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1 Streamflow Data Assimilation - Field requirements and results -

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1 1 Streamflow Data Assimilation - Field requirements and results -

2 2 Motivation, Field Site & Instrumentation

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

4 State of the Art

5 Location of Study Catchment Melbourne Newcastle Sydney 1000km0km

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


9 Installation of Soil Moisture Sensors

10 Weather Stations Soil Moisture Sites Stream Gauges Location of Instrumentation

11 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

12 12 Data Assimilation

13 Sequential Data Assimilation model output error

14 Analogy 1 Initial state Update

15 Variational Data Assimilation model output

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

17 Methodology (NLFIT) Kuczera, 1982

18 18 The Results

19 Location of Study Catchments Streamgauge Soil Moisture Climate

20 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%

21 Streamflow Assimilation - Single catchment - DischargeSoil Moisture

22 Streamflow Assimilation - Single catchment - Root ZoneSurface Layer

23 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?

24 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)

25 Errors and Biases of Forcing Data BiasError Rainfall25% Radiation0%15%

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

27 Focus Catchments Upper Catchment Lower Catchment

28 Unmonitored Catchments Upper Catch. Lower Catch. TruthDegrad.Assim. Catchment Deficit 221.744 270.119 150.461 148.909 228.773 253.190 Root Zone Excess -5.76858 -3.607990.0 0.0 -3.21003 Surface Excess -0.00615 -0.46736 0.79978 0.97535 0.51269 -6.7E-05

29 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.

30 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.

31 Thank you!

32 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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