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Application of Monte Carlo Simulation Technique to Design Flood Estimation:

A Case Study for North Johnstone River in Queensland, Australia

Article  in  Water Resources Management · September 2013

DOI: 10.1007/s11269-013-0398-9

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Water Resources ManagementAn International Journal - Publishedfor the European Water ResourcesAssociation (EWRA) ISSN 0920-4741Volume 27Number 11 Water Resour Manage (2013)27:4099-4111DOI 10.1007/s11269-013-0398-9

Application of Monte Carlo SimulationTechnique to Design Flood Estimation: ACase Study for North Johnstone River inQueensland, Australia

James Charalambous, Ataur Rahman &Don Carroll

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Application of Monte Carlo Simulation Techniqueto Design Flood Estimation: A Case Study for NorthJohnstone River in Queensland, Australia

James Charalambous & Ataur Rahman & Don Carroll

Received: 12 March 2013 /Accepted: 23 June 2013 /Published online: 5 July 2013# Springer Science+Business Media Dordrecht 2013

Abstract The traditional rainfall-runoff modelling based on the Design Event Approach hassome serious limitations as this ignores the probabilistic nature of the key flood producingvariables in the modelling except for rainfall depth. A more holistic approach of design floodestimation such as the Joint Probability Approach/Monte Carlo simulation can overcomesome of the limitations associated with the Design Event Approach. The Monte Carlosimulation technique is based on the principle that flood producing variables are randomvariables instead of fixed values. This allows accounting for the inherent variability in theflood producing variables in the rainfall-runoff modelling. This paper applies the MonteCarlo simulation technique and hydrologic model URBS to a large catchment with multiplepluviograph and stream gauging stations. It has been found that it is quite feasible to applythe Monte Carlo simulation technique to large catchments. The Monte Carlo simulationtechnique has much greater flexibility than the Design Event approach and can provide morerealistic design flood estimates with multiple scenarios, which is likely to replace the DesignEvent Approach. The method developed here can be applied to other catchments in Australiaand other countries.

Keywords Floods . Monte Carlo Simulation . URBS . Australian Rainfall and Runoff .

Rainfall runoff modelling . Joint Probability approach

Water Resour Manage (2013) 27:4099–4111DOI 10.1007/s11269-013-0398-9

J. CharalambousBrisbane City Council (BCC), GPO Box 1434, Brisbane QLD 4001, Australia

J. Charalambouse-mail: james.charalambous@brisbane.qld.gov.au

A. Rahman (*)School of Computing, Engineering and Mathematics, University of Western Sydney, Sydney, Australiae-mail: A.Rahman@uws.edu.au

D. CarrollDon Carroll Pty Ltd, Queensland, Australia

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1 Introduction

Flood is one of the worst natural disasters that cause loss of human lives, livestock andcrops, damage to houses, roads and other infrastructures, disruption to communication andother services, and increased river erosion, sediment and nutrient loads in the rivers. Annualspending on infrastructure requiring flood estimation in Australia is about $1 billion whilethe average annual flood damage cost is about $400 million. Episodes of serious floodingcan affect large areas, such as experienced in the 2010–11 season in Queensland, whenflooding impacted around 70 % of the State and resulted in total damage to publicinfrastructure estimated to exceed $5 billion (PWC 2011).

Due to global climate change (resulting from greenhouse effects), the severity andfrequency of floods and associated damage would increase significantly at many locationsin the near future in Australia (Bureau of Meteorology 2012). Hence, development of a moreaccurate flood risk assessment technique will be of a greater benefit to the Australianeconomy as this will help to reduce the overall flood damage cost. Reliable flood estimationmethods provide the foundation for living with floods, which will be much more needed innear future under extreme weather regime (Zaman et al. 2012).

Flood risk assessment is widely used in water resources management e.g. hydrologicalmodelling, design of hydraulic structures, flood plain management, and environmental andecological studies (Haddad and Rahman 2012; Haddad et al. 2012). The currently recom-mended [by Australian Rainfall and Runoff (ARR), Engineers Australia (I. E. Aust.)]rainfall-based design flood estimation method known as the Design Event Approach hassome serious limitations (Weinmann et al. 1998; Rahman et al. 2002a,b; Nathan et al. 2003;Kuczera et al. 2006; Svensson et al. 2013). The key assumption involved in this approach isthat the annual exceedance probability (AEP) of the input rainfall depth can be preserved inthe final flood output by selecting representative design values of other inputvariables/model parameters (such as initial loss and temporal pattern) at different steps inthe rainfall-runoff modelling (IE Aust 1987). The success of this approach is cruciallydependent on how well this assumption is satisfied.

