Assimilation of Streamflow and Surface Soil Moisture Observations into a Land Surface Model Christoph Rüdiger, Jeffrey P. Walker Dept. of Civil & Env.

Slides:



Advertisements
Similar presentations
A streamflow assimilation system for ensemble streamflow forecast over France G. Thirel (1), E. Martin (1), J.-F. Mahfouf (1), S. Massart (2), S. Ricci.
Advertisements

MoistureMap: Mixed-pixel Retrieval Ye Nan Master of research University of Melbourne Jeffrey Walker, Dongryeol Ryu, Christoph Rüdiger, Robert Gurney, Edward.
Scaling and Assimilation of Soil Moisture And Streamflow (SASMAS) - Streamflow Data Assimilation - Christoph Rüdiger, Jeffrey Walker Dept. of Civil & Environmental.
A Catchment Based Study On Streamflow Data Assimilation Christoph Rüdiger a, Jeffrey P. Walker a, Jetse D. Kalma b, Garry R. Willgoose c and Paul R. Houser.
Streamflow Data Assimilation Christoph Rüdiger Supervisors: Jeffrey Walker, University of Melbourne, Australia Jetse Kalma, University of Newcastle, Australia.
Retrieving Soil Moisture States Using Streamflow Data Assimilation C. Rüdiger a Supervisors: Jeffrey P. Walker a, Jetse D. Kalma b, Garry R. Willgoose.
AMS’04, Seattle, WA. January 12, 2004Slide 1 HYDROS Radiometer and Radar Combined Soil Moisture Retrieval Using Kalman Filter Data Assimilation X. Zhan,
Jeffrey Walker Factors Affecting the Detection of a Soil Moisture Signal in Field Relative Gravity Measurements 1 Adam Smith, 1 Jeffrey Walker, 1 Andrew.
Using Flux Observations to Improve Land-Atmosphere Modelling: A One-Dimensional Field Study Robert Pipunic, Jeffrey Walker & Andrew Western The University.
Enhancing vegetation productivity forecasting using remotely-sensed surface soil moisture retrievals Wade T. Crow USDA Hydrology and Remote Sensing Laboratory,
GRACE in the Murray-Darling Basin: integrating remote sensing with field monitoring to improve hydrologic model prediction Kevin M. Ellett Department of.
Scaling and Assimilation of Soil Moisture and Streamflow (SASMAS): project overview and preliminary results G Willgoose (U. Leeds, UK), H Hemakumara (U.
Jeffrey Walker Australian Root Zone Soil Moisture: Assimilation of Remote Sensing Observations 1 Jeffrey Walker, 2 Nadia Ursino, 1 Rodger Grayson and 3.
Remote Sensing, Land Surface Modelling and Data Assimilation Christoph Rüdiger, Jeffrey Walker The University of Melbourne Jetse Kalma The University.
Walker, Merlin, Panciera, Kalma, Kim and Hacker NAFE National Airborne Field Experiment 3 rd Workshop – Sep
Testing Remotely Sensed Evapotranspiration Estimates Using Airborne and Ground Measurements May 2004 Cressida Savige, Andrew French, Andrew Western, Jeffrey.
Effects of Climate Change on Natural and Regulated Flood Risks in the Skagit River Basin and Prospects for Adaptation Se-Yeun Lee 1 Alan F. Hamlet 2,1.
Department of Civil, Surveying and Environmental Engineering The University of Newcastle AUSTRALIA Supervisor:Co-Supervisor: Supervisor:Co-Supervisor:
1 Streamflow Data Assimilation - Field requirements and results -
Recent advances in soil moisture measurement instrumentation and the potential for online estimation of catchment status for flood and climate forecasting:
Disaggregation of passive microwave data and assimilation into distributed hydrological models: The National Airborne Field Experiment (NAFE’05/06) Jetse.
Catchment Monitoring for Scaling and Assimilation of Soil Moisture and Streamflow C. Rüdiger a, R.E. Davidson b, H.M. Hemakumara b, J.P. Walker a, J.D.
Alan F. Hamlet Andy Wood Seethu Babu Marketa McGuire Dennis P. Lettenmaier JISAO Climate Impacts Group and the Department of Civil Engineering University.
Washington State Climate Change Impacts Assessment: Implications of 21 st century climate change for the hydrology of Washington Marketa M Elsner 1 with.
Land Surface Models & Surface Water Hydrology Cédric DAVID.
Kristie J. Franz Department of Geological & Atmospheric Sciences Iowa State University
U.S. Department of the Interior U.S. Geological Survey Using Advanced Satellite Products to Better Understand I&M Data within the Context of the Larger.
ABSTRACT One of the large challenges in data assimilation (DA) into distributed hydrologic models is how to reduce the degrees of freedom in the inverse.
J. Famiglietti 1, T. Syed 1, P. Yeh 1,2 and M. Rodell 3 1 Dept. of Earth System Science, University of California,Irvine, USA 2 now at: Institute of Industrial.
