Spatial analysis of hydrogen and oxygen stable isotopes (“isoscapes”) in Himalayan basins: improved prediction using Geographically Weighted Regression (GWR) models

Isotopic landscapes or “Isoscapes” are a valuable tool for studying hydro-climatic processes and their impact on water supplies at various spatial scales. These isoscapes are extremely useful because they enable the documentation and visualization of large-scale hydrological processes occurring on a regional, continental, or global scale. This study focuses on surface water isotope data (present study and published data) to interpolate, develop isoscapes of Himalayan basins (Indus, Ganga, and Brahmaputra), and analyze spatial variability from regional to local scale. We use physically based information of three basins from hydro-climatic variables such as actual evapotranspiration (AET), mean annual precipitation (MAP), mean annual runoff (MAR), and runoff coefficient, as well as basin variables such as elevation, slope, aspect, size, and land-use/land-cover (LULC), to develop geographically weighted regression (GWR) models. We identified a systematic spatial pattern in the stable isotopes (δ¹⁸O, δ²H, d-excess) of surface water that can be predicted using a GWR model. In the absence of long-term precipitation isotope records, an increased spatial and temporal sampling of surface water for isotopic isoscapes would significantly aid our understanding of hydrological processes, providing that catchment characteristics are taken into consideration. The GWR models used in this study demonstrated the ability to predict isotopic changes in the context of future climate and land-use change in these three major basins.

Keywords: Stable isotopes, Geographically Weighted Regression (GWR), Isoscapes, Himalayan basins.