1. INTRODUCTION
Global warming will give us a serious impact on our life. Frequencies and magnitudes of floods and sedimentation disasters are predicted to increase due to the change of precipitation extremes. The IPCC, the Intergovernmental Panel on Climate Change, 4th assessment report (WG1, WG2; 2007) describes increase of global average surface temperatures, and potential increase of frequency of heavy rainfall, and so on based on long term observations. The report also shows the projections of climate change ac- l cording to several greenhouse gas emission scenarios and the impacts of climate change on water-related disasters and water resources. To cope with water-related disasters induced by climate change, both mitigation measures and adap- tation measures are essential. For adaptation measures, prediction of future water resources is a key issue. In this paper, a distributed hydrologic model of the Chao Phraya River basin is developed. Then, future river discharge is projected using the latest GCM output to detect the hotspots of river discharge change. It is found a change of flow char- acteristics at the tributaries of the Chao Phraya River, and water resource at the Pasak River basin is de- creased. Thus, we develop a water resources as- sessment model at the Pasak River basin, which in- cludes a distributed hydrologic model, a dam reser- voir water storage prediction model, and a plant growth model for rice production assessment.
2. DISTRIBUTED HYDROLOGIC MODEL
(1) Watershed model
The topography of the catchment is modeled using the eight direction method which assumes the flow direction one-dimensionally to the steepest gradient direction illustrated in Fig. 1. Each slope element de- termined by the flow direction is represented by a rec- tangle formed by the two adjacent nodes of grid cells. The watershed model is developed using a digital ele- vation model, DEM, included in HydroSHED, which cover the globe with about 100m spatial resolution. Figure 2 shows the delineated catchments of the Chao Phraya River basin (160,400km²), Thailand using the DEM. The area, length, and gradient of each rectan- gular slope element used for runoff and channel routing are calculated according to the watershed model.
(2) Flow model
The kinematic wave model is applied to all rec- tangular slope elements to route the water to down- stream according to the derived watershed model. The continuity equation for each rectangular slope element is:
where t is time; x is distance; A is cross-sectional area; Q is discharge; and q(t) is the lateral inflow per unit length of slope or channel given as runoff gen- eration provided by MRI-AM20km. The Manning type relation of the discharge and the cross-sectional area:
water, where i 0 is slope; n is roughness coefficient; and B is width of the flow. The slope i 0 is determined according to the watershed model. The model pa- rameters of the flow model are B and n . The value of B is determined using the regression relationship B=aSc , where S is the catchment area, and a and c is constant parameters. The value of n is determined to 0.03m־173 s when the size of the catchment is larger than 250km² and 11.0m -1/3 s when smaller than 250km². These values were tuned to reproduce the observed data at two different catchments and ap- plied to all basins. The flow model was applied to all catchments and 75 years runoff simulations were conducted. The simulated river discharge data of daily maximum and daily mean were stored for each day with about 1km spatial resolution.
3. GCM DATA USED FOR RIVER FLOW
PROJECTION
The projection data used here is simulated by the general circulation model (MRI-AM20km) devel- oped by the Meteorological Research Institute in Japan. MRI-AM20km realizes 1920×960 of grid cells of about 20 km spatial resolutions (Kitoh et al., 2009). The products of MRI-AM20km consists of various atmospheric and hydrologic variables of the present climate experiment (1979-2003), the near future climate experiment (2015-2039), and the fu- ture climate experiment (2075-2099), which were simulated under the SRES A1B scenario. The river discharge for the Chao Phraya River basins is predicted by feeding the future climate projection data into the 1km-spatial resolution dis- tributed hydrologic model. The hydrologic projec- tion variables related to river discharge is shown in Figure 3. The inputted data to the distributed hy- drologic model is daily surface runoff generation and daily sub-surface runoff generation data, which are simulated by the land-surface process model em- bedded in the MRI-AM20km. The time-scale of daily runoff generation data is insufficient to reproduce the hourly flood peak dis- charge, thus it was downscaled using the time-series of hourly precipitation data of MRI-AM20km to add the same hourly distribution pattern into the daily surface runoff generation data. We confirmed that discharge simulation with river the    timely downscaled surface runoff generation and daily subsurface runoff generation data successfully re- produced almost similar river discharge simulated by using hourly precipitation, daily snowmelt, daily evaporation and daily transpiraion (Takino et al., 2010). Thus, the timely downscaled surface runoff generation data and daily subsurface runoff genera- tion data were used as inputted data to the distributed hydrologic model.
