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CRAFT Project Case Study UpdateScenario-neutral climate impact assessment of a stormwater capture
and managed aquifer recharge scheme
Goyder Forum 2017
Project team: Seth Westra (UoA), Lu Zhang (CSIRO), Bree Bennett (UoA), Nick Potter (CSIRO), Marie Ekstrom (CSIRO), Mark Thyer (UoA), Holger Maier (UoA)
Additional in-kind contributions from: Francis Chiew (CSIRO) and Sam Culley (UoA)
Application of framework to stormwater capture and managed aquifer recharge scheme
Problem definition
Set performance measures
Identify variables and processes that could lead to changes in system performance
Develop system model
‘Stress test’ the system
Visualise system performance over the exposure space
Superimpose climate information onto exposure space
• Expert knowledge, Historical variability (paleo climate information), Climate projections
Understanding an existing system
Problem definition
Set performance measures
Identify variables and processes that could lead to changes in system performance
Develop system model
‘Stress test’ the system
Visualise system performance over the exposure space
Superimpose climate information onto exposure space
• Expert knowledge, Historical variability (paleo climate information), Climate projections
Understanding an existing system
Reliability - capture & supply
Problem definition
Set performance measures
Identify variables and processes that could lead to changes in system performance
Develop system model
‘Stress test’ the system
Visualise system performance over the exposure space
Superimpose climate information onto exposure space
• Expert knowledge, Historical variability (paleo climate information), Climate projections
Understanding an existing system
Rainfall
• Volumes
• Extremes
• Intermittency
• Seasonality
• Variability
Reliability - capture & supply
Problem definition
Set performance measures
Identify variables and processes that could lead to changes in system performance
Develop system model
‘Stress test’ the system
Visualise system performance over the exposure space
Superimpose climate information onto exposure space
• Expert knowledge, Historical variability (paleo climate information), Climate projections
Understanding an existing system
Reliability - capture & supply
Problem definition
Set performance measures
Identify variables and processes that could lead to changes in system performance
Develop system model
‘Stress test’ the system
Visualise system performance over the exposure space
Superimpose climate information onto exposure space
• Expert knowledge, Historical variability (paleo climate information), Climate projections
Understanding an existing system
Reliability - capture & supply
‘Stress test’ the system
Rain, PETRain, PET
Time
Rai
n
Parafield system performance as a function of volumetric reliability
Stress test - simple scaling
Are all the system sensitivities exposed?
Extremes
Volumes
Seasonality
Rainfall intermittency
Variability
Rain, PETRain, PET
Have we examined our identified variables?
What about combinations of changes in these variables?
∆ A
vera
ge W
et S
pe
ll Le
ngt
h
∆ 99th Percentile Rainfall
∆ A
vera
ge S
um
mer
Rai
nfa
ll
∆ Average Winter Rainfall
An inverse approachStochastically generated perturbations
∆ A
vera
ge W
et S
pe
ll Le
ngt
h
∆ 99th Percentile Rainfall
Stochastic weather generator
Search algorithm
Time
Rai
n
Guo, D., Westra, S. & Maier, H., 2016, An inverse approach to perturb historical rainfall data for scenario-neutral climate impact studies, Journal of Hydrology
Problem definition
Set performance measures
Identify variables and processes that could lead to changes in system performance
Develop system model
‘Stress test’ the system
Visualise system performance over the exposure space
Superimpose climate information onto exposure space
• Expert knowledge, Historical variability (paleo climate information), Climate projections
Understanding an existing system
Reliability - capture & supply
R package to generate perturbed scenarios
• Generation of hydroclimate scenarios - time series
• Exposure space sampling• Regular grid
• Multiple perturbation approaches• Simple scaling
• Inverse approach (stochastic generation)
• Simple interface• One function call
• Open source
CustomiseClimate Attributes Optimisation
Length
• Change optimisation settings• Iterations• Favour key attributes• Parallel computing
• Inbuilt domain knowledge• Australian bounds on search
space
• Variable simulation length
Model configuration
• Richardson-type models• Annual*• Seasonal*• Monthly*• Harmonics*
• Rainfall*• Temperature*• Other hydroclimate variables – PET,
relative humidity, solar radiation, wind Help
• Guidance - suitability of chosen models to perturb chosen attributes
• Help documentation• Worked examples - vignette
Automatic Diagnostics Generation
MAR stochastic perturbations• Annual rainfall total - P_ann_tot_m• Seasonal average rainfall wet day amounts - P_DJF_dyWet_m, P_JJA_dyWet_m• Annual average wet-spell duration - P_ann_avgWSD_m• Annual number of wet days - P_ann_nWet_m• Annual average daily rainfall amount - P_ann_dyAll_m• Annual 99th percentile rainfall - P_ann_P99_m• Seasonal average rainfall total - P_SON_tot_m
Baseline climate simulation
Baseline climate simulation
• Month specific attributes not used – avoid over constraining scenarios
Problem definition
Set performance measures
Identify variables and processes that could lead to changes in system performance
Develop system model
‘Stress test’ the system
Visualise system performance over the exposure space
Superimpose climate information onto exposure space
• Expert knowledge, Historical variability (paleo climate information), Climate projections
Understanding an existing system
Reliability - capture & supply
Next Steps
CRAFT
21
Acknowledgements:
This work undertaken as part of the Goyder Institute ‘Climate resilienceanalysis framework and tools’ project (project number CA.16.01)
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