There are no definite guidelines on how to select the representative values of the inputvariables/model parameters that are likely to convert a rainfall depth of a particular AEP tothe design flood of the same AEP. There are many methods to determine an input value, thechoice of which is largely dependent on various assumptions and preferences of theindividual designer. Due to the non-linearity of the transformation in the rainfall-runoffprocess, it is generally not possible to know a priori how a representative value for an inputshould be selected to preserve the AEP of the input rainfall depth in the final flood output.Furthermore, this problem is exacerbated when input variables such as initial loss are subjectto wide variability (Rahman et al. 2002a).

The arbitrary treatment of the probabilistic aspects of various flood-producing variables,as done in the current Design Event Approach, can lead to inconsistencies and significantbias in flood estimates for a given AEP and has been widely criticised (e.g. Muzik 2002;Heneker et al. 2002; Rahman et al. 2002a; Kuczera et al. 2006; Gioia et al. 2008; Kjeldsen etal. 2010; Cecilia et al. 2011). Other researchers working on improvements to specificcomponents of the current approach have noted that their efforts were largely unsuccessfulbecause of interactions with other components, which the existing method could not dealwith satisfactorily (Kjeldsen et al. 2010).

A significant improvement in design flood estimates can be achieved through rigoroustreatment of the probabilistic aspects of the major input variables/model parameters in therainfall-runoff models. This can be done through a Joint Probability Approach, which uses

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probability-distributed input variables/model parameters and their correlation structure toobtain probability-distributed flood output (Weinmann et al. 1998). An alternative methodbased on copula can also be used in modeling the joint dependence structure of floodvariables as proposed by Reddy and Ganguli (2012). A similar approach can also be usedin joint-probabilistic modelling of water reservoir system as explained in Chen et al. (2013).

While ARR (IE Aust 1987) recommended the Design Event Approach to rainfall-baseddesign flood estimation, it recognised the importance of considering the probabilistic natureof the flood-producing input variables. It thus recommended further investigation into theJoint Probability approaches. For example, Hill and Mein (1996), in a study of incompat-ibilities between storm temporal patterns and losses for design flood estimation, mentioned,“a holistic approach will perhaps produce the next significant improvement in design floodestimation procedures”. Kuczera et al. (2006) mentioned, “Looking to the future, ARR needsto move towards event and total Joint Probability Approaches that are underpinned by arigorous joint probability framework.” Further studies (e.g. Caballero et al. 2011 andLoveridge and Rahman 2012) have highlighted the importance of adopting a Joint Proba-bility Approach in design flood estimation. More recently, the National Committee on WaterEngineering in Australia is advocating the use of the Joint Probability Approach to floodestimation in preference to the Design Event Approach (NCWE 2013).

Rahman et al. (1998) reviewed previous studies (e.g., Eagleson 1972; Russell et al. 1979;Diaz-Granados et al. 1984; Sivapalan et al. 1990; Hromadka 1996) on the Joint Probabilityapproaches to flood estimation and found that most were limited to theoretical studies;mathematical complexity, difficulties in parameter estimation and limited flexibility gener-ally preclude the application of these techniques to practice.

More recently, the Joint Probability Approach has been adopted in flood risk assessmentin a number of international studies. For example, Gioia et al. (2008) proposed a two-component derived distribution based on two runoff thresholds characterized by differentscaling behaviour. Haberlandt et al. (2008) introduced Monte Carlo simulation of meteoro-logical inputs coupled with lumped, distributed or semi-distributed hydrological models inflood modelling. Viglione and Bloschl (2009) investigated the role of critical storm durationin the framework of the Joint Probability Approach. Giola et al. (2011) applied JointProbability Approach to investigate the spatial variability of the coefficient of skewnessand its impacts on flood peak estimation. Iacobellis et al. (2011) tested the Joint ProbabilityApproach in the context of regional analysis by means of an objective jack knife procedure.All these studies have demonstrated that the Joint Probability Approach provided moreaccurate and realistic flood estimates than the Design Event Approach.

