Predicting available water of soil from particle-size distribution and bulk density in an oasis-desert transect in northwestern China

JOURNAL OF HYDROLOGY

卷:538

Article

Li, Danfeng¹  Gao, Guangyao^1,2  Shao, Ming'an³  Fu, Bojie^1,2 

[1] Chinese Acad Sci, Res Ctr Ecoenvironm Sci, State Key Lab Urban & Reg Ecol, Beijing 100085, Peoples R China

[2] Joint Ctr Global Change Studies, Beijing 100875, Peoples R China

[3] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China

关键词: Soil water characteristic curve;    Soil available water;    Pedotransfer functions;    Oasis-desert transect;   

DOI  :  10.1016/j.jhydrol.2016.04.046

来源: Elsevier

【 摘 要 】

A detailed understanding of soil hydraulic properties, particularly the available water content of soil, (AW, cm(3) cm(-3)), is required for optimal water management. Direct measurement of soil hydraulic properties is impractical for large scale application, but routinely available soil particle-size distribution (PSD) and bulk density can be used as proxies to develop various prediction functions. In this study, we compared the performance of the Arya and Paris (AP) model, Mohammadi and Vanclooster (MV) model, Arya and Heitman (AH) Model, and Rosetta program in predicting the soil water characteristic curve (SWCC) at 34 points with experimental SWCC data in an oasis-desert transect (20 x 5 km) in the middle reaches of the Heihe River basin, northwestern China. The idea of the three models emerges from the similarity of the shapes of the PSD and SWCC. The AP model, MV model, and Rosetta program performed better in predicting the SWCC than the AH model. The AW determined from the SWCCs predicted by the MV model agreed better with the experimental values than those derived from the AP model and Rosetta program. The fine-textured soils were characterized by higher AW values, while the sandy soils had lower AW values. The MV model has the advantages of having robust physical basis, being independent of database related parameters, and involving subclasses of texture data. These features make it promising in predicting soil water retention at regional scales, serving for the application of hydrological models and the optimization of soil water management. (C) 2016 Elsevier B.V. All rights reserved.

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