HWRS2015_Ayre-Paper_99-REVISED_FINAL

Reconciliation of Design Flood Estimates for the Brisbane River
Catchment Flood Study
R. Ayre
Technical Director, Aurecon, Brisbane, Australia
E-mail: Rob.Ayre@aurecongroup.com
F.L.M. Diermanse
Expert Researcher, Deltares, Delft, The Netherlands
E-mail: ferdinand.diermanse@deltares.nl
D.G. Carroll
Director, Don Carroll Project Management, Brisbane, Australia
E-mail: don.carroll@optusnet.com.au
P. Hart
Principal Consultant – Royal HaskoningDHV, Sydney, Australia
E-mail: paul.hart@rhdhv.com
L. Toombes
Senior Engineer, Aurecon, Brisbane, Australia
E-mail: Luke.Toombes@aurecongroup.com
The State of Queensland initiated a comprehensive hydrologic assessment as part of the Brisbane
River Catchment Flood Study (BRCFS) in response to the devastating floods of January 2011 and
subsequent recommendations of the Queensland Floods Commission of Inquiry. The goal of the
hydrologic assessment is to produce a set of competing methods for estimating design floods in the
Brisbane River catchment. These techniques include; Flood Frequency Analysis (FFA), the standard
Design Event Approach (DEA) as outlined in Australian Rainfall and Runoff (EA, 2003) and the
application of Monte Carlo Simulations (MCS).
The three methods were applied to estimate design flows throughout the 13,500km2 catchment for two
different scenarios: ‘no-dams’ and ‘with-dams’ conditions. With the Flood Frequency Analysis method,
statistics of peak flows and flow volumes are estimated directly from observed flow records. The
Design Event Approach and Monte Carlo Simulation methods both rely on rainfall statistics in
combination with a runoff-routing model to compute peak flows and flow volumes at locations of
interest. The Design Event Approach is a more traditional rainfall-based method which relies on a
number of simplifications including the application of uniform rainfall over the catchment and the
assumption of AEP neutrality. Monte-Carlo Simulation removes many of the limitations common to
Design Event methodologies.
In the reconciliation process that was used to assimilate the design flood estimates from the various
methods that were applied, significant effort was spent on obtaining mutual consistency in results for
the three methods and on the validation of the methods using data records. Furthermore, estimated
design flows were verified extensively for mutual consistency between results of different locations.
The paper describes the three estimation methods used and the approach adopted for the
reconciliation process that has achieved the desired project outcome.
1. INTRODUCTION
The Queensland Floods Commission of Inquiry Final Report (QFCOI, 2012) contained a
recommendation (Recommendation 2.2) that required a flood study be conducted of the Brisbane
River catchment. In accordance with this recommendation, the State of Queensland is managing the

Reconciliation of Design flood Estimates for the Brisbane River Catchment Flood Study Toombes
HWRS 2015 Diermanse, Carroll, Hart, Ayre and Toombes 2 of 9
conduct of this study in a number of separate phases. The second phase consists of a comprehensive
hydrologic assessment. The objective of this assessment is to produce a set of competing methods to
provide best estimates of a range of flood flows across the entire Brisbane River system. The study
includes the implementation of three different estimation techniques for 22 locations situated within the
Brisbane River catchment. Refer to Figure 1 for details of the specified locations where design flood
estimates were derived.
This paper describes the methods used to derive the design flood estimates and the reconciliation
process adopted to determine the desired study outcomes. All of the approaches used in this study
have been developed independently of any previous investigation.
2. FLOOD PROCESSES IN THE BRISBANE RIVER CATCHMENT
The main factors that influence flood levels in the Brisbane River catchment are:
1. Rainfall depth: Rainfall is the driving force of flood events
2. Event duration: High flood flows in the Lower Brisbane River are typically associated with
periods of rainfall lasting between three to five days whereby several hundreds of millimetres
fall over most of the contributing catchment.
