Russian Meteorology and Hydrology

, Volume 39, Issue 4, pp 271–282 | Cite as

Application of statistical downscaling in GCMs at constructing the map of precipitation in the Mekong River basin

  • K. Parajuli
  • K. Kang


This study used the Statistical Downscaling Model (SDSM) to increase the resolution of the Global Circulation Model (GCM) at forecasting the amount of precipitation in the Mekong River basin. The model was initially calibrated using the reanalysis data by National Centers for Environmental Prediction (NCEP) and the data on observed precipitation. The results of comparison between the SDSM calculations and the observational data were used to generate the distribution of precipitation until 2099 using HadCM3, SRES A2 and B2 scenarios. After total annual precipitation had been downscaled, the percentage change in precipitation was interpolated among the selected stations in order to create precipitation maps. Both A2 and B2 scenario indicate the possibility of remarkable increase in annual precipitation in the Mekong basin, which may amount to 150 and 110%, respectively. The December–January–February precipitation is likely to increase significantly in the most part of the region, and in some areas, almost by three times. On the contrary, the June–July–August precipitation will remarkably decrease in the different parts of the territory under study. As the water resource sector is the backbone of the economics of this region including hydropower and agricultural sector, the changes in the amount of precipitation and its interannual variability can put the usual water business into stress. Thus, proper adaptive measures should be applied both at local and at regional levels for the benefit of all associated countries utilizing the resource of the Mekong River.


RUSSIAN Meteorology Statistical Downscaling Global Circulation Model Statistical Downscaling Model Mekong River Commission 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Allerton Press, Inc. 2014

Authors and Affiliations

  1. 1.Asian Institute of Technology (AIT)Klong Luang, PathumthaniThailand
  2. 2.Sejong UniversitySeoulRepublic of Korea

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