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Making the best with what you have - Design flood estimation with
and without observed data
Steve Muncaster
Associate, Water Technology Pty Ltd
Email: steve.muncaster@watech.com.au
Warwick Bishop
Associate, Water Technology Pty Ltd
Email: warwick.bishop@watech.com.au
Stephen Duggan
Project engineer, Water Technology Pty Ltd
Email: stephen.duggan@watech.com.au
Abstract
Flood risk assessment is underpinned by the analysis of flood event likelihood over a range of flood
magnitude. Generally, flood likelihood is assessed via a range of design flood estimation techniques.
The techniques employed reflect the required outcomes of the investigations (range of flood
magnitudes, peak flow estimates, flood hydrographs, etc) and the available data. Design flood
techniques can be broken into two broad categories: streamflow based and rainfall based. Both
categories of techniques rely on rainfall and streamflow from the site/region of interest to assess the
frequency of the flood events. Often the site/region of interest lacks significant long term streamflow
and rainfall data to enable a “text book” application of design flood estimation techniques.
This paper explores a number of innovative design flood estimation approaches applied for the
assessment of flood likelihoods. These approaches have been tailored to draw efficiently on the
available streamflow and flood information. Underpinning the approaches has been a philosophy of
maintaining consistency, where appropriate, between the various available flood information sources.
Further, these approaches look to assess the uncertainty surrounding design flood estimates.
The paper discusses case studies from the Regional Murray River and Violet Town catchment flood
investigations. The paper provides a board discussion of the impact of data scarcity on flood
investigations.
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INTRODUCTION
The Victoria Flood Management Strategy (DNRE 1998) outlines a risk-based framework for floodplain
management practice. Within this framework, the Victoria Flood Management Strategy (DNRE 1998)
defines flood risk as the product of flood likelihood and flood consequence. This paper explores the
central role of design flood estimation to the evaluation of flood likelihood.
Australian Rainfall Runoff (I.E.Aust 1999) provides a number of design flood estimation approaches.
These approaches form the basis of current practice in Australia. The applicable approach for a given
situation, firstly depends on the flood characteristic(s) required in the evaluation of flood risk. The peak
flow may be the most relevant characteristic for a floodplain with limited storage, whereas flood
volume is pivotal to assess flood behaviour in floodplains with significant available storage. Secondly,
availability of observed flood data, principally streamflow and rainfall data, shapes the choice of the
applicable design flood estimation approach. The efficient use of data from hydrologically similar
catchments can strengthen the reliability of design flood estimates.
This paper is organised into three parts. The first section briefly outlines available design flood
estimation approaches. The second section presents the design flood estimation approaches applied
in two case studies, Murray River Regional Flood Study and Violet Town Flood Study. These two case
studies demonstrate the way in which various influences, discussed above, impact on the selection of
an applicable design flood estimation approach. Finally, key conclusions are presented.
2.
DESIGN FLOOD ESTIMATION APPROACHES
2.1
Overview
Design flood estimation approaches can be categorised as either streamflow-based or rainfall-based.
Doran and Pilgrim (1986) outlined the factors influencing the choice of the most suitable approach for
different situations:
 Length of available streamflow record.
 Length and quality of the available rainfall data upon which the design rainfall estimates
are based.
 Available calibration data for flood hydrograph model.
Design flood estimation has two distinct phases. A development and/or calibration phase determines
estimates of the associated model parameters. The application phase implements the model with the
adopted parameters to calculate the design flood estimates. Tables 2.1 and 2.2 outline the modelling
components in each of the design flood estimation phases, and highlights the differences between the
different approaches.