4. CHANGE OF BASIC METEOROLOGI
-CAL PARAMETERS
According to the MRI-AM20km projection data for the near future climate experiment, the surface temperature found to increase roughly by 0.5 to 1 degree from the present temperature whereas the temperature increased by about 2.5 to 3 degrees in the future climate. Figure 4 shows a spatial pattern of the change of the difference of the mean of the annual average daily temperature in the near future and the future climate experiment with respect to the present climate experiment. Figure 5 shows a spatial pattern of the change of the percentage difference of the mean annual rainfall in the near future and the future climate experiment with respect to the present climate experiment. The percentage difference PD is obtained as
where r t is the annual mean precipitation in the near future or the future climate experiment and r p is the annual mean precipitation in the present climate ex- periment. 1% to 5% decreases of annual precipitation is observed in the central region of Thailand and 1% to 10% of increase in annual precipitation is observed in the southern region in the near future climate ex- periment. In the future climate conditions, it is ex- pected to have 1% to 10% of increase in annual pre- cipitation in the north mountainous region and 5% to 10% increase in the northeast region. Figure 6 shows a spatial pattern of the change of the percentage difference of the annual maximum hourly rainfall in the near future and the future cli- mate experiment with respect to the present climate experiment. It is observed that a clear increasing trend of the annual maximum hourly rainfall over Thailand for both time periods of the climate ex- periments. Notably, in the future climate experiment, about 20% increase of the annual maximum hourly rainfall in many parts of the region. Figure 7 shows a spatial pattern of the change of the percentage difference of the mean annual daily potential evapotranspiration in the near future and the future climate experiment with respect to the present climate experiment. It is noticed that the potential evappotranspiration increase by 4% to 6% in the near future climate experiment and 4% to 10% in the fu- ture climate experiment.
5. IMPACT OF CLIMATE CHANGE ON
RIVER FLOW REGIME
Runoff simulations of 75 years for the present climate experiment (1979-2003), the near future climate experiment (2015-2039), and the future cli- mate experiment (2075-2099) were conducted. Runoff simulation data of hourly maximum and daily mean are stored for each day with about 1km spatial resolution. The simulated discharge data were ana- lyzed to discuss the change of the flood risk and water resources. 4
(1) Change of flood
Annual maximum hourly discharge data was compiled and the statistical characteristics were an- alyzed. Figure 8 shows the change ratio of the mean of the annual maximum hourly discharge for the present climate experiment and the near future cli- mate experiment. Generally, the annual maximum discharge of the main stream of the Chao Phraya River does not change, however the one of the trib- utaries changes location to location. Figure 9 shows the change ratio of the 10-year return period river discharge in the future climate experiment with respect to the future climate ex- periment using the Gumbel distribution. The spatial pattern is similar to the change of the mean of the annual maximum river discharge. It is important to recognize that the change of the discharge would appear especially at the tributaries.
(2) Change of water resources
Figure 10 shows the change ratio of the mean October discharge in the future climate experiment with respect to the present climate experiment. Generally, the monthly discharge on October de- creases in the middle and lower part of the Chao Phraya River basin. Figure 12 shows the last 10% daily flow duration curve for each 25 years climate experiment at the lower reach of the Pasak River shown in Figure 11 in the red circle. The figure shows the decrease ten- dency of the low discharge at the area. Figure 13 shows the projected monthly discharge at the lower reach of the Pasak River. The figure shows the clear pattern of the decrease of the October discharge.
6. WATER RESOURSES ASSESSMENT
MODEL AT THE PASAK RIVER BASIN
Through the river discharge projection research of the entire Chao Phraya River, it was revealed that the Pasak River basin would be one of the hotspot basins for the river discharge change. Thus, we focus the catchment and develop a water resources assessment model to analyze the change of hydrologic cycle, water resources, and its impact on rice production (Tachikawa and Yorozu, 2009). Figure 14 shows the detail topographic modeling using HydroSHED. Left figure shows the entire Chao Phraya River basin and right figure shows the Pasak River basin. The to- pography data is used for a detail distributed hydro- logic model. Figure 15 shows the irrigated area at the lower part of the Pasak Dam, where water is taken from the lower reach of the Pasak River for paddy fields ex- tended over the lower part of the Pasak River basin. To predict a potential rice crop yield for the irrigated area, a development of a crop production model combined with a distributed hydrologic model and a dam storage prediction model is in progress. We have installed a hydro-meteorological observation in- strument at the Rama VI office, RID to develop a crop yield model (Fig. 16).
7. SUMARRY
The impact of climate change on river flow re- gimes in the Chao Phraya River basin was analyzed. A possible change of water resources are indicated in the near future climate experiment and it becomes clearer in the future climate experiment. We found the Pasak River basin would be one of hotspots of river discharge change. Thus, the following re- searches are in progress at the Pasak River basin: ● Development of a detailed distributed hydro- logic model at the Pasak River basin, ● Development of the Pasak Dam storage predic- tion model, ● In-situ observation of crop production cycle at the irrigation area of the Pasak dam reservoir, ● Development of crop production model, and • Hydro-meteorological observation at the study area. ACKNOWLEDGMENT: The hydrologic projec- tion data used for the river flow simulations was provided by the Meteorological Research Institute, Japan. The hydrologic observation data at the Pasak River basin was provided by the Royal Irrigation Department, Thailand.
REFERENCES
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