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Spatialtemporal modelling of extreme rainfall

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... Bayesian Hierarchical modelling to. Amalgamate ... Spatial Modelling of GEV parameters, to borrow 'strength' ... Spatial-temporal modelling of extreme rainfall ... – PowerPoint PPT presentation

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Title: Spatialtemporal modelling of extreme rainfall


1
Spatial-temporal modelling of extreme rainfall
Climate Adaptation Flagship
  • Mark Palmer
  • TechFest 2009

2
Spatial-temporal modelling of extreme rainfall
  • Acknowledgements
  • Santosh Aryal (CLW),
  • Bryson Bates (CMAR),
  • Eddy Campbell, (CMIS),
  • Yun Li (CMIS),
  • Neil Viney (CLW),
  • IOCI,
  • BoM
  • WA Water Corp.  
  • Australian Greenhouse Office (Department  of
    Climate Change)
  • Upper Parramatta River Catchment Trust
  • Sydney Water 
  • Sydney Metropolitan Catchment Management
    Authority 
  • Hunter-Central Rivers Catchment Management
    Authority
  • Southern Rivers Catchment Management Authority

3
Spatial-temporal modelling of extreme rainfall
  • How does it fit in to Tech Fest 2009
  • (relatively) large data sets
  • Use of Bayesian Hierarchical modelling to
  • Amalgamate data sets,
  • Reduce dimensionality.

4
Spatial-temporal modelling of extreme rainfall
5
Spatial-temporal modelling of extreme rainfall
  • Where is this changing, clearly not uniformly
  • WA Wheatbelt, formerly most reliable wheat
    growing area in Australia
  • The Eastern urban fringe, relies on dam water
  • Kimberley rainfall is increasing
  • What about changes in extreme rainfall, this may
    be more dramatic?

6
Spatial-temporal modelling of extreme rainfall
  • Why are we doing this?
  • With climate change, engineering specifications
    will need to change dams, culverts roads.
  • Farming practices may need to change
  • So
  • Can we pickup changes?
  • Can we relate changes to climate drivers?
  • Can we link our analyses to climate models?

7
Spatial-temporal modelling of extreme rainfall
  • What are we really interested in?
  • Return Period/Levels, IDFs and DA curves
  • Need to look at this from both a spatial and
    temporal aspect
  • So, how are we going to proceed
  • Data
  • Characterize extremes at a station
  • Allow characteristics of extremes to vary
    spatially, and be driven by covariates

8
Spatial-temporal modelling of extreme rainfall
  • Return Period
  • The return period T, for a given duration and
    intensity i(d), is the average time interval
    between exceedance of the value i(d)
  • Intensity Duration Frequency Curves
  • X-axis duration
  • Y-axis intensity
  • Each line corresponds to a fixed return period
  • How have these been derived?
  • Generally in an adhoc manner
  • No measures of variability associated with them
  • Generally not (very) specific to a location

9
Spatial-temporal modelling of extreme rainfall
  • Data
  • Daily data 1950-2003
  • Pluvio data essentially continuous recording,
    but aggregated into sub to super daily rainfall
    records
  • There is an issue with daily versus 24 hour data
  • Data divided into summer and winter (region
    specific)
  • Numbers of sites
  • SW WA 1501 made up of (1227, 274)
  • UPRCT 607 (346, 261)

10
Spatial-temporal modelling of extreme rainfall
  • Bayesian Hierarchical Model
  • Model rainfall at a station
  • GEV distribution for extremes
  • Spatial Modelling of GEV parameters, to borrow
    'strength
  • Geostatistical approach based on GPs, such
    askriging for a single variable is well
    understood and developed, shall pursue an
    approach that induces GPs.
  • Include covariates
  • looking for trends in parameters of the GEV,
    covariates (time, drivers eg SOI, AAO, SST, heat
    content of oceans)
  • Hierarchical framework
  • Allows us to combine the above
  • Bayesian
  • Coherent approach to inferences
  • Applications of the model
  • Use it to derive return levels, IFD curves, areal
    statistics such as Depth Area curves

11
Spatial-temporal modelling of extreme rainfall

Di-graph representation of the Spatial-Temporal
model for extreme rainfall
  • Issues
  • Implemented correctly? (Gelman, simulation)
  • Does the model fit?

12
Spatial-temporal modelling of extreme rainfall
  • Examples UPRCT GEV surfaces

13
Spatial-temporal modelling of extreme rainfall
  • Differences in 50 year Return Level Surfaces
    (2003 1953)

14
Spatial-temporal modelling of extreme rainfall
  • IDF curves

Sample of IDF curves for the pluviograph station
566038, for a 50 year return period (Ocean heat
anomaly 0, ie effectively the historical long
term average), drawn from the MCMC procedure.
Estimated average IDF curves, for return periods
of (a) 5 years, (b) 20 years and (c) 50 years.
Each figure shows the IDF curves calculated using
an ocean heat anomaly of -2.5, 0.0 and 2.5
respectively.
15
Spatial-temporal modelling of extreme rainfall
Driven by covariates
16
Spatial-temporal modelling of extreme rainfall
  • Perils of climate research
  • Syd Levitus, John Antonov, Tim Boyer (March 2008)
  • Improved estimates of upper-ocean warming and
    multi-decadal sea-level rise (Catia M.
    Domingues1, John A. Church1,2, Neil J. White1,2,
    Peter J. Gleckler3, Susan E. Wijffels1, Paul M.
    Barker1 Jeff R. Dunn1) CSIRO June 2008, Nature

17
Spatial-temporal modelling of extreme rainfall
  • Whats achieved
  • A spatial model for GEVs which characterizes
    extreme rainfall at gauged and ungauged locations
    over a range of durations
  • Can be driven by covariates (either measured or
    derived from other models)
  • Can derive objects of interest such as IDFs and
    measures of variability

18
Spatial-temporal modelling of extreme rainfall
  • Whats next predictor selection
  • Large numbers of covariates, (both 2D and 3D)
  • L1, Lasso, RChip
  • What about spatial correlation (2 and 3 D data)?
  • What about time lags?

19
Thank you
CSIRO Mathematical and Information Sciences Mark
Palmer Phone (08) 9333 6293 Email
mark.palmer_at_csiro.au Web www.csiro.au/cmis
Climate Adaptation Flagship
Contact Us Phone 1300 363 400 or 61 3 9545
2176 Email Enquiries_at_csiro.au Web www.csiro.au
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