Table 2.1 Design Flood Estimation: Model Development/Calibration Phase
Component
Option
Modelling
Rainfall Based Approaches
Streamflow Based
Approaches
Approaches
Continuous Hydrologic Modelling
Discrete Event Modelling
Peak Flow Estimation
Model
Observed Time-series Data
Observed Discrete Event Data
Design Rainfall Depths
Observed Series of
over a
Instantaneous Data
Input Data
Potential
Daily
Pluviographic
Evaporation
Rainfall
Rainfall
Daily Rainfall
Pluviographic
range of AEPs
Flood Frequency Analysis
Peak Flow
Rainfall
Flood
Volume
Catchment Characteristics
Model Calibration
Observed Streamflow Time-series
Observed Discrete Flood Hydrograph
Data
Observed Peak Flow
Observed
Series
Peak Flow
Observed
Flood
Series
Volume
Series
Model Parameters
Runoff Generation Model Parameters
Rainfall Loss Model Parameters
Derived by
Calibration
Direct Model
Runoff
Probability Distribution and
Coefficient
Parameters
Instantaneous Peak Flow
Flood Statistics
Runoff Routing Model Parameters
Simulated Streamflow Time-series
Simulated Discrete Flood Hydrograph
Output
_______________________________________________________________________________________
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Table 2.2 Design Flood Estimation: Model Application Phase
Component
Option
Modelling
Rainfall-Based Approaches
Streamflow-Based
Approaches
Approaches
Design
Continuous Simulation
Design Time-series Data
Discrete Event Modelling
Peak Flow Estimation
Flood Frequency Analysis
Design Discrete Event Data for a Selected AEP and Event Duration
Selected
Input
Potential
Rainfall
Rainfall Spatial
Rainfall Spatial
Rainfall
Uniform
Data
Evaporation
Depth
Pattern
Pattern
Temporal
Rainfall
Rainfall
(Catchment Data for use of
Pattern
Spatial
Temporal
Regional Frequency Analysis)
Pattern
Pattern
Uniform
AEP of Interest
Rainfall Depth
Adopted Model
Runoff Generation Model Parameters
Rainfall Loss Model Parameters
Parameter
Runoff
Distribution and Parameters
Coefficient
Runoff Routing Model Parameters
Direct Model Output
Method of Output
Simulated Streamflow Time-series
Simulated Discrete Flood Hydrograph
Simulated Peak Flow for a
for a Selected AEP
Selected AEP
Frequency Analysis
Flood Quantiles
Direct
Analysis
Design Flood
Peak Flow, Flood Volume,
Peak Flow, Flood Volume,
Characteristic(s)
Flood Duration
Flood Duration
2.2
Peak Flow
Peak Flow, Flood Volume
Streamflow Based Approaches
The main characteristic of streamflow-based approaches is the primary reliance on observed
streamflow data for the development of the approach. The approach assumes that a series of
independent observations of flood characteristics (peak flow, flood volume) fits an underlying
probability distribution. Three available approaches include:
 At-Site Analysis - based on data available at site of interest.
 At-Site/Regional Analysis - based on data at site of interest and from other hydrologically
similar sites.
 Regional Analysis - based on data from hydrologically similar sites.
The flood characteristic series may consist of annual maxima (annual series) or independent peak
flows/volumes over a specified threshold (partial series). The use of an annual series is suitable for the
estimation of infrequent floods, while the partial series provides useful estimates for frequent floods
(Laurenson, 1987).
Common to the above approaches is the choice of a suitable probability distribution to describe the
series of peak flows/volumes. The distributions employed range from a simple line-of-best-fit drawn by
hand to complex multi-parameter theoretical probability distributions. The selection of an appropriate
distribution remains an area of contention with “no rigorous analytical proof that any particular
probability distribution for floods is the correct theoretical distribution”(I.E.Aust, 1987).
Discussion in this paper is limited to the use of frequency analysis with an annual series.
At-Site Analysis - Annual Series
ARR99 (I.E.Aust, 1999) recommends the use a Log Pearson Type III (LPIII) distribution (3
parameters) with parameters estimated by the method of moments for use with an annual maxima
series of peak flows. However, this recommendation was not intended to be prescriptive and
designers are encouraged to examine alternative procedures. In addition, ARR87 generally limits atsite flood frequency analysis to the estimation of floods up to the 1% AEP event. No recommendations
are made regarding the appropriate distribution for flood volumes.
Much discussion has centred about the use of the Log Pearson III distribution and the estimation of
associated parameters. Nathan and Weinmann (1992) suggested the use of a two-parameter
distribution if only a short length of streamflow data is available, as the streamflow record contains
insufficient information on which to estimate the third parameter. Cordery and Cloke (1994) showed
the length of streamflow data has a large influence on the determination of the skew. The results,
based on 41 Australian catchments, showed that estimates of skew stabilised when the streamflow
record lengths were greater than 60 years. Even with long record lengths, the occurrence of a large
event may result in a biased estimate of skew. These studies point to the possible errors involved in
extrapolating flood frequency curves.
