Multi-scale assimilation of root zone soil water predictions

A methodology is presented for quantifying the uncertainty on soil hydraulic parameters arising from within soil class variability and for assessing its impact on soil moisture modelling. This method differs from former research because (i) the construction of the parameter uncertainty does not rely on experimental data but on soil hydraulic parameters predicted with pedotransfer functions, (ii) all moments of the parameter probability distribution function are preserved and (iii) the dependence between the soil hydraulic parameters is taken into account. Results show that the soil hydraulic model parameters are not normally distributed and that this parameter uncertainty results in non-normal distributions of the modelled soil moisture. Especially when the soil is very dry, the deviation from the normal distribution will hamper the optimality of classical linear filters for data assimilation. As an extension of this research, we have shown that the central point of a soil class is ...

Journal of Hydrometeorology, 2007

A long term data acquisition effort of profile soil moisture is under way in southwestern France at 13 automated weather stations. This ground network was developed in order to validate remote sensing and model soil moisture estimates. In this paper, both those in situ observations and a synthetic data set covering continental France are used to test a simple method to retrieve root zone soil moisture from a time series of surface soil moisture information. A recursive exponential filter equation using a time constant, T , is used to compute a soil water index. The Nash and Sutcliff coefficient is used as a criterion to optimise the T parameter for each ground station and for each model pixel of the synthetic data set. In general, the soil water indices derived from the surface soil moisture observations and simulations agree well with the reference root-zone soil moisture. Overall, the results show the potential of the exponential filter equation and of its recursive formulation to derive a soil water index from surface soil moisture estimates. This paper further investigates the correlation of the time scale parameter T with soil properties and climate conditions. While no significant relationship could be determined between T and the main soil properties (clay and sand fractions, bulk density and organic matter content), the modelled spatial variability and the observed inter-annual variability of T suggest that a weak climate effect may exist.

2011

Soil Science Society of America Journal, 2012

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Journal of the American Water Resources Association, 2007

A hybrid data assimilation (DA) methodology that combines two state-of-the-art techniques, support vector machines (SVMs) and ensemble Kalman filter (EnKF), is applied for soil moisture DA in this work. The SVM methodology provides a statistically sound and robust approach to solving the inverse problem, and thus to building statistical models. EnKF is an extension of the Kalman Filter (KF), a well-known tool in prediction updating. In the present research, ground measurements were used to build a SVM-type soil moisture predictor. Subsequent observations and their statistics were assimilated to update predictions from the SVM model by coupling it with EnKF. In this way, both model predictions and ground data, as well as their statistics, are fused thus minimizing the prediction error and making the predictions and observations statistically consistent. The results are shown for two approaches; one in which update is done at every time step and the other which assumes that data is only available at alternate time steps (in window of 10 time steps) and hence update is performed at those occasions. The SVM-EnKF coupling is shown to improve soil moisture forecasts in an example using data from the Soil Climate Analysis Network site at Ames, Iowa.

Water Resources Research, 2001

An operational framework is presented for assimilating surface soil moisture remote sensing measurements into a soil-vegetation-atmosphere-transfer (SVAT) model for the robust prediction of root zone moisture time series. The proposed approach is based on analytical treatment of the dynamical equations coupling surface and deeper soil reservoirs. The resulting framework uses biases between observed and modeled time rates of change of surface soil moisture to quantify biases between modeled and actual root zone average soil moisture contents. The approach is based on the popular interactions between soil-biosphere-atmosphere (ISBA) force-restore SVAT model. An experimental data set, collected near Cork, Ireland, is analyzed both for a long data series of 183 days and four short periods that were selected to focus on different hydrometeorological conditions. The results demonstrated that the proposed framework performs uniformly robust over 3 orders of magnitude of misspecification of saturated hydraulic conductivity. In the presence of uncertain initial conditions, the results demonstrated a marked increase in model skill (over the original ISBA model) for periods when average precipitation was less than average potential evaporation.

Esaim-control Optimisation and Calculus of Variations, 2004

The soil water movement model governed by the initial-boundary value problem for a quasilinear 1-D parabolic equation with nonlinear coefficients is considered. The generalized statement of the problem is formulated. The solvability of the problem is proved in a certain class of functional spaces. The data assimilation problem for this model is analysed. The numerical results are presented.

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.

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.

Water

Real-time in situ measurements are increasingly being used to improve the estimations of simulation models via data assimilation techniques such as particle filter. However, models that describe complex processes such as water flow contain a large number of parameters while the data available are typically very limited. In such situations, applying particle filter to a large, fixed set of parameters chosen a priori can lead to unstable behavior, i.e., inconsistent adjustment of some of the parameters that have only limited impact on the states that are being measured. To prevent this, in this study correlation-based variable selection is embedded in the particle filter, so that at each step only a subset of the most influential parameters is adjusted. The particle filter used in this study includes genetic algorithm operators and Monte Carlo Markov Chain for alleviating filter degeneracy and sample impoverishment. The proposed method was applied to a water flow model (Hydrus-1D) in ...