The Monte Carlo simulation technique is a simplified version of the Joint Probabilityapproach in that the distribution of the flood outputs can be determined by simulating therainfall-runoff events that are likely to produce floods (Lohkas et al. 1996). The stochasticevents used in the simulation are generated as random combinations of the rainfall and lossinputs, which determine the flood outputs. In each run of the Monte Carlo simulation, a setof input/parameter values is selected by randomly drawing a value from their respectivedistributions (for probability distributed variables) and by choosing a representative valuefor other variables. Any significant correlation between the input variables is allowed for byusing conditional probability distributions. For example, the strong correlation betweenrainfall duration and intensity is allowed for by first drawing a value of duration and thena value of intensity conditioned on that duration interval. The results of the run (e.g. floodpeaks at the catchment outlet) are stored and the Monte Carlo simulation procedure isrepeated a sufficiently large number of times to fully reflect the range of variation ofinput/parameter values in the generated output. The output values of a selected flood

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characteristic (e.g. flood peak) can then be subjected to a frequency analysis to determine thederived flood frequency curve. Monte Carlo simulation technique is widely used in hydrologyto assess uncertainty in the modelling (e.g. Abolverdi and Khalili 2010 and Zakaria et al. 2012).

Rahman et al. (2002a) developed a Monte Carlo simulation technique for flood estima-tion based on the principles of joint probability that can employ many of the commonlyadopted flood estimation models and design data existing in Australia. The proposedtechnique demonstrated enough flexibility for its adoption in practical situations and dem-onstrated the potential to provide more accurate design flood estimates than the existingDesign Event Approach.

A Monte Carlo simulation technique was also adopted by Muzik (2002) where hegenerated rainfall duration and intensity data by allowing a 25 % increase in the mean andstandard deviation of Gumbel distribution of rainfall depth for storm durations from 6 to48 h, and secondly, a 50 % increase in the standard deviation only. The HEC-1 watershedmodel and the soil conservation service runoff curve method for abstractions were used in aMonte Carlo simulation to assess the impacts of climate change on flood frequency curves.

Aronica and Candela (2007) adopted a Monte Carlo procedure to Sicily (Italy) similar toRahman et al. (2002b) and Muzik (2002) for deriving frequency distributions of peak flowsusing a semi-distributed stochastic rainfall-runoff model. The adopted method was based onthree modules: a stochastic rainfall generator module, a hydrologic loss module and a floodrouting module. The procedure was tested on six practical case studies where synthetic floodfrequency curves were obtained starting from model variables distributions by simulating5000 flood events combining 5000 values of total rainfall depth for the storm duration andantecedent moisture conditions. The results showed how Monte Carlo simulation techniquecan reproduce the observed flood frequency curves with reasonable accuracy over a widerange of return periods.

Svensson et al. (2013) adopted a Monte Carlo simulation technique to UK catchments.The adopted method allowed all the input variables to take on values across the full range oftheir individual marginal distributions. These values were then brought together in allpossible combinations as input to a rainfall–runoff model in a Monte Carlo simulationapproach. The developed Monte Carlo simulation technique produced a long string ofevents (on average 10 per year), where dependencies from one event to the next, as wellas between different variables within a single event, were explicitly accounted for. Frequen-cy analysis was then applied to the annual maximum peak flows and flow volumes to obtainderived distributions.

Monte Carlo simulation approaches presented by Rahman et al. (2002a), Muzik (2002),Weinmann et al. (2002), Svensson et al. (2013) and Caballero and Rahman (2013) attempt tosolve the total probability theorem by an empirical procedure by drawing thousands ofrandom events by allowing for correlation among various input variables and then use thesegenerated variable sets to generate flood peaks using a calibrated rainfall runoff model. Inanother approximate Joint Probability Approach, attempts have been made to use the fixedduration and rainfall intensity data and then simulate random values of temporal pattern andlosses to obtain derived flood frequency curves (e.g. Mirfenderesk et al. 2013; Sih et al.2012; Nathan and Weinmann 2013). This approach however does not consider a completerandom variation of all the possible input variables; however, this is useful as it attempts toutilise standard intensity-frequency-duration data to make the method more user-friendlyand easy to apply in practice.

