Identifying future climate change and drought detection using CanESM2 in the upper Siem Reap River,Cambodia |
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| Institution: | 1. School of Environment, the University of Auckland, Auckland, 1010, New Zealand;2. Faculty of Engineering, the University of Auckland, Auckland, 1010, New Zealand;3. Ministry of Water Resources and Meteorology, Phnom Penh, 12300, Cambodia;1. Dept. of Atmospheric Sciences, School of Marine Sciences, Cochin University of Science and Technology, Kochi, India;2. Advance Centre for Atmospheric Radar Research, Cochin University of Science and Technology, Kochi, India;3. Physical Oceanography Division, National Institute of Oceanography, Dona-Paula, India;1. Department of Civil Engineering, Indian Institute of Technology Madras, Chennai 600036, India;2. International Pacific Research Center and Department of Oceanography, University of Hawaii, USA;1. Institute of Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou, 450045, China;2. CSIRO Land and Water, Wembley, WA, 6913, Australia;3. State Key Laboratory of Hydrology-Water Resource and Hydraulic Engineering, College of Hydrology and Water Resources, Hohai University, Nanjing, 210098, China;4. School of Land Resources and Urban and Rural Planning, Hebei GEO University, Shijiazhuang, 050031, China;1. School of Environmental Science and Engineering, Shandong University, Qingdao 266237, China;2. Institute for Energy, Environment and Sustainable Communities, University of Regina, Regina, Saskatchewan S4S0A2, Canada;3. Shandong Key Laboratory of Water Pollution Control and Resource Reuse, School of Environmental Science and Engineering, Shandong University, Qingdao 266237, China;4. Environment and Energy Systems Engineering Research Center, School of Environment, Beijing Normal University, Beijing 100875, China |
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| Abstract: | Cambodia is one of the most vulnerable countries to climate change impacts such as floods and droughts. Study of future climate change and drought conditions in the upper Siem Reap River catchment is vital because this river plays a crucial role in maintaining the Angkor Temple Complex and livelihood of the local population since 12th century. The resolution of climate data from Global Circulation Models (GCM) is too coarse to employ effectively at the watershed scale, and therefore downscaling of the dataset is required. Artificial neural network (ANN) and Statistical Downscaling Model (SDSM) models were applied in this study to downscale precipitation and temperatures from three Representative Concentration Pathways (RCP 2.6, RCP 4.5 and RCP 8.5 scenarios) from Global Climate Model data of the Canadian Earth System Model (CanESM2) on a daily and monthly basis. The Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) were adopted to develop criteria for dry and wet conditions in the catchment. Trend detection of climate parameters and drought indices were assessed using the Mann-Kendall test. It was observed that the ANN and SDSM models performed well in downscaling monthly precipitation and temperature, as well as daily temperature, but not daily precipitation. Every scenario indicated that there would be significant warming and decreasing precipitation which contribute to mild drought. The results of this study provide valuable information for decision makers since climate change may potentially impact future water supply of the Angkor Temple Complex (a World Heritage Site). |
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| Keywords: | Statistical downscaling ANN and SDSM SPI and SPEI Drought index Mann-Kendall Angkor temple |
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