3. Spatial-temporal distribution of rainfall: This distribution is relevant because it determines
for example, the percentage of rainfall falling upstream of the major mitigation dams, and the
co-incidence of flood peaks from the tributaries and runoff percentages
4. Antecedent soil moisture conditions (initial losses): The antecedent soil moisture
conditions strongly affect initial rainfall losses and, hence, runoff percentages.
5. Reservoir volumes: The reservoir capacity at the commencement of a rainfall event has a
significant influence on the volume of floodwater that can be safely stored for the purpose of
flood peak attenuation by the mitigation dams of Somerset and Wivenhoe.
6. Moreton Bay (Ocean) water levels: High ocean water levels can increase flood levels in the
Lower Brisbane River and Lower Bremer River.
There are considerable challenges associated with capturing the influence of the main flood forcing
factors in a realistic manner for this catchment, given the spatial and temporal aspects of the rainfall
and the location of the main mitigation dams in relation to the downstream tributaries and urban
centres which are the focus of the dam operations. The interaction of the various factors results in a
large range of possible design flood estimates due to the variability of key inputs.
An assessment of the uncertainty of the estimates for the locations considered suggest that quite wide
confidence limits are expected for most locations for the more frequently occurring events. The
uncertainty tends to increase with increasing flood magnitude and with the lack of direct evidence for
assessing the accuracy of the estimates, it needs to be recognised that a significant degree of
uncertainty remains with the derived flood frequency curves, particularly for the range of rare to
extreme events. However, some greater reliance can be placed on estimates for locations where there
is consistency between the three independent methods.
3. DESIGN FLOOD ESTIMATION TECHNIQUES
3.1. Flood Frequency Analysis
Flood frequency analysis (FFA) uses statistical analysis of recorded flood characteristics to estimate
the magnitude of floods of a selected probability of exceedance. The procedures are typically applied
to peak discharges. They may sometimes be applied to flood volumes or even maximum flows over
some time period such as a month, although relatively little evidence is available on appropriate types
of probability distributions in these cases. Flood frequency analysis is dependent upon the assumption
that the variable being examined can be considered to be drawn randomly from a well-behaved
statistical distribution, which is more or less invariant over the period of observations.

Independent at-site flood frequency assessment was undertaken for peak flows at ten primary gauge
and seven secondary gauge locations within the Brisbane River catchment that were considered to
have reliable gauge and rating information, whilst three primary gauge locations were included in the
flood volume at-site flood frequency assessment. These assessments were done for the no-dam
condition. Both the Generalised Extreme Value (GEV) and Log-Pearson III (LPIII) distributions were
fitted along with the 90% confidence interval. The flood frequency estimates of the primary sites were
assessed using FLIKE’s Bayesian inference method with Gaussian prior distributions to include
weighted catchment skew and standard deviation parameters. The weighted catchment skew and
standard deviation were also applied to secondary gauges to improve the overall consistency of the
estimates.
Overall 17 of the nominated 22 sites were assessed using flood frequency analysis techniques as the
remaining sites did not have sufficient record length or record of suitable quality to warrant
consideration. Analysis of the Brisbane River catchment sites identified that the GEV distribution could
usually provide a reasonable representation of the upper or lower tails of the gauge data, but in many
cases when fitted to the full available range of data produced an upper tail that did not appear
consistent with the expected frequency distribution, where the GEV distribution appears to diverge
significantly above 1 in 50 AEP. In most situations the Log-Pearson III distribution provided a good
overall representation of the full data set, as well as being relatively consistent with the design event
and Monte-Carlo simulation methodologies. Since the primary objective of the FFA is to provide a
consistent assessment flows across the range of 1 in 2 to 1 in 100 AEP and to reconcile with other
methods at and above this range, the Log-Pearson III distribution was adopted as the standard
probability function for all gauges.
3.2. Design Event Approach
The Design Event Approach is a rainfall based assessment of flood hydrographs using runoff-routing
models. It is a well-accepted procedure that is described in AR&R (Engineers Australia, 1998). The
primary input into the runoff-routing model is the design rainfall depth and associated temporal
distribution of the rainfall. Loss rates, representing the antecedent condition of the catchment can also
have a significant influence on the resultant design flood estimates.