The advantages of the Monte Carlo simulation technique are that this allows examiningthe impacts of many possible combinations of the input variables in hydrologic modelling incontrast to the Design Event approach which considers only one design event. Furthermore,

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the Monte Carlo simulation technique is relatively easier to apply with the industry-basedhydrologic models as compared to the traditional Joint Probability approach (e.g. Eagleson1972), which is too complex to conceptualise for real catchment applications. The MonteCarlo simulation technique has been tested to smaller gauged catchments in Australia (e.g.Rahman et al. 2002a, b, c; Caballero et al. 2011 and Loveridge and Rahman 2012). Thispaper extends the Monte Carlo simulation technique to large catchments having multiplerainfall and stream gauging stations. This paper adopts industry based hydrologic modelURBS (URBS 2013) to simulate the streamflow hydrographs based on the Monte Carlosimulation technique.

2 Method

In the currently recommended Design Event Approach, rainfall duration is regarded as‘fixed’ and pre-determined (IE Aust 1987); however, in the application of the Monte Carlosimulation technique, rainfall duration is taken as a random variable. Consequently, in theapplication of Monte Carlo simulation technique, a new definition is required for stormevents which can produce rainfall events with rainfall duration, intensity and temporalpatterns as random variables. Hoang et al. (1999) and Rahman et al. (2002a) defined a‘complete storm’ (see Fig. 1) in which it is described as the period of significant rainpreceded and followed by an arbitrary selected period of ‘dry hours’ (e.g. 6 h). Other dryhours such as 8 h can also be adopted; however, we adopted 6 h similar to Rahman et al.(2002a). The corresponding storm-core is selected as the period within a complete storm thathas the highest rainfall intensity ratio compared to the 0.40×2-year ARI design rainfallintensity. The rationale of using 0.40×2-year ARI design rainfall intensity as a threshold isexplained in Rahman et al. (2002a) and Hoang et al. (1999), which allows selecting about 2to 8 partial series storm-core events per year on average. If the fraction 0.40 is replaced by ahigher value e.g. 0.60, it would result in a smaller number of storm events per year beingselected and vice versa.

The selected storm-core events are then analysed to identify probability distributions ofrainfall duration, intensity and temporal pattern. We selected 2 to 8 storm-cores on averageper year at a given pluviograph station based on principle of partial duration series where athreshold of rainfall intensity was selected as a value equal to 0.40 times of 2 years ARI

0

1

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9

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1 4 7 10 13 16 19 22 25 28 31 34 37 40 43

Rai

nfal

l int

ensi

ty (

mm

/h)

Time (h)

Start of complete storm

Storm-core

End of complete storm

Fig. 1 Complete storm and storm-core

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rainfall intensity. Finally, the selected storm-cores are analysed to derive probability distrib-uted rainfall duration, intensity and temporal patterns.

A probability distribution was fitted to the storm-core duration (dc) data. The conditionaldistribution of storm-core rainfall intensity (Ic) was expressed in the form of intensity-frequency-duration table following the approach described in Rahman et al. (2002a).

For estimation of losses, concurrent rainfall and streamflow events were selected andlosses are estimated. Here, the initial loss was assumed to be the rainfall that occurred beforethe start of surface runoff hydrograph (Rahman et al. 2002b). The initial loss for completestorm (ILs) was converted to initial loss for storm-core (ILc) using the following equation:

ILc ¼ ILs 0:5þ 0:25log10 dcð Þ½ � ð1ÞTo generate surface runoff, URBS hydrologic model was used (URBS 2013). This model

subdivides a catchment into a number of sub-catchments. To apply the URBS-MCST to largecatchments, it would ideally be necessary to specify probability distributions of the inputvariables at each of the sub-catchments, which is not possible in most of the cases due tounavailability of pluviograph and/or streamflow data at different parts of a catchment. In thisstudy, it was assumed that the distributions of rainfall duration, rainfall temporal pattern andinitial loss remained same over all the sub-catchments; only the distribution of rainfall intensitywas assumed to be different over sub-catchments. Rahman and Carroll (2004) found that thisassumption is likely to introduce insignificant error on the final flood frequency curve.

The adopted URBS-MCST involved the following steps:

1. From the analysis of recorded pluviograph and/or streamflow data, identify the proba-bility distributions of rainfall storm-core duration (dc), intensity (Ic) and initial loss (ILs)and pool the dimensionless temporal pattern (TPc) data.