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Vogel et al. (1993) investigated the suitability of a number of probability distributions for use with
annual peak flow series from 61 Australian catchments. L-moments diagrams were employed to
evaluate the suitability of probability distributions. Results show that the Generalised Pareto (GPA),
log Pearson III (LP3), three-parameter log-normal (LN3), Generalised Extreme Value (GEV), and
Wakeby (WA5) distributions provide satisfactory approximations to the observed peak flow series.
Overall, GPA and LP3 performed best for all regions across Australia. The GPA showed the best
performance on the South-Eastern coast, while GEV lead to better results in winter rainfall dominated
regions such as the South-West coast of Western Australia and Tasmania.
2.3
Rainfall Based Design Flood Estimation
Rainfall-based approaches rely on the ability of a model to convert rainfall into streamflow. This
approach relies on the assumption that the model employed preserves the probabilistic relationship
between the input rainfall and the resultant flood. Rainfall-based design flood estimation approaches
are applied in the absence of sufficient streamflow data for the application of flood frequency analysis,
or where a complete flood hydrograph is an important design requirement.
Peak flow estimation and discrete event modelling deal only with discrete events. Underpinning these
methods is the assumption that a design rainfall event of a certain AEP will result in a design flood
with the same AEP. On the other hand, continuous hydrologic simulation provides a time-series of
simulated streamflow that nominally accounts for antecedent conditions prior to a flood that influence
rainfall losses This simulated streamflow time-series can be subjected to a frequency analysis in order
to obtain design flood characteristics.
Discussion in this paper is limited to the use of discrete event models.
Discrete Event Modelling
Unlike the peak flow estimation methods, discrete event modelling methods produce a complete
design flood hydrograph from a design rainfall event. Hence this method enables the computation of
flood volume and duration. This section discusses the advantages and limitations of the current
discrete event modelling methods. Also, recent improvements to the current discrete event modelling
methods are outlined.
At each step in the development and application of the procedure, a number of parameters are
evaluated. The choice of each parameter can influence the AEP of the resultant flood from a design
storm. Disregarding the interactions between each of the components introduces bias and uncertainty
into design flood estimates (Siriwardena and Weinmann, 1997). ARR87 (I.E.Aust, 1987) outlines
several techniques to aid in the determination of the AEP of a design flood from a design storm with a
given AEP:
 Adoption of median losses from observed rainfall events would result in the AEP of the
rainfall depth and the flood peak discharge to be approximately equivalent.
 Adjustment of rainfall losses so that the AEP of a peak discharge from rainfall based
design estimation equals the AEP of that peak discharge from a flood frequency analysis.
 Consideration of the joint probability of the various factors affecting flood magnitude from a
given rainfall event.
Generally research has focused on two areas:
 Determination of appropriate design rainfall losses and their interaction with design rainfall
spatial and temporal patterns (Hill et al., 1996).
 Estimation of runoff routing model parameters from historical floods and their suitability for
use in design (Kuczera, 1991; Bates et al., 1991).
Design Rainfall and Design Rainfall Losses
The recommended design losses in ARR87 (I.E.Aust, 1987) are based on the development of models
via calibration to large flood events. Little attention has been paid to the loss rates involved with large
rainfall events that have resulted in small floods due to dry antecedent conditions. Hence the loss
rates obtained are biased towards wet catchment conditions (ie. conservatively low). A further problem
identified in ARR87 is the incompatibility between the derivation of rainfall losses from complete
historical flood events and their application to design storms (Hill and Mein, 1996). Design storms do
not represent complete storm events but have been formulated from rainfall bursts within storm
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events. However, rainfall losses obtained from calibration to historical events are for the entire storm
event being modelled. As a result, the losses derived will tend to be conservatively high for design
applications. ARR87 suggests it is possible for the two biased loss estimates (one low, one high) to
compensate each other.
Hill et al. (1996), in a study of rural catchments in South Eastern Australia, found the use of
recommended design losses from ARR87 resulted in overestimation of peak flows over a range of
AEPs for eight out of eleven catchments. The comparison was made with flood estimates from a flood
frequency analysis. Further, this study developed design losses consistent with the derivation of the
design rainfall. In combination with revised areal reduction factors (Siriwardena and Weinmann, 1996)
the new design losses were shown to remove the bias in flood estimates due to the use of the
ARR87(I.E.Aust, 1987) losses.