The design flood estimates were derived using a runoff-routing model of the Brisbane River
hydrological model as developed by Seqwater (2013) which was modified during the course of the
BRCFS by the Aurecon Team. The model is based on the URBS hydrological model (Carroll, 2012a).
URBS is a rainfall runoff-routing networked model of sub-catchments based on centroidal inflows.
Each storage component is conceptually represented as a non-linear storage. Seqwater divided the
Brisbane River catchment into seven distinct sub-catchment models based on review of topography,
drainage patterns, and major dam locations. Seqwater also considered the key locations of interest for
real time flood operations of the dams and the best use of available data including water level gauges.
Refer to Figure 1.
Design rainfall intensities were derived from the latest BoM IFD data (BoM, 2013) for a range of events
extending from 1 in 2 AEP up to the 1 in 100 AEP. The design rainfall frequency curve was extended
by use of CRC-Forge estimates (Hargraves, 2005) up to the limit of creditable extrapolation 1 on 2,000
AEP. Estimates of the Probable Maximum Precipitation (PMP) for each of the reporting locations were
also derived using the Generalised Methods of PMP estimation, GSDM (BoM, 2003a) and GTSMR
(BoM, 2003b). The intermediate design rainfall estimates were then derived by using the interpolation
procedure prescribed in Book VI of AR&R (Engineers Australia, 1998).
Areal Reduction Factors (ARF) were calculated as per AR&R Project 2 (Engineers Australia, 2013) for
the IFD data, whereas CRC-Forge and PMP estimates were derived as areal estimates.
The AEP of the PMP varies in accordance with the catchment area of the location under consideration
based upon the current recommendation contained in AR&R (Engineers Australia, 1998). This meant
that the AEP of the PMP varied from 1 in 80,000 to 1 in 10,000,000 for the sites under investigation. A
combined rainfall frequency curve was developed for each site for a range of storm durations varying
from 3 hours to 168 hours.

Figure 1 Brisbane River Catchment Reporting Locations

Rainfall temporal patterns adopted for the DEA were based on the North-East Coast Zone 3, AR&R
Volume 2 (Engineers Australia, 1987), as well as the GSDM and GTSMR Average Variability Method
recommendations.
Review of loss parameters obtained for the calibration events produced mean initial and continuing
losses of around 60 mm and 2.3 mm/h respectively, however significant variation is observed.
Assessment of historical events does not necessarily produce loss values that are suitable for design
application as:
 The calibration events are rainfall events that produced significant runoff, however there are
likely to be events of similar rainfall on dry catchments where much lower flows occurred. The
events will therefore be biased towards wet catchment conditions
 Design rainfalls and temporal patterns using AR&R methods are not complete storms, and do
not include any lower intensity antecedent rainfall occurring before the main design ‘burst’
Review of the calibration event losses identified that losses in the Lockyer Creek catchment are
typically higher than the mean value while the Stanley and Bremer River catchments are typically
lower. These observations are consistent with known characteristics. Consequently Design Event
losses applied to these catchments have been scaled up and down by a similar amount.
Design event modelling commonly uses higher losses for high AEP events. This is often an effect of
calibration to match design event flows to other data (e.g. flood frequency analysis), however there is
some theoretical justification as large rainfall events have a higher probability of occurring in wet
periods when the higher frequency and intensity of antecedent rainfall are likely to reduce the
infiltration capacity of the catchment.
