Recent Advances On Soil Moisture Data Assimilation (original) (raw)
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Integration of soil moisture remote sensing and hydrologic modeling using data assimilation
Water Resources Research, 1998
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. 1. Introduction Soil 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] that the best, operational soil moisture estimates might be obtained through a synthesis between remote-sensing data and hydrologic modeling. Remote-sensing data, when combined with numerical simulation and other data, should provide estimates of soil moisture with higher spatial and temporal resolution 1Department of Hydrology and Water Resources, University of Arizona, Tucson.
Journal of Hydrometeorology, 2011
The contributions of precipitation and soil moisture observations to soil moisture skill in a land data assimilation system are assessed. Relative to baseline estimates from the Modern Era Retrospective-analysis for Research and Applications (MERRA), the study investigates soil moisture skill derived from (i) model forcing corrections based on large-scale, gauge- and satellite-based precipitation observations and (ii) assimilation of surface soil moisture retrievals from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). Soil moisture skill (defined as the anomaly time series correlation coefficient R) is assessed using in situ observations in the continental United States at 37 single-profile sites within the Soil Climate Analysis Network (SCAN) for which skillful AMSR-E retrievals are available and at 4 USDA Agricultural Research Service (“CalVal”) watersheds with high-quality distributed sensor networks that measure soil moisture at the scale of land ...
A comparison of methods for a priori bias correction in soil moisture data assimilation
Water Resources Research, 2012
1] Data assimilation is increasingly being used to merge remotely sensed land surface variables such as soil moisture, snow, and skin temperature with estimates from land models. Its success, however, depends on unbiased model predictions and unbiased observations. Here a suite of continental-scale, synthetic soil moisture assimilation experiments is used to compare two approaches that address typical biases in soil moisture prior to data assimilation: (1) parameter estimation to calibrate the land model to the climatology of the soil moisture observations and (2) scaling of the observations to the model's soil moisture climatology. To enable this research, an optimization infrastructure was added to the NASA Land Information System (LIS) that includes gradient-based optimization methods and global, heuristic search algorithms. The land model calibration eliminates the bias but does not necessarily result in more realistic model parameters. Nevertheless, the experiments confirm that model calibration yields assimilation estimates of surface and root zone soil moisture that are as skillful as those obtained through scaling of the observations to the model's climatology. Analysis of innovation diagnostics underlines the importance of addressing bias in soil moisture assimilation and confirms that both approaches adequately address the issue. (2012), A comparison of methods for a priori bias correction in soil moisture data assimilation, Water Resour. Res., 48, W03515,
IEEE International Geoscience and Remote Sensing Symposium, 2002
A data assimilation scheme has been applied using field data to determine the effects of the temporal frequency at which remote observations are assimilated to adjust model soil moisture profiles. It was found that, in terms of near-surface soil moisture, there is generally a gradual decrease in performance as the update frequency decreases. Performance for update periods longer than 8 days is nearly identical to that of a simulation performed without assimilation.
Hydrologic Remote Sensing and Land Surface Data Assimilation
Sensors, 2008
Accurate, reliable and skillful forecasting of key environmental variables such as soil moisture and snow are of paramount importance due to their strong influence on many water resources applications including flood control, agricultural production and effective water resources management which collectively control the behavior of the climate system. Soil moisture is a key state variable in land surface-atmosphere interactions affecting surface energy fluxes, runoff and the radiation balance. Snow processes also have a large influence on land-atmosphere energy exchanges due to snow high albedo, low thermal conductivity and considerable spatial and temporal variability resulting in the dramatic change on surface and ground temperature. Measurement of these two variables is possible through variety of methods using ground-based and remote sensing procedures. Remote sensing, however, holds great promise for soil moisture and snow measurements which have considerable spatial and temporal variability. Merging these measurements with hydrologic model outputs in a systematic and effective way results in an improvement of land surface model prediction. Data Assimilation provides a mechanism to combine these two sources of estimation. Much success has been attained in recent years in using data from passive microwave sensors and assimilating them into the models. This paper provides an overview of the remote sensing measurement techniques for soil moisture and snow data and describes the advances in data assimilation techniques through the ensemble filtering, mainly Ensemble Kalman filter (EnKF) and Particle filter (PF), for improving the model prediction and reducing the uncertainties involved in prediction process. It is believed that PF provides a complete representation of the probability distribution of state variables of interests (according to sequential Bayes law) and could be a strong alternative
Journal of Geophysical Research, 2004
The multiple-agency/university North American Land Data Assimilation System (NLDAS) project is designed to provide enhanced soil and temperature initial conditions for numerical weather/climate prediction models. Currently, four land surface models (LSMs) are running in NLDAS both in retrospective mode and in real-time mode. All LSMs are driven by the same meteorologic forcing data and are initiated at the same time with the same relative soil wetness. This study intercompares these NLDAS soil moisture fields with each other and ...
