Improving hydrologic predictions of a catchment model via assimilation of surface soil moisture

This paper examines the potential for improving Soil and Water Assessment Tool (SWAT) hydrologic predictions of root-zone soil moisture, evapotranspiration, and stream flow within the 341 km² Cobb Creek Watershed in southwestern Oklahoma through the assimilation of surface soil moisture observations using an Ensemble Kalman filter (EnKF). In a series of synthetic twin experiments assimilating surface soil moisture is shown to effectively update SWAT upper-layer soil moisture predictions and provide moderate improvement to lower layer soil moisture and evapotranspiration estimates. However, insufficient SWAT-predicted vertical coupling results in limited updating of deep soil moisture, regardless of the SWAT parameterization chosen for root-water extraction. Likewise, a real data assimilation experiment using ground-based soil moisture observations has only limited success in updating upper-layer soil moisture and is generally unsuccessful in enhancing SWAT stream flow predictions. Comparisons against ground-based observations suggest that SWAT significantly under-predicts the magnitude of vertical soil water coupling at the site, and this lack of coupling impedes the ability of the EnKF to effectively update deep soil moisture, groundwater flow and surface runoff. The failed attempt to improve stream flow prediction is also attributed to the inability of the EnKF to correct for existing biases in SWAT-predicted stream flow components.     

Comparison of ensemble-based state and parameter estimation methods for soil moisture data assimilation

Model parameters are a source of uncertainty that can easily cause systematic deviation and significantly affect the accuracy of soil moisture generation in assimilation systems. This study addresses the issue of retrieving model parameters related to soil moisture via the simultaneous estimation of states and parameters based on the Common Land Model (CoLM). The state-parameter estimation algorithms AEnKF (Augmented Ensemble Kalman Filter), DEnKF (Dual Ensemble Kalman Filter) and SODA (Simultaneous optimization and data assimilation) are entirely implemented within an EnKF framework to investigate how the three algorithms can correct model parameters and improve the accuracy of soil moisture estimation. The analysis is illustrated by assimilating the surface soil moisture levels from varying observation intervals using data from Mongolian plateau sites. Furthermore, a radiation transfer model is introduced as an observation operator to analyze the influence of brightness temperature assimilation on states and parameters that are estimated at different microwave signal frequencies. Three cases were analyzed for both soil moisture and brightness temperature assimilation, focusing on the progressive incorporation of parameter uncertainty, forcing data uncertainty and model uncertainty. It has been demonstrated that EnKF is outperformed by all other methods, as it consistently maintains a bias. State-parameter estimation algorithms can provide a more accurate estimation of soil moisture than EnKF. AEnKF is the most robust method, with the lowest RMSE values for retrieving states and parameters dealing only with parameter uncertainty, but it possesses disadvantages related to increasing sources of uncertainty and decreasing numbers of observations. SODA performs well under the complex situations in which DEnKF shows slight disadvantages in terms of statistical indicators; however, the former consumes far more memory and time than the latter.     

A soil water and heat transfer model including changes in soil frost and thaw fronts

Freeze-thaw processes in soils,including changes in frost and thaw fronts(FTFs),are important physical processes.The movement of FTFs affects soil hydrothermal characteristics,as well as energy and water exchanges between the land surface and the atmosphere and hydrothermal processes in the land surface.This paper reduces the issue of soil freezing and thawing to a multiple moving-boundary problem and develops a soil water and heat transfer model which considers the effects of FTF on soil hydrothermal processes.A local adaptive variable-grid method is used to discretize the model.Sensitivity tests based on the hierarchical structure of the Community Land Model(CLM)show that multiple FTFs can be continuously tracked,which overcomes the difficulties of isotherms that cannot simultaneously simulate multiple FTFs in the same soil layer.The local adaptive variable-grid method is stable and offers computational efficiency several times greater than the high-resolution case.The simulated FTF depths,soil temperatures,and soil moisture values fit well with the observed data,which further demonstrates the potential application of this simulation to the land-surface process model.     

A land surface data assimilation framework using the land information system: Description and applications

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.     

