Abstract
Abstract. 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.
Highlights
Soil moisture is a key variable of the land surface water budget
land surface model (LSM) soil moisture predictions can be enhanced by assimilating near-surface satellite soil moisture observations through a land data assimilation system (LDAS)
This study aims at investigating the impact of model and rainfall forcing uncertainty on soil moisture fields simulated by the NASA Catchment Land Surface Model
Summary
Soil moisture is a key variable of the land surface water budget. It has an impact on water, energy and biogeochemical cycles; it plays a major role in many research fields, such as hydrology, agriculture and ecology. Hossain and Anagnostou (2006c), and recently Maggioni et al (2011), have investigated the implication of using SREM2D in representing temporal and spatial uncertainty of soil moisture prediction in a land surface model forced with satellite rainfall data. This study aims at investigating the impact of model and rainfall forcing uncertainty on soil moisture fields simulated by the NASA Catchment Land Surface Model (hereinafter CLSM, or Catchment model; Koster et al, 2000; Ducharne et al, 2000) It builds upon the recent study by Maggioni et al (2011) that investigated soil moisture prediction uncertainty associated with errors in rainfall forcing alone.
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