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首页> 外文期刊>Paddy and Water Environment >To improve model soil moisture estimation in arid/semi-arid region using in situ and remote sensing information
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To improve model soil moisture estimation in arid/semi-arid region using in situ and remote sensing information

机译：利用原位和遥感信息改善干旱/半干旱地区的土壤湿度模型估算

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摘要

Soil moisture plays a key role in water and energy exchange in the land hydrologic process. Effective soil moisture information can be used for many applications in weather and hydrological forecasting, water resources, and irrigation system management and planning. However, to accurate modeling of soil moisture variation in the soil layer is still very challenging. In this study, in situ and remote sensing information of near-surface soil moisture is assimilated into the Noah land surface model (LSM) to estimate deep-layer soil moisture variation. The sequential Monte Carlo-Particle Filter technique, being well known for capability of modeling high nonlinear and non-Gaussian processes, is applied to assimilate surface soil moisture measurement to the deep layers. The experiments were carried out over several locations over the semi-arid region of the US. Comparing with in situ observations, the assimilation runs show much improved from the control (non-assimilation) runs for estimating both soil moisture and temperature at 5-, 20-, and 50-cm soil depths in the Noah LSM.
机译：在土地水文过程中，土壤水分在水和能量交换中起着关键作用。有效的土壤水分信息可用于天气和水文预报，水资源以及灌溉系统的管理和规划中的许多应用。但是，要对土壤层中土壤水分的变化进行精确建模仍然是非常困难的。在这项研究中，将近地表土壤水分的原位和遥感信息同化为Noah地表模型（LSM），以估算深层土壤水分的变化。顺序蒙特卡洛颗粒过滤技术因其对高非线性和非高斯过程建模的能力而闻名，可用于将表层土壤水分测量值同化到深层。实验是在美国半干旱地区的多个地点进行的。与原位观察相比，在Noah LSM的5、20和50厘米土壤深度处，同化运行相对于对照（非同化）运行要好得多，而对照（非同化）运行可以提高土壤湿度和温度。

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  • 来源
    《Paddy and Water Environment》 |2012年第3期|p.165-173|共9页
  • 作者

    Kuolin Hsu; Jialun Li; Soroosh Sorooshian;

  • 作者单位

    Department of Civil and Environmental Engineering, Center for Hydrometeorology and Remote Sensing, UC Irvine, Irvine, CA, USA;

    Department of Civil and Environmental Engineering, Center for Hydrometeorology and Remote Sensing, UC Irvine, Irvine, CA, USA;

    Department of Civil and Environmental Engineering, Center for Hydrometeorology and Remote Sensing, UC Irvine, Irvine, CA, USA;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Soil moisture; Land surface model; Data assimilation; Sequential Monte Carlo;

    机译：土壤水分土地表面模型数据同化顺序蒙特卡罗;

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