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

被引：3

作者：

Gulakhmadov, Manuchekhr ^{[1
,2
,3
,4
,5
]}

Chen, Xi ^{[1
,2
]}

Gulakhmadov, Aminjon ^{[1
,2
,4
]}

Nadeem, Muhammad Umer ^{[6
,7
]}

Gulahmadov, Nekruz ^{[1
,3
]}

Liu, Tie ^{[1
,2
]}

机构：

[1] Chinese Acad Sci, Xinjiang Inst Ecol & Geog, State Key Lab Desert & Oasis Ecol, Urumqi 830011, Peoples R China

[2] Chinese Acad Sci, Xinjiang Inst Ecol & Geog, Res Ctr Ecol & Environm Cent Asia, Urumqi 830011, Peoples R China

[3] Univ Chinese Acad Sci, Beijing 100049, Peoples R China

[4] Natl Acad Sci Tajikistan, Inst Water Problems Hydropower & Ecol, Dushanbe 734042, Tajikistan

[5] Comm Environm Protect Govt Republ Tajikistan, Dushanbe 734034, Tajikistan

[6] Natl Agr Res Ctr, Climate Energy & Water Res Inst, Islamabad 44000, Pakistan

[7] PMAS Arid Agr Univ, Fac Agr Engn & Technol, Dept Land & Water Conservat Engn, Rawalpindi 46000, Pakistan

来源：

REMOTE SENSING | 2023年 / 15卷 / 05期

基金：

中国国家自然科学基金;

关键词：

satellite-based precipitation datasets; performance analysis; IMERG; PERSIANN-CDR; probability density function; mountains of central Asia; RIVER-BASIN; SATELLITE; PRODUCTS; RAINFALL;

D O I：

10.3390/rs15051420

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

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.

引用

页数：27