Location: Hydrology and Remote Sensing LaboratoryTitle: Improving operational flood ensemble prediction by the assimilation of satellite soil moisture: comparison between lumped and semi-distributed schemes
|ALVAREZ, C. - University Of Melbourne|
|RYU, D. - University Of Melbourne|
|SU, CHUN-HSU - University Of Melbourne|
|ROBERTSON, D. - Collaborator|
|LEAHY, C. - Collaborator|
Submitted to: Hydrology and Earth System Sciences
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/1/2015
Publication Date: 5/1/2015
Citation: Alvarez, C., Ryu, D., Su, C., Crow, W.T., Robertson, D., Leahy, C. 2015. Improving operational flood ensemble prediction by the assimilation of satellite soil moisture: comparison between lumped and semi-distributed schemes. Hydrology and Earth System Sciences. 19:1659-1676. doi: 10.5194/hess-19-1659-20151659-1676.
Interpretive Summary: Estimating runoff from agricultural catchments is important for efforts to: minimize the impact of agricultural management on water quality, forecast downstream flooding, and monitor the availability of water resources during periods of drought. A promising approach for improving runoff estimates is the integration of remotely-sensed soil moisture estimates into a hydrologic model. Since soil moisture conditions determine the land surface's ability to infiltrate future rainfall, the improved estimation of pre-storm soil moisture conditions should facilitate better monitoring of stream flow conditions. With this goal in mind, this paper evaluates the degree to which stream flow predictions made by a hydrologic model in a semi-arid catchment can be improved by integrating satellite-derived surface soil moisture estimates into the model. The results of this analysis can be used to improve the operational monitoring of stream-flow conditions in agricultural catchments. Better monitoring of this conditions will - in turn - allow local water resource manager to minimize the impact of agricultural management on water quality and better manage water resources in semi-arid areas.
Technical Abstract: Assimilation of remotely sensed soil moisture data (SM-DA) to correct soil water stores of rainfall-runoff models has shown skill in improving streamflow prediction. In the case of large and sparsely monitored catchments, SM-DA is a particularly attractive tool.Within this context, we assimilate active and passive satellite soil moisture (SSM) retrievals using an Ensemble Kalman filter to improve operational flood prediction within a large semi-arid catchment in Australia (>40,000km2). We assess the importance of accounting for channel routing and the spatial distribution of forcing data by applying SM-DA to a lumped and a semi-distributed scheme of the probability distributed model (PDM). Our scheme also accounts for model error representation and seasonal biases and errors in the satellite data. Before assimilation, the semi-distributed model provided more accurate streamflow prediction (Nash-Sutcliffe efficiency, NS=0.77) than the lumped model (NS=0.67) at the catchment outlet. However, this did not ensure good performance at the “ungauged” inner catchments. After SM-DA, the streamflow ensemble prediction at the outlet was improved in both the lumped and the semi-distributed schemes: the root mean square error of the ensemble was reduced by 27% and 31%, respectively; the NS of the ensemble mean increased by 7% and 38%, respectively; the false alarm ratio was reduced by 15% and 25%, respectively; and the ensemble prediction spread was reduced while its reliability was maintained. Our findings imply that even when rainfall is the main driver of flooding in semi-arid catchments, adequately processed SSM can be used to reduce errors in the model soil moisture, which in turn provides better streamflow ensemble prediction.We demonstrate that SM-DA efficacy is enhanced when the spatial distribution in forcing data and routing processes are accounted for. At ungauged locations, SM-DA is effective at improving streamflow ensemble prediction, however, the updated prediction is still poor since SM-DA does not address systematic errors in the model.