Selection of the Most Suitable Gridded Precipitation and Temperature Datasets for the Kabul River Basin based on Statistical Indices - A Transboundary Basin between Pakistan and Afghanistan

Accurate and reliable long term meteorological data is very difficult to be obtained in developing countries especially in hard and mountainous regions. This paper focuses to select the most suitable and reliable gridded datasets for the two most important meteorological parameters i.e., precipitation and temperature in a sparsely gauged transboundary Kabul River Basin (KRB) between Pakistan and Afghanistan. Novelty of this study is that gridded datasets were evaluated for precipitation and temperature based on monthly, seasonal and annual timescales against the available observed stations data on both sides of the KRB. Based on the literature studies, the five most frequently used datasets namely; National Centers for Environmental Prediction, Climate Forecast System Reanalysis (NCEP-CFSR), Asian Precipitation Highly Resolved Observational Data Integration towards Evaluation (APHRODITE v1101), Global Precipitation Climatology Centre (GPCC), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) and Climate Research Unit (CRU TS v4.02) with different spatial and temporal resolutions were selected and evaluated. Analyses were done using the four most widely used statistical indices i.e., Modified Index of Agreement (dm), Pearson's Correlation Coefficient (r), Root Mean Square Error (RMSE), and Relative Bias (RB%). Results revealed that based on the statistical indices scores; APHRODITE (v1101) showed the best results followed by GPCC for precipitation while for temperature, CRU (TS v4.02) was found better compared to other datasets in the study basin. These findings can be used with confidence by the researchers for the future studies whose outcomes could be utilized by the water resource managers, planners and agriculturists.