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									    School of Civil, Environmental and Mining Engineering




                              Wednesday, 4th April 2012




   Changes to sub-daily
     rainfall in Australia



Dr Seth Westra                           Life Impact | The University of Adelaide
Slide 1
Slide 2
Slide 3
Slide 4
Presentation overview

• Part 1: The sub-daily rainfall dataset in Australia
• Part 2: The observed relationship between
  temperature, humidity and rainfall intensity
• Part 3: Detection of trends in sub-daily rainfall
• Part 4: Towards a downscaling algorithm for sub-daily
  rainfall
• Part 5: Evaluating regional climate model (WRF)
  performance using the diurnal cycle of sub-daily
  precipitation


                                                        Slide 5
Part 1: Australian rainfall record
• More than 19000 daily precipitation stations (read at
  9am daily)
• More than 1500 pluviograph stations (6-minute
  resolution)
Pluviograph (sub-daily)   Slide 7
Daily (only locations > 100 years)
                                     Slide 8
Australian rainfall record – record length
        Pluviograph       Daily




                                        Slide 9
Part 2: Link between temperature and extreme
rainfall




Extreme rainfall will
scale at C-C rate of
~7%/C or “super C-C”
rate of ~15%/C




                                          Slide 10
Methodology

• Reproduce this work using Australia-wide data:
  – 137 long pluviograph records (average length 32
    years, with average of 6% missing)
  – Mean and maximum daily 2m air temperature
    extracted for each wet day
  – Data grouped into 15 bins by temperature – and
    different percentile (e.g. 50, 99%ile) rainfall
    extracted in each bin
  – Where available, relative humidity also extracted
Methodology




Hardwick-Jones, R., Westra, S. & Sharma,
A., 2010, “Observed relationships between
extreme sub-daily precipitation, surface
temperature, and relative humidity”,
Geophysical Research Letters, 37, L22805
                                            Slide 12
 60-minute rainfall intensity against
 average daily temperature




Blue = 99 percentile rainfall (representing behaviour of ‘extremes’)
Red = 50 percentile rainfall (representing behaviour of ‘average’
events)
                                                  60-minute rainfall intensity against
                                                  average daily temperature
                                                  2         ALICE SPRINGS AIRPORT 015590                                                             2           DARWIN AIRPORT 014015
                                             10                                                                                                 10
Maximum daily 60-minute precipitation (mm)




                                                                                                   Maximum daily 60-minute precipitation (mm)
                                                  1                                                                                                  1
                                             10                                                                                                 10




                                                  0                                                                                                  0
                                             10                                                                                                 10
                                                      10   15            20            25     30                                                     24   25   26      27        28         29   30   31
                                                                Mean Daily Temperature (C)                                                                      Mean Daily Temperature (C)



                                             Blue = 99 percentile rainfall (representing behaviour of ‘extremes’)
                                             Red = 50 percentile rainfall (representing behaviour of ‘average’
                                             events)
                  Scaling of 99th percentile maximum daily 60-minute burst
School of Civil, Environmental and Mining Engineering
                            with mean daily surface temperature
                                                 150  E
                       E         135 E                           165  E
                  1 20
                          Wednesday, 4th April 2012


                        NORTH

  
15 S

                                     EST
                           CENTRAL- W

        
     3 0 S                                                                      13% to 20%. C-1
                                                                                          
                                                                                7% to 13%. C-1
                                                                                         




                                                                   EAST
                             SOUTH
                                                                                2% to 7%. C-1
                                                                                        
                                                                                -2% to 2%. C-1
                                                                                         

                                                                               -7% to -2%.C-1
           45 S
                                                                                -13% to -7%. C-1
                                                                                           
                                     Life Impact | The University of Adelaide   -17% to -13%. C-1
                                                                                            
                                                  Regional scaling of 99th percentile
                                3         60-minute burst precipitation with surface temperature
                            School of Civil, Environmental and Mining Engineering
                           10
                                                 East Region
                                                 North Region
                                                 South Region 4th April 2012
                                                       Wednesday,
                                                 Central Region
                                                 Clausius-Clapeyron Relationship
Precipitation Depth (mm)




                              2
                           10




                                1
                           10




                                0
                           10                                  Impact | The University of Adelaide
                                                          Life15
                                    0    5        10                      20            25           30   35   40
                                                               Temperature (C)
                                             Regional scaling of 99th percentile daily precipitation
                                4                          with surface temperature
                            School of Civil, Environmental and Mining Engineering
                           10
                                                 East Region
                                                 North Region
                                                 South Region 4th April 2012
                                                       Wednesday,
                              3                  Central Region
                           10
                                                 Clausius-Clapeyron Relationship
Precipitation Depth (mm)




                                2
                           10




                                1
                           10




                                0
                           10                                  Impact | The University of Adelaide
                                                          Life15
                                    0    5         10                     20            25           30   35   40
                                                               Temperature (C)
Slide 18
Slide 19
Does relative humidity stay constant with temp?
                                                              PERTH AIRPORT 009021                                                                                    DARWIN AIRPORT 014015
                                      100                                                                                                      100