Application of data assimilation with the Root Zone Water Quality Model for soil moisture profile estimation in the upper Cedar Creek,Indiana

Data assimilation techniques have been proven as an effective tool to improve model forecasts by combining information about observed variables in many areas. This article examines the potential of assimilating surface soil moisture observations into a field‐scale hydrological model, the Root Zone Water Quality Model, to improve soil moisture estimation. The Ensemble Kalman Filter (EnKF), a popular data assimilation technique for nonlinear systems, was applied and compared with a simple direct insertion method. In situ soil moisture data at four different depths (5, 20, 40, and 60 cm) from two agricultural fields (AS1 and AS2) in northeastern Indiana were used for assimilation and validation purposes. Through daily update, the EnKF improved soil moisture estimation compared with the direct insertion method and model results without assimilation, having more distinct improvement at the 5 and 20 cm depths than for deeper layers (40 and 60 cm). Local vertical soil property heterogeneity in AS1 deteriorated soil moisture estimates with the EnKF. Removal of systematic bias in the forecast model was found to be critical for more successful soil moisture data assimilation studies. This study also demonstrates that a more frequent update generally contributes in enhancing the open loop simulation; however, large forecasting error can prevent more frequent update from providing better results. In addition, results indicate that various ensemble sizes make little difference in the assimilation results. An ensemble of 100 members produced results that were comparable with results obtained from larger ensembles. Copyright © 2011 John Wiley & Sons, Ltd.     

Using a H∞ filter assimilation procedure to estimate root zone soil water content

Root zone soil water content impacts plant water availability, land energy and water balances. Because of unknown hydrological model error, observation errors and the statistical characteristics of the errors, the widely used Kalman filter (KF) and its extensions are challenged to retrieve the root zone soil water content using the surface soil water content. If the soil hydraulic parameters are poorly estimated, the KF and its extensions fail to accurately estimate the root zone soil water. The H‐infinity filter (HF) represents a robust version of the KF. The HF is widely used in data assimilation and is superior to the KF, especially when the performance of the model is not well understood. The objective of this study is to study the impact of uncertain soil hydraulic parameters, initial soil moisture content and observation period on the ability of HF assimilation to predict in situ soil water content. In this article, we study seven cases. The results show that the soil hydraulic parameters hold a critical role in the course of assimilation. When the soil hydraulic parameters are poorly estimated, an accurate estimation of root soil water content cannot be retrieved by the HF assimilation approach. When the estimated soil hydraulic parameters are similar to actual values, the soil water content at various depths can be accurately retrieved by the HF assimilation. The HF assimilation is not very sensitive to the initial soil water content, and the impact of the initial soil water content on the assimilation scheme can be eliminated after about 5–7 days. The observation interval is important for soil water profile distribution retrieval with the HF, and the shorter the observation interval, the shorter the time required to achieve actual soil water content. However, the retrieval results are not very accurate at a depth of 100 cm. Also it is complex to determine the weighting coefficient and the error attenuation parameter in the HF assimilation. In this article, the trial‐and‐error method was used to determine the weighting coefficient and the error attenuation parameter. After the first establishment of limited range of the parameters, ‘the best parameter set’ was selected from the range of values. For the soil conditions investigated, the HF assimilation results are better than the open‐loop results. Copyright © 2010 John Wiley & Sons, Ltd.     

Data assimilation for distributed hydrological catchment modeling via ensemble Kalman filter

Catchment scale hydrological models are critical decision support tools for water resources management and environment remediation. However, the reliability of hydrological models is inevitably affected by limited measurements and imperfect models. Data assimilation techniques combine complementary information from measurements and models to enhance the model reliability and reduce predictive uncertainties. As a sequential data assimilation technique, the ensemble Kalman filter (EnKF) has been extensively studied in the earth sciences for assimilating in-situ measurements and remote sensing data. Although the EnKF has been demonstrated in land surface data assimilations, there are no systematic studies to investigate its performance in distributed modeling with high dimensional states and parameters. In this paper, we present an assessment on the EnKF with state augmentation for combined state-parameter estimation on the basis of a physical-based hydrological model, Soil and Water Assessment Tool (SWAT). Through synthetic simulation experiments, the capability of the EnKF is demonstrated by assimilating the runoff and other measurements, and its sensitivities are analyzed with respect to the error specification, the initial realization and the ensemble size. It is found that the EnKF provides an efficient approach for obtaining a set of acceptable model parameters and satisfactory runoff, soil water content and evapotranspiration estimations. The EnKF performance could be improved after augmenting with other complementary data, such as soil water content and evapotranspiration from remote sensing retrieval. Sensitivity studies demonstrate the importance of consistent error specification and the potential with small ensemble size in the data assimilation system.     

