ARIES, ARtificial Intelligence for Ecosystem Services

ARIES, ARtificial Intelligence for
Ecosystem Services
ARIES website:
http://ARIESonline.org/
Basics
• Modelling framework
• Defined structure & ontology
• Spatial Bayesian modelling
• 4 elements
– Source
– Sink
– Users (Rival, Non-Rival)
• Service Flow routines using SPAN model
algorithms
Conceptual Ecosystem
Service model
Aesthetic Viewshed model, view of mountains
Source, Sink, Users
Modelled service flow
to Users
Visualising Uncertainty
Mapping uncertainty in Water Infiltration
model, Mexico catchment
Possibilities for use of ARIES
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Defining models based on expert judgement
Develop, test, calibrate on existing data
Model wrapping
(Using SPAN models to map service flow to
beneficiaries)
Ecosystem service assessment
Model development for Soy production in Brazil
Model fit
‘Source model’ for Soy yield in ARIES :
-Based on climate, soil, human inputs
-Incorporates yield gap (can also be a separate ARIES model)
-Calibrated on Earthstat Soy yield
Thinkcap Interface
Ecosystem service assessment
Model testing for Soy production in Brazil
Soy yield, Earthstat
ARIES model Soy
yield
Model fit
-Based on climate, soil, human inputs
-Calibrated on Earthstat Soy yield
-Model fit generally OK, but poor fit in some areas
Ecosystem service assessment
-Land use change & Soy production in Brazil
2050: SSP1 =
Heaven
Land use change from CLUE
model.
Soy yield modeled using ARIES
2050: SSP5 = Worst
Case
• Extensive soya expansion
under worst case scenario
Summary:
Advantages
• Rigorous methodology
• Flexible
• Sophisticated representation of service flow to Users
• Explicitly incorporates spatial context
• Can illustrate uncertainty
• Can apply models in data-poor areas
Disadvantages
• Complexity
• Can’t handle feedbacks (easily)
• Awkward get-arounds for dealing with negative numbers
• Outputs are discrete, not continuous (at the moment)