Download - Assimilation of Streamflow and Surface Soil Moisture Observations into a Land Surface Model

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


State of Art


Location of Study Catchment

Melbourne

NewcastleSydney

1000km0km


Location of Study Catchment

Streamgauge

Soil Moisture

Climate

www.sasmas.unimelb.edu.au


Methodology (NLFIT)

Kuczera, 1982


Streamflow Assimilation- Single catchement -

Discharge Soil Moisture

Assimilation with "wrong" forcing data (profile mc)

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Assimilation with "wrong" forcing data (runoff)

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Streamflow Assimilation- Single catchement -

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 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)

Download - Assimilation of Streamflow and Surface Soil Moisture Observations into a Land Surface Model

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