scholarly journals Three-dimensional soil moisture profile retrieval by assimilation of near-surface measurements: Simplified Kalman filter covariance forecasting and field application

2002 ◽  
Vol 38 (12) ◽  
pp. 37-1-37-13 ◽  
Author(s):  
Jeffrey P. Walker ◽  
Garry R. Willgoose ◽  
Jetse D. Kalma
Keyword(s):  
Soil Moisture ◽  
Kalman Filter ◽  
Three Dimensional ◽  
Field Application ◽  
Moisture Profile ◽  
Near Surface ◽  
Surface Measurements ◽  
Soil Moisture Profile

scholarly journals Frozen soil parameterization in a distributed biosphere hydrological model

2009 ◽  
Vol 6 (6) ◽  
pp. 6895-6928
Author(s):  
L. Wang ◽  
T. Koike ◽  
K. Yang ◽  
R. Jin ◽  
H. Li
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Hydrological Model ◽  
Frozen Soil ◽  
Surface Radiation ◽  
Moisture Profile ◽  
Flood Peak ◽  
Radiation Fluxes ◽  
Soil Moisture Profile ◽  
The Web

Abstract. In this study, a frozen soil parameterization has been modified and incorporated into a distributed biosphere hydrological model (WEB-DHM). The WEB-DHM with the frozen scheme was then rigorously evaluated in a small cold area, the Binngou watershed, against the in-situ observations from the WATER (Watershed Allied Telemetry Experimental Research). In the summer 2008, land surface parameters were optimized using the observed surface radiation fluxes and the soil temperature profile at the Dadongshu-Yakou (DY) station in July; and then soil hydraulic parameters were obtained by the calibration of the July soil moisture profile at the DY station and by the calibration of the discharges at the basin outlet in July and August that covers the annual largest flood peak of 2008. The calibrated WEB-DHM with the frozen scheme was then used for a yearlong simulation from 21 November 2007 to 20 November 2008, to check its performance in cold seasons. Results showed that the WEB-DHM with the frozen scheme has given much better performance than the WEB-DHM without the frozen scheme, in the simulations of soil moisture profile at the DY station and the discharges at the basin outlet in the yearlong simulation.

Retrieval of soil moisture profile by combined C-band scatterometer data and a surface hydrological model

2000 ◽  
Author(s):  
Claudia Notarnicola ◽  
Angelo C. D'Alessio ◽  
Francesco Posa ◽  
Vincenzo Sabatelli ◽  
Domenico Casarano
Keyword(s):  
Soil Moisture ◽  
Hydrological Model ◽  
Moisture Profile ◽  
Soil Moisture Profile

scholarly journals Dynamic Calibration with an Ensemble Kalman Filter Based Data Assimilation Approach for Root-Zone Moisture Predictions

2007 ◽  
Vol 8 (4) ◽  
pp. 910-921 ◽  
Author(s):  
Nicola Montaldo ◽  
John D. Albertson ◽  
Marco Mancini
Keyword(s):  
Soil Moisture ◽  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Root Zone ◽  
Sensitivity Analyses ◽  
Surface Model ◽  
Early Model ◽  
Model Errors ◽  
Model Bias ◽  
Near Surface

Abstract In the presence of uncertain initial conditions and soil hydraulic properties, land surface model (LSM) performance can be significantly improved by the assimilation of periodic observations of certain state variables, such as the near-surface soil moisture (θg), as observed from a remote platform. In this paper the possibility of merging observations and the model optimally for providing robust predictions of root-zone soil moisture (θ2) is demonstrated. An assimilation approach that assimilates θg through the ensemble Kalman filter (EnKF) and provides a physics-based update of θ2 is developed. This approach, as with other common soil moisture assimilation approaches, may fail when a key LSM parameter, for example, the saturated hydraulic conductivity (ks), is estimated poorly. This leads to biased model errors producing a violation of a main assumption (model errors with zero mean) of the EnKF. For overcoming this model bias an innovative assimilation approach is developed that accepts this violation in the early model run times and dynamically calibrates all the components of the ks ensemble as a function of the persistent bias in root-zone soil moisture, allowing one to remove the model bias, restore the fidelity to the EnKF requirements, and reduce the model uncertainty. The robustness of the proposed approach is also examined in sensitivity analyses.

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