Discussion and Future Work With an explicit representation of river network, CHARMS is capable of capturing the seasonal variability of streamflow, although.
Earth Science Division National Aeronautics and Space Administration 18 January 2007 Paper 5A.4: Slide 1 American Meteorological Society 21 st Conference.
Streamflow Predictability Tom Hopson. Conduct Idealized Predictability Experiments Document relative importance of uncertainties in basin initial conditions.
Pang-Wei Liu 1, Roger De Roo 2, Anthony England 2,3, Jasmeet Judge 1 1. Center for Remote Sensing, Agri. and Bio. Engineering, U. of Florida 2. Atmosphere,
Jeffrey Walker et al. PLMR Data Jeffrey Walker and Valerio Paruscio Dept of Civil and Env Engg The University of Melbourne, Australia Ed Kim Hydrospheric.
Enhancing the Value of GRACE for Hydrology
A Variational Ensemble Streamflow Prediction Assessment Approach for Quantifying Streamflow Forecast Skill Elasticity AGU Fall Meeting December 18, 2014.
SeaWiFS Highlights February 2002 SeaWiFS Views Iceland’s Peaks Gene Feldman/SeaWiFS Project Office, Laboratory for Hydrospheric Processes, NASA Goddard.
September 16, 2008 R. Edward Beighley Civil, Construction and Environmental Engineering San Diego State University SWOT Hydrology Workshop The Ohio State.
Printed by Joint assimilation of in-situ and remotely sensed surface soil moisture and LAI observations in a simplified variational.
Understanding hydrologic changes: application of the VIC model Vimal Mishra Assistant Professor Indian Institute of Technology (IIT), Gandhinagar
# # # # An Application of Maximum Likelihood Ensemble Filter (MLEF) to Carbon Problems Ravindra Lokupitiya 1, Scott Denning 1, Dusanka Zupanski 2, Kevin.
Pg. 1 Using the NASA Land Information System for Improved Water Management and an Enhanced Famine Early Warning System Christa Peters-Lidard Chief, Hydrological.
Merging of microwave rainfall retrieval swaths in preparation for GPM A presentation, describing the Merging of microwave rainfall retrieval swaths in.
S. Munier, A. Polebitski, C. Brown, G. Belaud, D.P. Lettenmaier.
Natural and human induced changes in the water cycle: Relative magnitudes and trends Dennis P. Lettenmaier Department of Geography University of California,
Goal: to understand carbon dynamics in montane forest regions by developing new methods for estimating carbon exchange at local to regional scales. Activities:
The lower boundary condition of the atmosphere, such as SST, soil moisture and snow cover often have a longer memory than weather itself. Land surface.
Dr. Christa D. Peters-Lidard NASA/GSFC GLASS Workshop 26-Aug-03 GLASS “LOcal-COupled” Project: “LOCO” Christa D. Peters-Lidard NASA’s Goddard Space Flight.
Matt Rodell NASA GSFC Multi-Sensor Snow Data Assimilation Matt Rodell 1, Zhong-Liang Yang 2, Ben Zaitchik 3, Ed Kim 1, and Rolf Reichle 1 1 NASA Goddard.
Hydrologic Data Assimilation with a Representer-Based Variational Algorithm Dennis McLaughlin, Parsons Lab., Civil & Environmental Engineering, MIT Dara.
DIAS INFORMATION DAY GLOBAL WATER RESOURCES AND ENVIRONMENTAL CHANGE Date: 09/07/2004 Research ideas by The Danish Institute of Agricultural Sciences (DIAS)
EVALUATION OF A GLOBAL PREDICTION SYSTEM: THE MISSISSIPPI RIVER BASIN AS A TEST CASE Nathalie Voisin, Andy W. Wood and Dennis P. Lettenmaier Civil and.
Mahkameh Zarekarizi, Hamid Moradkhani,
Upper Rio Grande R Basin
Retrieving Soil Moisture States Using Streamflow Data Assimilation
European Geosciences Union General Assembly 2015
Hydrologic Considerations in Global Precipitation Mission Planning
Combining COSMOS and Microwave Satellite Data
Streamflow Simulations of the Terrestrial Arctic Regime
Analysis of influencing factors on Budyko parameter and the application of Budyko framework in future runoff change projection EGU Weiguang Wang.
Retrieving Soil Moisture States Using Streamflow Data Assimilation
Multimodel Ensemble Reconstruction of Drought over the Continental U.S
Kostas M. Andreadis1, Dennis P. Lettenmaier1
Long-Lead Streamflow Forecast for the Columbia River Basin for
Andy Wood and Dennis P. Lettenmaier
Results for Basin Averages of Hydrologic Variables
A Multimodel Drought Nowcast and Forecast Approach for the Continental U.S.  Dennis P. Lettenmaier Department of Civil and Environmental Engineering University.
Multimodel Ensemble Reconstruction of Drought over the Continental U.S
Off-line 3DVAR NOx emission constraints
Ben Zaitchik, Matt Rodell, Rolf Reichle, Rasmus Houborg, Bailing Li,
Presentation transcript:

