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

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2023-03, Vol.15 (5), p.1420
• Main Authors: Gulakhmadov, Manuchekhr, Chen, Xi, Gulakhmadov, Aminjon, Nadeem, Muhammad Umer, Gulahmadov, Nekruz, Liu, Tie
• Format: Article
• Language: English
• Subjects: Accuracy; Altitude; Bias; Climate change; Climatic conditions; Correlation coefficient; Correlation coefficients; Datasets; Domains; Ecological studies; Environmental aspects; False alarms; Hydrologic cycle; Hydrology; IMERG; Mountain regions; Mountains; mountains of central Asia; performance analysis; Performance evaluation; PERSIANN-CDR; Precipitation; Precipitation (Meteorology); probability density function; Probability density functions; Root-mean-square errors; Satellite imaging; satellite-based precipitation datasets; Satellites; Sensors; Summer; Topography; TRMM satellite; Water shortages
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15051420

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