Overview of Urban Drainage Overview of Urban Drainage Flood Prediction Methods and Flood Prediction Methods and

ApproachesApproaches

J.Y. ChenJ.Y. Chen11 and B.J. Adams and B.J. Adams22

1.1.  Water Survey Division, Environment CanadaWater Survey Division, Environment Canada2. Department of Civil Engineering, University of Toronto2. Department of Civil Engineering, University of Toronto

2008. 05. 062008. 05. 06

Presentation OutlinePresentation Outline

Urban drainage modeling approaches

Analytical model development

Model evaluation and comparison

Conclusions

Methods for Urban Drainage Methods for Urban Drainage Flood Prediction Flood Prediction

Statistical/stochastic methodsFlood frequency analysisRegional flood frequency analysisTime series analysis

Deterministic methods

Conceptual models

Deterministic Conceptual Deterministic Conceptual Modeling Methods Modeling Methods

Model inputsModel inputs Water budgetWater budget

Runoff routingRunoff routing

Model calibrationModel calibration

Model verificationModel verification

Model structure

PredictionPrediction

Approaches Used for This StudyApproaches Used for This Study

Design storm approach

Simulation approach

Continuous simulation

Event simulation

Derived probability distribution approach

Analytical Model DevelopmentAnalytical Model Development

Closed-form analytical models are developed

with derived probability distribution theory

Probability distributions of runoff volumes and

peak flow rates can be derived from probability

distributions of rainfall characteristics

Rainfall-Runoff TransformationRainfall-Runoff Transformation

Runoff coefficient based

dd

d

r SvSv

Svv

;

;0

Extended form

dpdpdpdidp

dpdidi

di

r

SvShhSvhh

SvSSvh

Sv

v

;11

;

;0

Rainfall-Runoff Rainfall-Runoff Transformation Transformation (Cont’d)(Cont’d)

Infiltration based

tfSSvtfhSShhSv

tfSSvSSvh

Sv

v

ciwdpciwdpdi

ciwdpdidi

di

r

;11

;

;0

Analytical ModelAnalytical Model

Statistic analysis of rainfall recordsStatistic analysis of rainfall records

Rainfall characteristics, e.g., rainfall event volume,duration & interevent time

Rainfall characteristics, e.g., rainfall event volume,duration & interevent time

Probability distributionsof rainfall characteristicsProbability distributionsof rainfall characteristics

Rainfall-runoff transformationRainfall-runoff transformation

PDF or CDF of runoff event volumePDF or CDF of runoff event volume

Expected value of runoff event volumeExpected value of runoff event volume

Average annual runoff volumeAverage annual runoff volume

Storagefacility

Exceedance probability of a runoff spill volumeExceedance probability of a runoff spill volume

Average annual number of spillsAverage annual number of spills

Average annual volume of spillsAverage annual volume of spills

Average annual runoff control efficiencyAverage annual runoff control efficiency

Overflow

Rational MethodRational Method

Peak flow rate

Runoff volume

Storage

AiQp

Tbase=2tc or 2.67tc

Post-development peak Pre-development

peak

prepostbase QQT2

1R

Simulation ModelsSimulation Models

Event simulation models

OTTHYMO (Canada)

HEC-HMS (US)

SWMM (US)

Continuous simulation model

SWMM

Case study:Case study:Don River Don River WatershedWatershed

Case study:Case study:Don River Don River WatershedWatershed

Harding Park stormwater Harding Park stormwater detention facilitydetention facility

Harding Park stormwater Harding Park stormwater detention facilitydetention facility

Model CalibrationModel Calibration

Rational methodRational method

OTTHYMOOTTHYMO

Model Calibration Model Calibration (Cont’d)(Cont’d)

HEC-HMSHEC-HMS

SWMMSWMM

Calibration of Analytical ModelsCalibration of Analytical Models

Model VerificationModel Verification

Model Verification Model Verification (Cont’d)(Cont’d)

ConclusionsConclusions

Peak flow rates from event simulation models

appear to be lower than the results from

continuous simulation model

Event simulation models appear to be more

conservative than continuous simulation model

for runoff volume estimation

Conclusions Conclusions (Cont’d)(Cont’d)

The closed-form analytical models developed with derived probability distribution theory, are capable of providing comparable results to continuous simulation results

Different models may vary not only in modeling approach, but also in the level of complexity, it can be challenging to select an appropriate model with a desired level of performance