3.3. Monte-Carlo Simulation
The variability of the flood generating factors in the Brisbane River catchment was captured within the
Monte Carlo Simulation (MCS) framework by establishing the relevant statistical properties, including
mutual correlations of each of the factors. Furthermore, the respective influence of these factors on
flood flows needed to be assessed. The following statistical dependencies (correlations) between
random variables were identified as relevant and have been incorporated in the Monte Carlo
simulations:
a)Spatial and temporal correlation of rainfall. This dependence is taken into account in the BoM
synthetic rainfall patterns, which are incorporated in the Monte Carlo Framework
b)Mutual correlations between antecedent moisture conditions (initial losses) of the various sub-
catchments. These correlations are taken into account in the Monte Carlo simulations using a
Gaussian copula model
c) Correlation between rainfall and ocean water levels. This is modelled with a threshold-excess
logistic model, as provided by AR&R
d)Correlation between rainfall and reservoir volumes. Reservoir volumes at the beginning of high
rainfall events are on average significantly higher than reservoir volumes at any given day. For this
reason, marginal distribution functions of reservoir volumes are based on observed reservoir
volumes at the beginning of high rainfall events. The ‘remaining’ correlation is weak, ie the
correlation between the total rainfall depth of a high rainfall event and the reservoir volume at the
beginning of such an event. The latter is therefore not included in the MCS framework
e)Mutual correlations of initial dam water levels. These are simulated with the skewed student-t
copula model
The MCS framework adopts the same hydrologic model configuration that was adopted for the
simulation of the Design Event Approach. This includes the use of the URBS runoff-routing model and
an initial loss/continuing loss model to determine the rainfall excess. However, stochastic space-time
patterns were utilized in lieu of the BoM temporal patterns for frequently occurring events.

The three components of the MCS framework can be summarized as follows:
1. Pre-processing: a combination of advanced statistical techniques to generate a large set of
realistic and representative synthetic flood events. These events are characterised by rainfall,
antecedent moisture conditions, initial reservoir volumes and ocean water levels
2. Processing: simulation of the synthetic events with a combination of a hydrological model
(URBS) and a reservoir simulation model (RTC tools) to obtain peak discharges and flow
volumes at each location of interest
3. Post-processing: Statistical techniques to combine the results of I and II to derive annual
exceedance probabilities for a range of flood flows across the entire Brisbane River system
The computation scheme provides a joint probability approach for the derivation of design flows and
volumes, taking into account spatial and temporal variation of rainfall over the Brisbane River
catchment and variability of initial losses, reservoir volumes and ocean water levels. The Monte Carlo
framework is implemented in Delft-FEWS (Werner et al., 2013). Delft-FEWS is a component-based
modeling framework that incorporates a wide range of general data handling utilities and open
interfaces to many hydrological and hydraulic models. FEWS is mainly used for flow forecasting, e.g.
by the Environment Agency (UK), the National Weather Service (US) and the Bureau of Meteorology
(Australia), but it is also used for the purpose of operational reservoir management, for example by
Seqwater (Australia).
4. RECONCILIATION PROCESS
4.1. Sources of Flow Estimates
When reconciling the available design flood estimates it is important to recognize the strengths and
limitations of each method.
Flood frequency analysis is an assessment of flows measured directly at the site. The reliability of the
design flow estimates is dependent on the physical and statistical reliability of the available data,
including the accuracy of the flow rating curve, the length of the data record, and the statistical
representativeness of the rated flows in that period of record. Flood frequency analysis is considered
most reliable for frequent flood events. Extrapolation to large and rare events can be strongly
influenced by the presence (or lack of) extreme events in the data record.
The Design Event Approach and Monte Carlo Simulations are both rainfall based methods, which
means flow statistics are based on rainfall statistics in combination with simulations of hydrological
processes and reservoir operations. These approaches have the advantage of allowing changes in
catchment conditions, such as the presence of dams, to be explicitly modelled. The primary advantage
of Monte Carlo Simulations and Design Event Approaches over flood frequency analysis is that the
typically longer record and spatial consistency of rainfall records makes extrapolation to extreme
events more reliable than site-specific stream gauge records. Another advantage is that the
application of hydrologic model enables these approaches to capture effects of physical limits in the
system, such as flow capacities, on flood frequencies.
The Design Event Approach has numerous limitations. It is dependent on hydrologic modelling to
convert rainfall to runoff, which infers assumptions of adopted temporal pattern and spatially uniform
rainfall distribution (uniform with respect to AEP) across the catchment. A fundamental assumption in
this method is that flood AEP is equal to the AEP of the causal rainfall, which is not necessarily the
case. It is necessary to adopt ‘AEP neutral’ losses that are typically higher for frequent events and
which decrease with flood magnitude.
