Impact of soil hydraulic parameter uncertainty on soil moisture modelling and its implications with respect to data assimilation (original) (raw)
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CATENA, 2018
Obtaining reliable soil hydraulic properties is essential for correct simulations of soil water content (SWC), which is a key variable in countless applications such as agricultural management, soil remediation, aquifer protection, etc. Soil hydraulic properties can be measured in the laboratory; however, the procedures are laborious and costly, and may provide estimates different from those observed in the field. An alternative approach is to obtain soil hydraulic properties using a soil water flow model in conjunction with SWC monitoring data. The goal of the present study was to analyze the efficiency of obtaining hydraulic properties utilizing data assimilation (DA) based on the Ensemble Kalman Filter method. Two soil textures in homogeneous soil profiles, and four climatic conditions were considered; observations of soil moisture data were synthetically generated using HYDRUS-1D and subsequently perturbed by the application of the conditional multivariate normal distribution. When observed SWC varied in relatively narrow range as a consequence of the forcing imposed by dry climate atmospheric boundary conditions, data assimilation provided sets of properties that led to good Richards model performance, with the RMSE below 0.02 and/or R 2 above 0.8 after a period of just 100 days and above 0.98 after a period of three years in all climate/soil conditions. However, the closeness of parameters from DA to the parameters used to generate the synthetic data depended on weather conditions and soil properties. One year was adequate to obtain reliable soil hydraulic properties with data assimilation.
Soil hydraulic parameters estimated from satellite information through data assimilation
International Journal of Remote Sensing, 2011
Leaf area index (LAI) and actual evapotranspiration (ET a ) from satellite observations were used to estimate simultaneously the soil hydraulic parameters of four soil layers down to 60 cm depth using the combined soil water atmosphere plant and genetic algorithm (SWAP-GA) model. This inverse model assimilates the remotely sensed LAI and/or ET a by searching for the most appropriate sets of soil hydraulic parameters that could minimize the difference between the observed and simulated LAI (LAI sim ) or simulated ET a (ET asim ). The simulated soil moisture estimates derived from soil hydraulic parameters were validated using values obtained from soil moisture sensors installed in the field. Results showed that the soil hydraulic parameters derived from LAI alone yielded good estimations of soil moisture at 3 cm depth; LAI and ET a in combination at 12 cm depth, and ET a alone at 28 cm depth. There appeared to be no match with measurement at 60 cm depth. Additional information would therefore be needed to better estimate soil hydraulic parameters at greater depths. Despite this inability of satellite data alone to provide reliable estimates of soil moisture at the lowest depth, derivation of soil hydraulic parameters using remote sensing methods remains a promising area for research with significant application potential. This is especially the case in areas of water management for agriculture and in forecasting of floods or drought on the regional scale.
Recent Advances On Soil Moisture Data Assimilation
Physical Geography, 2008
This study reviews recent progress on soil moisture data assimilation. Data assimilation is a process of merging observations with a system dynamic model to provide an improved estimate of the states of the environment. The application of data assimilation in hydrology is relatively new, however, rapid progress has been made in the last decade or so with the available remotely sensed soil moisture data. After briefing the history of soil moisture data assimilation, the review focuses on the most common data assimilation methods and recent progress made in soil moisture data assimilation through a case study of the soil moisture initialization activities for NASA's seasonal and interannual climate prediction. The example demonstrates that soil moisture data assimilation has made great progress in the last decade, however is still in its infancy. Good quality remotely sensed soil moisture data with accurate uncertainty information at continental and global scale are needed to ensure the success of the operational use of soil moisture data assimilation technique. Further advancement on the current soil moisture data assimilation methods is necessary to be able to assimilate multisource hydrological remote sensing data into land surface models for the best use of various remote sensing data sources at continental and global scales.
Hydrology and Earth System Sciences Discussions, 2015
Two data assimilation methods are compared for their ability to produce a deterministic soil moisture analysis on the Météo-France land surface model: (i) SEKF, a Simplified Extended Kalman Filter, which uses a climatological background-error covariance, (ii) EnSRF, the Ensemble Square Root Filter, which uses an ensemble background-error covariance and approximates random forcing errors stochastically. The accuracy of the deterministic analysis is measured on 12 sites with in situ observations and various soil textures in Southwest France (SMOSMANIA network). In the experiments with real observations, the two methods perform similarly and improve on the open loop. Both methods suffer from incorrect linear assumptions which are particularly degrading to the analysis during water-stressed conditions: the EnSRF by a dry bias and the SEKF by an over-sensitivity of the model Jacobian between the surface and the root zone layers. These problems are less severe for sandy soils than clay so...
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,
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 ...
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.