One-dimensional soil moisture profile retrieval by assimilation of near-surface observations: a comparison of retrieval algorithms

This paper investigates the ability to retrieve the true soil moisture and temperature profiles by assimilating near-surface soil moisture and surface temperature data into a soil moisture and heat transfer model. The direct insertion and Kalman filter assimilation schemes have been used most frequently in assimilation studies, but no comparisons of these schemes have been made. This study investigates which of these approaches is able to retrieve the soil moisture and temperature profiles the fastest, over what depth soil moisture observations are required, and the effect of update interval on profile retrieval. These questions are addressed by a desktop study using synthetic data. The study shows that the Kalman filter assimilation scheme is superior to the direct insertion assimilation scheme, with retrieval of the soil moisture profile being achieved in 12 h as compared to 8 days or more, depending on observation depth, for hourly observations. It was also found that profile retrieval could not be realised for direct insertion of the surface node alone, and that observation depth does not have a significant effect on profile retrieval time for the Kalman filter. The observation interval was found to be unimportant for profile retrieval with the Kalman filter when the forcing data is accurate, whilst for direct insertion the continuous Dirichlet boundary condition was required for an increasingly longer period of time. It was also found that the Kalman filter assimilation scheme was less susceptible to unstable updates if volumetric soil moisture was modelled as the dependent state rather than matric head, because the volumetric soil moisture state is more linear in the forecasting model.     

Investigating soil moisture sensitivity to precipitation and evapotranspiration errors using SiB2 model and ensemble Kalman filter

Accurate soil moisture information is useful in agricultural practice, weather forecasting, and various hydrological applications. Although land surface modeling provides a viable approach to simulating soil moisture, many factors such as errors in the precipitation can affect the accuracy of soil moisture simulations. This paper examined how precipitation rate and evapotranspiration rate affect the accuracy of soil moisture simulation using simple biosphere model with and without data assimilation through ensemble Kalman filter (EnKF). For each of the two variables, seven levels of relative errors (?20, ?10, ?5, 0, 5, 10 and 20 %) were introduced independently, thus a total of 49 combined cases were investigated. Observations from Wudaogou Hydrology Experimental site in the Huaihe River basin, China, were used to drive and verify the simulations. Results indicate that when the error of precipitation rate is within 10 % of the observations, the resulting error in soil moisture simulations is less significant and manageable, thus the simulated precipitation can be used to drive hydrological models in poorly gauged catchments when observations are not available. When the error of evapotranspiration rate is within 20 % of the observations, which is partly caused by model structural and parameterization errors, its impact on soil moisture simulation is less significant and can be acceptable. This study also demonstrated that the EnKF can perform consistently well to improve soil moisture simulation with less sensitivity to precipitation errors.     

Simultaneous estimation of soil moisture and hydraulic parameters using residual resampling particle filter

Land data assimilation (DA) has gradually developed into an important earth science research method because of its ability to combine model simulations and observations. Integrating new observations into a land surface model by the DA method can correct the predicted trajectory of the model and thus, improve the accuracy of state variables. It can also reduce uncertainties in the model by estimating some model parameters simultaneously. Among the various DA methods, the particle filter is free from the constraints of linear models and Gaussian error distributions, and can be applicable to any nonlinear and non-Gaussian state-space model; therefore, its importance in land data assimilation research has increased. In this study, a DA scheme was developed based on the residual resampling particle filter. Microwave brightness temperatures were assimilated into the macro-scale semi-distributed variance infiltration capacity model to estimate the surface soil moisture and three hydraulic parameters simultaneously. Finally, to verify the scheme, a series of comparative experiments was performed with experimental data obtained during the Soil Moisture Experiment of 2004 in Arizona. The results show that the scheme can improve the accuracy of soil moisture estimations significantly. In addition, the three hydraulic parameters were also well estimated, demonstrating the effectiveness of the DA scheme.     

