Presenting objective and subjective uncertainty
  information for spatial system-based models.

                          ...
Presenting Objective and Subjective
 Uncertainty Information for Spatial
              System-based Models
               ...
The Rise of Models

 Land management increasingly holistic
     Multiple outcome questions
     Systems-science

 More...
Project Context
 Victorian Government Water White Paper
     Action 2.20 – Water and forest plantations
     Modelling ...
“Spatial Viewer”

 Increased
  flexibility for
  non-
  technical
  model users
Spatial Viewer Summary
 Land-use change among:
     Pasture, Crop, Forest

 Impact on eight factors:
       Aquifer re...
CAT Model Background
 Catchment Analysis Tool (CAT)
 Underpinning hydrological model
 Linked single-purpose
  landscape...
CAT Calibration
Calibrated for each catchment
Data
     Bore holes (water depth)   80000


     Climate (rainfall)      ...
Implications for Uncertainty

 Objective estimation not possible for all
  parameters

 Truly independent validation not...
Policy Makers . . . .
 “I understand all that, but all I want to know is if the
  model estimates are „good‟.”
Solution
 Spatial and statistical uncertainty information

 Statistical on stream gauges
     Coefficient of efficiency...
Interpretation of CE
 >0.6 “Satisfactory”; > 0.8 “Good”
                                                                 ...
Spatial Uncertainty
 Reflects the calibration data and method

 For example….
     Stream gauges +         Limits to num...
Uncertainty Surfaces
For Users of Model Outputs




                                                   S tr e a m flo w (C o r a n g a m ite )
...
Conclusions

 Model uncertainty can be communicated
  without hard statistics.

 Combining numerical/objective and
  qua...
The Environment
    Institute
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Presenting objective and subjective uncertainty information for spatial system-based models

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Presented by Kim Lowell as part of the 2009 Place and Purpose Symposium run by the Landscape Science Cluster

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Presenting objective and subjective uncertainty information for spatial system-based models

  1. 1. Presenting objective and subjective uncertainty information for spatial system-based models. Kim Lowell
  2. 2. Presenting Objective and Subjective Uncertainty Information for Spatial System-based Models Kim Lowell1,2, Brendan Christy1, Greg Day1 1Department of Primary Industries 2CRC for Spatial Information, University of Melbourne
  3. 3. The Rise of Models  Land management increasingly holistic  Multiple outcome questions  Systems-science  More reliance on models for Public Policy  Increased model use demands increased model meta-data  Uncertainty especially
  4. 4. Project Context  Victorian Government Water White Paper  Action 2.20 – Water and forest plantations  Modelling to identify best” locations
  5. 5. “Spatial Viewer”  Increased flexibility for non- technical model users
  6. 6. Spatial Viewer Summary  Land-use change among:  Pasture, Crop, Forest  Impact on eight factors:  Aquifer recharge  Evapotranspiration (ET)  Flow to stream  Plant carbon  Erosion  In-stream phosphorous  In-stream nitrogen  In-stream salt load  Depth-to-water table
  7. 7. CAT Model Background  Catchment Analysis Tool (CAT)  Underpinning hydrological model  Linked single-purpose landscape models  Erosion  Tree growth  Etc.
  8. 8. CAT Calibration Calibrated for each catchment Data  Bore holes (water depth) 80000  Climate (rainfall) 70000 60000 50000  Streamflow (outflow) 40000 30000 Realm_Strm Modelled_Strm 20000 10000 0  Method Jul-74 Jul-77 Jul-80 Jul-83 Jul-86 Jul-89 Jul-92 Jul-95 Jul-98  Numerical optimisation  Expert knowledge  Voodoo
  9. 9. Implications for Uncertainty  Objective estimation not possible for all parameters  Truly independent validation not possible  Model complexity limits numerical evaluation  Size of error  Form of error distribution
  10. 10. Policy Makers . . . .  “I understand all that, but all I want to know is if the model estimates are „good‟.”
  11. 11. Solution  Spatial and statistical uncertainty information  Statistical on stream gauges  Coefficient of efficiency: – CE = 1 - Σ(Oi - Pi)2/Σ(Oi – OBar)2 (1) – where Oi and Pi are Observed and Predicted – OBar is the average observed value over entire period
  12. 12. Interpretation of CE  >0.6 “Satisfactory”; > 0.8 “Good” B a s e flo w (C o r a n g a m ite ) Q u ic k flo w (C o r a n g a m ite ) C o e ffic ie n t o f E ffic ie n c y C o e ffic ie n t o f E ffic ie n c y 1 1 0. 8 0.8 0. 6 0.6 0. 4 0.4 0. 2 0.2 0 0 0 5 10 15 20 25 30 35 0 5 10 15 20 25 30 S tre a m G a u g e ID S tr e a m G a u g e ID In -s tr e a m S a lt (C o r a n g a m ite ) S tr e a m flo w (C o r a n g a m ite ) C o e ffic ie n t o f E ffic ie n c y C o e ffic ie n t o f E ffic ie n c y 1 1 0.8 0. 8 0.6 0. 6 0.4 0. 4 0.2 0. 2 0 0 0 5 10 15 20 25 30 0 5 10 15 20 25 30 S tr e a m G a u g e ID S tr e a m G a u g e ID
  13. 13. Spatial Uncertainty  Reflects the calibration data and method  For example…. Stream gauges + Limits to numerical flow directions evaluation
  14. 14. Uncertainty Surfaces
  15. 15. For Users of Model Outputs S tr e a m flo w (C o r a n g a m ite ) C o e ffic ie n t o f E ffic ie n c y 1 0. 8 0. 6 0. 4 0. 2 0 0 5 10 15 20 25 30 S tr e a m G a u g e ID
  16. 16. Conclusions  Model uncertainty can be communicated without hard statistics.  Combining numerical/objective and qualitative/subjective information is useful.  Uncertainty representation must reflect model fundamentals.
  17. 17. The Environment Institute

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