Monte-Carlo Simulation removes many of the limitations common to Design Event methodologies. It
explicitly considers all relevant factors that contribute to flood events, including rainfall depth, spatial
and temporal distribution of rainfall, antecedent soil moisture conditions, initial reservoir volumes and

ocean water levels. Furthermore, the likelihood of combined occurrences of these factors is taken into
account. The AEP of the simulated flows are based on the ordering of all simulated flows which means
that flood AEP is generally not equal to the AEP of the causal rainfall. Monte Carlo Simulations are
particularly advantageous in capturing the joint probability of flooding from the Brisbane River and its
major tributaries (e.g. Bremer River and Lockyer Creek), and from catchment and storm tide flooding.
The method is therefore considered to be especially advantageous for locations along the Lower
Brisbane River. The MCS approach has also the advantage, for the with-dams scenario, of capturing
the influence of varying initial water levels in storages.
4.2. Process for No-dams Condition
In order to reconcile design flow estimates, initial and continuing loss parameters in the DEA and MCS
models were chosen in such a way that DEA and MCS results are as much as possible in accordance
with FFA results for frequent events. This reconciliation procedure is constrained by the requirements
that:
 Loss values need to be consistent with those generally adopted in practice
 Loss values should be relatively consistent (within rational explanation) across sub-
catchments
Sensitivity runs were carried out for the DEA and MCS models to analyze which loss parameters
would provide a good match with FFA results. Subsequently, DEA and MCS runs were carried out for
all locations with the selected loss values and results were compared with FFA estimates. For this
purpose, figures are produced for each location in the catchment, containing:
 Plotting positions of rated flows
 Derived frequency curves of FFA, MCS and DEA
The figures were analyzed extensively to verify whether the frequency curves of MCS and DEA are in
accordance with FFA (and rated flows). Refer to Figure 2 for an example. For locations where this is
not the case, a probable cause was identified and a decision was made on whether the following
needs to be reconsidered:
 The selected loss values for DEA and MCS
 The applied FFA probability distribution function and/or fit method
 The reliability of the series of rated peak flows
 The reliability of IFD curves as used in the DEA and MCS methods
In the end, the approach that produced design flows that are considered most ‘realistic’ was adopted.
In the cases were DEA and MCS methods provided similar results, the MCS method is the preferred
choice. The main reasons are that the MCS method is expected to provide more reliable design flow
estimates for the ‘with-dams’ scenario and also more realistic design flow hydrographs.
There are a few locations for which no (reliable) rated flows are available and, hence, no FFA results
as well. Reconciled estimates therefore in principle should be based on either DEA or MCS results
only. However, this may lead to inconsistencies with reconciled results of nearby locations for which
reconciled design flow estimates were based on FFA results or probability estimates from rated flows.
To improve consistency in peak flows of nearby locations, the rated flows of the nearby locations are
included in the reconciliation process for locations for which no (reliable) rated flows are available.
As a final verification, the flood frequency curves are verified for internal consistency between
locations. For any given AEP, the following Figures are made:
1. Peak flow (Q) versus catchment area (A) for all locations
2. Q/A versus A for various (all) locations
The first relationship should reveal an increasing trend; the second should reveal a decreasing trend.
If this is not the case for some locations, physical characteristics of the specific catchments under
consideration were examined to see if a rational explanation for the behaviour could be identified. If no

such explanation was found, the reconciliation process was repeated until a satisfactory outcome was
achieved.
Figure 2 Brisbane River at Savages Crossing – No Dams Conditions
4.3. Process for With-dams Condition
Flood frequency analysis of stream gauge records for ‘with-dams’ conditions is considered to be of
limited benefit, particularly for the locations on the Brisbane River downstream of Wivenhoe as:
Consistent post-dam data record is limited (approximately 30 years)
The data will not fit a known statistical distribution
Data is influenced by dam operations and therefore not fully homogeneous due to changing
operational procedures
Because of these issues, traditional FFA methods, including calculation of a probability distribution
and the subsequent fitting of confidence limits cannot be conducted. However, rated flows can be
assigned a probability estimate (‘plotting position’) to allow a general comparison with flow probability
estimates from the MCS and DEA approaches. This comparison can only be made for AEP values
above 1 in N, where N is the length of the length of the series of rated flows in years.
