干旱区生态环境知识资源中心(Arid): An Appraisal of Dynamic Bayesian Model Averaging-based Merged Multi-Satellite Precipitation Datasets Over Complex Topography and the Diverse Climate of Pakistan

Arid

DOI	10.3390/rs12010010
	An Appraisal of Dynamic Bayesian Model Averaging-based Merged Multi-Satellite Precipitation Datasets Over Complex Topography and the Diverse Climate of Pakistan
	Rahman, Khalil Ur 1; Shang, Songhao 1; Shahid, Muhammad 1,2; Wen, Yeqiang 1
通讯作者	Shang, Songhao
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2020
卷号	12 期号:1
英文摘要	Merging satellite precipitation products tends to reduce the errors associated with individual satellite precipitation products and has higher potential for hydrological applications. The current study evaluates the performance of merged multi-satellite precipitation dataset (daily temporal and 0.25 degrees spatial resolution) developed using the Dynamic Bayesian Model Averaging algorithm across four different climate regions, i.e., glacial, humid, arid and hyper-arid regions, of Pakistan during 2000-2015. Four extensively evaluated SPPs over Pakistan, i.e., Tropical Rainfall Measurement Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42V7, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), Climate Prediction Center MORPHing technique (CMORPH), and Era-Interim, are used to develop the merged multi-satellite precipitation dataset. Six statistical indices, including Mean Bias Error, Mean Absolute Error, Root Mean Square Error, Correlation Coefficient, Kling-Gupta efficiency, and Theil's U coefficient, are used to evaluate the performance of merged multi-satellite precipitation dataset over 102 ground precipitation gauges both spatially and temporally. Moreover, the ensemble spread score and standard deviation are also used to depict the spread and variation of precipitation of merged multi-satellite precipitation dataset. Skill scores for all statistical indices are also included in the analyses, which shows improvement of merged multi-satellite precipitation dataset against Simple Model Averaging. The results revealed that DBMA-MSPD assigned higher weights to TMPA (0.32) and PERSIANN-CDR (0.27). TMPA presented higher skills in glacial and humid regions with average weights of 0.32 and 0.37 as compared to PERSIANN-CDR of 0.27 and 0.25, respectively. TMPA and Era-Interim depicted higher skills during pre-monsoon and monsoon seasons, with average weights of 0.31 and 0.52 (TMPA) and 0.25 and 0.21 (Era-Interim), respectively. Merged multi-satellite precipitation dataset overestimated precipitation in glacial/humid regions and showed poor performance, with the poorest values of mean absolute error (2.69 mm/day), root mean square error (11.96 mm/day), correlation coefficient (0.41), Kling-Gupta efficiency score (0.33) and Theil's U (0.70) at some stations in glacial/humid regions. Higher performance is observed in hyper-arid region, with the best values of 0.71 mm/day, 1.72 mm/day, 0.84, 0.93, and 0.37 for mean absolute error, root mean square error, correlation coefficient, Kling-Gupta Efficiency score, and Theil's U, respectively. Merged multi-Satellite Precipitation Dataset demonstrated significant improvements as compared to TMPA across all climate regions with average improvements of 45.26% (mean bias error), 30.99% (mean absolute error), 30.1% (root mean square error), 11.34% (correlation coefficient), 9.53% (Kling-Gupta efficiency score) and 8.86% (Theil's U). The ensemble spread and variation of DBMA-MSPD calculated using ensemble spread score and standard deviation demonstrates high spread (11.38 mm/day) and variation (12.58 mm/day) during monsoon season in the humid and glacial regions, respectively. Moreover, the improvements of DBMA-MSPD quantified against fixed weight SMA-MSPD reveals supremacy of DBMA-MSPD, higher improvements (40-50%) in glacial and humid regions.
英文关键词	satellite precipitation merged multi-satellite precipitation dataset Dynamic Bayesian Model Averaging regional and seasonal evaluation complex topography diverse climate Pakistan
类型	Article
语种	英语
国家	Peoples R China ; Pakistan
开放获取类型	Green Submitted, gold
收录类别	SCI-E
WOS记录号	WOS:000515391700010
WOS关键词	SATELLITE-BASED PRECIPITATION ; SURFACE-TEMPERATURE FORECASTS ; GLOBAL PRECIPITATION ; ANALYSIS TMPA ; HYDROLOGICAL APPLICATION ; PRODUCTS ; ENSEMBLE ; PERFORMANCE ; RADAR ; RESOLUTION
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
来源机构	清华大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/315400
作者单位	1.Tsinghua Univ, Dept Hydraul Engn, State Key Lab Hydrosci & Engn, Beijing 100084, Peoples R China; 2.Natl Univ Sci & Technol Islamabad, SCEE, NICE, Islamabad 44000, Pakistan
推荐引用方式 GB/T 7714	Rahman, Khalil Ur,Shang, Songhao,Shahid, Muhammad,et al. An Appraisal of Dynamic Bayesian Model Averaging-based Merged Multi-Satellite Precipitation Datasets Over Complex Topography and the Diverse Climate of Pakistan[J]. 清华大学,2020,12(1).
APA	Rahman, Khalil Ur,Shang, Songhao,Shahid, Muhammad,&Wen, Yeqiang.(2020).An Appraisal of Dynamic Bayesian Model Averaging-based Merged Multi-Satellite Precipitation Datasets Over Complex Topography and the Diverse Climate of Pakistan.REMOTE SENSING,12(1).
MLA	Rahman, Khalil Ur,et al."An Appraisal of Dynamic Bayesian Model Averaging-based Merged Multi-Satellite Precipitation Datasets Over Complex Topography and the Diverse Climate of Pakistan".REMOTE SENSING 12.1(2020).