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

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

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

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

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

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

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

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

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

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

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: – 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

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

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

Uncertainty Surfaces

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

Conclusions  Model uncertainty can be communicated without hard statistics.  Combining numerical/objective and qualitative/subjective information is useful.  Uncertainty representation must reflect model fundamentals.

The Environment Institute

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

by University of Adelaide on Oct 02, 2009

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