Runoff Routing Model Parameter Estimation
Runoff routing model parameters are typically estimated via calibration to historical flood events. This
leads to a unique parameter set for each calibration event. A single parameter set, based on an
assessment of the various calibrated parameters, is then adopted for design use. Design storms are
generally much larger than storm events used in calibration. This gives rise to questions regarding the
errors involved in the use of routing model parameters in the estimation of large design floods (Bates
et al., 1993).
Parameter estimates from a study of five catchments in both New South Wales and Western Australia
showed a high level of variability between storm events (Bates et al., 1993). Results revealed
considerable uncertainty in parameter estimates obtained from historical events. The study questioned
the concept of a set of catchment parameters and their use in design flood estimation, particularly
where the required design flood is significantly larger than the available calibration events.
3.
CASE STUDIES
3.1
Violet Town Flood Study
Scope
Significant flood events in 1993 and 1999 resulted in evacuations, property damage, road closures
and associated hardship to the Violet Town community. In response, Strathbogie Shire Council and
the Goulburn Broken Catchment Management Authority commissioned a flood study to determine the
current flood risks and potential flood mitigation measures (Water Technology 2007a).
For the flood study, design flood hydrographs (10, 20, 50, 100, 200 and 500 year ARI events) were
required for Honeysuckle Creek (upstream of the Hume Highway), Long Gully Creek (upstream of the
Hume Highway Road), and for two small sub-catchments upstream of the Hume Highway.
RORB model – Local catchment calibration
A RORB model was employed as the principal tool in the design flood estimation. The calibration of a
RORB model requires the comparison of modelled flood hydrographs with observed flood
hydrographs at streamflow gauge(s) throughout the catchment. One appropriate streamflow gauge
exists on the upper catchment of Honeysuckle Creek (405294A), with an approximate catchment area
of 25 km2 to the gauge. There is also a streamflow gauge downstream of Violet Town on Stony Creek,
however the catchment area at this locations is approximately 6 times larger than the Honeysuckle
Creek catchment to Violet Town. The catchment downstream of Violet Town is also topographically
dissimilar to the catchment upstream of Violet Town. The parameters derived through calibration of
the RORB model to this downstream gauge were considered unlikely to provide representative
parameters for design flood estimates at Violet Town. As a result the RORB model only calibrated to
the Honeysuckle Creek gauge (405294A).
RORB model calibration is preferably undertaken with historical floods of a similar magnitude to those
being developed for design flood estimation. This is to ensure the calibrated RORB model parameters
correctly reproduce the catchment response to rainfall for the range of design flood magnitudes being
considered. However, only three flood events had reliable concurrent streamflow and rainfall data.
These calibration flood events had peak flows ranging from 4.8 m3/s to 8.5 m3/s. Unfortunately,
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streamflow data for the October 1993 flood, the flood of record in Violet Town, was incomplete and
could therefore not be used for calibration purposes.
Overall, reasonably good fits between the observed and calibrated hydrographs are considered to
have been achieved by the RORB model. The modelled peak flows and general hydrograph shape
are in good agreement for all three calibration events. A range of kc values, between 19 and 35, were
found to provide good representation of peak flow and general hydrograph shape for the calibration
events.
It is noted that the flood magnitudes for the calibration events are small compared to the expected
magnitude of the design floods. The kc values determined during the calibration may therefore not be
representative of catchment behaviour during large floods.
RORB model – Regional parameters relationships and scaling
As discussed, only minor flood events were available for calibration and subsequently, the derived kc
may not be representative of large flood events. To improve the reliability of the kc value adopted for
design flood estimation, alternative kc estimates were evaluated. A number of regional kc estimation
equations exist that are based on characteristics such as catchment area or geometry. An additional
estimate of kc for Honeysuckle Creek has been developed by scaling kc based on the ratio of kc and
the average flow distance from the sub-catchment to the outlet (dav), from a calibrated RORB model
of Sevens Creek to Euroa developed by Hill et al. (1996). The Sevens Creek catchment to Euroa is
adjacent to the Honeysuckle Creek catchment and both catchments share similarities in topography
and geology. Table 3.1 displays a comparison between the kc values determined from the RORB
model calibration and those determined from regional estimates for the Honeysuckle Creek
catchment.