It is observed that the existence of the dams result in the following reduction in 1 in 100 AEP design
peak flows:
 Nearly 50% at Somerset Dam and Wivenhoe Dam
 Between 29% and 41% at locations along the Brisbane River downstream of Wivenhoe Dam
 8% at Ipswich
5. SUMMARY
This paper describes the process for deriving reconciled design flows at 22 locations in the Brisbane
River catchment for a range of AEP’s for ‘no-dams’ and ‘with-dams’ conditions based on the results
from three different estimation techniques. For ‘no-dams’ conditions, the reconciled design flows for

the majority of the locations are based on a combination of
 Empirical estimates from rated flows for frequent events
 Flood frequency analysis results for frequent to large events
 Monte Carlo Simulations results for large to extreme events
For ‘with-dams’ conditions, the reconciled design flows for these locations are based on a combination
of:
 Empirical estimates from rated flows for (very) frequent events
 Monte Carlo Simulations results for frequent events to extreme events
These reconciled ‘with-dams’ estimates were successfully validated for spatial consistency by
comparing plots of peak flow versus catchment area and (peak flow/catchment area) versus
catchment area.
6. ACKNOWLEDGMENTS
The authors want to express their gratitude for the valuable comments on the work described in this
paper by representatives of the Queensland Government, by members of the Technical Working
Group and by members of the Independent Panel of Experts, all of whom are involved in the Brisbane
River Catchment Flood Study.
7. REFERENCES
Aurecon, (2015), Recalibration of Hydrologic Model. Brisbane River Catchment Flood Study –
Hydrology Phase, May 2015.
Engineers Australia (1987), Australian Rainfall and Runoff – A guide to flood estimation. Institution of
Engineers Australia.
Engineers Australia (1998), Australian Rainfall and Runoff - A guide to flood estimation, revised edition
1998, Institution of Engineers Australia.
Engineers Australia (2013), AR&R Revision Projects. Project 2 – Spatial Patterns of Design Rainfall:
Collation and Review of Areal Reduction Factors from Applications of the CRC-Forge Method
in Australia FINAL REPORT (P2/S2/012)
Bureau of Meteorology, (2003a), The Estimation of Probable Maximum Precipitation in Australia:
Generalised Short-Duration Method, Bureau of Meteorology, Melbourne, Australia, June 2003,
(39pp)
Bureau of Meteorology, (2003b), Guidebook of the Estimation of Probable Maximum Precipitation:
Generalised Tropical Storm Method, Hydro-meteorological Advisory Service, Bureau of
Meteorology, March 2004
Carroll, D.G. (2012a), URBS (Unified River Basin Simulator) V 5.00 December 2012
Carroll, D.G. (2012b), URBS Monte Carlo Modelling Training Notes
Green, J.H., Johnson, F.M., Xuereb, K., The, C. and Moore, G. (2012), Revised Intensity-Frequency-
Duration (IFD) Design Rainfall Estimates for Australia – An Overview. Engineers Australia
Hydrology and Water Resources Symposium. Sydney.
Hargraves, G, (2005), Final Report Extreme Rainfall Estimation Project, Resource Sciences Centre,
Brisbane, 2005
QFCOI, (2012): Queensland Floods Commission of Inquiry Final Report, 2012,
http://www.floodcommission.qld.gov.au/__data/assets/pdf_file/0007/11698/QFCI-Final-Report-
March-2012.pdf.
Seqwater (2013), Brisbane River Flood Hydrology Models, Seqwater Final Report, December 2013.
SKM (2013), Brisbane River Catchment Dams and Operational alternatives study, SKM, October 2013
Werner, M., Schellekens, J., Gijsbers, P., van Dijk, M., Van den Akker, O., Heynert, K. (2013), The
Delft-FEWS flow forecasting system, Environmental Modelling & Software Volume 40,
February 2013, Pages 65–77.

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