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The aim of SASMAS

The aim of SASMAS. Estimation of Spatial Distribution of Soil Moisture Estimation of Temporal Variation of Soil Moisture Scaling between Satellite Soil Moisture Products and On-Site Point Measurements Connecting Soil Moisture Measurements with Streamflow Measurements

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The aim of SASMAS

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  1. The aim of SASMAS • Estimation of Spatial Distribution of Soil Moisture • Estimation of Temporal Variation of Soil Moisture • Scaling between Satellite Soil Moisture Productsand On-Site Point Measurements • Connecting Soil Moisture Measurements with Streamflow Measurements • Validation of AMSR-E data Christoph Rüdiger

  2. Subprojects • Scaling Studies (Manju Hemakumara) • Soil Moisture Data Assimilation (Riki Davidson) • Streamflow Data Assimilation (Christoph Rüdiger) • Satellite Data Validation (eg. AMSR-E) (all candidates) Christoph Rüdiger

  3. Areas of Application • Climatic Modelling • Flood Forecasting • Agriculture • … Christoph Rüdiger

  4. Field Site • Goulburn River Catchment • Proximity to Newcastle • Size and geophysical properties • Cleared areas • Division into 4 subcatchments Christoph Rüdiger

  5. Subcatchments Stanley Krui River Merriwa River Christoph Rüdiger

  6. Surface conditions Christoph Rüdiger

  7. Instrumentation • Installation of … • 2 weather stations and several rain gauges • 26 soil moisture monitoring sites • 1 flume and 2 river gauges • Use of … • 3 existing weather stations • 3 stream gauges Christoph Rüdiger

  8. Locationof Instrumentation Weather Stations Soil Moisture Sites Stream Gauges Future Stream Gauges Christoph Rüdiger

  9. Schematic of a Soil Moisture Site 30cm Logger T 0 - 30cm 30 – 60 cm 60 - 90cm SM SM SM 30cm 60cm 75cm CS616 Logger backfilled soil 30cm 30cm N Christoph Rüdiger

  10. Temperature Sensor Weather Station at Spring Hill Soil Moisture Sensor Instrumentation Christoph Rüdiger

  11. Instrumentation Christoph Rüdiger

  12. Streamflow Data Assimilation • Why the Focus on Streamflow Data • Updating of Soil Moisture States • Soil Moisture Estimation under Vegetation Canopy Christoph Rüdiger

  13. Streamflow Data Assimilation • How • Gauging stations, flume, soil moisture sensors and satellite remote sensed data • Implementing Streamflow Routing Model into Rainfall-Runoff Models • Variational Approach • Ensemble Kalman Filter Christoph Rüdiger

  14. Approach to Streamflow Modelling - Creating model - Synthetic study on model • Application of Mahurangi (NZ) data sets • Application of Goulburn data sets Christoph Rüdiger

  15. Acknowledgements • Australian Research Council (Research Grant) • NASA, Hydrological Sciences Branch (Instrumentation) Christoph Rüdiger

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