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www.geosci-model-dev-discuss.net/2/551/2009/
doi:10.5194/gmdd-2-551-2009
© Author(s) 2009. This work is distributed
under the Creative Commons Attribution 3.0 License.
Streamflow data assimilation for soil moisture analysis
Institute for Physics and Meteorology, University of Hohenheim, Stuttgart, Germany
Abstract. Streamflow depends on the soil moisture of a river catchment and can be measured with relatively high accuracy. The soil moisture in the root zone influences the latent heat flux and hence the quantity and spatial distribution of atmospheric water vapour and precipitation. As numerical weather forecast and climate models require a proper soil moisture initialization for their land surface models, we enhanced an Ensemble Kalman Filter to assimilate streamflow timeseries into the multi-layer land surface model TERRA-ML of the regional weather forecast model COSMO. The impact of streamflow assimilation was studied by an observing system simulation experiment in the Enz River catchment (located at the downwind side of the northern Black Forest in Germany). The results demonstrate a clear improvement of the soil moisture field in the catchment. We illustrate the potential of streamflow data assimilation for weather forecasting and discuss its spatial and temporal requirements for a corresponding, automated river gauging network.
Discussion Paper (PDF, 1032 KB) Interactive Discussion (Closed, 4 Comments) Final Revised Paper (GMD)
Notice on Discussion StatusCitation: Warrach-Sagi, K. and Wulfmeyer, V.: Streamflow data assimilation for soil moisture analysis, Geosci. Model Dev. Discuss., 2, 551-579, doi:10.5194/gmdd-2-551-2009, 2009. Bibtex EndNote Reference Manager XML
