A partitioned update scheme for state-parameter estimation of distributed hydrologic models based on the ensemble Kalman filter

2013 ◽  
Vol 49 (11) ◽  
pp. 7350-7365 ◽  
Author(s):  
Xianhong Xie ◽  
Dongxiao Zhang
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
State Parameter ◽  
Hydrologic Models

scholarly journals Simultaneous estimation of land surface scheme states and parameters using the ensemble Kalman filter: identical twin experiments

2011 ◽  
Vol 15 (8) ◽  
pp. 2437-2457 ◽  
Author(s):  
S. Nie ◽  
J. Zhu ◽  
Y. Luo
Keyword(s):  
Parameter Estimation ◽  
Soil Moisture ◽  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Land Surface ◽  
Estimation Method ◽  
State Parameter ◽  
Hydraulic Parameters ◽  
Constrained Estimation ◽  
Estimated Parameters

Abstract. The performance of the ensemble Kalman filter (EnKF) in soil moisture assimilation applications is investigated in the context of simultaneous state-parameter estimation in the presence of uncertainties from model parameters, soil moisture initial condition and atmospheric forcing. A physically based land surface model is used for this purpose. Using a series of identical twin experiments in two kinds of initial parameter distribution (IPD) scenarios, the narrow IPD (NIPD) scenario and the wide IPD (WIPD) scenario, model-generated near surface soil moisture observations are assimilated to estimate soil moisture state and three hydraulic parameters (the saturated hydraulic conductivity, the saturated soil moisture suction and a soil texture empirical parameter) in the model. The estimation of single imperfect parameter is successful with the ensemble mean value of all three estimated parameters converging to their true values respectively in both NIPD and WIPD scenarios. Increasing the number of imperfect parameters leads to a decline in the estimation performance. A wide initial distribution of estimated parameters can produce improved simultaneous multi-parameter estimation performances compared to that of the NIPD scenario. However, when the number of estimated parameters increased to three, not all parameters were estimated successfully for both NIPD and WIPD scenarios. By introducing constraints between estimated hydraulic parameters, the performance of the constrained three-parameter estimation was successful, even if temporally sparse observations were available for assimilation. The constrained estimation method can reduce RMSE much more in soil moisture forecasting compared to the non-constrained estimation method and traditional non-parameter-estimation assimilation method. The benefit of this method in estimating all imperfect parameters simultaneously can be fully demonstrated when the corresponding non-constrained estimation method displays a relatively poor parameter estimation performance. Because all these constraints between parameters were obtained in a statistical sense, this constrained state-parameter estimation scheme is likely suitable for other land surface models even with more imperfect parameters estimated in soil moisture assimilation applications.

scholarly journals Dual state–parameter estimation of hydrological models using ensemble Kalman filter

2005 ◽  
Vol 28 (2) ◽  
pp. 135-147 ◽  
Author(s):  
Hamid Moradkhani ◽  
Soroosh Sorooshian ◽  
Hoshin V. Gupta ◽  
Paul R. Houser
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
State Parameter ◽  
Hydrological Models

scholarly journals Simultaneous estimation of land surface scheme states and parameters using the ensemble Kalman filter: idealized twin experiments

2011 ◽  
Vol 8 (1) ◽  
pp. 1433-1468
Author(s):  
S. Nie ◽  
J. Zhu ◽  
Y. Luo
Keyword(s):  
Surface Layer ◽  
Parameter Estimation ◽  
Soil Moisture ◽  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Land Surface ◽  
Root Zone ◽  
State Parameter ◽  
Near Surface ◽  
Estimated Parameters

Abstract. The performance of the ensemble Kalman filter (EnKF) in soil moisture assimilation applications is investigated in the context of simultaneous state-parameter estimation in the presence of uncertainties from model parameters, initial soil moisture condition and atmospheric forcing. A physically-based land surface model is used for this purpose. Using a series of idealized twin experiments, model generated near-surface soil moisture observations are assimilated to estimate soil moisture state and three hydraulic parameters (the saturated hydraulic conductivity, the saturated soil moisture suction and a soil texture empirical parameter) in the model. The single imperfect parameter can be successfully estimated using the EnKF. Results show that all the three estimated parameters converge toward their respective true values, while the root mean squared errors (RMSE) of soil moisture associated with these parameters is on average reduced by 54% and 53% comparing with the non-parameter-estimation benchmark RMSE for near-surface layer and root zone layer, respectively. The performance of simultaneous multi-parameter estimation is significant degraded, mainly because the inherent balance relationship of these parameters is broken and the degree of freedom increases in assimilation processes. By introducing constraints between estimated parameters, the performance of the constraint-based simultaneous multi-parameter estimations are as good as that of single-parameter cases even assimilating temporal-sparse observations. In terms of the relative root mean squared error (RRE), the constraint-based estimation cases can achieve 36% to 53% in near-surface layer and 25% to 50% in root zone layer for different assimilation intervals ranging from 1-day to 40-days. This result suggests that the greatest advantage of this method can be displayed with a proper temporal-sparse assimilation interval of 10-days as actual measurement interval of conventional in situ soil moisture observations. As these obtained constraints are mostly in statistical sense, this constraint-based simultaneous state-parameter estimation scheme is supposed to be suitable for other land surface models in soil moisture assimilation applications.

Sign in / Sign up

Export Citation Format

Share Document