A land surface model incorporated with soil freeze/thaw and its application in GAME/Tibet

Land surface process is of great importance in global climate change, moisture and heat exchange in the interface of the earth and atmosphere, human impacts on the environment and eco- system, etc. Soil freeze/thaw plays an important role in cold land surface processes. In this work the diurnal freeze/thaw effects on energy partition in the context of GAME/Tibet are studied. A sophisti- cated land surface model is developed, the particular aspect of which is its physical consideration of soil freeze/thaw and vapor flux. The simultaneous water and heat transfer soil sub-model not only reflects the water flow from unfrozen zone to frozen fringe in freezing/thawing soil, but also demon- strates the change of moisture and temperature field induced by vapor flux from high temperature zone to low temperature zone, which makes the model applicable for various circumstances. The modified Picard numerical method is employed to help with the water balance and convergence of the numerical scheme. Finally, the model is applied to analyze the diurnal energy and water cycle char- acteristics over the Tibetan Plateau using the Game/Tibet datasets observed in May and July of 1998. Heat and energy transfer simulation shows that: (i) There exists a negative feedback mechanism between soil freeze/thaw and soil temperature/ground heat flux; (ii) during freezing period all three heat fluxes do not vary apparently, in spite of the fact that the negative soil temperature is higher than that not considering soil freeze; (iii) during thawing period, ground heat flux increases, and sensible heat flux decreases, but latent heat flux does not change much; and (iv) during freezing period, soil temperature decreases, though ground heat flux increases.     

Characteristics of land surface heat and water exchange under different soil freeze/thaw conditions over the central Tibetan Plateau

Freezing and thawing processes at the soil surface play an important role in determining the nature of Tibetan land and atmosphere interactions. In this study, land surface water and heat exchanges under different freezing and thawing conditions over the central Tibetan Plateau were investigated using observations from the Coordinated Enhanced Observing Period/Asia‐Australia Monsoon Project on the Tibetan Plateau, and the Simultaneous Heat and Water Model. During the freezing and thawing stages, significant diurnal variation of soil temperature resulted in a diurnal cycle of unfrozen water content at the surface. Radiation and energy components and evapotranspiration averaged over four freeze/thaw stages also changed diurnally. On average, the surface albedo (0·68) during the completely frozen stage was sharply higher than those during the freezing, thawing, and completely thawed stages due to the snow cover. The Bowen ratios were 3·1 and 2·5 in the freezing and thawing stages, respectively, but the ratio was only 0·5 in the completely thawed stage. Latent heat flux displayed distinctly better correlation with unfrozen soil water content during the freezing and thawing stages than during the completely frozen and thawed stages. This implies that the diurnal cycle of unfrozen soil water, resulting from diurnal freeze/thaw cycles at the surface, has a significant impact on latent heat flux. A surface energy imbalance problem was encountered, and the possible sources of error were analysed. Copyright © 2011 John Wiley & Sons, Ltd.     

Improvement of multi-layer soil moisture prediction using support vector machines and ensemble Kalman filter coupled with remote sensing soil moisture datasets over an agriculture dominant basin in China

Soil moisture prediction is of great importance in crop yield forecasting and drought monitoring. In this study, the multi-layer root zone soil moisture (0-5, 0-10, 10-40 and 40-100 cm) prediction is conducted over an agriculture dominant basin, namely the Xiang River Basin, in southern China. The support vector machines (SVM) coupled with dual ensemble Kalman filter (EnKF) technique (SVM-EnKF) is compared with SVM for its potential capability to improve the efficiency of soil moisture prediction. Three remote sensing soil moisture products, namely SMAP, ASCAT and AMSR2, are evaluated for their performance in multi-layer soil moisture prediction with SVM and SVM-EnKF, respectively. Multiple cases are designed to investigate the performance of SVM, the effectiveness of coupling dual EnKF technique and the applicability of the remote sensing products in soil moisture prediction. The main results are as follows: (a) The efficiency of soil moisture prediction with SVM using meteorological variables as inputs is satisfactory for the surface layers (0-5 and 0-10 cm), while poor for the root zone layers (10-40 and 40-100 cm). Adding SMAP as input to SVM can improve its performance in soil moisture prediction, with more than 47% increase in the R-value and at least 11% reduction in RMSE for all layers. However, adding ASCAT or AMSR2 has no improvement for its performance. (b) Coupling dual EnKF can significantly improve the performance of SVM in the soil moisture prediction of both surface and the root zone layers. The increase in R-value is above 80%, while the reduction in BIAS and RMSE is respectively more than 90% and 63%. However, adding remote sensing soil moisture products as inputs can no further improve the performance of SVM-EnKF. (c) The SVM-EnKF can eliminate the influence of remote sensing soil moisture extreme values in soil moisture prediction, therefore, improve its accuracy.     

