Title: Spatialtemporal modelling of extreme rainfall
1Spatial-temporal modelling of extreme rainfall
Climate Adaptation Flagship
- Mark Palmer
- TechFest 2009
2Spatial-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
3Spatial-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.
4Spatial-temporal modelling of extreme rainfall
5Spatial-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?
6Spatial-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?
7Spatial-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
8Spatial-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
9Spatial-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)
10Spatial-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
11Spatial-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?
12Spatial-temporal modelling of extreme rainfall
- Examples UPRCT GEV surfaces
13Spatial-temporal modelling of extreme rainfall
- Differences in 50 year Return Level Surfaces
(2003 1953)
14Spatial-temporal modelling of extreme rainfall
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.
15Spatial-temporal modelling of extreme rainfall
Driven by covariates
16Spatial-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
17Spatial-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
18Spatial-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?
19Thank 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 UsPhone 1300 363 400 or 61 3 9545
2176Email Enquiries_at_csiro.au Web www.csiro.au