 |
| Titel |
The impact of model and rainfall forcing errors on characterizing soil moisture uncertainty in land surface modeling |
| VerfasserIn |
V. Maggioni, E. N. Anagnostou, R. H. Reichle |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1027-5606
|
| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 16, no. 10 ; Nr. 16, no. 10 (2012-10-04), S.3499-3515 |
| Datensatznummer |
250013510
|
| Publikation (Nr.) |
 copernicus.org/hess-16-3499-2012.pdf |
|
|
|
|
|
| Zusammenfassung |
| The contribution of rainfall forcing errors relative to model (structural
and parameter) uncertainty in the prediction of soil moisture is
investigated by integrating the NASA Catchment Land Surface Model (CLSM),
forced with hydro-meteorological data, in the Oklahoma region.
Rainfall-forcing uncertainty is introduced using a stochastic error model
that generates ensemble rainfall fields from satellite rainfall products.
The ensemble satellite rain fields are propagated through CLSM to produce
soil moisture ensembles. Errors in CLSM are modeled with two different
approaches: either by perturbing model parameters (representing model
parameter uncertainty) or by adding randomly generated noise (representing
model structure and parameter uncertainty) to the model prognostic
variables. Our findings highlight that the method currently used in the NASA
GEOS-5 Land Data Assimilation System to perturb CLSM variables poorly
describes the uncertainty in the predicted soil moisture, even when combined
with rainfall model perturbations. On the other hand, by adding model
parameter perturbations to rainfall forcing perturbations, a better
characterization of uncertainty in soil moisture simulations is observed.
Specifically, an analysis of the rank histograms shows that the most
consistent ensemble of soil moisture is obtained by combining rainfall and
model parameter perturbations. When rainfall forcing and model prognostic
perturbations are added, the rank histogram shows a U-shape at the domain
average scale, which corresponds to a lack of variability in the forecast
ensemble. The more accurate estimation of the soil moisture prediction
uncertainty obtained by combining rainfall and parameter perturbations is
encouraging for the application of this approach in ensemble data
assimilation systems. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|