Performance Analysis of Precipitation Datasets at Multiple Spatio-Temporal Scales over Dense Gauge Network in Mountainous Domain of Tajikistan, Central Asia

Cryospheric and ecological studies become very complicated due to the absence of observed data, particularly in mountainous regions Central Asia. Performance analysis Satellite-Based Precipitation Datasets (SBPD) is critical before their direct hydro-climatic applications. This study assessed ground validation four SBPDs (IMERG, TRMM, PERSIANN-CDR, PERSIANN-CSS). From January 2000 December 2013, all SBPD data were analyzed on daily, monthly, seasonal (winter, spring, summer, autumn), annual scales at entire spatial domain point-to-pixel scale. The performance was by using evaluation indices (root mean square error (RMSE), correlation coefficient (CC), bias, relative bias (r-Bias)) along with categorical (false alarm ratio (FAR), probability detection (POD), success (SR), index (CSI). Results revealed that: (1) IMERG’s spatiotemporal tracking ability better as compared other datasets appropriate ranges (CC > 0.8 r-BIAS (±10)). more capable a monthly scale daily (2) In terms POD, IMERG outperformed scales. All showed underestimations summer season, PERSIANN-CCS most significant underestimation (−70). Moreover, signposted satisfactory seasons. (3) capturing light precipitation events indicated Probability Density Function (PDF%). PERSIANN-CDR TRMM acceptable low topography; poor diverse topographical climatic conditions over Tajikistan. Therefore, we advocate use estimations product for applications