2. Simulation starts with generation of a dc value from the distribution of dc, which isassumed to be the same for all the sub-catchments.

3. Using the dc value and a randomly selected value of ARI, a value of Ic is generated usingthe IFD table for each sub-catchment. The ARI is assumed to be constant for all the sub-catchments.

4. A random TPc is then selected from the pooled TPc data, based on the value of dc. TheTPc selected is kept constant for all the sub-catchments.

5. The value of ILs is then generated from the ILs distribution, which is assumed to beconstant for all sub-catchments. ILc was estimated from the generated ILs using Eq. 1.

6. To convert point rainfall into areal rainfall, an areal reduction factor is applied based onthe area of the catchment following the approach by Siriwardena and Weinmann (1996).A constant continuing loss (CL) is applied.

The above steps allowed formulation of the ‘rainfall hyetograph’, which was then routedthrough the calibrated URBS model to produce a runoff hydrograph. A design baseflow wasthen added to the generated hydrograph. The hydrograph peak was noted. This procedurewas repeated 10,000 times; the simulated 10,000 flood peaks were then used to obtain thederived flood frequency curve. Here, the ARI of each of the simulated flood peaks wereobtained using Eq. 2.

ARI ¼ N þ 0:2

m−0:4� 1

λð2Þ

where N is the number of simulated peaks, m is the rank and λ is the average number ofstorm-core events per year.

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3 Data Description

To apply the URBS-MCST the North Johnstone River in North Queensland was selected(Fig. 2). This is 1,000 km2 in size and is located in a high rainfall area and damaging floodscan occur during the summer wet season as a result of tropical storms and cyclones.

Continuous rainfall data was sourced from 14 ALERT stations situated across the entirecatchment. Approximately 7 years of continuous rainfall data was available for each station.Continuous streamflow data was obtained at Tung Oil (Fig. 2).

4 Results

The statistics of the storm-core durations (dc) for various alert rainfall stations are listed inTable 1. The observed distribution of dc at Millaa Millaa is illustrated in Fig. 2. Anexponential distribution was fitted to the dc data. A hypothesis test using Anderson DarlingTest showed that the hypothesis of an exponential distribution for the dc data could not berejected at 10 % significance level and hence this was adopted to specify the distribution ofdc data. The study by Haddad and Rahman (2011) and Caballero and Rahman (2013) alsofound that exponential distribution is suitable for specifying the distribution of the dc data forAustralia. The conditional distribution of storm-core rainfall intensity (Ic) was represented inthe form of intensity-frequency-duration (IFD) curves, as shown in Fig. 3.

The temporal patterns (TPc), in the form of dimensionless mass curves, were extracted forall the ALERT rainfall stations. These temporal patterns were then ‘pooled’ from all thestations and were divided into two groups: a) up to 12 h duration and b) greater than 12 hduration. An example of TPc data is presented in Fig. 4.

Fig. 2 Pluviograph and stream gauging locations for North Johnstone catchment in Queensland, Australia

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In estimating the initial loss, catchment average rainfall was estimated by using ALERTrainfall station data and ‘Thiessen Polygon weightings’. The observed initial losses (ILs) forthe North Johnstone River catchments based on 38 observed events range 1 mm to 93 mm,with a mean, standard deviation and skewness of 14 mm, 15 mm and 3.9 mm, respectively.The distribution of ILs is presented in Fig. 5. A four-parameter Beta distribution was fitted to

Table 1 Statistics of the storm-core duration (dc)—Johnstone River Catchment

AlertStation I.D.

ALERT StationName

ObservedEvents

RecordLength

AverageEventsper year

Statistics of Storm-Core Durations(Dc)—hours

Mean Standard Deviation Skew-ness

2500 Millaa Millaa 38 7 5 17 22 1.3

2510 Malanda 40 6 14 19 1.8

2515 Topaz 45 7 6 33 51 3.1

2520 McKell Rd 38 7 5 12 16 1.7

2525 Greenhaven 23 7 3 16 23 1.5

2530 Bartle View 31 7 4 17 27 2.6

2535 Sutties Creek 30 7 4 11 20 2.7

2540 Crawford’s Lookout 29 7 4 19 30 2.5

2545 Menavale 48 7 7 15 21 1.6

2550 Corsi’s 37 7 5 14 21 1.8

2555 Central Mill 45 7 6 14 21 1.9

2560 Nerada 32 7 5 13 18 2.1

2565 Tung Oil 47 7 7 16 22 1.5

2570 Innisfail 51 7 7 15 23 2.3

Average 38 7 5 16 24 2.0

Storm-core duration (dc), hour

70.060.0

50.040.0

30.020.0

10.00.0

Millaa Millaa30

20

10

0

Std. Dev = 22.49

Mean = 16.6

N = 38.00

Fig. 3 Distribution of storm-core durations (dc) for Johnstone River Catchment at Millaa Millaa

