A Modular Framework for Modeling Unsaturated Soil Hydraulic Properties Over the Full Moisture Range
A Modular Framework for Modeling Unsaturated Soil Hydraulic Properties Over the Full Moisture Range
复制标题
用于模拟全湿度范围内非饱和土水力特性的模块化框架
DOI:
10.1029/2018wr024584
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发表时间:
2019
影响因子:
5.4
通讯作者:
E. Diamantopoulos
中科院分区:
文献类型:
--
作者:
Tobias K. D. Weber;W. Durner;T. Streck;E. Diamantopoulos
A generalized modular framework for partitioning soil hydraulic property (SHP) functions into a capillary and a noncapillary part is developed. The full water retention curve (WRC) is modeled as a weighted sum of a parametric capillary saturation function and a new general model for the noncapillary saturation function. This model is directly computed from any selected capillary saturation function. With it, a physically complete, continuous, and flexible representation of the WRC is achieved, ensuring zero water content at oven dryness. In a modular and hierarchical framework, the expressions for the capillary and noncapillary saturation function are used to calculate the respective hydraulic conductivity curves (HCC). This is achieved by adopting Mualem's integral for the capillary part of the HCC only and calculating the noncapillary HCC directly from the new noncapillary saturation function. This leads to consistent descriptions of measured HCC data, including the often observed change in slope beyond −100 cm pressure head. Compared to the classical van Genuchten‐Mualem approach, it requires only one additional model parameter. The SHP framework model describes both WRC and HCC adequately and coherently. We demonstrate the suitability of the SHP framework and versatility by describing measured WRC and HCC data across the full moisture range using soil samples from a wide range of textures and origins. The modular framework was implemented in the soil physics and soil hydrology (spsh) R‐package, available from the Comprehensive R Archive Network. It contains several SHP models, model parameter estimation, and features options for goodness of fit statistics, and model selection.
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影响因子:
5.4
作者:
Andre Peters;W. Durner
通讯作者:
Andre Peters;W. Durner
影响因子:
4.7
作者:
S. Iden;A. Peters;W. Durner
通讯作者:
S. Iden;A. Peters;W. Durner
影响因子:
4.7
作者:
Diamantopoulos;W. Durner
通讯作者:
W. Durner
影响因子:
2.8
作者:
E. Diamantopoulos;W. Durner
通讯作者:
E. Diamantopoulos;W. Durner
影响因子:
6.2
作者:
Ingwersen;Wizemann;Warrach-Sagi;Streck
通讯作者:
Streck