干旱区生态环境知识资源中心(Arid): Reconstructing monthly 0.25° terrestrial evapotranspiration data in a remote arid region using Bayesian-driven ensemble learning method

Arid

DOI	10.1016/j.jhydrol.2024.131115
	Reconstructing monthly 0.25° terrestrial evapotranspiration data in a remote arid region using Bayesian-driven ensemble learning method
	Ochege, Friday Uchenna; Yuan, Xiuliang; Ezekwe, Ifeanyichukwu C.; Ling, Qing; Nzabarinda, Vincent; Kayiranga, Alphonse; Xie, Mingjuan; Shi, Haiyang; Luo, Geping
通讯作者	Yuan, XL ; Luo, GP
来源期刊	JOURNAL OF HYDROLOGY
ISSN	0022-1694
EISSN	1879-2707
出版年	2024
卷号	634
英文摘要	This study presents a Bayesian-driven ensemble learning framework to minimize errors in evapotranspiration (ET) data assimilation, thereby generating a continuous-scale ET estimation with improved accuracy for the data-scarce arid Central Asia (CA). Five state-of-the-art gridded ET products were extensively tested and utilized to reconstruct new long-term ET data at grid scale, including two Global Land Data Assimilation System (GLDAS)-Land Surface Models (LSM) (Catchment and NOAH), two reanalysis products (European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) and Modern-Era Retrospective Analysis for Research and Applications - ver. 2 (MERRA2)), along with the Global Land Evaporation Amsterdam (GLEAM) satellite ET product ver. 3.6b. Taylor Skill Score was employed to evaluate the input gridded ET datasets, using in situ measurements across four arid biomes - cropland, shrubland, farmland, and grassland -as benchmarks. Bayes-Markov Chain Monte Carlo (Bayes-MCMC)-derived logarithmically transformed predictors from the posterior ensemble anomalies were standardized to resolve the mismatches in the cumulative distribution functions of ET data relating to the in situ observations. We found that the constrained residual errors were more profound between 45th and 95th percentiles for the rescaled total ET anomalies across all the input ET datasets at 95 % confidence interval. Moreover, the weighted ensemble estimates derived from the Bayes-MCMC were instrumental in correcting ET anomalies, leading to the robust reconstruction of a more reliable ensemble mean ET data for Central Asia (CAET(ens)) at monthly temporal and 0.25 degrees spatial resolutions, spanning from 2003 to 2020. Overall, CAET(ens )exhibited superior performance, achieving the highest averaged Kling-Gupta Efficiency of 0.75 and registering the lowest Root Mean Square Error of 6.85 mm/m compared to the standalone gridded ET products. Comparisons of spatial distributions of fine-scale features, and evaluations using standard metrics revealed that all ET products varied significantly (p < 0.05) by biome types. Remarkably, only CAET(ens) exhibited a reasonable low positive bias and pronounced ability to resolve the complex spatiotemporal patterns associated with the input gridded ET products. The study bridges the gap in continuous ET monitoring for sustainable water resources management in CA.
英文关键词	Reconstructed evapotranspiration (ET) Bayesian ensemble learning Gridded ET evaluation Arid Central Asia
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001224045000001
WOS关键词	TRIPLE COLLOCATION ; LAND EVAPORATION ; SATELLITE ; PRODUCTS ; FLUX ; COMBINATION ; PERFORMANCE ; MANAGEMENT ; ALGORITHM ; FUTURE
WOS类目	Engineering, Civil ; Geosciences, Multidisciplinary ; Water Resources
WOS研究方向	Engineering ; Geology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/404577
推荐引用方式 GB/T 7714	Ochege, Friday Uchenna,Yuan, Xiuliang,Ezekwe, Ifeanyichukwu C.,et al. Reconstructing monthly 0.25° terrestrial evapotranspiration data in a remote arid region using Bayesian-driven ensemble learning method[J],2024,634.
APA	Ochege, Friday Uchenna.,Yuan, Xiuliang.,Ezekwe, Ifeanyichukwu C..,Ling, Qing.,Nzabarinda, Vincent.,...&Luo, Geping.(2024).Reconstructing monthly 0.25° terrestrial evapotranspiration data in a remote arid region using Bayesian-driven ensemble learning method.JOURNAL OF HYDROLOGY,634.
MLA	Ochege, Friday Uchenna,et al."Reconstructing monthly 0.25° terrestrial evapotranspiration data in a remote arid region using Bayesian-driven ensemble learning method".JOURNAL OF HYDROLOGY 634(2024).