Im Impa pact cts s asse ssess ssmen ents s of of water er - PowerPoint PPT Presentation

Im Impa pact cts s asse ssess ssmen ents s of of water er re resour sources ces in n the he Ka Kalu Ganga nga ba basi sin, , Sri ri La Lanka nka & Pre reliminar ary y ana nalys ysis of of dow downscal nscaling ng sc sche heme e by by GSMaP MaP Cho ho Tha handa nda Nyunt yunt Hi Hiroshima a Un Univer ersity The 7 th GEOSS Asia-Pacific Symposium Tokyo, Japan, 27 May2014

Deficiencies in GCM 4 Extreme rainfall Seasonal rainfall Wet day frequency 1 2 3 Regional Bias of GCM Low horizontal Climate  Under resolution estimation of Insufficient for Simulation  extreme rainfall basin scale run off  Asia Monsoon  Poor seasonality simulation  ITCZ  Frequency of wet A lot of uncertainty  days Spatial Statistical Multi-GCM disaggregation Bias selection correction (Downscaling)

GCM Selection Parameter • Precipitation (GPCP) • Outgoing Long wave Radiation (NOAA) • Sea Surface Temperature (HADLEY) • Sea level pressure (JRA-25) • Specific Humidity (JRA-25) • Zonal wind (JRA-25) • Meridonial wind (JRA-25) • Geopotential Height (JRA-25) Time Window • Wet/rainy season Analyzed Window • Local domain • Regional domain Scoring • Average over all GCM • Scorr > Scorr_allgcm_avg  1 • RMSE < RMSE_allgcm_avg  1 • Both Scorr & RMSE  1  final 1 • Either Scorr or RMSE  1  final 0 • Neither Scorr nor RMSE  1  final -1 Criteria • Highest total scores • Precipitation (both Scorr and RMSE satisfied) • Inter-seasonality (bimodal or single mode )

Bias correction scheme 9 Rain Type Threshold Correction - > 99% of daily precipitation during analysis period Generalized Pareto Distribution Extreme - same frequencies of extreme as insitu station as -Non every year statistics in GCM -Extreme (long or short tailed) fitting -Peak over threshold method Extreme Ranking order statistics No rain day - frequency of no rain day in GCM is same as station No rain day - less than no rain day threshold change zero rainfall. GCM=0 Gamma Distribution Normal - monthly CDF of GCM mapping to Extreme No rain day monthly CDF of station - inverse of Gamma CDF in Normal rain each month is corrected rain GCM

Climate Change Climate Change Assessment Impact Assessment Insi-tu Rain(1981-2000) Framework CMIP3 20C3M(1981-2000) A1Bscenario(2046-2065) Multi-GCM Selection Statistical Bias Correction WEB-DHM Spatial disaggregation Rainfall Downscaling DEM Temperature Soil Relative Humidity Land Use Wind Speed WEB-DHM Vegetation Solar Radiation JRA25 Reanalysis Calibration Basin Impact & Validation Vulnerability Basin Parameter Assessment Fl Floo ood Drou ought Decision Making & Planning IWRM CMIP3 : Coupled Modeled Intercomparison Project Phase-3 20C3M : 20th Century Numerical Reproductive Experiment

Sri Lanka Kalu Ganga River Basin

GCM Selections Region and Parameter East Asia Tibetan plateau monsoon South Asia monsoon Ocean  Spatial distribution of 10 parameters  4 region  May - August  Monthly Spatial Correlation  Monthly Root mean square error

GPCP Miroc3_2_hires Iap_fgoals Ncar_ccsm30 Mri_cgcm2_3_2 Giss_model_e_r Ncar_pcm1 Gfdl_cm2_0 Cnrm_cm3 Inmcm3_0 Ipsl_cm4 Miroc3_2_medres Ukmo_hadgem1 Cccma_cgcm3_1t63 Csiro_mk3_5 Mub_echo_g Giss_model_e_h Cccma_cgc_3_1 Mpi_echam5 Gfdl_cm2_1 Ukmo_hadcm3 Ingv_echam4 Giss_aom Csiro_mk3_0 Bccr_bcm2_0

Bias correction results at point Scale (1981-2000) Before Bias-Correction After Bias-correction Top 1% Extreme Extreme Frequency Analysis Normal rainfall (1981-2000)

Bias correction results for Basin Scale Gfdl_cm21 Ingv_echam4 Insitu Rain Gauge (May) Miroc32_hires Raw GCM rain Bias corrected rain

The Hydrological Model: WEB-DHM Source: Wang, et al., 2009, JGR; Wang, et al., 2009, HESS The Water and Energy Budget-based Distributed Hydrological Model ▪ Fully couples a simple biosphere scheme SiB2 with a hillslope hydrological model GBHM. ▪ Allows consistent descriptions of water, energy and CO 2 fluxes at the basin scale. Benefits of WEB-DHM over other models: flexible, low computational requirements; and comparable to commercially available models

MaP Pre reliminar ary y ana nalys ysis of of dow downscal nscaling ng sc sche heme e by by GSMaP

GSMaP: Global Satellite Mapping of Precipitation Dataset GSMap_MVK+ Observed point R Rain g gauge uge 0.1 d 1 degree l lat at/lon on Grid resolution 1 d 1 day ay 1 d 1 day ay Temporal resolution 60S) scale Global obal(60N 60N-60 Basi sin sc Available Domain Monthly( y(20 2003 03-200 2008) 8) Daily( y(19 1981 81-200 2008) 8) Available period Daily( y(20 2003 03-200 2008) 8) Hourly( y(20 2003 03-200 2008) 8)

Downscaling (Spatial disaggregation) Scheme Delineating monthly distributed weight 25 Bias Corrected GCM 1.1  3 Degree GSMapraw Vs Insitu stations based Monthly GSMaP corrected rainfall based 0.1 Degree gridded GSMap 0.1 Degree correction spatial distributed weights Map IDW Bias corrected Downscaled rain Insitu rainfall station Basin Scale Basin Scale 0.01 Degree Basin Scale 0.01 Degree

Sensitivity of numbers of rain gauges Sensitivity of Numbers of Rain gauges Over 3000 km 2

Bias corrected + Downscaled Rainfall 41 465.597 490.04 mm/month Observed mm/month Miroc32_hire 488.6577 471.5055 mm/month mm/month

42 Mean(mm/day) Stdev RMSE (mm/day) Observed 10.39 14.05 Cccma_cgcm31 11.38 14.88 12.04 Cccma_cgcm_t63 11.39 14.95 12.09 Gfdl_cm20 11.94 14.65 11.9 Gfdl_cm21 11.96 14.49 11.86 Ingv_echam4 11.35 14.83 12.04 Miroc32_hire 11.47 14.93 12.2 Miroc32_medres 11.38 15.04 11.81 Mpi_echam5 10.94 15.24 12.11