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Geophysical imaging of watershed subsurface patterns and prediction of soil texture and water holding capacity

机译：流域地下模式的地球物理成像以及土壤质地和持水量的预测

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摘要

The spatial distribution of subsurface soil textural properties across the landscape is an important control on the hydrological and ecological function of a watershed. Traditional methods of mapping soils involving subjective assignment of soil boundaries are inadequate for studies requiring a quantitative assessment of the landscape and its subsurface connectivity and storage capacity. Geophysical methods such as electromagnetic induction (EMI) provide the possibility of obtaining high-resolution images across a landscape to identify subtle changes in subsurface soil patterns. In this work we show how EMI can be used to image the subsurface of a ～38 ha watershed. We present an imaging approach using kriging to interpolate and sequential Gaussian simulation to estimate the uncertainty in the maps. We also explore the idea of difference EC_a mapping to try to exploit changes in soil moisture to identify more hydrologically active locations. In addition, we use a digital elevation model to identify flow paths and compare these with the EC_a measurement as a function of distance. Finally, we perform a more traditional calibration of EC_a with clay percentage across the watershed and determine soil water holding capacity (SWHC). The values of SWHC range from 0.07 to 0.22 m~3 m~(-3) across the watershed, which contrast with the uniform value of 0.13 derived from the traditional soil survey maps. Additional work is needed to appropriately interpret and incorporate EMI data into hydrological studies; however, we argue that there is considerable merit in identifying subsurface soil patterns from these geophysical images.
机译：整个景观中地下土壤质地特性的空间分布是对流域水文和生态功能的重要控制。涉及主观分配土壤边界的传统土壤图绘制方法不足以进行需要定量评估景观及其地下连通性和存储能力的研究。地球物理方法（例如电磁感应（EMI））提供了在整个景观中获取高分辨率图像以识别地下土壤模式中细微变化的可能性。在这项工作中，我们展示了如何使用EMI来成像约38公顷流域的地下。我们提出一种成像方法，使用克里金插值和顺序高斯模拟来估计地图中的不确定性。我们还探讨了差异EC_a映射的想法，以尝试利用土壤水分的变化来确定更具水文活性的位置。此外，我们使用数字高程模型来识别流路，并将其与EC_a测量值进行比较，以作为距离的函数。最后，我们使用跨流域的粘土百分比对EC_a进行更传统的校准，并确定土壤持水量（SWHC）。流域的SWHC值范围为0.07至0.22 m〜3 m〜（-3），与传统土壤调查图得出的统一值0.13形成对比。需要做额外的工作来适当地解释电磁干扰数据并将其纳入水文研究中；然而，我们认为从这些地球物理图像中识别地下土壤模式具有相当大的优势。

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  • 来源
    《Water resources research》 |2010年第4期|p.W00D018.1-W00D018.10|共10页
  • 作者

    H. Abdu; D. A. Robinson; M. Seyfried; S. B. Jones;

  • 作者单位

    Department of Biological and Irrigation Engineering, Utah State University, Logan, UT 84322, USA;

    Department of Food Production, University of the West Indies, St. Augustine, Trinidad and Tobago;

    Northwest Watershed Research Center, ARS, USDA, 800 Park Boulevard, Plaza IV, Suite 105, Boise, ID 83712, USA;

    Department of Plants, Soils and Climate, Utah State University, Logan, UT 84322, USA;

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