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Overview of Urban Drainage Flood Prediction Methods and Approaches J.Y. Chen1 and B.J. Adams2 1. Water Survey Division, Environment Canada 2. Department.

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Presentation on theme: "Overview of Urban Drainage Flood Prediction Methods and Approaches J.Y. Chen1 and B.J. Adams2 1. Water Survey Division, Environment Canada 2. Department."— Presentation transcript:

1 Overview of Urban Drainage Flood Prediction Methods and Approaches J.Y. Chen1 and B.J. Adams  Water Survey Division, Environment Canada 2. Department of Civil Engineering, University of Toronto Good afternoon, our topic of the presentation is “overview of urban drainage flood prediction methods and approaches”, the topic looks quite broad, but we will try to stay focused.

2 Presentation Outline Urban drainage modeling approaches
Analytical model development Model evaluation and comparison Conclusions We start with an introduction to urban drainage modeling approaches, or model classification with emphasis in the development of analytical models.Since some of you may be not quite familiar with the methodologies, we try to paint a broad picture for the analytical model development by sharing our thoughts and experience with you. Model evaluation and comparison is based on a case study of the subcatchment located in Don river watershed. From the results of the case study, we attempt to draw conclusions.

3 Methods for Urban Drainage Flood Prediction
Statistical/stochastic methods Flood frequency analysis Regional flood frequency analysis Time series analysis Deterministic methods Conceptual models Different types of methods or models may be used for urban drainage flood prediction, these methods generally fall into two broad categories, namely, statistical methods and deterministic methods. For the statistical methods, flood frequency analysis may be used at a gauged site to estimate flood quantiles with a specified return period, regional flood frequency analysis can be used for an ungauged site; time series analysis includes auto regression analysis, stepwise regression analysis, etc. An important type of deterministic model is conceptual model, which attempts to simulate major components of rainfall-runoff physical processes

4 Deterministic Conceptual Modeling Methods
Water budget Model inputs Model structure Runoff routing Model calibration Conceptual models normally consist of two major components, namely water budget and runoff routing components, the water budget component establishes water balance among rainfall, evaporation, soil moisture content, and different runoff components, the routing component reflects the damping effect of catchment storage. This type of model is normally characterized by the water budget component. In model applications, significant involvement is needed in model calibration and verification. Model verification Prediction

5 Approaches Used for This Study
Design storm approach Simulation approach Continuous simulation Event simulation Derived probability distribution approach Design storm, simulation and derived probability distribution approaches are used for this study. Depending on the time scale is used, simulation may be further divided into event and continuous simulation. Nevertheless, different approaches may have its advantages and disadvantages. For example design storm approach cannot consider antecedent soil moisture condition, and continuous simulation is usually considered more accurate, but time consuming.

6 Analytical 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 The key element in the development of analytical model is the rainfall-runoff transformation, based on which the probability distributions of the rainfall characteristics are mathematically transformed to create the probability distributions of system outputs. such as, runoff volume and peak flow rates. We will give you some highlights of these transformations

7 Rainfall-Runoff Transformation
Runoff coefficient based Extended form

8 Rainfall-Runoff Transformation (Cont’d)
Infiltration based

9 Analytical Model Statistic analysis of rainfall records
Rainfall characteristics, e.g., rainfall event volume, duration & interevent time Overflow Storage facility Exceedance probability of a runoff spill volume Probability distributions of rainfall characteristics Rainfall-runoff transformation Average annual volume of spills Average annual number of spills PDF or CDF of runoff event volume Expected value of runoff event volume Average annual runoff volume Average annual runoff control efficiency

10 Rational Method Peak flow rate Runoff volume Storage Post-development
Pre-development peak Tbase=2tc or 2.67tc

11 Simulation Models Event simulation models Continuous simulation model
OTTHYMO (Canada) HEC-HMS (US) SWMM (US) Continuous simulation model SWMM

12 Case study: Don River Watershed

13 Harding Park stormwater detention facility

14 Model Calibration Rational method OTTHYMO

15 Model Calibration (Cont’d)

16 Calibration of Analytical Models

17 Model Verification

18 Model Verification (Cont’d)

19 Conclusions 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

20 Conclusions (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

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