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Title: Effects of forcing uncertainties in the improvement skills of assimilating satellite soil moisture retrievals into flood forecasting models

Author
item ALVAREZ, CAMILLA - University Of Melbourne
item RYU, DONGRYEOL - University Of Melbourne
item WESTERN, ANDREW - University Of Melbourne
item ROBERTSON, DAVID - Commonwealth Scientific And Industrial Research Organisation (CSIRO)
item Crow, Wade
item LEAHY, CHRISTOPHER - Collaborator

Submitted to: International Geoscience and Remote Sensing Symposium Proceedings
Publication Type: Abstract Only
Publication Acceptance Date: 7/1/2013
Publication Date: 7/26/2013
Citation: Alvarez, C., Ryu, D., Western, A., Robertson, D., Crow, W.T., Leahy, C. 2013. Effects of forcing uncertainties in the improvement skills of assimilating satellite soil moisture retrievals into flood forecasting models [abstract]. 2013 International Geoscience and Remote Sensing Symposium Proceedings, July 21-26, 2013, Melbourne, Australia. 2013 CDROM.

Interpretive Summary:

Technical Abstract: Floods have negative impacts on society, causing damages in infrastructures and industry, and in the worst cases, causing loss of human lives. Thus early and accurate warning is crucial to significantly reduce the impacts on public safety and economy. Reliable flood warning can be generated using real-time flood forecasting models forced by accurate meteorological input data. As an effort to further improve the forecast accuracy, various ancillary observations (e.g., soil moisture and stream discharge) have been assimilated into the model. However, the benefit of assimilating the ancillary observations can be compromised by the accuracy of input forcing data. For example, assimilating soil moisture into rainfall runoff models can have very marginal effect when input forcing error dominates their prediction uncertainty. In order to quantify the impacts of input uncertainty on soil moisture assimilation, a series of synthetic experiments are conducted over a semi-arid catchment in inner Australia. The Probability Distributed Model (PDM) is employed to conduct flood forecasting in the sparsely instrumented Warrego River catchment in Queensland and New South Wales, Australia. Real-time satellite precipitation data are used as input forcing and satellite soil moisture retrievals are used to update soil water states of the model. Future work includes the estimation of an optimal observational operator for transforming satellite observed soil moisture into the model space and the estimation of an optimal strategy for ensemble generation in the assimilation scheme.