Table 3.1 RORB model - kc values
Source
RORB calibration events
Pearse et al (2000)
(kc = 1.25 dav)
ARR99 – Victoria Mean annual
rainfall > 800 mm
(kc= 2.57 A0.45)
Scaled Seven Creeks at Euroa
kc value
19 (1996)
25 (1998)
35 (2000)
19.4
17.1
10.1
Design rainfall losses
The selection of design rainfall losses has a significant impact on the magnitude of design flood
estimates. As the magnitude of the floods employed in calibrating the RORB model are significantly
smaller than the magnitude of design floods required to be estimated, loss values derived from the
calibration are not considered applicable for design flood estimation. Regional loss relationships
developed by Hill et al. (1996) were applied, with a runoff co-efficient of 0.36 determined for the 100
year ARI event. Additionally, the loss values determined by Hill et al. (1996) for Seven Creeks at
Euroa were applied for comparison with a runoff co-efficient of 0.62.
Alternative design flood estimation techniques
The following alternative techniques were also applied:

Hydrological Recipes (CRC-CH, 1996) – ‘Extending a short flow record’
The methodology as outlined in Hydrological Recipes (CRC-CH, 1996) was applied to
develop a relationship between flow and catchment area at Violet Town with the
adjoining Sevens Creek catchment to Euroa. The Sevens Creek catchment to Euroa
is geographically close to the Honeysuckle Creek catchment and topographically
similar, as both are situated in the Strathbogie Ranges.
 Rational Method Peak Flow Estimation
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The rational method for estimation of peak flows on small to medium rural catchments
has been applied to the Honeysuckle Creek and Long Gully Creek catchment as
outlined in ARR 1987.
 Hydrological Recipes (CRC-CH, 1996) – ‘Estimating Extreme Flood Discharges’
The methodology as outlined in Hydrological Recipes (CRC-CH, 1996) was applied
for estimating the magnitude of the 100 year ARI flood at Violet Town. The method is
based on a regression relationship relating catchment area to the magnitude of 1 in
100 year floods developed from approximately 100 sites either side of the Great
Dividing Range in Victoria Rural Catchments.
 October 1993 Flood Estimate
The significant flood event that occurred in October 1993 was provisionally estimated
to be of order of a 100 year ARI flood at Violet Town (GeoEng, 2002). Analyses
undertaken by the GBCMA and GeoEng (2002) deduced flows in Honeysuckle Creek
ranging from 105 to 116 m3/s.
Adopted Design Flood Estimates
As discussed, the provisional estimate of the October 1993 flood was that it was in the order of a
100 year ARI flood. Various hydrological methods were employed in order to gain some guidance on
the expected magnitudes of design flows at Violet Town. Based on the hydrological analysis and
analysis of the October 1993 flood in the hydraulic model, the weight of evidence would appear to
suggest that the October 1993 flood in Honeysuckle Creek was representative of a 100 year ARI
flood. Figure 3.1 displays a comparison of the design peak flow estimates.
In particular, it is considered important to note that the adoption of the scaled Euroa RORB model
parameters provides some confidence that the parameters adopted for Honeysuckle Creek are
broadly representative of the catchment characteristics (topographic relief, geology, vegetation cover)
and regional setting of catchments located in the north-western slopes of the Strathbogie Ranges.
For the reasons outlined above the study team in consultation with the project steering committee
adopted the October 1993 flow estimates developed in this study as representative of a 100 year ARI
flood at Violet Town. The adoption of the October 1993 flood as representative of a 100 year ARI flood
allowed design flood hydrographs to be developed from the RORB model for various other recurrence
interval floods.
Figure 3.1 Violet Town Flood Study – Design peak flows comparison
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3.2
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Murray River Regional Flood Study
Context
The Murray River Regional Flood Study is an investigation of flood behaviour (depth, extent, velocity)
and risk for an area along the Murray River from Dicks/Seppelts Levee Spillway to downstream of
Ulupna Island. The study area includes the towns of Cobram, Barooga and Tocumwal. The study is
being undertaken for the Goulburn Broken Catchment Management authority (GBCMA), Moira Shire
Council (MSC) and Berrigan Shire Council (BSC).
The key aim of the hydrologic analysis was the determination of design flood hydrographs for the
Murray River at Yarrawonga. The estimates of the design flood hydrographs were required for 10 year
to 200 year ARI events.
