Simulated Precipitation and Reservoir Inflow in the Chao Phraya River Basin by Multi-model Ensemble CMIP3 and CMIP5
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Climate Change caused by global warming is a growing public concern throughout the world. It is well accepted within the scientific community that an ensemble of different projections is required to achieve robust climate change information for a specific region. For this purpose we have compiled a Multi-Model Ensemble and performed statistical downscaling for 9 GCMs of CMIP3 and CMIP5. The observed precipitation data from 83 stations around the country were interpolated to grid data using the Inverse Distance Weighted method. The precipitation projection was downscaled by the Distribution Mapping for the near-future (2010–2039), the mid-future (2040–2069) and the far-future (2070–2099). The nonlinear autoregressive neural network with exogenous input (NARX) was used to forecast the mean monthly inflow to reservoirs. The projection inflow for the future periods are shown to increase in inflow in the wet season. A possibility of increase in hydrological extreme flood in the wet season may be indicated by these findings.
KeywordsCMIP3 CMIP5 Nonlinear autoregressive neural network with exogenous input Statistical downscaling Chao Phraya river basin
The authors wish to thank the Royal Irrigation Department of Thailand and the Thai Meteorological Department for providing their observed precipitation. The Electricity Generating Authority of Thailand for the Bhumibol and Sirikit reservoirs inflow characteristic. Also, the climate model datasets were obtained from PCMDI.
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