Effect of Spatial Extent of Atmospheric Variables on Development of Statistical Downscaling Model for Monthly Precipitation in Yamuna-Hindon Interbasin, India

Atmospheric variables (being predictors) play a crucial role in statistical downscaling (SD) studies, which subsequently are used to assess the hydrological impacts of climate change on water resources development and management. The spatial extent of these variables has an utmost importance in development of the SD model and for projecting the precipitation series. In this paper, an attempt is made to identify the spatial extent of atmospheric variables in terms of National Center for Environmental Prediction (NCEP) grid points that result in development of an efficient SD model for precipitation. A case study of the Yamuna-Hindon interbasin is considered to illustrate the applicability of the methodology proposed for capturing the effect of spatial domain of atmospheric variables on development of the SD model. The precipitation in the area is likely to be influenced by surrounding climate, topography, atmospheric circulation, thermodynamic processes, etc. Various cases of spatial extent, which covers a combination of NCEP grid points (containing atmospheric data), are constructed and SD models are developed for each case. SD Models are evaluated for performance using statistical measures. The statistical analysis revealed that though the complete coverage of atmospheric variables gives a better result, the atmospheric activities on NCEP grid points located in Himalayan region have a higher influence on precipitation in the Yamuna-Hindon interbasin. The best performing SD model is then practiced with various emission scenarios.