Impact of observation error structure on satellite soil moisture assimilation into a rainfall-runoff model

Authors: ALVAREZ, C - University Of Melbourne; RYU, D - University Of Melbourne; WESTERN, A - University Of Melbourne; Crow, Wade; ROBERTSON, D - Collaborator

Submitted to: International Congress on Modeling and Simulation Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 12/1/2013
Publication Date: 12/25/2014
Citation: Alvarez, C., Ryu, D., Western, A., Crow, W.T., Robertson, D. 2014. Impact of observation error structure on satellite soil moisture assimilation into a rainfall-runoff model. Proceedings of the 20th International Congress of Modeling and Simulation. p. 3071-3077.

Interpretive Summary:

Technical Abstract: In Ensemble Kalman Filter (EnKF)-based data assimilation, the background prediction of a model is updated using observations and relative weights based on the model prediction and observation uncertainties. In practice, both model and observation uncertainties are difficult to quantify and they have been often assumed to be spatially and temporally independent Gaussian random variables. Nevertheless, it has been shown that incorrect assumptions regarding the structure of these errors can degrade the performance of the stochasticdata assimilation. This work investigates the autocorrelation structure of the microwave satellite soil moisture retrievals and explores how assumed observation error structure affects the streamflow prediction skills when assimilating these observations into a rainfall-runoff model. An AMSR-E soil moisture product and the Probability Distribution Model (PDM) are used for this purpose. Satellite soil moisture data is transformed with an exponential filter for make it comparable to the root zone soil moisture state of the model. The exponential filter formulation explicitly incorporates an autocorrelation component in the rescaled observation. However, the error structure of this widely used observation operator has been treated until now as an independent Gaussian process. In this work, a mean variance of the rescaled observations is estimated based on the residuals from the rescaled satellite soil moisture and the calibrated model soil moisture state. Next, the observation error structure is treated as a Gaussian independent process with time-variant variance; a weak autocorrelated random process (with autocorrelation coefficient of 0.2) and a strong autocorrelated random process (with autocorrelation coefficient of 0.8). These experiments are compared with a control case which corresponds to the commonly used assumption of time-invariant observation variance. Model error is represented by perturbing both forcing data and soil moisture state. These perturbations are assumed to represent all forcing and model structure errors. Error parameters are calibrated by applying two discharge ensemble verification criteria. Assimilation results are compared and the impacts of the observation error structure assumptions are assessed. The study area is the semi-arid 42,870 km^2 Warrego at Wyandra River catchment, located in Queensland, Australia. This catchment is chosen for its flooding history, along with having geographical and climatological conditions that enable soil moisture satellite retrievals to have higher accuracy than in other areas. These conditions include large area, semi-arid climate and low vegetation cover. Moreover, the catchment is poorly instrumented, thus satellite data provides valuable information. Results show a consistent improvement of the model forecast accuracy of the control case and in all experiments. The assumed observation error structures do not show significant effect in the assimilation results. This case study provides useful information about the assimilation of satellite soil moisture retrievals in poor instrumented semi-arid catchments.