month). 4Use normal distributions for daily minimum and maximum temperatures. These distributions are conditioned on the wet/dry status of the day. Constant Lag-1 autocorrelation and cross-correlation correlation are assumed. 4Use semi-empiricalempirical distribution for daily solar radiation. This distribution is conditioned on the wet/dry status of the day. Constant Lag-1 autocorrelation is assumed. 19

20 Statistical Downscaling Model (SDSM) (Wilby et al., 21) n Generation of synthetic series of daily weather data at a local site based on empirical relationships between local-scale predictands (daily temperature and precipitation) and large- scale predictors (atmospheric variables) n Procedure: 4Identify large-scale predictors (X) that could control the local parameters (Y). 4Find a statistical relationship between X and Y. 4Validate the relationship with independent data. 4Generate Y using values of X from GCM data. 2

21 Geographical locations of sites under study. Geographical coordinates of the stations Station Lat ( o ) Long ( o ) Alt (m) Dorval 45 o o Drummondville 45 o o Maniwaki 46 o o Montreal McGill 45 o 3-73 o

22 DATA: n Observed daily yp precipitation p and temperature extremes at four sites in the Greater Montreal Region (Quebec, Canada) for the period. n NCEP re-analysis daily data for the period. n Calibration: ; validation: Variable Level of measurement Mean sea level pressure Airflow strength surface 85 hpa 5 hpa Zonal velocity surface 85 hpa 5 hpa Meridional velocity surface 85 hpa 5 hpa Vorticity surface 85 hpa 5 hpa Wind direction surface 85 hpa 5 hpa Divergence surface 85 hpa 5 hpa Specific humidity near surface 85 hpa 5 hpa Geopotential height 85 hpa 5 hpa 22

23 No Code Unit Time scale Description 1 Prcp1 % Season Percentage of wet days (daily precipitation i i 1 mm) 2 SDII mm/r.day Season Daily Mean: sum of daily precipitations / number of wet days 3 CDD days Season Maximum number of consecutive dry days (daily precipitation < 1 mm) 4 R3days mm Season Maximum 3-day precipitation total 5 Prec9p mm Season 9 th percentile of daily precipitation amount 6 Precip_mean mm/day Month Sum of daily precipitation in a month / number of days in that month 7 Precip_sd mm Month Standard deviation of daily precipitation in a month Evaluation indices and statistics 23

24 (mm) The mean of daily precipitation for the period of Dorval J F M A M J J A S O N D OBSERVED vs. SDSM-GENERATED MEAN (mm) (mm) Dorval ova Dorval OBS SDSM LARS J F M A M J J A S O N D 6 BIAS = Mean (Obs.) Mean (Est.) J F M A M J J A S O N D OBSERVED vs. LARS-WG-GENERATED MEAN 24

25 The mean of daily tmax for the period of (oc) 3 McGill J F M A M J J A S O N D OBSERVED vs. SDSM- AND LARS-WG-MEAN OF TMAX MGill McGill LARS SDSM OBS BIAS (oc) J F M A M J J A S O N D 25

26 GCM and Downscaling Results (Precipitation Extremes ) 1- Observed 2- SDSM [CGCM1] 3- SDSM [HADCM3] 4- CGCM1-Raw data 5- HADCM3-Raw data From CCAF Project Report by Gachon et al. (25) 26

27 SUMMARY Downscaling is necessary!!! LARS-WG and SDSM models could provide good good but generally biased biased estimates of the observed statistics of daily precipitation at a local site. GCM-Simulated Daily Precipitation Series Is it feasible? Daily Extreme Precipitations Is it feasible? Sub-Daily Extreme Precipitations 27

28 The Scaling Generalized Extreme-Value (GEV) Distribution. The scaling concept f ( t ) = C ( λ ). C ( λ ) = λ λ f ( λ t ) β k μ k = E { f ( t )} = α ( k ) β k The cumulative distribution function: F ( x ) = The quantile: exp 1 κ ( x ξ ) α t 1 / κ α X ( F ) = ξ + F ] κ { κ 1 [ ln } 28

29 The Scaling GEV Distribution The Scaling GEV Distribution ) ( ) (λ ) ( ) ( ) ( ) ( t t t t α λ λ α κ λ κ β = ) ( ) ( ) (. ) ( t t t t ξ λ λ ξ α λ λ α β β = = ) (. ) ( ) (. ) ( t X t X t t T T λ λ ξ λ λ ξ β = = ) ( ) ( T T ) ( where 1 t λ μ λ β = 29 ) ( μ 1 t

30 APPLICATION: Estimation of Extreme Rainfalls for Gaged Sites Data used: Raingage network: 88 stations in Quebec (Canada). Rainfall durations: from 5 minutes to 1 day. Record lengths: from 15 yrs. to 48 yrs. 3

