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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
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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
(JJA) Background Koster et al., JHM, 2000
Location of Study Catchment Newcastle Sydney Melbourne 0km 1000km
Field Site Goulburn River Catchment (NSW) • Proximity to Newcastle • Size and geophysical properties • Cleared areas • Division into subcatchments • Distance to the sea
Weather Stations Soil Moisture Sites Stream Gauges Location of Instrumentation
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
Sequential Data Assimilation model output error
Analogy 1 Initial state Update Update Update Update Update Update
Variational Data Assimilation model output
Initial state Analogy 2 Avail. Info Avail. Info Forecast Forecast Avail. Info Forecast
Methodology (NLFIT) Kuczera, 1982
Location of Study Catchments Streamgauge Climate Soil Moisture www.sasmas.unimelb.edu.au
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%
Streamflow Assimilation- Single catchment - Discharge Soil Moisture
Streamflow Assimilation- Single catchment - Root Zone Surface Layer
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?
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)
Variational-type Surface Soil Moisture Assimilation Surface SM Root Zone SM Runoff Profile SM
Focus Catchments Upper Catchment Lower Catchment
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.
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.
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)