Revised intensity frequency-duration (ifd) design rainfalls estimates for wa - janice green

Revised Intensity-Frequency-

Duration (IFD) Design Rainfalls

Estimates for WA

Janice Green

Bureau of Meteorology 24 October 2012

IFD Revision Team

• Team members

– Cathy Beesley *Chris Lee

– Fiona Johnson *Maria Levtova

– Catherine Jolly *William Tall

– Garry Moore *Max Monahan

– Cynthia The *Damian Chong

– Karin Xuereb *Murray Henderson

*Ceredwyn Ealanta

– Mike Hutchinson (ANU) (Honorary team member)

– University of Western Sydney

– Student contractors

Current IFDs – AR&R87

Current IFDs – AR&R87

Current IFDs – CDIRS On-line

Current IFDs – CDIRS On-line

Current IFDs – CDIRS On-line

Current IFDs – CDIRS On-line

Current IFDs

• Developed by Bureau of Meteorology over 20 years ago

• Used a database comprising information primarily from the Bureau’s network of daily read and pluviograph stations

• Adopted statistical techniques considered appropriate at the time

• Focus of the IFDs was the design of structures on relatively large rural catchments and therefore durations of less than five minutes were not considered necessary.

Current IFDs – Adopted Approach

Aspect ARR87

Data BoM stations only

Record length ~ up to 1983; 7500 daily read > 30 years; 600 pluviographs > 6 years

Frequency analysis Annual maximum series; method of moments; Log-Pearson Type III

Daily to sub-daily Principal Component Analysis

Mapping Subjective (meteorological analysis)

Frequency ARIs 1 year to 100 year

Duration 5 minute to 72 hour (3 day)

Dissemination Maps; HAS; CDIRS on-line

Climate change Stationary climate assumed;

climatic trends negligible effect on IFDs

Choice of variables

Gridding technique

Basic approach

Rainfall data

Series of extreme values

Frequency Analysis

Gridding

Annual Maximum Series

Choice of fitting technique and distribution

Extraction of L-moments

Supplementing of sub-daily statistics

Establishment of data base – Bureau and Water Regs

Quality controlling of data

Dissemination Outputs

Medium

Regionalisation Index rainfall approach

Regions of Influence

Data base

• Bureau of Meteorology Australian Data Archive for Meteorology (ADAM )

– Contains 19711 daily read rainfall stations (both open & closed) for period from 1800 to 2011

– Contain 1467 continuous stations – both pluviograph & TBRG

• Water Act 2007 identified the Bureau’s new responsibilities including collecting and publishing water information

• Water Regulations 2008 provided ready access to rainfall data collected by other organisations

• In particular, data from dense continuous rainfall networks operated by urban water utilities and councils:

– ~350 daily read rainfall stations

– ~2175 continuous rainfall stations

Spatial coverage of daily read rainfall stations

Spatial coverage of continuous rainfall stations

•

Data base

• Finalisation of data base

– Initially December 2010

Data base

• Finalisation of data base

– Initially December 2010

Data base

• Finalisation of data base

– Initially December 2010

– Updated to December 2011

Data base

• Finalisation of data base

– Initially December 2010

– Updated to December 2011

Data base

• Finalisation of data base

– Initially December 2010

– Updated to December 2011

– Updated to March 2012

– ?????

Quality Control

• Previous work had undertaken QCing on a largely manual basis

• Enormous amount of data that needed to be quality controlled

– > 20 000 daily read stations - Bureau

– > 1500 pluviograph stations – Bureau

– ~350 daily read rainfall stations – Water Regulations

– ~2175 continuous rainfall stations – Water Regulations

• Disparate amount and type of QCing undertaken by data providers

• Necessitated automating as much of QCing as possible

• However still required manual QCing using Bureau’s Quality Monitoring System (QMS)

QCing daily read data

• Quality Controlling of daily read data:

– Infilling of missing data

– Disaggregation of flagged accumulated daily rainfall totals

– Detection of suspect data, identification and correction of:

• Unflagged accumulated totals

• Time shifts

– Identification of gross errors - data inconsistent with neighbouring records but not either of the above two categories