31 Scaling of NCMs of extreme rainfalls with durations: 5-min to 1-hour and 1-hour to 1-day. 1.E+ 6 MONTREAL JEAN BREBEUF 1.E+ 6 MONTREAL McGILL 1.E+ 6 MONTREAL DORVAL 1.E+ 5 1.E+ 5 1.E+ 5 1.E+ 4 1.E+ 4 1.E+ 4 1.E+ 3 1.E+ 3 1.E+ 3 1.E+ 2 1.E+ 2 1.E+ 2 1.E+ 1 1.E+ 1 1.E+ 1 1.E Durations (min.) 1.E Durations (min.) 1.E Durations (min.) 1.E+ 6 SHAWVILLE 1.E+ 6 MANIWAKI 1.E+ 6 QUEBEC CITY 1.E+ 5 1.E+ 5 1.E+ 5 1.E+ 4 1.E+ 4 1.E+ 4 1.E+ 3 1.E+ 3 1.E+ 3 1.E+ 2 1.E+ 2 1.E+ 2 1.E+ 1 1.E+ 1 1.E+ 1 1E+ 1.E Durations (min.) 1E+ 1.E Durations (min.) 1E+ 1.E Durations (min.) 1.E+ 6 RIMOUSKI 1.E+ 6 AMOS 1.E+ 6 OKA LA TRAPPE 1.E+ 5 1.E+ 5 1.E+ 5 1E+4 1.E+4 1E+4 1.E+4 1E+4 1.E+4 1.E+ 3 1.E+ 3 1.E+ 3 1.E+ 2 1.E+ 2 1.E+ 2 1.E+ 1 1.E+ 1 1.E+ 1 1.E+ 1.E+ 1.E Durations (min.) Duratio ns (min.) Durations (min.) red: 1 st NCM; blue: 2 nd NCM; black: 3 rd NCM; markers: observed values; lines: fitted regression 31

32 Results on scaling regimes: Non-central moments are scaling. Two scaling regimes: 5-min. to 1-hour interval. 1-hour to 1-day interval. Based on these results, two estimations were made: 5-min. extreme rainfalls from 1-hr rainfalls. 1-hr. extreme rainfalls from 1-day rainfalls. 32

33 5-min Extreme Rainfalls estimated from 1-hour Extreme Rainfalls 2 MONTREAL DORVAL 2 MONTREAL JEAN BREBEUF 2 L'ASSOMPTION CDA Pro b ab ility Probability Probability 2 DRUMMONDVILLE 2 STE. AGATHE DES MONTS 3 MONTREAL McGILL Probability Pro b ab ility Probability 2 MANIWAKI 1 FORESTVILLE 2 SHAWINIGAN Pro b ab ility Probability Pro b ab ility markers: observed values lines: values estimated by scaling method markers: observed values lines: values estimated by scaling method 33

34 1-hour Extreme Rainfalls estimated from 1-day Extreme Rainfalls 6 MONTREAL DORVAL 6 STE. ANNE DE BELLEVUE 4 WEST DITTON Pro b ab ility Pro b ab ility Pro b ab ility 5 MONTREAL JEAN BREBEUF 9 MONTREAL McGILL 4 L'ASSOMPTION CDA Pro b ab ility Pro b ab ility Pro b ab ility 5 STE. AGATHE DES MONTS 5 MANIWAKI 3 SEPT-ILES AIRPORT Pro b ab ility Pro b ab ility Pro b ab ilty markers: observed values lines: values estimated by scaling method 34

35 The Spatial-Temporal Downscaling: Application Study Region n Precipitation records from a network of 15 raingages in Quebec (Canada). Data n GCM outputs: 4HadCM3A2, HadCM3B2, 4CGMC2A2, CGCM2B2, 4Periods: , 199, 22s, 25s, 28s. n Observed data: 4Daily precipitation data, 4AMP for 5 min., 15 min., 3 min., 1hr., 2 hrs., 6 hrs., 12 hrs. 4Periods:

36 Daily AMPs estimated from GCMs versus observed daily AMPs at Dorval. Calibration period: Dist. of AM Daily Precip. before and after adjustment, , 1975 Dorval 1 Dist. of AM Daily Precip. before and after adjustment, , 1975 Dorval 9 9 AM Daily Precipitation (m mm) m) AM Daily Precipitation (m Observed CGCM2A2 Adj-CGCM2A Return period (years) 4 Observed HadCM3A2 Adj-HadCM3A Return period (years) CGCMA2 HadCM3A2 36

37 Residual = Daily AMP (GCM) - Observed daily AMP (local) Calibration period: CGCM2A2 estimates vs Residuals, HadCM3A2 estimates vs Residuals, Residuals 8 6 Residuals Residuals Fitted curve CGCM2A2 estimates Residuals Fitted curve HadCM3A2 estimates CGCMA2 HadCM3A2 37

