Evaluation of Gridded Precipitation Datasets for the Kabul Basin, Afghanistan (2017)

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ABSTRACTThe availability of high-resolution gridded precipitation products based on satellite estimations and/or interpolated observations provides a good opportunity to monitor precipitation over large and remote areas of poorly gauged basins. Precipitation is the key input in hydrological modelling for the assessment and management of water resources. However, it is necessary to validate the accuracy of these precipitation products before their application towards the planning and management of the water resources. The objective of this study, therefore, was to validate gridded precipitation time series data in Climate Prediction Centre ? Rainfall Estimates (CPC-RFE 2.0), Global Satellite Mapping of Precipitation Microwave-IR Combined Product (GSMaP_MVK V5), Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA V7), and Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources, Monsoon Asia (APHRODITE MA V1101) using the data from recently established rain gauges over the Kabul basin in Afghanistan from 2004 to 2007 (the common period of observations). These products were evaluated at different spatial and temporal resolutions (daily, monthly, and annual). The validation approach used here includes continuous (mean absolute error, root mean square error, correlation coefficient (r), and multiplicative bias) and categorical (probability of detection and false alarm ratio) verification statistics. Furthermore, the spatial performance was evaluated by mapping the data and analysing the distribution of precipitation as a function of elevation. The results of continuous and categorical verification statistics suggest that the APHRODITE MA V1101 dataset performs better than other gridded datasets for the study basin. The estimates from four tested products showed a relatively good detection of the amount and distribution of precipitation in the Kabul basin.
Year: 2017
Language: English
In: International Journal of Remote Sensing, 38: 3317-3332 p.

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