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HAE MODEL Panut Manoonvoravong Bureau of research development and hydrology Department of water resources BACKGROUND TO THE HYDROLOGICAGRONOMIC-ECONOMIC MODEL (HAE MODEL) This model was funded by World Bank in May 2009 for the Lower Mekong Basin (LMB) to assist the Thai Government to develop policy tools for adapting to climate change impacts on the water and natural resources of the Mekong River Basin building local knowledge and capacity on climate change The pilot area for the HAE model development project was set in Kong-Chi-Mun the Northeast of Thailand basins (the sub-basin areas of lower Mekong tributaries). The models are required to assess the impact of climate change on: a) the hydrologic regime, b) water usage, c) agricultural production and d) socio-economic conditions. Topography ECHAM4 GCM The ECHAM climate model has been developed from the ECMWF atmospheric model. It provided a future climate scenario data of a spatial resolution approximates to about 2.8º longitude/latitude and the time-step of 24 minutes. The model condition based on the emission scenario of IPCC SRES A2 & B2. ROLE OF PRECIS MODEL ON THE PROJECT To provide the future climate scenario data based on ECHAM4 GCM’s future Climate data and the emission scenarios of IPCC SRES A2 & B2. 6 variables used in the HAE model are precipitation (mm) max & min temperature (Degree Celsius) solar radiation (MJ/m2) Relative humidity Wind speed (m/s) Future climate projection using PRECIS model Precipitation PRECIS vs. Observation Future Precipitation Change A2 & B2 Other Meteorological Parameters PRECIS vs. Observation Future Change A2 & B2 Evapotranspiration Baseline PRECIS data Observation in term of climatology PRECIS’ baseline & future climate scenario data comparison DATA PREPARING FOR HAE MODEL To find the bias adjustment coefficient for PRECIS data To apply for the future climate scenario data HAE model Bias adjusted climate scenario data PRECIS VERIFICATION 13 observation stations of TMD were selected to compare with PRECIS simulation WAY TO ADJUST PRECIS: PRECIPITATION 0.2 Grid PRECIS Observed To Interpolate into each grid an approach in which variable bias adjustment was applied to the PRECIS data depending upon the ranking of the PRECIS simulated data amongst all rain-day events in a particular month WAY TO ADJUST PRECIS: OTHER METEOROLOGICAL PARAMETERS The approach developed was based on monthly corrections to PRECIS data determined from the differences between the mean monthly observed and PRECIS simulated data at the 13 TMD stations. By using adjusted PRECIS meteorological data, Daily potential evapotranspiration was calculated using the FAO Penman method. Data were calculated on each of the PRECIS grids. FUTURE CLIMATE SCENARIO PROJECTIONS Summary of future climate change in Kong-Chi-Mun basin Kong Chi Mun - Higher mean annual rain - Rainy season: higher rainfall intensity - Increase rainfall in annual A2 heaviest rainy day - Longer hot duration - Higher mean annual rain on eastern side - Rainy season: higher rainfall intensity - Increase rainfall in annual heaviest rainy day - Longer hot duration - Mean annual rain is close to present - Rainy season: seasonal rain is close to present - Increase rainfall in annual heaviest rainy day - Longer hot duration -Mean annual rain is close to present - Increase variation in annual rainfall - Rainy season: seasonal B2 rain is close to present - Dry season: Less rainfall during dry season - Longer hot duration -Mean annual rain is close to present - Increase variation in annual rainfall - Rainy season: seasonal rain is close to present - Dry season: Less rainfall during dry season - Longer hot duration -Mean annual rain is close to present - Increase variation in annual rainfall - Rainy season: seasonal rain is close to present -Dry season: Less rainfall during dry season - Longer hot duration FUTURE CLIMATE SCENARIO Summary of future climate scenarios 19802009 A2 20402069 Kong Chi Mun Annual Rainfall 1600 mm 1200 mm 1300 mm Max temp. (Mar-May) 34.5C 35C 35C Min temp. (Dec-Feb) 16C 17.5C 18 C Annual Rainfall (+9%) (+7%) Rainy seasonal rain (+12%) (+10%) Dry seasonal rain (-6%) Annual heaviest rainday Max temp. (Mar-May) Hot duration (+1C) (+1C) (+30-45 days) (+30-45 days) (+1C) (+45-60 days) Annual Rainfall Rainy seasonal rain B2 20402069 Dry seasonal rain (-14%) (-11%) (-7%) (+1C) (+1C) (+1C) Annual heaviest rainday Max temp. (Mar-May) Hot duration (+30-45 days) (Increase) (+30-45 days) No change (+45-60 days) (Decrease) FLOOD DAMAGES SWAT Model Time Series Routed Mean Daily Flows at Selected Locations Associated Rating Curve Associated Flood Depth Flood Depth-Area Table For Location Associated Flood Area and Duration Annual Loss Calculations Data requirement to estimate yield and crop water uses by DSSAT crop model 2.1 Weather data 2.2 Soil data 2.3 Crops and crop management practices Time Frame Baseline (BS, 1980-2009) Future A2 (2050s, 2040-2069) Future B2 (2050s, 2040-2069) Data requirement to estimate yield and crop water uses by DSSAT crop model 2.1 Weather data 2.2 Soil data 2.3 Crops and crop management practices Figure 2.5 Soil group in the basins ECONOMIC ANALYSIS OVERVIEW Agriculture Time Series Net Production Values Present Value of Production Flood Damage Time Series Flood Damages Direct/Indirect Annual Damages Hydropower Time Series Net Production Values Present Value of Production Present Value Adaptation and Mitigation Costs Revised Present Value Production and Damages with Mitigation Benefit : Cost Ratios of Adaptation and Mitigation Measures THANK YOU FOR YOU ATTENTION