POWRE: Soil Hydraulic Properties as Affected by Soil Solution Chemical Composition

[00:74841 . lebron1]这个项目解决了量化溶液组成对土壤水力特性影响的问题。了解这些特性对于评估土壤中污染物的迁移和随后的补救措施、预测二氧化碳的释放和封存以及解释和改进遥感土壤数据的使用是必要的，例如根据电阻率数据确定盐度和根据卫星信息确定含水量。土壤中水和溶质的流动和运输受土壤孔隙度的大小、几何形状和特征的控制。土壤孔隙的大部分特征是微观的，如粗糙度和圆形。传统的液体分布、流动和溶质运移模型完全依赖于圆柱形孔隙，而忽略了表面积、角度和连通性的作用。神经网络已被用于利用宏观和微观参数预测土壤的保水特性。这些模型具有巨大的潜力，可以推导出土壤传递函数（ptf）来预测水力参数。然而，文献中可用的ptf没有考虑土壤溶液的化学成分，也没有考虑土壤中粘土矿物的矿物学的数据。众所周知，粘土矿物和化学成分是膨胀粘土的水力特性的关键决定因素，特别是当它们受到碱度的影响时。该项目的主要目标是量化盐度、碱度和pH对土壤孔隙空间几何形状、大小和分布的影响。这种量化将为使用神经网络和自举方法开发ptf提供信息，并旨在提高我们预测土壤水力特性的能力。

0074841LebronThis project addresses the problem of quantifying the influence of solution composition on soil hydraulic properties. The knowledge of these properties is necessary to assess transport of contaminants in soil and subsequent remediation, to predict release and sequestration of CO2, and to interpret and improve the use of remotely sensed soil data such as determination of salinity from electrical resistivity data and determination of water content from satellite information. Flow and transport of water and solutes in soils are controlled by size, geometry and characteristics of the soil porosity. Most of the characteristics of the soil pores are microscopic, such as roughness and circularity. Conventional models of liquid distribution, flow and solute transport rely solely on cylindrical pores, while ignoring the role of surface area, angularity, and connectivity. Neural networks have been used to predict water retention properties in soils using macro and microscopic parameters. These models have tremendous potential to derive pedotransfer functions (PTFs) to predict hydraulic parameters. However the PTFs available in the literature do not consider the chemical composition of the soil solution nor data considering the mineralogy of the clay minerals in the soil. Clay mineral and chemical composition are known to be critical determinants of the hydraulic properties in soils with swelling clays, especially if they are affected by sodicity. The main objective of this project is to quantify the influence of salinity, sodicity and pH on the geometry, size, and distribution of the soil pore space. This quantification will provide the information for developing PTFs using neural network and bootstrap methodology and is intended to improve our capability to predict soil hydraulic properties.