– Manual correction gross errors identified as having a high probability of being incorrect

QCing daily read data using QMS

QCing of continuous data

• QCing of continuous rainfall data considerably more complicated:

– significantly more data due to shorter time step

– sparsity of continuous station network means fewer stations with which to compare

– small rainfall depths extremely difficult to QC

• Needed to reduce amount of data to be QC’d to a manageable amount

• Developed approach QC’d the PDS

– 5 x number of years of record

• Durations of:

– 5, 10, 15, 30 minutes

– 1, 2, 3, 6, 12 hours

– 1, 2, 3 days

• Number of PDS values to be QC’d > 1,000,000

QCing continuous data

• Issues with Continuous Rainfall Data

– Time shifts of clock - DINES

– Missed pulses – TBRG

• QCing procedure compared values to:

– AWAP (Australian Water Availability Product) gridded daily rainfall data

– Co-located or nearby daily read stations

– AWS (Automatic Weather Stations)

– Synoptic stations

Trialling of frequency distributions

• Frequency analysis

– Previously adopted Log-Pearson Type III fitted by method of moments

– Used 58 Bureau long-term continuous rainfall stations to trial a range of distributions

• From each of the 58 continuous rainfall stations extracted both the AMS and the PDS

Trialling of frequency distributions

• AMS & PDS extracted for durations of :

– 6, 12, 18, and 30 minutes

– 1, 2, 3, 6, and 12 hours

• Calculated L-moments and fit five distributions:

– Generalised Logistic (GLO)

– Generalised Extreme Value (GEV)

– Generalised Normal (GNO)

– Pearson Type III (PE3)

– Generalised Pareto (GPA).

• Assessed goodness of fit using Hosking and Wallis (1997) goodness of fit measure ZDist

Trialling of frequency distributions

• AMS

– the GEV gave the most acceptable fit for all durations except 3 and 12 hours

– however, with the exception of the GPA, the other distributions also showed acceptable fits

• PDS

– the GPA gave the most acceptable fit for all durations

Extraction of L-moments

• L-moments used to summarise statistical properties of AMS and PDS

– Index rainfall (mean)

– L-skewness

– L-CV

• L-moments expected to be more robust against large outliers in the data, particularly for the higher order moments.

• To reduce uncertainty in the parameter estimates, minimum station record lengths have been adopted

– 30 years for daily rainfall stations and

– 9 years for sub daily rainfall stations.

Estimation of sub-daily rainfalls

• Shift in focus to urban design on small catchments necessitating the provision of IFD estimates for durations as short as one minute

• Far fewer continuous rainfall stations than daily read rainfall stations

Spatial coverage of daily read rainfall stations

Spatial coverage of continuous rainfall stations

•

Derivation of short duration IFDs

• Need a method to improve spatial coverage of sub-daily data

• Most commonly done using information from daily stations

– Statistics of sub-daily data are inferred from those of daily data

• Techniques adopted include:

– Factoring of the 24 hour IFDs

– Principal component analysis (PCA)

– Partial least squares regression (PLSR)

Derivation of short duration IFDs

• However, major weakness of the previously adopted approaches is their inability to account for:

– Variation in record lengths from site to site

– Inter-station correlation

• An approach that avoids these problems is Bayesian Generalised Least Squares Regression (BGLSR)

Approach to be adopted

• Statistics to be derived (predictands) are:

– Index rainfall (mean)

– L-skewness

– L-CV

• Predictors to be used are:

– Location (latitude & longitude)

– Elevation

– Slope

– Aspect

– Distance from coast

– Mean annual rainfall

– Index rainfall, L-skewness & L-CV at 24, 48 & 72 hours

Regionalisation

• Regionalisation recognises for stations with short records

– considerable uncertainty when estimating the parameters of probability distributions and

– short records can bias estimates of rainfall statistics

• Overcome by combining information from multiple rainfall stations

– more accurate estimates of the probability distribution parameters can be made

Regionalisation

• Index rainfall approach adopted to do this (Hosking & Wallis)

• Station point estimates have been regionalised using a Region of Influence Approach (ROI).