The observed flood behaviour in this study area is dependent on peak flow, flood volume and duration.
Flood events with similar peak flows but different flood volumes and durations result in different flood
behaviour. Hence consideration of peak flows, flood volumes and durations is an important aspect of
the hydrologic analysis.
The contributing catchment for the Murray River to Yarrawonga is approximately 27,300 km2. The
catchment area can be broken into three main sub-catchments, the Upper Murray River (above Lake
Hume), the Kiewa River and the Ovens River. Flood flows in the study area can arise from varied
contributions from these three sub-catchments.
Rainfall-based approaches require assumptions to be made about the temporal and spatial variation
of rainfall input to a runoff routing model. As discussed, the contributing catchment to the study area is
large, and the assignment of appropriate rainfall temporal and spatial patterns can be difficult and
would be accompanied with a high degree of uncertainty. Hence, the suitability of the rainfall-based
approach is limited for this application.
Streamflow based approaches analyse available streamflow data to assess flood characteristics (peak
flow and volume). Streamflow based approaches rely on the length and reliability of observed
streamflow data. These approaches assess individual flood characteristics (peak flow and volumes)
separately and combine these individual characteristics to yield design flood hydrographs. Streamflow
records for approximately 100 years are available within the study area, thus facilitating the use of
streamflow based approaches for the estimate of events up to the 100 year ARI.
Accordingly, this hydrologic analysis has adopted a streamflow based (flood frequency) approach to
the design flood hydrograph estimation.
Available Data
As discussed, this hydrologic analysis adopted a streamflow based approach. The robustness of the
design estimates from streamflow based approaches relies on the length and reliability of the available
streamflow data. Three annual peak flow data sets are available for the Murray River at Yarrawonga.
These three data sets, for the purpose of this analysis, are referred to as follows:
 Agency gauged data: Available for period 1938 – 2004.
 SRWSC-A: Peak flow from SRWSC/DWR working files. Available for period 1905- 1979.
 SRWSC-B: Peak flow from SRWSC/DWR working files. Available for period 1905- 1979.
The design peak flow estimates from a flood frequency analysis are heavily influenced by the reliability
of streamflow used, and in particular by the large flood events. Figure 3.2 provides a comparison of
peak flows from several large flood events from the three available data sets.
Examination of the SRWSC-A data set reveals several inconsistencies, with the 1974 and 1975 peak
flows being significantly larger than the estimates from the other two data sets. Particularly, the
SRWSC-A 1975 estimate is considerably larger than the other estimates for the 1917 event. The 1917
flood event is considered to be the largest recorded event in terms of peak flow (RWCV et. al. 1986).
Conversely, the SRWSC-A 1917 estimate is considerably less the other estimates. These
inconsistencies in the SRWSC-A record raise concerns over the reliability of this data-set. It was
considered the inconsistencies in the SRWSC-A record were sufficient to set aside this data for the
purpose of the analysis. However, it is noted that definitive examination of the derivation of the dataset is difficult due to the lack of available documentation.
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The agency gauged data is the most recently derived data-set and may contain revisions since the
derivation of the SRWSC-A and SRWSC-B data. However, the documentation of any revisions was
not available. Given that this is the most recent derivation, the agency gauged data was adopted for
the Murray River at Yarrawonga over the period 1938 to 2004.
As significant flood events occurred prior to 1938, inclusion of these flood events in the flood
frequency analysis was considered desirable. The inconsistencies contained in the SRWSC-A raised
concerns over the reliability of this data set. In absence of any other data source, the SRWSC-B data
set was adopted for the period 1905-1937. It is recognised that the reliability of the SRWSC-B data set
is difficult to assess thoroughly. However, the inclusion of the pre -1938 period was considered
worthwhile.
500000
450000
Agency gauged data
400000
SRWSC-A data
SRWSC - B data
Peak flow (ML/d)
350000
300000
250000
200000
150000
100000
50000
0
1917
1931
1955
1956
1958
1970
1973
1974
1975
Flood event
Figure 3.2 Murray River at Yarrawonga: Comparison of peak flows for significant flood events
Further, the 1867 and 1870 flood events have been documented to be as large or larger than the 1917
flood (RWCV et al 1986), however no peak flow estimates are available for these events. The
occurrence of these flood events, 1870 and 1917, underscores the importance of the longer period of
streamflow data in the peak flow frequency analysis. The following section discusses the techniques
available for the inclusion of historical data in the peak flow frequency analysis.
