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Soil moisture data assimilation at multiple scales and estimation of representative field scale soil moisture characteristics.

机译：多尺度土壤水分数据同化和代表性田间尺度土壤水分特征的估算。

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摘要

Soil moisture is a key variable in understanding the hydrologic processes and energy fluxes at the land surface. Therefore, accurate prediction of soil moisture in the vadose zone benefits irrigation planning and crop management, flooding and drought prediction, water quality management, climate change forecasts, and weather prediction. The three objectives of this study are to: (1) investigate the effects of surface soil moisture data assimilation on hydrological responses at the field scale using in situ soil moisture measurements, (2) explore the effect of surface soil moisture data assimilation on each hydrologic process in a simulation model, including the effect of spatially varying inputs on the potential capability of surface soil moisture assimilation at the watershed scale, and (3) link two different scales of soil moisture estimates by upscaling single point measurements to field averages.;First, a well-proven data assimilation technique, the Ensemble Kalman Filter (EnKF), is applied to a field scale water quality model, the Root Zone Water Quality Model, with in situ soil moisture data from two agricultural fields in Indiana. Through daily update, the EnKF improves all statistical results compared to the direct insertion method and model results without assimilation for the 5 cm and 20 cm depths while less improvement is achieved for deeper layers. Optimal update interval and ensemble size are also tested for the operational potential of data assimilation. This study demonstrates the potential of surface soil moisture assimilation to improve water quality and crop yield simulation, as well as, soil moisture estimation at the agricultural field scale.;Second, the EnKF is coupled with a watershed scale, semi-distributed hydrologic model, the Soil and Water Assessment Tool. Results show that daily assimilation of surface soil moisture with the EnKF improves model predictions of almost all hydrological processes. However, the EnKF does not produce as much of a significant improvement in streamflow predictions as compared to soil moisture estimates in the presence of large precipitation errors and due to the limitations of the infiltration-runoff model mechanism. Distributed errors of the soil water content show effects of spatially varying inputs such as soil and landuse types on the assimilation results. Results from this study suggest that soil moisture update through data assimilation can be a supplementary way to overcome the errors created by limited or inaccurate rainfall data.;Proper linkage of soil moisture estimates across different scales of observations and model predictions is essential for the validation of remotely-sensed soil moisture products, as well as the successful application of data assimilation techniques. Thus, this study also examines different upscaling methods to transform point measurements to field averages in representing small agricultural watersheds (∼ 2 ha). The cumulative distribution function (CDF) matching approach is found to provide best estimates of field average soil moisture out of several statistical methods. Tests for temporal and spatial (horizontal and vertical) transferability of the upscaling equations indicate that they are transferable in space, but not in time. Rainfall characteristics and crop types are most likely major factors affecting the success of the transferability. In addition, the CDF matching approach is found to be an effective method to estimate spatial soil moisture variance from single point measurements.;Overall, the results presented in this work can be utilized to improve applications of soil moisture data assimilation at field and watershed scales and better evaluate the scaling behavior of soil moisture.
机译：土壤湿度是了解陆地表面水文过程和能量通量的关键变量。因此，对渗流带土壤湿度的准确预测有利于灌溉计划和作物管理，洪水和干旱预测，水质管理，气候变化预测以及天气预测。这项研究的三个目标是：（1）使用原位土壤水分测量方法研究表层土壤水分数据同化对田间尺度水文响应的影响，（2）探索表面土壤水分数据同化对每种水文的影响模拟模型中的过程，包括在分水岭尺度上空间变化的输入对地表土壤水分同化的潜在能力的影响，以及（3）通过将单点测量值扩大到田间平均值将两个不同尺度的土壤水分估算联系起来; ，经过充分验证的数据同化技术Ensemble Kalman过滤器（EnKF）被用于田间尺度水质模型，即根区水质模型，其中包含来自印第安纳州两个农田的土壤水分数据。通过每天更新，与直接插入方法和模型结果相比，EnKF改进了所有统计结果，而对于5 cm和20 cm深度没有同化，而对较深层的改进较少。还测试了最佳更新间隔和合奏大小，以了解数据吸收的潜在操作能力。这项研究证明了表层土壤水分同化在改善水质和农作物产量以及在农业规模上估算土壤水分的潜力。第二，EnKF与分水岭规模，半分布式水文模型相结合，土壤和水评估工具。结果表明，每天用EnKF吸收地表土壤水分可以改善几乎所有水文过程的模型预测。但是，由于存在较大的降水误差，并且由于入渗径流模型机制的局限性，EnKF与土壤湿度估计值相比，在流量预测方面并没有产生太大的显着改善。土壤含水量的分布误差表明，土壤和土地利用类型等空间变化输入对同化结果的影响。这项研究的结果表明，通过数据同化来更新土壤水分可能是克服因降雨数据有限或不准确而造成的误差的补充方法。;在不同规模的观测值和模型预测之间正确关联土壤水分估计值对于验证土壤湿度至关重要遥感土壤水分产品，以及数据同化技术的成功应用。因此，本研究还研究了不同的放大方法，以将点测量值转换为代表小型农业流域（约2公顷）的田间平均值。发现累积分布函数（CDF）匹配方法可以从几种统计方法中提供田间平均土壤水分的最佳估计。升迁方程的时间和空间（水平和垂直）传递性测试表明，它们在空间上是可传递的，但在时间上是不可传递的。降雨特征和作物类型最有可能是影响可转移性成功的主要因素。另外，发现CDF匹配方法是一种从单点测量估算空间土壤水分变化的有效方法。总体而言，这项工作中提出的结果可用于改进土壤水分数据在田间和流域尺度上的同化应用并更好地评估土壤水分的结垢行为。

著录项

  • 作者

    Han, Eunjin.;

  • 作者单位

    Purdue University.;

  • 授予单位 Purdue University.;
  • 学科 Engineering Civil.
  • 学位 Ph.D.
  • 年度 2011
  • 页码 175 p.
  • 总页数 175
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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