38 Daily AMPs estimated from GCMs versus observed daily AMPs at Dorval. Validation period: Dist. of AM Daily Precip. before and after adjustment, , 1975 Dorval 1 Dist. of AM Daily Precip. before and after adjustment, , 1975 Dorval 9 9 AM Daily Precipitation (m mm) m) AM Daily Precipitation (m Observed CGCM2A2 Adj-CGCM2A Return period (years) 4 Observed HadCM3A2 Adj-HadCM3A Return period (years) CGCMA2 HadCM3A2 Adjusted Daily AMP (GCM) = Daily AMP (GCM) + Residual 38

39 13 Dist. of AM Daily Precip. after adjustment (CGCM2A2),Dorval 11 Dist. of AM Daily Precip. after adjustment (HadCM3A2),Dorval ) AM Daily Precipitation (mm) s 4 25s 28s Return period (years) 2 18 GEV Dist. of AM 5 min Precip. after adjustment (CGCM2A2), Dorval ) AM Daily Precipitation (mm) s 25s 28s Return period (years) GEV Dist. of AM 5 min Precip. after adjustment (HadCM3A2), Dorval AM 5 min Precipitation (mm) s 6 25s CGCMA2 28s Return period (years) Precipitation (mm) AM 5 min s 25s HadCM3A2 28s Return period (years) 39

40 CGCMA2 HadCM3A s 25s 28s A 5-year design storm by Peyron et al. based on CGCMA2 and HadCM3A2 scenarios 4

41 s 25s 28s A square area of 1 ha with 65% imperviousness 41

42 CONCLUSIONS (1) Significant advances have been achieved regarding the global climate modeling. However, GCM outputs are still not appropriate for assessing climate change impacts on the hydrologic cycle. Downscaling methods provide useful tools for this assessment. In general, LARS-WG and SDSM models could provide good but biased estimates of the observed statistical properties of the daily precipitation process at a local site. Differences between quantile estimates from observed daily AMPs and from GCM-based daily AMPs could be described by a second-order order non-linear function. Observed AMPs in Quebec exhibit two different scaling regimes for time scales ranging from 1 day to 1 hour, and from 1 hour to 5 minutes. 42

43 CONCLUSIONS (2) The proposed scaling GEV method could provide accurate AMP quantiles for sub-daily durations from daily AMPs. It is feasible to link daily GCM-simulated climate variables with sub-daily AMPs based on the proposed spatial- temporal downscaling method. IDF relations and design storms for different climate change scenarios could be constructed. AMPs derived from CGCM2A2 outputs show a large increasing trend for future periods, while those given by HadCM3A2 did NOT exhibit a large (increasing or decreasing) trend. Similar results were found for peak flows and volumes from urban areas of different sizes, shapes, and impervious conditions. 43

44 Thank you for your attention! 44

45 Slides required for presentations 45

46 I (mm/hr) True image time (hr) I (mm/hr) time (hr) 46

47 47

48 Climate Scenario development need: from coarse to high resolution A mismatch of scales between what climate models can supply and what environmental impact models require. Impact models require... 1km 1m km 5 3km GCMs or RCMs supply... Point P. Gachon 48

49 Data Points Regression curves 2 years 2 years 5 years 5 years 1 years 1 years 5 years 5 years 1 years 1 years Duration (min) Intensity-Duration-Frequency curves for Montreal area. 49

50 I 7 ntensity (mm/hr) Time (min) Return Period: 2 years 5 years 1 years 5 years 1 years I ( t ) = Chicago a ( b + t) IDF c i a[(1 c)( τ / ρ) [( τ / ρ) + b] = c+ 1 τ = t p τ = t t p t i( τ ) dτ = I ( t) t t a t i( τ ) dτ = (b b + t ) + b] t and ρ = r = t and ρ = 1 r b Design Storm / D for t t for t t p c p 5

51 Frequency of Natural Disasters in Canada (19-25) 16 Wildfires Avalanches 14 Cold Waves/Heat Waves Droughts Disasters Numb ber of Natural Earthquakes/Landslides Floods Freezing Rain Hail/Thunderstorms Hurricane/Typhoon Storms Tornados Tsunamis/Storm Surges 6 years data! Year Period Environment Canada (H. Auld) 51

52 Climate Trends and Variability Maximum and minimum temperatures have increased at similar rate Warming in the south and west, and cooling in the northeast (winter & spring) Trends in Winter Mean Temp ( C / 49 years) Trends in Spring Mean Temp ( C / 49 years) Trends in Summer Mean Temp ( C / 49 years) Trends in Fall Mean Temp ( C / 49 years) From X. Zhang, L. Vincent, B. Hogg and A. Niitsoo, Atmosphere-Ocean, 2 52

53 Validation of GCMs for Current Period ( ) Winter Temperature ( C) Model mean =all flux & non-flux corrected results (vs NCEP/NCAR dataset) [Source: IPCC TAR, 21, chap. 8] 53