Soil moisture controls the land surface water and energy budget and plays a crucial role in land surface processes. Based on certain mathematical rules, data assimilation can merge satellite observations and land surface models, and produce spatiotemporally continuous profile soil moisture. The two mainstream assimilation algorithms (variational-based and sequential-based) both need model error and observation error estimates, which greatly impact the assimilation results. Moreover, the performance of land data assimilation relies heavily on the specification of model parameters. However, it is always challenging to specify these errors and model parameters. In this study, a dual-cycle assimilation algorithm was proposed for addressing the above issue. In the inner cycle, the Ensemble Kalman Filter (EnKF) is run with parameters of both model and observation operators and their errors, which are provided by the outer cycle. Both the analyzed state variable and the innovation are reserved at each analysis moment. In the outer cycle, the innovation time series kept by the inner cycle are fed into a likelihood function to adjust the values of parameters of both the model and observation operators and their errors through an optimization algorithm. A series of assimilation experiments were first performed based on the Lorenz-63 model. The results illustrate that the performance of the dual-cycle algorithm substantially surpasses those of both the classical parameter calibration and the standard EnKF. Subsequently, the Advanced Microwave Scanning Radiometer of earth Observing System (AMSR-E) brightness temperatures were assimilated into the simple biosphere model scheme version 2 (SiB2) with a radiative transfer model as the observation operator in two experimental areas, namely Naqu on the Tibetan Plateau and a Coordinate Enhanced Observing (CEOP) reference site in Mongolia. The results indicate that the dual-cycle assimilation algorithm can simultaneously estimate model parameters, observation operator parameters, model error, and observation error, thus improving surface soil moisture estimation in comparison with other assimilation algorithms. Since the dualcycle assimilation algorithm can estimate the observation errors, it provides the potential for assimilating multi-source remote sensing data to generate physically consistent land surface state and flux estimates.
Journal of Geophysical Research: Atmospheres, 2001
Because of its long-term persistence, accurate initialization of land surface soil moisture in fully coupled global climate models has the potential to greatly increase the accuracy of climatological and hydrological prediction. To improve the initialization of soil moisture in the NASA Seasonal-to-Interannual Prediction Project (NSIPP), a onedimensional Kalman filter has been developed to assimilate near-surface soil moisture observations into the catchment-based land surface model used by NSIPP. A set of numerical experiments was performed using an uncoupled version of the NSIPP land surface model to evaluate the assimilation procedure. In this study, "true" land surface data were generated by spinning-up the land surface model for 1987 using the International Satellite Land Surface Climatology Project (ISLSCP) forcing data sets. A degraded simulation was made for 1987 by setting the initial soil moisture prognostic variables to arbitrarily wet values uniformly throughout North America. The final simulation run assimilated the synthetically generated near-surface soil moisture "observations" from the true simulation into the degraded simulation once every 3 days. This study has illustrated that by assimilating near-surface soil moisture observations, as would be available from a remote sensing satellite, errors in forecast soil moisture profiles as a result of poor initialization may be removed and the resulting predictions of runoff and evapotranspiration improved. After only 1 month of assimilation the root-meansquare error in the profile storage of soil moisture was reduced to 3% vol/vol, while after 12 months of assimilation, the root-mean-square error in the profile storage was as low as 1% vol/vol.
IEEE Transactions on Geoscience and Remote Sensing, 2001
In coming years, Land Data Assimilation Systems (LDAS) two-dimensional (2-D) arrays of the relevant land-surface model) are likely to become the routine mechanism by which many predictive weather and climate models will be initiated. If this is so, it will be via assimilation into the LDAS that other data relevant to the land surface, such as remotely sensed estimates of soil moisture, will find value. This paper explores the potential for using low-resolution, remotely sensed observations of microwave brightness temperature to infer soil moisture in an LDAS with a "mosaicpatch" representation of land-surface heterogeneity, by coupling the land-surface model in the LDAS to a physically realistic microwave emission model. The past description of soil water movement by the LDAS is proposed as the most appropriate, LDASconsistent basis for using remotely sensed estimates of surface soil moisture to infer soil moisture at depth, and the plausibility of this proposal is investigated. Three alternative methods are explored for partitioning soil moisture between modeled patches while altering the area-average soil moisture to correspond to the observed, pixel-average microwave brightness temperature, namely, 1) altering the soil moisture by a factor, which is the same for all the patches in the pixel, 2) altering the soil moisture by adding an amount that is the same for all the patches in the pixel, and 3) altering the change in soil moisture since the last assimilation cycle by a factor which is the same for all the patches in the pixel. In each case, an iterative procedure is required to make the adjustment. Comparison is made between these alternative procedures for a hypothetical pixel that contains eight individual patches (three different vegetation types growing both in clay and sand, plus one patch of bare soil and one of open water) using a mosaic-patch version of the MICRO-SWEAT model. When the applied forcing variables are artificially degraded, all three methods provide similar, improved descriptions of the time-evolution of soil moisture in the pixel as a whole and of the deep soil moisture for each patch. However, in each case, the ability of the LDAS to correctly describe the separate evolution of surface soil moisture in each patch is imperfect.