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
Keywords:
Evaluation, Gridded datasets, Statistical indices, Bilinear weighted interpolation technique, Kabul River BasinAbstract
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 (d ), Pearson's Correlation m
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.
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