Design flood hydrograph estimation
ARR99 (IEAust 1999) provides a methodology for the derivation of flood hydrographs from frequency
analyses of peak flow and flood volume. This methodology is underpinned by the assumption that a
design flood hydrograph has a peak flow and flood volume with the same ARI.
The key components of the ARR methodology are summarises as follows:
 Peak flow frequency analysis: evaluates the frequency and magnitude of peak flows.
 Flood volume frequency analysis: evaluates the frequency and magnitude of flood
volumes.
 Flood event rank comparison: assesses the relative rank of peak flows and flood volumes
from selected historical events.
 Peak flow – flood volume ratio: determines the peak flow to flood volume ratios from
selected historical and design flood events.
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 Historical flood hydrograph selection: examines historical flood hydrographs with peak flow
– volume ratios similar to the design flood events and selects representative historical flood
hydrographs suitable for use as design flood hydrographs.
 Design flood hydrograph scaling: determines design flood hydrographs by scaling
representative historical flood hydrographs.
Discussion in this paper is limited to the peak flow frequency analysis and peak flow- flood volume
aspects. Further details are provided in Water Technology (2007b).
Peak flow frequency analysis
From the three data sets, a series of annual maximum peak flows was determined at Yarrawonga. A
peak flow frequency analysis involves the fitting of a probability distribution to observed series of
annual maximum peak flows. In this analysis the following probability distributions were trialled:
 Log-Normal Distribution (LP3)
 Generalised Pareto (GP)
It was found that the Generalised Pareto (GP) distribution provided overall the best fit to the peak flow
series at Yarrawonga. As such, the results from the GP distribution are presented. To enable
comparison with the MRFPM study (RWCV et. al. 1986), results from the LP3 distribution are also
provided.
As discussed, no peak discharge data exists or can be derived for the 1870 and 1867 historical flood
events. This typically suggests that these flow events must be excluded from any conventional flood
frequency analysis. However a technique is available whereby ungauged floods may be included in
the analysis. The use of ‘Censored Flows’ allows the inclusion of historical flood events for which no
gauged discharge exists by considering the number of floods in the pre-gauging period greater than a
threshold discharge. This method has been implemented in this analysis to establish the impact of the
1867 and 1870 historic floods on the flood frequency estimates for Yarrawonga. The threshold
discharge chosen was 390,000 ML/d as both floods are generally regarded as being larger than the
1917 event. Table 3.2 displays the design peak flow estimates for the Murray River at Yarrawonga.
Table 3.2 Design peak flow estimates at Yarrawonga (409025)
Average
Recurrence
Interval
(years)
1905-2004
LP3
Distribution
ML/d
1905-2004
GP
distribution
ML/d
1938-2004
GP
distribution
ML/d
1870-2004
GP
distribution
ML/d
1867-2004
GP
distribution
ML/d
MRFPM
1986
(LP3)
ML/d
10
178,000
186,000
152,000
193,000
215,000
--
20
240,000
236,000
190,000
251,000
292,000
235,000
50
334,000
300,000
236,000
328,000
406,000
325,000
100
416,000
346,000
269,000
387,000
445,000
390,000
200
507,000
392,000
300,000
448,000
527,000
--
The LP3 distribution predicts design flood magnitudes at Yarrawonga that are consistent with those
developed as part of the MRFPM (RWCV et. al. 1986) study. The GP distribution predicts significantly
lower estimates than those of the MRFPM study for the 1 in 50 year and greater events. As discussed
above, the GP distribution is considered to fit the observed data significantly better than the LP3
distribution.
Limiting the analysis to the period 1938 to 2004, yields significantly lower design peak estimates for
both the distributions. This is due to the exclusion of the significant flood events in 1906, 1909, 1917,
1922 and 1931. The 100 year ARI GP distribution peak flow increases from 269,000 ML to 346,000
ML/d with inclusion of the 1905-1937 data. This is a 28% increase for the 100 year ARI peak flow
estimate.
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Peak flow – flood volume ratio
The magnitude of the peak flow – volume ratio reflects the peakiness of the flood hydrograph i.e.
higher the ratio the peakier the flood hydrograph. Higher peak flow – volume ratios occurred for the
1917, 1975, 1993 and 1974 events with the lower ratios occurring for the 1939, 1981 and 1990 event.