Soil water content in southern England derived from a cosmic‐ray soil moisture observing system – COSMOS‐UK

Cosmic‐ray soil moisture sensors have the advantage of a large measurement footprint (approximately 700 m in diameter) and are able to operate continuously to provide area‐averaged near‐surface (top 10–20 cm) volumetric soil moisture content at the field scale. This paper presents the application of this technique at four sites in southern England over almost 3 years. Results show the soil moisture response to contrasting climatic conditions during 2011–2014 and are the first such field‐scale measurements made in the UK. These four sites are prototype stations for a UK COsmic‐ray Soil Moisture Observing System, and particular consideration is given to sensor operating conditions in the UK. Comparison of these soil water content observations with the Joint UK Land Environment Simulator 10‐cm soil moisture layer shows that these data can be used to test and diagnose model performance and indicate the potential for assimilation of these data into hydro‐meteorological models. The application of these large‐area soil water content measurements to evaluate remotely sensed soil moisture products is also demonstrated. Numerous applications and the future development of a national COsmic‐ray Soil Moisture Observing System network are discussed. Copyright © 2016 John Wiley & Sons, Ltd.     

Dual state-parameter estimation of root zone soil moisture by optimal parameter estimation and extended Kalman filter data assimilation

With well-determined hydraulic parameters in a hydrologic model, a traditional data assimilation method (such as the Kalman filter and its extensions) can be used to retrieve root zone soil moisture under uncertain initial state variables (e.g., initial soil moisture content) and good simulated results can be achieved. However, when the key soil hydraulic parameters are incorrect, the error is non-Gaussian, as the Kalman filter will produce a persistent bias in its predictions. In this paper, we propose a method coupling optimal parameters and extended Kalman filter data assimilation (OP-EKF) by combining optimal parameter estimation, the extended Kalman filter (EKF) assimilation method, a particle swarm optimization (PSO) algorithm, and Richards’ equation. We examine the accuracy of estimating root zone soil moisture through the optimal parameters and extended Kalman filter data assimilation method by using observed in situ data at the Meiling experimental station, China. Results indicate that merely using EKF for assimilating surface soil moisture content to obtain soil moisture content in the root zone will produce a persistent bias between simulated and observed values. Using the OP-EKF assimilation method, estimates were clearly improved. If the soil profile is heterogeneous, soil moisture retrieval is accurate in the 0-50 cm soil profile and is inaccurate at 100 cm depth. Results indicate that the method is useful for retrieving root zone soil moisture over large areas and long timescales even when available soil moisture data are limited to the surface layer, and soil moisture content are uncertain and soil hydraulic parameters are incorrect.     

A dual-pass data assimilation scheme for estimating surface fluxes with FY3A-VIRR land surface temperature

In this work, a dual-pass data assimilation scheme is developed to improve predictions of surface flux. Pass 1 of the dual-pass data assimilation scheme optimizes the model vegetation parameters at the weekly temporal scale, and Pass 2 optimizes the soil moisture at the daily temporal scale. Based on ensemble Kalman filter(EnKF), the land surface temperature(LST) data derived from the new generation of Chinese meteorology satellite(FY3A-VIRR) are assimilated into common land model(CoLM) for the first time. Six sites, Daman, Guantao, Arou, BJ, Miyun and Jiyuan, are selected for the data assimilation experiments and include different climatological conditions. The results are compared with those from a dataset generated by a multi-scale surface flux observation system that includes an automatic weather station(AWS), eddy covariance(EC) and large aperture scintillometer(LAS). The results indicate that the dual-pass data assimilation scheme is able to reduce model uncertainties and improve predictions of surface flux with the assimilation of FY3A-VIRR LST data.     

Effect of macropores on soil freezing and thawing with infiltration

An understanding of heat transport and water flow in unsaturated soils experiencing freezing and thawing is important when considering hydrological and thermal processes in cold regions. Macropores, such as cracks, roots, and animal holes, provide efficient conduits for enhanced infiltration, resulting in a unique distribution of water content. However, the effects of macropores on soil freezing and thawing with infiltration have not been well studied. A one‐directional soil‐column freezing and thawing experiment was conducted using unsaturated sandy and silt loams with different sizes and numbers of macropores. During freezing, macropores were found to retard the formation of the frozen layer, depending on their size and number. During thawing, water flowed through macropores in the frozen layer and reached the underlying unfrozen soil. However, infiltrated water sometimes refroze in a macropore. The ice started to form at near inner wall of the macropore, grew to the centre, and blocked flow through the macropore. The blockage ice in the macropore could not melt until the frozen layer disappeared. Improving a soil freezing model to consider these macropore effects is required. Copyright © 2016 John Wiley & Sons, Ltd.     

