1 streamflow data assimilation - field requirements and results -
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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
(JJA)
Background
Koster et al., JHM, 2000
State of the Art
Location of Study Catchment
Melbourne
NewcastleSydney
1000km0km
Field Site
Goulburn River Catchment (NSW)– Proximity to Newcastle– Size and geophysical
properties– Cleared areas– Division into subcatchments– Distance to the sea
Vegetation and Soils
Installation of Soil Moisture Sensors
Weather Stations
Soil Moisture SitesStream 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
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Data Assimilation
Sequential Data Assimilation
model outp
ut
err
or
Analogy 1
Initial state
Up
date
Up
date
Up
date
Up
date
Up
date
Up
date
Variational Data Assimilation
model outp
ut
Analogy 2In
itia
l st
ate
Avail. Info ForecastAvail. Info
Forecast
Fore
cast
Avail.
Info
Methodology (NLFIT)
Kuczera, 1982
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The Results
Location of Study Catchments
Streamgauge
Soil Moisture
Climate
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
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
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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
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)
Errors and Biases of Forcing Data
Bias Error
Rainfall 25% 25%
Radiation 0% 15%
Variational-type Surface Soil Moisture Assimilation
Surf
ace
SM
Run
off
Root
Zone S
M
Pro
file
SM
Focus CatchmentsUpper Catchment
Lower Catchment
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
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.
Thank you!Thank you!
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)