                                                                                                                                                95
                                       90
                                                                                                                                                90




                                                                                                         Mean daily relative humidity (%)
   Mean daily relative humidity (%)




                                       80
                                                                                                                                                85
                                       70
                                                                                                                                                80

                                       60                                                                                                       75

                                                                                                                                                70
                                       50
                                                                                                                                                65
                                       40
                                                                                                                                                60
                                       30                                                                                                                Jun to Nov
                                                 Jun to Nov                                                                                     55
                                                 Dec to May                                                                                              Dec to May
                                       20                                                                                                       50
                                            5    10        15           20         25         30    35                                            22       24             26           28            30   32
                                                       Mean daily surface temperature (C )                                                                     Mean daily surface temperature (C )

                                                       ALICE SPRINGS AIRPORT 015590                                                                          SYDNEY (OBSERVATORY HILL) 066062
                                      100                                                                                                      100

                                       90
                                                                                                                                                90
 Mean daily relative humidity (%)




                                       80                                                                   Mean daily relative humidity (%)
                                                                                                                                                80
                                       70

                                       60                                                                                                       70


                                       50                                                                                                       60

                                       40
                                                                                                                                                50
                                       30
                                                                                                                                                40
                                       20        Jun to Nov                                                                                              Jun to Nov
                                                 Dec to May                                                                                              Dec to May
                                       10                                                                                                       30
                                            5   10      15         20        25        30      35   40                                               5     10             15           20            25   30
                                                       Mean daily surface temperature (C )                                                                     Mean daily surface temperature (C )
Summary of temperature scaling work

• Clear scaling of rainfall with temperature across
  Australia
• Scaling depends on duration of storm burst, and
  exceedance probability
• Scaling also depends on atmospheric temperature –
  negative scaling with high temperatures!
   – Likely to be due to access to atmospheric moisture
• BUT: Does a historical scaling relationship imply
  similar future changes?


                                                      Slide 21
Part 2: Detection of trends in Australian rainfall
• We wish to detect whether there are trends or
  other types of climatic non-stationarity in
  extreme precipitation data

• Consider the following hypothetical example:

  – ‘Extreme’ precipitation will scale at a rate of
    7%/C in proportion to the water holding capacity of
    the atmosphere

  – Global warming trend has been ~0.74C over the 20th
    century

  – Therefore would need to be able to detect a ~5%
    change
Motivation
• Assuming 50 years of data, such a trend would be
  detected at the 5% significance level in only 8% of
  samples (and a negative trend detected in 2% of
  samples!)
What is a max-stable process?
• Formal definition: suppose for         , i = 1,..., n,
  are independent realisations of a continuous process.
  If the limit:



  exists for all s with normalising constants an(s) and
  bn(s), then       is a max-stable process.

• Spatial analogue of multivariate extreme value
  models, which accounts for both data-level
  dependence and parameter-level dependence.
   – Distinct from ‘Spatial GEV’ models which only
     account for parameter-level dependence.
Illustration of max-stable process
• The ‘storm profile’ model:
Benefits for trend detection
• Can improve the strength of the trend that can be
  detected (given by value of parameter ‘β1’),
  depending on the amount of spatial correlation.
Application to Australian rainfall data

• Of Australia’s ~1400 sub-daily records,
  we selected the 35 most complete
  stations with records from 1965-2005.
   – Extracted annual maximum data for
     6-minute through to 72 hour storm
     bursts
• Also considered high quality daily data
  from 1910 to 2005
 Application to Australian rainfall

• Trends in annual
  maximum 6-minute
  rainfall

  – Blue/red indicates
    increasing/decreasing
    trend

  – Filled circles indicate
    statistically significant
    at the 5% level
Is there an increasing trend in
east-Australian precipitation?
Sensitivity to gauge changes

• Many sub-daily stations had at least one gauge
  change over the record, usually from Dines
  pluviograph to TBRG
• Tested sensitivity by extracting any ‘step change’ in
  the year the gauge change occurred, and then re-
  fitting the trend.
• Did not make any significant difference to the
  strength of the trends in the previous slide
  Summary of trend detection work
  • Max-stable processes provide an elegant way of
    detecting non-stationarity in hydroclimatic data
        – Enables substitution of ‘space-for-time’ while
          accounting for spatial dependence
  • In east-Australia an increasing trend in sub-daily
    (particularly sub-hourly) precipitation data could be
    detected, but not for daily data
  • This would suggest that sub-daily precipitation is
    increasing much more quickly than expected
  • Also highlights that daily data cannot be used
    for inference at shorter timescales
Westra, S. & Sisson, A., 2011, “Detection of non-stationarity in precipitation extremes using a max-stable
process model”, Journal of Hydrology, 406
Part 4: Disaggregating from daily to sub-daily
rainfall under a future climate

