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

Kim Lowell

Presenting Objective and Subjective Uncertainty Information for Spatial

System-based ModelsKim Lowell1,2, Brendan Christy1, Greg Day1

1Department of Primary Industries2CRC for Spatial Information, University of Melbourne

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

The Rise of Models

Victorian Government Water White Paper

Action 2.20 – Water and forest plantations

Modelling to identify best” locations

Project Context

Increased

flexibility for

non-

technical

model users

“Spatial Viewer”

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

Spatial Viewer Summary

Catchment Analysis Tool (CAT)

Underpinning hydrological model

Linked single-purpose

landscape models

Erosion

Tree growth

Etc.

CAT Model Background

Calibrated for each catchment

Data

Bore holes (water depth)

Climate (rainfall)

Streamflow (outflow)

Method

Numerical optimisation

Expert knowledge

Voodoo

CAT Calibration

0

10000

20000

30000

40000

50000

60000

70000

80000

Jul

-74

Jul

-77

Jul

-80

Jul

-83

Jul

-86

Jul

-89

Jul

-92

Jul

-95

Jul

-98

Realm_Strm

Modelled_Strm

Objective estimation not possible for all

parameters

Truly independent validation not possible

Model complexity limits numerical evaluation

Size of error

Form of error distribution

Implications for Uncertainty

“I understand all that, but all I want to know is if the

model estimates are „good‟.”

Policy Makers . . . .

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

Solution

>0.6 “Satisfactory”; > 0.8 “Good”

B ase flo w (C oran gam ite )

0

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0.8

1

0 5 10 15 20 25 30 35

S tre am G au g e ID

Co

eff

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nt

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y

Q u ic k flo w (C o ra n g a m ite )

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S tre a m G a u g e ID

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

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S tr e a m G a u g e ID

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In -s tre a m S a lt (C o ra n g a m ite )

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S tre a m G a u g e ID

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Interpretation of CE

Reflects the calibration data and method

For example….

Stream gauges +

flow directions

Limits to numerical

evaluation

Spatial Uncertainty

Uncertainty Surfaces

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

0

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S tr e a m G a u g e ID

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yFor Users of Model Outputs

Model uncertainty can be communicated

without hard statistics.

Combining numerical/objective and

qualitative/subjective information is useful.

Uncertainty representation must reflect model

fundamentals.

Conclusions

The Environment Institute