• Trialled various approaches => ROIs defined as circle which is expanded until it includes 500 station years of record

• Circular ROIs defined with distance defined in three dimensions

– Latitude

– Longitude

– Elevation

Region of influence

Region of influence

Region of influence

Gridding

• Regionalisation gave estimates of GEV parameters at all station locations

– Combined with the mean of the AMS (index) at that site to estimate rainfall quantiles for any required exceedance probability.

• However IFD estimates required across Australia, not just at station locations.

• Results of the analyses needed to be extended in some way to ungauged locations.

Gridding

• Translation from point to gridded rainfall estimates carried out with thin plate smoothing splines implemented using ANUSPLIN.

• ANUSPLIN (Hutchinson 2007) was chosen to grid the GEV parameters so that IFD estimates are available for any point in Australia.

• GEV parameters are being gridded in ANUSPLIN, as:

– earlier testing showed little difference in quantile estimates if point parameter or point rainfall depths gridded.

– Gridding rainfall parameters gives more flexibility in the choice of exceedance probabilities that can be extracted and

– requires fewer grids to be processed in ANUSPLIN.

• Appropriate level of smoothing chosen through generalised cross validation by minimising the predictive error of the fitted surface

Interpolation – point to grid

MEAN GRID

SCALE GRID

SHAPE GRID

Gridding

Index Alpha

Kappa

Y = 1 in 2 AEP Y = 1 in 5AEP

Y = 1 in 10 AEP Y = 1 in 20 AEP

Y = 1 in 50 AEP Y = 1 in 100 AEP

Interpolation – point to grid

MEAN GRID

SCALE GRID

SHAPE GRID

ANUSPLIN Output Example

Outputs

• Revised IFDs will be provided as depths in millimetres (not intensities)

• Revised IFDs will be provided for standard durations of:

– 1, 2, 3, 4, 5, 10, 15, 30 minutes

– 1, 2, 3, 6, 12 hours

– 1, 2, 3, 4, 5, 6, 7 days

– advice provided for IFDs < 1 minute and > 7 days

Outputs

• Revised IFDs will be provided for standard EY and AEPs of :

– 1EY (1 Exceedance Per Year) } New AR&R probability

– 50%, 20%, 10%, 5%, 2%, 1% AEP } terminology

• Revised IFDs also provided for sub-annual recurrences eg 2EY

• Revised IFDs blended with CRCFORGE estimate to enable smooth curve to be derived to an AEP of 0.05%

Revised IFDs

• Revised IFDs will be disseminated

– In electronic form

– Via new web page accessed from Bureau of Meteorology website

• Release of new webpages in 2 Phases

Phase 1

• Phase 1

– Revised IFDs for single point

– For standard durations & EY/AEPs

– Functionality of Phase 1 web pages => same as current CDIRS web page

– CDIRS and IFD 2012 run in parallel for ~ 6 months

Protoype

Phase 2

• Phase 2

– Multiple locations

– Dynamic map

– Duration range filters

– Uncertainty limits

– Temporal pattern

– Areal reduction factors

– Climate change adjustments

– Non-standard durations & EYs & AEPs

– Rainfall frequency curve to 0.05% AEP

Outputs

• Climate Change

– Revised IFDs will be for current climatic regime

– AR&R Revision Climate Change Research Strategy has been developed

– Objective to identify research priorities to enhance understanding of how projected climate change may alter the behaviours of factors used to estimate design floods

Outputs

• Climate Change

– Identified 5 research themes:

• Rainfall intensity-frequency-duration relationships

• Rainfall temporal patterns

• Continuous rainfall sequences

• Antecedent conditions (including baseflow)

• Simultaneous extremes

– Research to be undertaken over both short term (Stage 1 – one year) and longer term (Stage 2 – four years)

– Stage 1 of first two themes funded by GA

More information….

Leave your business card

Janice Green

(02) 6232 3558

j.green@bom.gov.au

or

ifdrevision@bom.gov.au

Protoype

Protoype

Protoype