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Streamflow Data Assimilation Christoph Rüdiger

Streamflow Data Assimilation Christoph Rüdiger. Supervisors: Jeffrey Walker, University of Melbourne, Australia Jetse Kalma, University of Newcastle, Australia Garry Willgoose, University of Leeds, United Kingdom. Overview. Research objectives

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Streamflow Data Assimilation Christoph Rüdiger

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  1. Streamflow Data Assimilation Christoph Rüdiger Supervisors: Jeffrey Walker, University of Melbourne, Australia Jetse Kalma, University of Newcastle, Australia Garry Willgoose, University of Leeds, United Kingdom

  2. Overview • Research objectives • Introduction to field sites and instrumentation • Approach to the problem and future tasks • Hydrological model • Data assimilation • Problems in general

  3. Research Objectives • Streamflow data assimilation for use of soil moisture predictions in a hydrological model • Joint assimilation of streamflow data and near surface soil moisture into a hydrological model

  4. Soil Moisture? What for? Drought monitoring Irrigation policies Flood prediction Weather forecasting

  5. Field Site (SASMAS) • Goulburn River Catchment • Proximity to Newcastle • Size and geophysical properties • Cleared areas • Division into 4 subcatchments

  6. 40km 50km Vegetation and Soils (SASMAS)

  7. Locationof Instrumentation Soil Moisture Sites DLWC Stream Gauges Weather Stations Project Stream Gauges Microcatchment Subcatchments

  8. Surface Conditions (MARVEX) (from Woods et al., 2001)

  9. Site Locations Stream gauges Raingauges Soil moisture sites (from Woods et al., 2001)

  10. Approach • Hydrological modelling (VIC-3L model; Liang et al. 2001) with synthetic and real data • Data assimilation • Routing model • Test on different climates and scales(Goulburn and Mahurangi catchments –subcatchments only and full sized) • Joint streamflow & near-surface soil moisture data assimilation

  11. Variable Infiltration Capacity bucket theory Xinanjiang distribution (from Wooldridge et al., 2001)

  12. What is Sequential Data Assimilation? model output error

  13. What is Variational Data Assimilation? model output

  14. Problems in General • Model assumptions (hydrological models & routing model) • Accuracy of instrument data (sensors & stream gauges) • Corruption of satellite data • Accessibility and representativeness of soil moisture sites (Grayson and Western, 1998)

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