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drainmod - model use, calibration, and Validation

机译:rainmod-模型使用,校准和验证

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摘要

DRAINMOD is a process-based, distributed, field-scale model developed to describe the hydrology of poorly drained and artificially drained soils. The model is based on water balances in the soil profile, on the field surface, and, in some cases, in the drainage system. This article briefly describes the model and the algorithms that are used to quantify the various hydrologic components. Inputs for soil properties, site parameters, weather data, and crop characteristics required in the application ofthe model are presented and discussed with respect to their role in calibration. Methods for determining field effective values of key inputs to the model, either independently or as a part of the calibration process, are demonstrated in a case study. The case study involved calibrating DRAINMOD with two years of field data for a subsurface drained agricultural field in eastern North Carolina, followed by testing or validation of the model with two additional years of data. Performance statistics indicated that the model with calibrated input data accurately predicted daily water table depths with Nash-Sutcliffe modeling efficiency (EF) values of 0.68 and 0.72, daily drainage rates (EF = 0.73 and 0.49), and monthly drainage volumes(EF = 0.87 and 0.77) for the two-year validation period.
机译:DRAINMOD是一种基于过程的分布式现场规模模型,旨在描述排水不良和人工排水土壤的水文状况。该模型基于土壤剖面,田间表面以及某些情况下的排水系统中的水平衡。本文简要介绍了用于量化各种水文组成部分的模型和算法。提出并讨论了模型应用中所需的土壤特性,场地参数,天气数据和农作物特性的输入,并讨论了它们在校准中的作用。在案例研究中演示了独立地或作为校准过程的一部分来确定模型的关键输入的现场有效值的方法。案例研究涉及使用北卡罗来纳州东部地下排水农田的两年现场数据校准DRAINMOD,然后使用另外两年的数据对模型进行测试或验证。性能统计数据表明,带有已校准输入数据的模型可以准确预测每日地下水位深度,纳什-萨特克利夫模型效率(EF)值为0.68和0.72,每日排水率(EF = 0.73和0.49),每月排水量(EF = 0.87)和0.77)的两年验证期。

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