Articles | Volume 19, issue 4
Hydrol. Earth Syst. Sci., 19, 1659–1676, 2015
https://doi.org/10.5194/hess-19-1659-2015
Hydrol. Earth Syst. Sci., 19, 1659–1676, 2015
https://doi.org/10.5194/hess-19-1659-2015
Research article
09 Apr 2015
Research article | 09 Apr 2015

Improving operational flood ensemble prediction by the assimilation of satellite soil moisture: comparison between lumped and semi-distributed schemes

C. Alvarez-Garreton et al.

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Cited articles

Albergel, C., Rüdiger, C., Pellarin, T., Calvet, J.-C., Fritz, N., Froissard, F., Suquia, D., Petitpa, A., Piguet, B., and Martin, E.: From near-surface to root-zone soil moisture using an exponential filter: an assessment of the method based on in-situ observations and model simulations, Hydrol. Earth Syst. Sci., 12, 1323–1337, https://doi.org/10.5194/hess-12-1323-2008, 2008.
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Albergel, C., de Rosnay, P., Gruhier, C., Muñoz-Sabater, J., Hasenauer, S., Isaksen, L., Kerr, Y., and Wagner, W.: Evaluation of remotely sensed and modelled soil moisture products using global ground-based in situ observations, Remote Sens. Environ., 118, 215–226, 2012.
Alvarez-Garreton, C., Ryu, D., Western, A. W., Crow, W. T., and Robertson, D. E.: Impact of observation error structure on satellite soil moisture assimilation into a rainfall-runoff model, in: MODSIM2013, 20th International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, edited by: Piantadosi, J., Anderssen, R., and Boland, J., 3071–3077, 2013.
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Short summary
We assimilate satellite soil moisture into a rainfall-runoff model for improving flood prediction within a data-scarce region. We argue that the spatially distributed satellite data can alleviate the model prediction limitations. We show that satellite soil moisture DA reduces the uncertainty of the streamflow ensembles. We propose new techniques for the DA scheme, including seasonal error characterisation, bias correction of the satellite retrievals, and model error representation.