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Developing novel ensemble models for predicting soil hydraulic properties in China's arid region

文献类型：期刊论文

作者Niu, Liantao2,3; Jia, Xiaoxu2,3,4; Li, Xiangdong5; Zhao, Chunlei2,3,4; Ren, Lidong4; Hu, Wei6; Zhu, Ping2,3; Li, Danfeng7; Zhang, Baoqing1; Shao, Ming'an2,3,4
刊名JOURNAL OF HYDROLOGY
出版日期2024-06-01
卷号636页码:131354
关键词Pedotransfer functions Soil hydraulic properties Machine learning Ensemble model Arid region
DOI10.1016/j.jhydrol.2024.131354
产权排序1
文献子类Article
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.
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研究方向Engineering ; Geology ; Water Resources
WOS记录号WOS:001293700000001
出版者ELSEVIER
源URL[http://ir.igsnrr.ac.cn/handle/311030/206947]  
专题黄河三角洲现代农业工程实验室_外文论文
通讯作者Jia, Xiaoxu
作者单位1.Lanzhou Univ, Coll Earth & Environm Sci, Key Lab Western Chinas Environm Syst, Minist Educ, Lanzhou 730000, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China
3.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100190, Peoples R China
4.Chinese Acad Sci, Yellow River Delta Modern Agr Engn Lab, Beijing 100101, Peoples R China
5.Yanan Univ, Coll Life Sci, Shaanxi Key Lab Chinese Jujube, Yanan 716000, Shaanxi, Peoples R China
6.New Zealand Inst Plant & Food Res Ltd, Private Bag 4704, Christchurch 8140, New Zealand
7.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China
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]. JOURNAL OF HYDROLOGY,2024,636:131354.
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,636,131354.
MLA Niu, Liantao,et al."Developing novel ensemble models for predicting soil hydraulic properties in China's arid region".JOURNAL OF HYDROLOGY 636(2024):131354.

入库方式： OAI收割

来源：地理科学与资源研究所

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