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 Earth & Biosphere Institute, School of Geography, University of Leeds Paul R. Houser Hydrological Sciences Branch, NASA Goddard Space Flight Center, Now: George Mason University & Center for Research on Environment and Water

Christoph Rüdiger EGU05 Background Koster et al., JHM, 2000

Christoph Rüdiger EGU05 State of Art

Christoph Rüdiger EGU05 Location of Study Catchment Melbourne Newcastle Sydney 1000km0km

Christoph Rüdiger EGU05 Location of Study Catchment Streamgauge Soil Moisture Climate

Christoph Rüdiger EGU05 Methodology (NLFIT) Kuczera, 1982

Christoph Rüdiger EGU05 Streamflow Assimilation - Single catchement - DischargeSoil Moisture

Christoph Rüdiger EGU05 Streamflow Assimilation - Single catchement - Root ZoneSurface Layer

Christoph Rüdiger EGU05 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 Rüdiger EGU05 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 Rüdiger EGU05 Errors and Biases of Forcing Data BiasError Rainfall25% Radiation0%15%

Christoph Rüdiger EGU05 Variational-type Surface Soil Moisture Assimilation Surface SM Runoff Root Zone SM Profile SM

Christoph Rüdiger EGU05 Focus Catchments Upper Catchment Lower Catchment

Christoph Rüdiger EGU05 Unmonitored Catchments Upper Catch. Lower Catch. TruthDegrad.Assim. Catchment Deficit Root Zone Excess Surface Excess E-05

Christoph Rüdiger EGU05 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 Rüdiger EGU05 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 Rüdiger EGU05 Acknowledgment Australian Research Council (ARC-DP grant ) 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)