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Estimation of root water uptake and soil hydraulic parameters from root zone soil moisture and deep percolation

机译：根带土壤水分水分和渗透渗透估算根水吸收与土壤液压参数

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摘要

For efficient irrigation management practices, an accurate prediction of water uptake in the root zone and soil information are foremost important. The present study deals with the identification and estimation of root water uptake (RWU) and soil hydraulic parameters using inverse modeling. These parameters were estimated by minimizing the difference between observed and model simulated soil moisture and deep percolation during the crop growth period. The linked simulation optimization model is tested for three different objective functions using hypothetically generated observed data. Results indicate that the optimizer with objective function defined by soil moisture, failed to provide unique estimate of RWU and soil hydraulic parameters. Further, it has been observed that with the objective function defined by deep percolation, soil hydraulic parameters were uniquely estimated but RWU parameter was not estimated accurately. However, with the objective function, that includes both soil moisture and deep percolation, these parameters were uniquely estimated. A Lysimeter experiments were conducted with four crops i.e. berseem (Trifolium alexandrinum), wheat (Triticum aestivurn), maize (Zea mays) and pearl millet (Penniseturn glaucum). Daily monitoring of soil moisture and deep percolation along with soil and crop parameter measurements were done for model validation. Inversely estimated soil hydraulic parameters were found to be in close agreement with laboratory obtained values. The results indicate that specifically for soils with high hydraulic conductivity, the information about deep percolation along with soil moisture is necessary for inverse estimation of root and soil parameters simultaneously. The moisture depletion pattern and deep percolation corresponding to optimized parameters for these crops were found to be in close agreement with observed values.
机译：为了有效灌溉管理实践，对根区和土壤信息中的水吸收准确预测最重要。本研究涉及使用逆建模的根水吸收（RWU）和土壤液压参数的识别和估计。通过最小化观察和模拟模拟土壤水分和作物生长期深的渗透性之间的差异来估计这些参数。使用假设产生的观察数据测试链接仿真优化模型三种不同的客观函数。结果表明，优化器具有土壤水分定义的客观函数，未能提供RWU和土壤液压参数的独特估计。此外，已经观察到，利用深渗滤所定义的目标函数，唯一估计土壤液压参数，但没有准确地估计RWU参数。然而，通过目标函数，包括土壤水分和深层渗透，这些参数唯一估计。用四种作物（Trifolium Alexandrinum），小麦（Triticum Aestivurn），玉米（Zea Mays）和珍珠米（Penniseturn肺泡）进行溶血仪实验。为模型验证进行了对土壤水分和土壤水分和深层渗透的日常监测，以进行模型验证。反向估计的土壤液压参数与实验室获得的价值密切一致。结果表明，专门针对具有高液压导电性的土壤，同时对土壤和土壤参数逆估计根和土壤水分的深层渗透的信息是必要的。发现与这些作物的优化参数相对应的水分耗尽图案和深渗透，与观察到的值密切一致。

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