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Assimilation of Streamflow and Surface Soil Moisture Observations into a Land Surface Model

Assimilation of Streamflow and Surface Soil Moisture Observations into a Land Surface Model. Christoph Rüdiger, Jeffrey P. Walker Dept. of Civil & Env. Engineering., University of Melbourne Jetse D. Kalma School of Engineering, University of Newcastle Garry R. Willgoose

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Assimilation of Streamflow and Surface Soil Moisture Observations into a Land Surface Model

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  1. Assimilation of Streamflow and Surface Soil Moisture Observations into a Land Surface Model Christoph Rüdiger, Jeffrey P. Walker Dept. of Civil & Env. Engineering., University of Melbourne Jetse D. Kalma School of Engineering, University of Newcastle Garry R. Willgoose Earth & Biosphere Institute, School of Geography, University of Leeds Paul R. Houser Hydrological Sciences Branch, NASA Goddard Space Flight Center, Now: George Mason University & Center for Research on Environment and Water

  2. Background Koster et al., JHM, 2000 Christoph Rüdiger EGU05

  3. State of Art Christoph Rüdiger EGU05

  4. Location of Study Catchment Newcastle Sydney Melbourne 0km 1000km Christoph Rüdiger EGU05

  5. Location of Study Catchment Streamgauge Climate Soil Moisture Christoph Rüdiger EGU05 www.sasmas.unimelb.edu.au

  6. Methodology (NLFIT) Kuczera, 1982 Christoph Rüdiger EGU05

  7. Streamflow Assimilation- Single catchement - Discharge Soil Moisture Christoph Rüdiger EGU05

  8. Streamflow Assimilation- Single catchement - Root Zone Surface Layer Christoph Rüdiger EGU05

  9. Surface Soil Moisture Assimilation • Eg. Walker et al. (2001) have shown that surface soil moisture assimilation is generally a viable tool for SM updating. • Can remote sensing data then be used to further constrain variational type assimilations? Christoph Rüdiger EGU05

  10. Adjustments to Experiment Runs • First initial state estimates are set to average values, rather than extremes • Maximum and minimum values are not allowed to be violated • Observation errors of forcing data are made more “realistic” by changing pure bias to bias and white noise errors (Turner et al., in review) Christoph Rüdiger EGU05

  11. Errors and Biases of Forcing Data Christoph Rüdiger EGU05

  12. Variational-type Surface Soil Moisture Assimilation Surface SM Root Zone SM Runoff Profile SM Christoph Rüdiger EGU05

  13. Focus Catchments Upper Catchment Lower Catchment Christoph Rüdiger EGU05

  14. Unmonitored Catchments Christoph Rüdiger EGU05

  15. Summary • Streamflow Assimilation in subhumid catchments can produce adequate estimates of initial moisture states. • DA of surface soil moisture observations can act as an additional constraint for the observed catchment. • Assimilation of both observations has potential for use in finding initial lumped moisture states for a LSM for ungauged upstream catchments. Christoph Rüdiger EGU05

  16. Conclusions • States of ungauged upstream basins can be retrieved to a certain extent. • Length of assimilation window will have to be variable for different conditions, esp. if errors in forcing are large and biased. • Some states may not have an impact on the objective function, but may be retrieved using additional observations of other variables. • First estimate of initial states can potentially be crucial to success of the proposed DA scheme, hence have to handled appropriately. Christoph Rüdiger EGU05

  17. Acknowledgment • Australian Research Council (ARC-DP grant 0209724) • Hydrological Sciences Branch, National Aeronautics and Space Administration (NASA), USA • University of Melbourne • Melbourne International Fee Remission Scholarship (MIFRS) • Postgraduate Overseas Research Experience Scholarship (PORES) Christoph Rüdiger EGU05

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