• We have shown that the scaling of rainfall with
  atmospheric temperature depends on storm burst
  duration, exceedance probability, and moisture
  availability
   – How can this be used for estimating change in sub-
     daily rainfall under a future climate?
• Various techniques are available for downscaling daily
  rainfall under a future climate
   – We have developed an algorithm to disaggregate
     from daily to sub-daily rainfall under a future
     climate.
                                                       Slide 32
Importance of seasonality on daily to sub-daily
scaling




• Scaling from daily to sub-daily rainfall strongly
  depends on atmospheric temperature                  Slide 33
Plotting against both temperature and day of year




• BUT – most of the annual variation can actually be
  attributed to atmospheric temperature!               Slide 34
Influence of location – before and after
regressing against atmospheric temperature




                                             Slide 35
Considering a broader range of
atmospheric variables...
Variable                Abbreviated name   Daily mean, maxima,     Units
                                           minima and/or diurnal
                                           range
2m surface temperature tmp2m               mean, maxima, minima,   Degrees Celsius
                                           range
500, 700 and 850hPa     t500, t700, t850   mean                    Degrees Celsius
temperature
Dew point temperature   Td                 maxima                  Degrees Celsius
Relative humidity       RH                 mean and maxima         Percentage (%)
Pressure reduced to     prmsl              mean and minima         Pa
mean sea level
850hPa wind strength    wnd850_str,        mean                    (derived from u and v
and direction           wnd850_theta                               components of wind;
                                                                   units of m/s)
10m wind strength and   wnd10m_str,        mean                    (derived from u and v
direction               wnd10m_theta                               components of wind;
                                                                   units of m/s)
500 and 850hPa          z500, z850         mean                    Geopotential meter
geopotential height                                                (gpm)
Algorithm

Assume we have future sequences of daily rainfall
available (e.g. from a statistical or dynamical downscaling
algorithm), as well as atmospheric covariates
1. Given a future daily rainfall amount and associated
   atmospheric covariates (e.g. temperature, relative
   humidity, geopotential height...)
2. Find days in the historical record which have a ‘similar’
   atmospheric state and daily rainfall amount and also
   the complete sub-daily rainfall sequence
3. Sample from one of those days

                                                         Slide 37
 A disaggregation algorithm for downscaling sub-daily rainfall




Westra, S., Evans, J., Mehrotra, R. & Sharma, A., “Disaggregating from daily to sub-daily rainfall under a   Slide 38

future climate”, submitted to Journal of Climate
Summary of sub-daily disaggregation

• Disaggregation algorithm is a simple ‘analogues’
  based approach for understanding sub-daily rainfall
  behaviour under a future climate
• Requires daily downscaling information, but such
  information is often readily available
• Shows substantial changes can be expected at hourly
  or sub-hourly timescales.




                                                        Slide 39
Part 5: Diurnal
cycle of modelled
and observed
rainfall


• Good performance of a
  dynamical model in
  capturing the diurnal
  cycle provides a
  positive indication that
  the processes of sub-
  daily precipitation are
  correctly represented.



Evans, J. & Westra, S., “Investigating the mechanisms of diurnal rainfall variability using a Regional Climate Slide 40
Model”, submitted to Journal of Climate
Diurnal cycle of different precipitation generating mechanisms




                                                          Slide 41
Conclusions and ongoing work
• Evaluated scaling relationships of sub-daily rainfall
  and found strong dependence on temperature and
  atmospheric moisture
• Trend detection work also shows increasing trends in
  fine time-scale (particularly sub-hourly) rainfall
   – Significant implications for urban flood risk and
     risk of flash flooding
• Developed statistical disaggregation algorithm to
  generate sub-daily rainfall sequences conditional to
  daily rainfall, under a future climate.
• Also collaborating with dynamical climate modellers
  to evaluate capacity of regional climate models to
  simulate sub-daily precipitation
                                                      Slide 42
Slide 43
References
• Hardwick-Jones, R., Westra, S. & Sharma, A., 2010, “Observed
  relationships between extreme sub-daily precipitation, surface
  temperature, and relative humidity”, Geophysical Research
  Letters, 37, L22805
• Westra, S. & Sisson, A., 2011, “Detection of non-stationarity in
  precipitation extremes using a max-stable process model”, Journal
  of Hydrology, 406
• Westra, S., Mehrotra, R., Sharma, A. & Srikanthan, S., 2012,
  Continuous rainfall simulation: 1. A regionalised sub-daily
  disaggregation approach, Water Resources Research, 48
  (W01535).
• Westra, S., Evans, J., Mehrotra, R. & Sharma, A., “Disaggregating
  from daily to sub-daily rainfall under a future climate”, submitted
  to Journal of Climate
• Evans, J. & Westra, S., “Investigating the mechanisms of diurnal
  rainfall variability using a Regional Climate Model”, submitted to
  Journal of Climate
                                                                   Slide 44

								
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