干旱区生态环境知识资源中心(Arid): Developing novel ensemble models for predicting soil hydraulic properties in China's arid region

Arid

DOI	10.1016/j.jhydrol.2024.131354
	Developing novel ensemble models for predicting soil hydraulic properties in China's arid region
	Niu, Liantao; Jia, Xiaoxu; Li, Xiangdong; Zhao, Chunlei; Ren, Lidong; Hu, Wei; Zhu, Ping; Li, Danfeng; Zhang, Baoqing; Shao, Ming'an
通讯作者	Jia, XX
来源期刊	JOURNAL OF HYDROLOGY
ISSN	0022-1694
EISSN	1879-2707
出版年	2024
卷号	636
英文摘要	Accurately quantifying the mass (water, nutrients, and carbon) and energy exchange processes between the Earth's atmosphere, biosphere, and lithosphere requires the accurate parameterization of soil hydraulic properties (SHPs) and their spatial heterogeneity. Because direct measurements of SHPs are difficult, time-consuming, and impossible at larger spatial scales, various pedotransfer functions (PTFs) have been developed in the last few decades, providing divergent estimates of SHPs from readily measurable variables. However, existing PTFs are mostly developed for specific regions and may not be suitable for other pedoclimatic conditions. Here, PTFs were developed using multiple machine-learning algorithms to estimate SHPs and examined the weaknesses and strengths of each method in estimating the average and variability of SHPs across China's arid region. The optimal PTFs were applied to a 1 x 1 km2 regional map of texture and bulk density, thus producing maps of the saturated hydraulic conductivity (Ks), the parameters of the van Genuchten (VG) formulation, field capacity (theta fc), wilting point (theta wp), plant available water (theta pa), and soil macroporosity (phi m) in the 0-2 m soil profile throughout the region. The results indicate that the ensemble model with the averaging method (EMA) is the most robust for estimating all SHPs. The EMA-PTFs for Ks yielded the best performance for sand textures, followed by sandy loam, loam, silty loam, silty clay loam, loamy sand, and clay loam textures. There were no significant differences in estimating soil water retention curve parameters among the different soil texture classes. Using the same observed data set, we demonstrated that the new EMA-PTFs outperformed those of existing PTFs, such as Rosetta and HYPRES, with RMSE values decreasing by 25-83 % depending on the SHPs. Furthermore, our SHP datasets exhibited significantly deeper soil profiles and higher accuracy than other available regional and global SHP products. The VG retention parameter alpha shows the greatest variation vertically throughout the 0-2 m soil profile, followed by ln(Ks), theta r, theta pa, theta fc, theta wp, phi m, theta s and n. All SHPs exhibit much lower variability in the desert than in other areas, likely due to the homogeneous particle size distribution of desert areas. This study provides more accurate SHP estimates in China's arid region than the existing SHP products by developing advanced PTFs and highlights the inconsistency in SHPs among different global or regional products; the uncertainties induced by PTFs should thus be considered in future terrestrial biosphere modeling.
英文关键词	Pedotransfer functions Soil hydraulic properties Machine learning Ensemble model Arid region
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001293700000001
WOS关键词	PARTICLE-SIZE DISTRIBUTION ; WATER RETENTION CHARACTERISTICS ; CATION-EXCHANGE CAPACITY ; PEDOTRANSFER FUNCTIONS ; BULK-DENSITY ; MOISTURE RETENTION ; AVAILABLE WATER ; CONDUCTIVITY ; PARAMETERS ; DATABASE
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/2XILL650/404586
推荐引用方式 GB/T 7714	Niu, Liantao,Jia, Xiaoxu,Li, Xiangdong,et al. Developing novel ensemble models for predicting soil hydraulic properties in China's arid region[J],2024,636.
APA	Niu, Liantao.,Jia, Xiaoxu.,Li, Xiangdong.,Zhao, Chunlei.,Ren, Lidong.,...&Shao, Ming'an.(2024).Developing novel ensemble models for predicting soil hydraulic properties in China's arid region.JOURNAL OF HYDROLOGY,636.
MLA	Niu, Liantao,et al."Developing novel ensemble models for predicting soil hydraulic properties in China's arid region".JOURNAL OF HYDROLOGY 636(2024).