Methods and examples for remote sensing data assimilation in land surface process modeling (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.
Land Data Assimilation: Harmonizing Theory and Data in Land Surface Process Studies
Reviews of Geophysics, 2024
Data assimilation plays a dual role in advancing the “scientific” understanding and serving as an “engineering tool” for the Earth system sciences. Land data assimilation (LDA) has evolved into a distinct discipline within geophysics, facilitating the harmonization of theory and data and allowing land models and observations to complement and constrain each other. Over recent decades, substantial progress has been made in the theory, methodology, and application of LDA, necessitating a holistic and in‐depth exploration of its full spectrum. Here, we present a thorough review elucidating the theoretical and methodological developments in LDA and its distinctive features. This encompasses breakthroughs in addressing strong nonlinearities in land surface processes, exploring the potential of machine learning approaches in data assimilation, quantifying uncertainties arising from multiscale spatial correlation, and simultaneously estimating model states and parameters. LDA has proven successful in enhancing the understanding and prediction of various land surface processes (including soil moisture, snow, evapotranspiration, streamflow, groundwater, irrigation and land surface temperature), particularly within the realms of water and energy cycles. This review outlines the development of global, regional, and catchment‐scale LDA systems and software platforms, proposing grand challenges of generating land reanalysis and advancing coupled land‒atmosphere DA. We lastly highlight the opportunities to expand the applications of LDA from pure geophysical systems to coupled natural and human systems by ingesting a deluge of Earth observation and social sensing data. The paper synthesizes current LDA knowledge and provides a steppingstone for its future development, particularly in promoting dual driven theory‐data land processes studies.
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
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.
Advances in Water …, 2008
The Land Information System (LIS) is an established land surface modeling framework that integrates various community land surface models, ground measurements, satellite-based observations, high performance computing and data management tools. The use of advanced software engineering principles in LIS allows interoperability of individual system components and thus enables assessment and prediction of hydrologic conditions at various spatial and temporal scales. In this work, we describe a sequential data assimilation extension of LIS that incorporates multiple observational sources, land surface models and assimilation algorithms. These capabilities are demonstrated here in a suite of experiments that use the ensemble Kalman filter (EnKF) and assimilation through direct insertion. In a soil moisture experiment, we discuss the impact of differences in modeling approaches on assimilation performance. Provided careful choice of model error parameters, we find that two entirely different hydrological modeling approaches offer comparable assimilation results. In a snow assimilation experiment, we investigate the relative merits of assimilating different types of observations (snow cover area and snow water equivalent). The experiments show that data assimilation enhancements in LIS are uniquely suited to compare the assimilation of various data types into different land surface models within a single framework. The high performance infrastructure provides adequate support for efficient data assimilation integrations of high computational granularity.
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.
Assimilating remote sensing data in a surface flux-soil moisture model
Hydrological Processes, 2002
A key state variable in land surface-atmosphere interactions is soil moisture, which affects surface energy fluxes, runoff and the radiation balance. Soil moisture modelling relies on parameter estimates that are inadequately measured at the necessarily fine model scales. Hence, model soil moisture estimates are imperfect and often drift away from reality through simulation time. Because of its spatial and temporal nature, remote sensing holds great promise for soil moisture estimation. Much success has been attained in recent years in soil moisture estimation using passive and active microwave sensors, but progress has been slow. One reason for this is the scale disparity between remote sensing data resolution and the hydrologic process scale. Other impediments include vegetation cover and microwave penetration depth. As a result, currently there is no comprehensive method for assimilating remote soil moisture observations within a surface hydrology model at watershed or larger scales.
Joint assimilation of surface soil moisture and LAI observations into a land surface model
Agricultural and Forest Meteorology, 2008
Joint assimilation of surface soil moisture and LAI observations into a land surface model. ABSTRACT 12 Land Surface Models (LSM) offer a description of land surface processes and set the 13 lower boundary conditions for meteorological models. In particular the accurate description of 14 those surface variables which display a slow response in time, like root zone soil moisture or 15 vegetation biomass, is of great importance. Errors in their estimation yield significant 16 inaccuracies in the estimation of heat and water fluxes in Numerical Weather Prediction 17 (NWP) models. In the present study, the ISBA-A-g s LSM is used decoupled from the 18 atmosphere. In this configuration, the model is able to simulate the vegetation growth, and 19 consequently LAI. A simplified 1D-VAR assimilation method is applied to observed surface 20 soil moisture and LAI observations of the SMOSREX site near Toulouse, in south-western 21 France, from 2001 to 2004. This period includes severe droughts in 2003 and 2004. The data 22 are jointly assimilated into ISBA-A-g s in order to analyse the root zone soil moisture and the 23 vegetation biomass. It is shown that the 1D-VAR improves the model results. The efficiency 24 score of the model (Nash criterion) is increased from 0.79 to 0.86 for root-zone soil moisture 25 and from 0.17 to 0.23 for vegetation biomass. 26 27
Hydrological Processes, 2002
A key state variable in land surface-atmosphere interactions is soil moisture, which affects surface energy fluxes, runoff and the radiation balance. Soil moisture modelling relies on parameter estimates that are inadequately measured at the necessarily fine model scales. Hence, model soil moisture estimates are imperfect and often drift away from reality through simulation time. Because of its spatial and temporal nature, remote sensing holds great promise for soil moisture estimation. Much success has been attained in recent years in soil moisture estimation using passive and active microwave sensors, but progress has been slow. One reason for this is the scale disparity between remote sensing data resolution and the hydrologic process scale. Other impediments include vegetation cover and microwave penetration depth. As a result, currently there is no comprehensive method for assimilating remote soil moisture observations within a surface hydrology model at watershed or larger scales.This paper describes a measurement-modelling system for estimating the three-dimensional soil moisture distribution, incorporating remote microwave observations, a surface flux-soil moisture model, a radiative transfer model and Kalman filtering. The surface model, driven by meteorological observations, estimates the vertical and lateral distribution of water. Based on the model soil moisture profiles, microwave brightness temperatures are estimated using the radiative transfer model. A Kalman filter is then applied using modelled and observed brightness temperatures to update the model soil moisture profile.The modelling system has been applied using data from the Southern Great Plains 1997 field experiment. In the presence of highly inaccurate rainfall input, assimilation of remote microwave data results in better agreement with observed soil moisture. Without assimilation, it was seen that the model near-surface soil moisture reached a minimum that was higher than observed, resulting in substantial errors during very dry conditions. Updating moisture profiles daily with remotely sensed brightness temperatures reduced but did not eliminate this bias.