Development of high resolution daily gridded precipitation and temperature dataset for potohar plateau of indus basin

Reliable spatial and temporal meteorological estimates are essential for accurately modeling hydrological, ecological, and climatic processes. High-resolution gridded datasets can be utilized for such applications, particularly in data-sparse regions. However, the validation of the accuracy of these products is necessary before their application in hydrological modeling for the assessment and management of water resources. In this study, high-resolution (0.08 & DEG; x 0.08 & DEG;), long-term station-based gridded datasets for precipitation and maximum and minimum temperatures were developed for the Potohar Plateau. Linear regression analysis was performed against the datasets and nearby stations for gap-filling during the base period. The observed gap-filled data were spatially interpolated using the ordinary kriging technique to obtain an observed gridded dataset. Five datasets for precipitation and three datasets for temperature were selected for evaluation in this study. GPCC, APHRODITE, ERA5-Land, MSWEP, and PERSIANN-CDR for precipitation, and CPC, CRU, and ERA5 for temperature were selected. The performance evaluation was performed using widely used statistical parameters (KGE, R2, MAE, and RMSE). Bias correction was performed by selecting the best technique between linear scaling and quantile mapping. The results revealed that GPCC and ERA5 were the best-performing datasets for precipitation and temperature, respectively, among the evaluated datasets. For GPCC, KGE, R2, MAE, and RMSE values were 0.75, 0.79, 21.22 mm, and 35.11 mm correspondingly, whereas, for ERA5, the aforementioned values were 0.87, 0.97, 1.5 mm, and 1.85 mm, and 0.92, 0.98, 1.05 mm, and 1.25 mm, respectively, for maximum and minimum temperature. Furthermore, linear scaling performed better than quantile mapping in bias correction. Finally, the GPCC and ERA5 datasets were bias-corrected to develop the final gridded dataset products for precipitation and temperature. This dataset will be utilized in hydro-climatological studies, which would be helpful in policy-making for sustainable water resources management.