The mean ratio for 15 historical events plus the 1917 event is 1.43 with the median ratio at 1.33.
Figure 3.3 displays the peak flows – 14 day flood volumes for both historical and design flood events.
The scatter of the historical peak flows – 14 day flood volumes highlights the variation in the
relationship of peak flow to flood volume i.e. hydrograph shape.
320000
1917
300000
1 in 200 year
280000
1 in 100 year
260000
240000
Peak flow (ML/d)
1975
1 in 50 year
220000
1956
200000
1974
1993
180000
160000
1958
1 in 10 year
1973
1996
1952
140000
1 in 20 year
1955
1970
1992
120000
1981
1964
100000
1939
Historical flood events
1990
Design flood events (1938-2004)
80000
80000
100000
120000
140000
160000
180000
200000
Average 14 -day flow (Flood volume) (ML/d)
Figure 3.3 Murray River at Yarrawonga: Peak flow – flood volume
It should be noted that the peak flow – volume ratios for the two significant historical floods, 1956 and
1975, were 1.28 and 1.76 respectively. These ratios differed considerably from the design event ratios
(1.40 – 1.59). The variation in these ratios reflects the nature of these two events. The 1956 event had
a long duration (i.e. lower peak flow – volume ratio) and the 1975 event was of shorter duration with a
high peak flow (i.e. higher ratio). This variation in peak flow – volume ratio compared to design peak
flows, is further highlighted by examination of the approximate ARI for the peak flow and flood
volumes as follows:
 1956 event peak flow ARI ~ 30 year and 14 day volume ARI ~ 50 years
 1975 event peak flow ARI ~ 50 year and 14 day volume ARI ~ 20 years
Discussion
Peak flow estimates at Yarrawonga are highly dependant on the period of record employed in the
frequency analysis. A number of significant flood events occurred during the early period (1905-1937).
The reliability of the peak flow data for the early period (1905-1937) is difficult to establish.
Further, significant water resources development has occurred in the Upper Murray catchment from
the 1930’s on, i.e. construction of Hume Dam in 1930’s and Dartmouth Dam in the 1970’s. It is likely
that this development has reduced the magnitude of flooding, particularly for more frequent events,
say up to 20 year ARI. However, the reduction in flood magnitude for larger events would be limited.
The MRFPM study (RWCV et al 1986) suggests that for the 1917 flood event the impact of Hume Dam
would be negligible.
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Major floods occurred prior to streamflow records, such as in 1870 as mentioned earlier. However,
peak flow estimates for these pre- streamflow record events are not available and hence these events
were not included in the initial frequency analysis. Based on anecdotal evidence that these events
were greater in magnitude than the 1917 flood, use of the ‘Censored Flows’ method shows that peak
design flow estimates would be significantly greater than those derived from the present gauge record,
as shown in Table 3.2. The sensitivity of design peak flow estimates to the range of historic floods
included in the analysis is demonstrated by the inclusion/exclusion of the early record period 19051937, the results of which are shown in Table 3.2.
Given the above factors, there is considerable uncertainty surrounding the peak flow estimates at
Yarrawonga. This study applied a different probability distribution (GP) to observed peak flow series
than used in previous studies. This GP distribution is considered to better fit the observed data and is
considered appropriate for adoption.
4.
CONCLUSION
The two case studies presented highlight the variety of design flood estimation approaches applied to
evaluate flood likelihood within the risk based framework for floodplain management. In each case, the
available data and required design flood characteristics guided the selection of the approach applied.
In the first case study, the use of design flood information from an adjacent catchment aided in the
determination of appropriate rainfall-runoff model parameters. The application of adjacent catchment
design information provided a means to overcome the lack of suitable calibration data.
In the second case study the inclusion of information on historical events, prior to the commencement
of systematic streamflow records refined the design peak flow estimates obtained from a peak flow
frequency analysis. As the included historical events were of considerable magnitude, the design flow
estimates obtained varied significantly.
Further, the application of several independent techniques provides some increased certainty in the
design flood estimates.
5.
ACKNOWLEDGEMENTS
The authors acknowledge the contributions made by the respective steering committees to the two
case studies presented in this paper.
6.
REFERENCES
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Laurenson, E. M. (1987). Back to Basics on Flood Frequency Analysis. Civil Engineering
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