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| Titel |
Simultaneous estimation of land surface scheme states and parameters using the ensemble Kalman filter: identical twin experiments |
| VerfasserIn |
S. Nie, J. Zhu, Y. Luo |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 15, no. 8 ; Nr. 15, no. 8 (2011-08-03), S.2437-2457 |
| Datensatznummer |
250012918
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| Publikation (Nr.) |
 copernicus.org/hess-15-2437-2011.pdf |
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| Zusammenfassung |
| 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. |
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