assimilation of streamflow and surface soil moisture observations into a land surface model
DESCRIPTION
Assimilation of Streamflow and Surface Soil Moisture Observations into a Land Surface Model. 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 - PowerPoint PPT PresentationTRANSCRIPT
Assimilation of Streamflow Assimilation of Streamflow and Surface Soil Moisture and Surface Soil Moisture
Observations Observations into a Land Surface Modelinto a Land Surface ModelChristoph Rüdiger, Jeffrey P. WalkerChristoph Rüdiger, Jeffrey P. Walker
Dept. 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. HouserHydrological Sciences Branch, NASA Goddard Space Flight Center,Hydrological Sciences Branch, NASA Goddard Space Flight Center,
Now: Now: George Mason University & Center for Research on Environment and George Mason University & Center for Research on Environment and WaterWater
Christoph RüdigerEGU05
Background
Koster et al., JHM, 2000
Christoph RüdigerEGU05
State of Art
Christoph RüdigerEGU05
Location of Study Catchment
Melbourne
NewcastleSydney
1000km0km
Christoph RüdigerEGU05
Location of Study Catchment
Streamgauge
Soil Moisture
Climate
www.sasmas.unimelb.edu.au
Christoph RüdigerEGU05
Methodology (NLFIT)
Kuczera, 1982
Christoph RüdigerEGU05
Streamflow Assimilation- Single catchement -
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
Christoph RüdigerEGU05
Streamflow Assimilation- Single catchement -
Root Zone Surface Layer
Christoph RüdigerEGU05
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?
Christoph RüdigerEGU05
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)
Christoph RüdigerEGU05
Errors and Biases of Forcing Data
Bias Error
Rainfall 25% 25%
Radiation 0% 15%
Christoph RüdigerEGU05
Variational-type Surface Soil Moisture Assimilation
Surf
ace
SM
Run
off
Root
Zone S
M
Pro
file
SM
Christoph RüdigerEGU05
Focus CatchmentsUpper Catchment
Lower Catchment
Christoph RüdigerEGU05
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
Christoph RüdigerEGU05
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.
Christoph RüdigerEGU05
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 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.
Christoph RüdigerEGU05
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)