Multi‐scale assimilation of root zone soil water predictions

When hydrology model parameters are determined, a traditional data assimilation method (such as Kalman filter) and a hydrology model can estimate the root zone soil water with uncertain state variables (such as initial soil water content). The simulated result can be quite good. However, when a key soil hydraulic property, such as the saturated hydraulic conductivity, is overestimated or underestimated, the traditional soil water assimilation process will produce a persistent bias in its predictions. In this paper, we present and demonstrate a new multi‐scale assimilation method by combining the direct insertion assimilation method, particle swarm optimisation (PSO) algorithm and Richards equation. We study the possibility of estimating root zone soil water with a multi‐scale assimilation method by using observed in situ data from the Wudaogou experiment station, Huaihe River Basin, China. The results indicate there is a persistent bias between simulated and observed values when the direct insertion assimilation surface soil water content is used to estimate root zone soil water contents. Using a multi‐scale assimilation method (PSO algorithm and direct insertion assimilation) and an assumed bottom boundary condition, the results show some obvious improvement, but the root mean square error is still relatively large. When the bottom boundary condition is similar to the actual situation, the multi‐scale assimilation method can well represent the root zone soil water content. The results indicate that the method is useful in estimating root zone soil water when available soil water data are limited to the surface layer and the initial soil water content even when the soil hydraulic conductivities are uncertain. Copyright © 2011 John Wiley & Sons, Ltd.     

Calibration of a Land Subsidence Model Using InSAR Data via the Ensemble Kalman Filter

The application of interferometric synthetic aperture radar (InSAR) has been increasingly used to improve capabilities to model land subsidence in hydrogeologic studies. A number of investigations over the last decade show how spatially detailed time‐lapse images of ground displacements could be utilized to advance our understanding for better predictions. In this work, we use simulated land subsidences as observed measurements, mimicking InSAR data to inversely infer inelastic specific storage in a stochastic framework. The inelastic specific storage is assumed as a random variable and modeled using a geostatistical method such that the detailed variations in space could be represented and also that the uncertainties of both characterization of specific storage and prediction of land subsidence can be assessed. The ensemble Kalman filter (EnKF), a real‐time data assimilation algorithm, is used to inversely calibrate a land subsidence model by matching simulated subsidences with InSAR data. The performance of the EnKF is demonstrated in a synthetic example in which simulated surface deformations using a reference field are assumed as InSAR data for inverse modeling. The results indicate: (1) the EnKF can be used successfully to calibrate a land subsidence model with InSAR data; the estimation of inelastic specific storage is improved, and uncertainty of prediction is reduced, when all the data are accounted for; and (2) if the same ensemble is used to estimate Kalman gain, the analysis errors could cause filter divergence; thus, it is essential to include localization in the EnKF for InSAR data assimilation.     

Simultaneous estimation of surface soil moisture and soil properties with a dual ensemble Kalman smoother

In this paper, a new state-parameter estimation approach is presented based on the dual ensemble Kalman smoother(DEn KS) and simple biosphere model(Si B2) to sequentially estimate both the soil properties and soil moisture profile by assimilating surface soil moisture observations. The Arou observation station, located in the upper reaches of the Heihe River in northwestern China, was selected to test the proposed method. Three numeric experiments were designed and performed to analyze the influence of uncertainties in model parameters, atmospheric forcing, and the model's physical mechanics on soil moisture estimates. Several assimilation schemes based on the ensemble Kalman filter(En KF), ensemble Kalman smoother(En KS), and dual En KF(DEn KF) were also compared in this study. The results demonstrate that soil moisture and soil properties can be simultaneously estimated by state-parameter estimation methods, which can provide more accurate estimation of soil moisture than traditional filter methods such as En KF and En KS. The estimation accuracy of the model parameters decreased with increasing error sources. DEn KS outperformed DEn KF in estimating soil moisture in most cases, especially where few observations were available. This study demonstrates that the DEn KS approach is a useful and practical way to improve soil moisture estimation.