Development of high resolution gridded Precipitation and Temperature datasets for Potohar Plateau of Pakistan

Abstract

Reliable spatial and temporal meteorological estimates are essential for accurately modeling hydrological, ecological, and climatic processes. High-resolution gridded datasets can be used for such applications, especially in data-sparse regions. However, the validation of the accuracy of these products is necessary before their application towards hydrological modeling for the assessment and management of water resources. In this study, high resolution (0.08°×0.08°), long-term station-based gridded datasets for precipitation, maximum and minimum temperature were developed for the Potohar region. Linear regression analysis was performed against the datasets and nearby stations for gap filling in the base period. The gap-filled observed data was spatially interpolated using the Ordinary Kriging technique to get an observed gridded dataset. Two gauge based and one reanalysis datasets, both for precipitation and temperature, were selected for evaluation in this study. GPCC, APHRODITE, and ERA5-Land for precipitation, while CPC, CRU, and ERA5 for temperature were selected. Performance evaluation was made using widely used statistical parameters (KGE, R2, MAE, and RMSE). Bias correction was performed by selecting the better technique between Linear Scaling and Quantile Mapping. The results revealed GPCC and ERA5 as the better performing datasets for precipitation and temperature, respectively, among all the selected datasets. Furthermore, linear scaling performed better than quantile mapping for bias correction. Finally, the GPCC and ERA5 datasets were bias corrected to develop the final gridded dataset products for precipitation and temperature, respectively. This dataset would be helpful in future hydrological and climate change impact studies. Moreover, it would be utilized to forecast the Potohar region's water availability