Integration of soil moisture remote sensing and hydrologic modeling using data assimilation

Abstract

Abstract. The feasibility of synthesizing distributed fields of soil moisture by the novel application of four-dimensional data assimilation (4DDA) applied in a hydrological model is explored. Six 160-km 2 push broom microwave radiometer (PBMR) images gathered over the Walnut Gulch experimental watershed in southeast Arizona were assimilated into the Topmodel-based Land-Atmosphere Transfer Scheme (TOPLATS) using several alternative assimilation procedures. Modification of traditional assimilation methods was required to use these high-density PBMR observations. The images were found to contain horizontal correlations that imply length scales of several tens of kilometers, thus allowing information to be advected beyond the area of the image. Information on surface soil moisture also was assimilated into the subsurface using knowledge of the surfacesubsurface correlation. Newtonian nudging assimilation procedures are preferable to other techniques because they nearly preserve the observed patterns within the sampled region but also yield plausible patterns in unmeasured regions and allow information to be advected in time. IntroductionSoil moisture is most often described as the water in the root zone that can interact with the atmosphere through evapotranspiration and precipitation. Because soil moisture links the hydrologic cycle and the energy budget of land surfaces by regulating latent heat fluxes, accurate assessment of the spatial and temporal variation of soil moisture is important for the study, understanding, and management of surface biogeophysical processes. Given the crucial role of soil moisture in land surface processes, it should be monitored with the same accuracy and frequency as other important environmental variables. However, because in situ soil moisture measurements are generally expensive and often problematic, no large-area soil moisture networks exist to measure soil moisture at the high frequency, multiple depths, and fine spatial resolution that is required for various applications. Remote sensing of soil moisture is limited by errors introduced by soil type, landscape roughness, vegetation cover, and inadequate coverage in both space and time. Alternatively, many reliable hydrologic models are available for calculating soil moisture, but these are prone to error in both structure and parameterization. It has been suggested [Wei, 1995] In the "statistical correction" data assimilation technique, = F(O, x, t) + GoWo(x, t)•o(x)(O•,-O) OtThe model's forcing terms are represented by F, 0•, is the observation at the model grid, and t is time. G 0 is the nudging factor that determines the magnitude of the nudging term relative to all other model processes, while the fourdimensional weighting function Wo specifies its spatial and temporal variation. The analysis quality factor • varies between 0 and 1 and is based on the quality and distribution of the observations. Equation (6) is implemented for all three TOPLATS soil layers, with the weighting factor decreasing with depth and time using...

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