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the ILs data. The hypothesis that ILs data followed an exponential distribution could not berejected at 10 % significance level.

A calibrated URBS runoff routing model of the North Johnstone River was obtained fromthe Bureau of Meteorology, Brisbane Office. The URBS model comprised of 42 sub-areasand used the ‘SPLIT’ model routine that routes the sub-catchment runoff and channel runoffseparately.

An IFD table generated from an ALERT rainfall station was assigned to each of theURBS model sub-catchments. Monte Carlo simulation was conducted to simulate 10,000rainfall and streamflow hydrograph events.

Millaa Millaa

1

10

100

1000

1 10 100

Storm-Core Duration (D c ), h

Rai

nfa

ll In

ten

sity

( Ic),

mm

/hr

ARI=500 years10020521

Fig. 4 IFD Curve—Milla Milla ALERT Station

0

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100

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Proportion of storm-core duration

Dim

ensi

on

less

cu

mu

lati

ve r

ain

fall

(%)

Fig. 5 Examples dimensionless temporal patterns for storm-cores

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As can seen in Fig. 6, the derived flood frequency curve compares very well with theobserved flood data for the North Johnstone River catchment except for the smaller ARIs.For the smaller ARIs, flood flows are likely to be affected by baseflow and hence somediscrepancy is not unexpected. Overall, the derived flood frequency curve fits the observedflood data better than the Design Event Approach (Fig. 7).

The result in Fig. 6 highlights that the Design Event Approach is slightly conservative innature for this application particularly at the higher ARIs. The new URBS-MCST approachhas an advantage when compared with the Design Event Approach in that it can simulate the

0

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10

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25

0 to10

11 to20

21 to30

31 to40

41 to50

51 to60

61 to70

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91 to100

Fre

qu

ency

Initial loss (ILs) (mm)

Fig. 6 Distribution of initial loss (ILs)

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7000

1 10 100Average Recurrence Interval (ARI) in years

Flo

w (

m3/

s)

UMCST

Design Event Approach

Observed partial series

Fig. 7 Comparison of URBS-MCST (UMCST) with observed floods and Design Event Approach at NorthJohnstone River Catchment at Tung Oil

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flood frequency curve with stochastic input variables, which does not require the specifica-tion of critical duration and representative values of the model input variables such as initialloss and temporal pattern. The URBS-MCST is, however, more data intensive whencompared with the Design Event Approach. For routine practical applications, the designdata for the URBS-MCST needs to be regionalised. It is expected that the on-going researchon the URBS-MCST will generate necessary design data for the input distributions ofrainfall duration, intensity, temporal pattern and losses for Australian catchments to allowAustralian water industries to apply the URBS-MCST in practice. In this regard, therecommendation by the NCWE (2013) favouring the MCST over the Design Event Ap-proach will play a major role.

5 Conclusion

This paper demonstrates the application of the Monte Carlo Simulation Technique withURBS hydrologic model to larger catchments with multiple rainfall stations and streamgauges. The major advantage of the Monte Carlo Simulation Technique when comparedwith the currently recommended Design Event Approach is its potential and flexibility todealing with ‘uncertainty’ by accounting for the stochastic nature of the input variables inflood modelling. In practice, the new approach can be adopted as a complementary methodto the Design Event Approach, in particular where greater confidence in the final floodestimates warrants the additional data and time needed for the implementation of the MonteCarlo Simulation Technique. For routine application of the Monte Carlo Simulation, thedistributions of rainfall durations, intensity, temporal patterns and losses should be regionalised.The Monte Carlo simulation technique has much greater flexibility than the Design Eventapproach and can provide more realistic design flood estimates with multiple scenarios, whichis likely to replace the Design Event Approach in Australia and other countries.

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