Effect of simultaneous state–parameter estimation and forcing uncertainties on root-zone soil moisture for dynamic vegetation using EnKF

Monsiváis-Huertero, Alejandro; Graham, Wendy D.; Judge, Jasmeet; Agrawal, Divya

doi:10.1016/j.advwatres.2010.01.011

Cited by 36 publications

(29 citation statements)

References 48 publications

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“…(12)) includes soil moisture at the 35 computational nodes and the imperfectly known parameters, namely, porosity (/), air entry pressure (w 0 ), pore-size index (k), and saturated hydraulic conductivity (K sat ) describing the first soil layer, assumed homogeneous. These four parameters were found to be the most influential for RZSM estimation, based upon time-dependent correlations of the RZSM estimates to the 16 parameters in the LSP model [37]. In this study, the second layer (1.7-2.7 m) of soil was assumed to have known constitutive properties, as shown in Table 2 obtained from Casanova and Judge [7].…”

Section: Implementation Of Assimilation Algorithmsmentioning

confidence: 99%

“…In this study, a Gaussian observation error with standard deviation equal to 12% of the observed value of precipitation/irrigation was introduced during events. No errors were introduced in the absence of the events [37].…”

Section: Uncertainty In Forcings Observations and Parametersmentioning

confidence: 99%

“…The DSSAT model provides LSP with vegetation characteristics that influence heat, moisture, and radiation transfer at the land surface and in the vadose zone [7]. The coupled LSP-DSSAT model has been used to implement the EnKF to understand impacts of simultaneous state-parameter estimation and forcing uncertainties on root-zone soil moisture for dynamic vegetation [37].…”

Section: Lsp-dssat Modelmentioning

confidence: 99%

“…Significant research has been conducted on EnKF-based assimilation for RZSM estimation [42,46,12,37]. However, only a few studies have been performed using PF [38,45].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Particle Filter-based assimilation algorithms for improved estimation of root-zone soil moisture under dynamic vegetation conditions

Nagarajan

Judge

Graham

et al. 2011

Advances in Water Resources

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Section: Implementation Of Assimilation Algorithmsmentioning

confidence: 99%

Section: Uncertainty In Forcings Observations and Parametersmentioning

confidence: 99%

Section: Lsp-dssat Modelmentioning

confidence: 99%

“…Significant research has been conducted on EnKF-based assimilation for RZSM estimation [42,46,12,37]. However, only a few studies have been performed using PF [38,45].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Particle Filter-based assimilation algorithms for improved estimation of root-zone soil moisture under dynamic vegetation conditions

Nagarajan

Judge

Graham

et al. 2011

Advances in Water Resources

Self Cite

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“…Most data assimilation strategies focus on reducing the discrepancy between observations and simulations by adjusting the uncertain model parameters, initial conditions [11,12], or model state variables [13][14][15]. Early data assimilation strategies based on optimum estimation algorithms failed to consider errors in observations and the model itself.…”

Section: Introductionmentioning

confidence: 99%

The Estimation of Regional Crop Yield Using Ensemble-Based Four-Dimensional Variational Data Assimilation

Jiang

Chen

et al. 2014

Remote Sensing

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To improve crop model performance for regional crop yield estimates, a new four-dimensional variational algorithm (POD4DVar) merging the Monte Carlo and proper orthogonal decomposition techniques was introduced to develop a data assimilation strategy using the Crop Environment Resource Synthesis (CERES)-Wheat model. Two winter wheat yield estimation procedures were conducted on a field plot and regional scale to test the feasibility and potential of the POD4DVar-based strategy. Winter wheat yield forecasts for the field plots showed a coefficient of determination (R 2 ) of 0.73, a root mean square error (RMSE) of 319 kg/ha, and a relative error (RE) of 3.49%. An acceptable yield at the regional scale was estimated with an R 2 of 0.997, RMSE of 7346 tons, and RE of 3.81%. The POD4DVar-based strategy was more accurate and efficient than the EnKF-based strategy. In addition to crop yield, other critical crop variables such as the biomass, harvest index, evapotranspiration, and soil organic carbon may also be estimated. The present study thus introduces a promising approach for operationally monitoring regional crop growth and predicting yield. Successful application of this assimilation model at regional scales must focus on uncertainties derived from the crop model, model inputs, data assimilation algorithm, and assimilated observations.

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Using a bias aware EnKF to account for unresolved structure in an unsaturated zone model

2014

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[1] When predicting flow in the unsaturated zone, any method for modeling the flow will have to define how, and to what level, the subsurface structure is resolved. In this paper, we use the Ensemble Kalman Filter to assimilate local soil water content observations from both a synthetic layered lysimeter and a real field experiment in layered soil in an unsaturated water flow model. We investigate the use of colored noise bias corrections to account for unresolved subsurface layering in a homogeneous model and compare this approach with a fully resolved model. In both models, we use a simplified model parameterization in the Ensemble Kalman Filter. The results show that the use of bias corrections can increase the predictive capability of a simplified homogeneous flow model if the bias corrections are applied to the model states. If correct knowledge of the layering structure is available, the fully resolved model performs best. However, if no, or erroneous, layering is used in the model, the use of a homogeneous model with bias corrections can be the better choice for modeling the behavior of the system. Citation: Erdal, D., I. Neuweiler, and U. Wollschl€ ager (2014), Using a bias aware EnKF to account for unresolved structure in an unsaturated zone model, Water Resour.

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Effect of simultaneous state–parameter estimation and forcing uncertainties on root-zone soil moisture for dynamic vegetation using EnKF

Cited by 36 publications

References 48 publications

Particle Filter-based assimilation algorithms for improved estimation of root-zone soil moisture under dynamic vegetation conditions

Particle Filter-based assimilation algorithms for improved estimation of root-zone soil moisture under dynamic vegetation conditions

The Estimation of Regional Crop Yield Using Ensemble-Based Four-Dimensional Variational Data Assimilation

Using a bias aware EnKF to account for unresolved structure in an unsaturated zone model

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