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Development of a Simulation Model for Estimation of Potential Recharge in a Semi-arid Foothill Region

机译:半干旱丘陵区电势估算模型的开发

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摘要

The SMPR (Soil Moisture and Potential Recharge) model is developed to simulate soil moisture content and potential recharge under semi-arid conditions. In SMPR model, infiltration and soil moisture redistribution follow two successive stages. In stage (I), precipitation infiltrates and is distributed into the soil profile utilizing the soil moisture accounting fashion and in stage (II), moisture is redistributed using simplified Richards' equation (neglecting matric-potential gradient). Liquid and vapor evaporation from bare soil are estimated based on Dual-Crop methodology [Ke and optimized Kcb (0.17)]. Two commonly applied unsaturated hydraulic conductivity functions [K(theta)] of B-C (Brooks and Corey) and VG (van-Genuchten); and an Empirical Exponential (E-E) equation are locally calibrated and used for potential recharge estimation (as main simulation objective). Model performance (calibration/validation) is based on reasonable estimation of potential recharge and acceptable simulation of soil moisture, considering local lysimeter data. According to results, B-C, V-G an E-E equations produced acceptable simulation of soil moisture content (NRMSE < 30%), however, potential recharge was underestimated/overestimated, using K(theta) by B-C/V-G. The best estimation of potential recharge (based on absolute annual recharge error,Delta Q < 10%) was achieved by the SMPR model with K(theta) of E-E. Results of the relative simple SMPR model [K(theta) by E-E equation] compared favorably with HYDRUS-1D sophisticated model [using locally calibrated V-G equation of K(theta)]. The proposed SMPR model requiring minimal data, can be used in regions with limited data.
机译:开发了SMPR(土壤水分和势能补给)模型来模拟半干旱条件下的土壤水分含量和势能补给。在SMPR模型中,入渗和土壤水分的再分配遵循两个连续的阶段。在阶段(I)中,降水渗透并利用土壤水分的计算方式分配到土壤剖面中;在阶段(II)中,水分利用简化的Richards方程(忽略矩阵势梯度)重新分布。根据双重作物方法[Ke和优化的Kcb(0.17)]估算了裸露土壤中的液体和蒸气蒸发。 B-C(布鲁克斯和科里)和VG(van-Genuchten)两个常用的不饱和导水率函数[Kθ];并局部校准了经验指数(E-E)方程,并将其用于势能补给估算(作为主要模拟目标)。模型性能(校准/验证)基于对潜在补给量的合理估计以及对土壤水分的可接受模拟,并考虑了当地的溶渗仪数据。根据结果​​,B-C,V-G和E-E方程对土壤水分含量(NRMSE <30%)进行了可接受的模拟,但是,使用B-C / V-G的Kθ,潜在的补给量被低估/高估了。潜在补给的最佳估计(基于绝对年度补给误差,Delta Q <10%)是通过EPR的K(theta)的SMPR模型获得的。相对简单的SMPR模型[通过E-E方程计算的Kθ]的结果与HYDRUS-1D复杂模型[使用Kθ的局部校准的V-G方程]相比,具有优势。所提出的需要最少数据的SMPR模型可用于数据有限的区域。

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