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

Abstract

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