Accounting for Non-Stationary Relationships between Precipitation and Environmental Variables for Downscaling Monthly TRMM Precipitation in the Upper Indus Basin

Satellite precipitation data downscaling is gaining importance for climatic and hydrological studies at basin scale, especially in the data-scarce mountainous regions, e.g., Upper Indus Basin (UIB). The relationship between environmental variables frequently utilized to statistically enhance spatial resolution; non-stationary has not yet been completely explored. present work designed downscale TRMM (Tropical Rainfall Measuring Mission) from 2000 2017 UIB, using stepwise regression analysis (SRA) filter first a geographically weighted (GWR) model later. As result, monthly annual with high resolution (1 km × 1 km) were obtained. study’s findings showed that elevation, longitude, Normalized Difference Vegetation Index (NDVI), latitude, highest correlations are most important downscaling. Environmental variable filtration followed by GWR performed better than directly when compared observation data. Generally, SRA method suitable downscaling, respectively, over complex heterogeneous topography of UIB. We conclude relationships exist have greatest potential affect which requires attention.