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

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

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. 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. Project Context
 Victorian Government Water White Paper
 Action 2.20 – Water and forest plantations
 Modelling to identify best” locations

5. “Spatial Viewer”
 Increased
flexibility for
non-
technical
model users

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. CAT Model Background
 Catchment Analysis Tool (CAT)
 Underpinning hydrological model
 Linked single-purpose
landscape models
 Erosion
 Tree growth
 Etc.

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. 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. Policy Makers . . . .
 “I understand all that, but all I want to know is if the
model estimates are „good‟.”

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. 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. Spatial Uncertainty
 Reflects the calibration data and method
 For example….
Stream gauges + Limits to numerical
flow directions evaluation

14. Uncertainty Surfaces

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. 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. The Environment
Institute