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Streamflow Data AssimilationStreamflow Data Assimilation- - Field requirements and resultsField requirements and results - -
Christoph Rüdiger, Jeffrey P. WalkerChristoph Rüdiger, Jeffrey P. WalkerDept. of Civil & Env. Engineering., University of MelbourneDept. of Civil & Env. Engineering., University of Melbourne
Jetse D. KalmaJetse D. KalmaSchool of Engineering, University of NewcastleSchool of Engineering, University of Newcastle
Garry R. WillgooseGarry R. WillgooseEarth & Biosphere Institute, School of Geography, University of LeedsEarth & Biosphere Institute, School of Geography, University of Leeds
Paul R. HouserPaul R. HouserGeorge Mason University & Center for Research on Environment and George Mason University & Center for Research on Environment and
Water Water
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Motivation, Field Site & Instrumentation
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(JJA)
Background
Koster et al., JHM, 2000
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State of the Art
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Location of Study Catchment
Melbourne
NewcastleSydney
1000km0km
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Field Site
Goulburn River Catchment (NSW)– Proximity to Newcastle– Size and geophysical
properties– Cleared areas– Division into subcatchments– Distance to the sea
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Vegetation and Soils
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Installation of Soil Moisture Sensors
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Weather Stations
Soil Moisture SitesStream Gauges
Location of Instrumentation
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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
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Data Assimilation
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Sequential Data Assimilation
model outp
ut
err
or
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Analogy 1
Initial state
Up
date
Up
date
Up
date
Up
date
Up
date
Up
date
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Variational Data Assimilation
model outp
ut
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Analogy 2In
itia
l st
ate
Avail. Info ForecastAvail. Info
Forecast
Fore
cast
Avail.
Info
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Methodology (NLFIT)
Kuczera, 1982
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The Results
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Location of Study Catchments
Streamgauge
Soil Moisture
Climate
www.sasmas.unimelb.edu.au
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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%
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Streamflow Assimilation- Single catchment -
Discharge Soil Moisture
Assimilation with "wrong" forcing data (profile mc)
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
01/08/03 08/08/03 15/08/03 22/08/03 29/08/03
Date
Vo
lum
etri
c M
ois
ture
Co
nte
nt
[-]
true
Assimilation with "wrong" forcing data (runoff)
0
100
200
300
400
500
600
01/08/03 08/08/03 15/08/03 22/08/03 29/08/03
Date
Dis
char
ge
[m^
3/s]
true
Assimilation with "wrong" forcing data (runoff)
0
100
200
300
400
500
600
01/08/03 08/08/03 15/08/03 22/08/03 29/08/03
Date
Dis
char
ge
[m^
3/s]
true
deg
Assimilation with "wrong" forcing data (runoff)
0
100
200
300
400
500
600
01/08/03 08/08/03 15/08/03 22/08/03 29/08/03
Date
Dis
char
ge
[m^
3/s]
true
deg
assim
Assimilation with "wrong" forcing data (profile mc)
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
01/08/03 08/08/03 15/08/03 22/08/03 29/08/03
Date
Vo
lum
etri
c M
ois
ture
Co
nte
nt
[-]
true
degr.
Assimilation with "wrong" forcing data (profile mc)
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
01/08/03 08/08/03 15/08/03 22/08/03 29/08/03
Date
Vo
lum
etri
c M
ois
ture
Co
nte
nt
[-]
true
degr.
assim
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Streamflow Assimilation- Single catchment -
Root Zone Surface Layer
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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?
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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)
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Errors and Biases of Forcing Data
Bias Error
Rainfall 25% 25%
Radiation 0% 15%
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Variational-type Surface Soil Moisture Assimilation
Surf
ace
SM
Run
off
Root
Zone S
M
Pro
file
SM
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Focus CatchmentsUpper Catchment
Lower Catchment
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Unmonitored Catchments
Upper Catch.Lower Catch.
Truth Degrad. Assim.
Catchment Deficit
221.744270.119
150.461 148.909
228.773253.190
Root Zone Excess
-5.76858-3.60799
0.00.0
0.0-3.21003
Surface Excess
-0.00615-0.46736
0.79978 0.97535
0.51269 -6.7E-05
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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.
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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.
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Thank you!Thank you!
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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)