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Streamflow Data Assimilation - Field requirements and results -

This study explores the field requirements and results of streamflow data assimilation in different catchments. Examining methods such as installation of soil moisture sensors, weather stations, and stream gauges, it emphasizes the importance of telemetry data assimilation. The research demonstrates the potential of assimilating both surface soil moisture and streamflow observations to improve the accuracy of hydrological models, especially in ungauged upstream catchments. Insights are provided on adjusting experiment runs and addressing errors and biases in forcing data for effective data assimilation. The study concludes by highlighting the significance of the initial state estimation in the success of data assimilation schemes.

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Streamflow Data Assimilation - Field requirements and results -

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  1. Streamflow Data Assimilation- Field requirements and results - 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 George Mason University & Center for Research on Environment and Water

  2. Motivation, Field Site & Instrumentation

  3. (JJA) Background Koster et al., JHM, 2000

  4. State of the Art

  5. Location of Study Catchment Newcastle Sydney Melbourne 0km 1000km

  6. Field Site Goulburn River Catchment (NSW) • Proximity to Newcastle • Size and geophysical properties • Cleared areas • Division into subcatchments • Distance to the sea

  7. Vegetation and Soils

  8. Installation of Soil Moisture Sensors

  9. Weather Stations Soil Moisture Sites Stream Gauges Location of Instrumentation

  10. Instrumentation • Currently installed … • 2 weather stations and several pluviometers • 26 soil moisture monitoring sites • 1 flume and 5 stream gauges • Use of … • 3 existing weather stations • 3 stream gauges • numerous rain gauges • To come … • Pluviometers at all 26 soil moisture sites • 0-6cm soil moisture measurements • Telemetry

  11. Data Assimilation

  12. Sequential Data Assimilation model output error

  13. Analogy 1 Initial state Update Update Update Update Update Update

  14. Variational Data Assimilation model output

  15. Initial state Analogy 2 Avail. Info Avail. Info Forecast Forecast Avail. Info Forecast

  16. Methodology (NLFIT) Kuczera, 1982

  17. The Results

  18. Location of Study Catchments Streamgauge Climate Soil Moisture www.sasmas.unimelb.edu.au

  19. Forcing Assumptions • No errors in forcing and other observations assumed for “true” run • Forcing biases are introduced to simulate uncertainties in observations • Precipitation +33% • Net radiation -20%

  20. Streamflow Assimilation- Single catchment - Discharge Soil Moisture

  21. Streamflow Assimilation- Single catchment - Root Zone Surface Layer

  22. 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?

  23. 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)

  24. Errors and Biases of Forcing Data

  25. Variational-type Surface Soil Moisture Assimilation Surface SM Root Zone SM Runoff Profile SM

  26. Focus Catchments Upper Catchment Lower Catchment

  27. Unmonitored Catchments

  28. 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.

  29. 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 extreme climatic conditions exist and/or 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.

  30. Thank you!

  31. 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)

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