ENVIRONMENTAL TRUST FUND

INLAND FLOOD ZONE MAPPING OF UNGAUGED WATERSHEDS

PHASE I – HYDROLOGICAL MODELLING

Anne-Marie Laroche

and

Wissi Mathilde Diramba Gabriel Dumont Nawres Yousfi

Climatic and Hydroscience Laboratory

March 2016

ii

ACKNOWLEDGMENTS

This study was funded by the Environmental Trust Fund of New Brunswick. The authors

wish to thank the Southeast Regional Service Commission for their assistance throughout the

project, especially Mister Sébastien Doiron and Mister James Bornemann for their support

and contributions to the improvement of the study. Our thanks also go to Madam Sabine

Dietz, project manager, Aster Group, who made a significant contribution through her

judicious and relevant comments. We cannot ignore the advice of Madam Annie Daigle and

Mister David Whyte of the Ministry of the Environment and Local Government in the

selection of watersheds to study.

iii

ABSTRACT

Due to its geographic location, the province of New Brunswick is prone to flooding. Indeed,

the sea defines the province to the north, east and south and the province has many rivers.

These floods can occur in several ways, either through overflows along the coast associated

with rising sea levels, either by overflowing rivers and streams in the inland. The

identification of flood zones allows taking proactive measures at the local and provincial

level to prevent potential risks and identify goods and people vulnerable to such events. In

addition, New Brunswick is facing the impact of climate change. The population of New

Brunswick will be faced with more changes in weather conditions in the near future. For the

province, the climate models have shown that the air temperature will be higher and extreme

precipitation events will occur more often. This could lead to more frequent flooding

episodes. The dissemination of information regarding flood zones to citizens as well as data

collection increases the knowledge of the effects of climate change and flooding.

The objective of this project is to update the information associated with the floodplain maps

which sometimes date back more than 30 years. This project must take place in three phases.

The first phase is to develop a method to simulate flood hydrographs for ungauged

watersheds in the Southeast region of New Brunswick using a hydrological model. The

second phase will be to calibrate a hydraulic model, and the last phase will consist in the

creation of a Geographical Information System (GIS) to map inland flood zones. These

endmost two steps will run from 2016 to 2018.

This report contains details of the methodology and results obtained during the first phase;

modelling of flood hydrographs for ungauged watersheds in the Southeast region of New

Brunswick. This document also contains an application of different scenarios of rainfall. To

do this, the HEC-HMS model was calibrated on distinct ungauged watersheds from

hydrological processes measured on gauged watersheds.

Keywords: HEC-HMS - Watershed - Hydrological modelling - Flood hydrograph

TABLE OF CONTENTS

ACKNOWLEDGMENTS .................................................................................................... I

ABSTRACT ........................................................................................................................ III

LIST OF TABLES ............................................................................................................. VI

LIST OF FIGURES ........................................................................................................... VI

INTRODUCTION ................................................................................................................ 1

MATERIALS AND METHODS ......................................................................................... 2

DATA USED .......................................................................................................................... 2

WATERSHEDS STUDIED ........................................................................................................ 2

GEOGRAPHICAL DATA .......................................................................................................... 2

HYDROLOGICAL MODELLING ............................................................................................... 4

Calibration and validation ............................................................................................ 10

PERFORMANCE PARAMETERS ............................................................................................. 11

METHODS OF REGIONALIZATION ........................................................................................ 12

RESULTS AND DISCUSSION ......................................................................................... 13

SIMULATED HYDROGRAPHS – GAUGED WATERSHEDS ...................................................... 13

REGIONALIZATION OF THE CALIBRATION PARAMETERS ..................................................... 17

SIMULATED HYDROGRAPHS – UNGAUGED WATERSHEDS.................................................. 19

SIMULATION SCENARIOS ................................................................................................... 20

CONCLUSION ................................................................................................................... 24

BIBLIOGRAPHY ............................................................................................................... 25

APPENDIX .......................................................................................................................... 28

LIST OF TABLES

TABLE 1 : PHYSIOGRAPHIC CHARACTERISTICS OF STUDIED WATERSHEDS ............................... 4

TABLE 2 : CALIBRATION PARAMETERS FOR THE HEC-HMS MODEL ....................................... 9

TABLE 3 : RAINFALL-RUNOFF EVENTS USED FOR CALIBRATION AND VALIDATION ................ 11

TABLE 4: CALIBRATED PARAMETERS OF THE HEC-HMS MODEL – TURTLE WATERSHED ..... 14

TABLE 5: CALIBRATED PARAMETERS OF THE HEC-HMS MODEL – COAL BRANCH

WATERSHED .................................................................................................................. 15

TABLEAU 6: CALIBRATED PARAMETERS OF THE HEC-HMS MODEL – PETITCODIAC

WATERSHED .................................................................................................................. 16

TABLE 7: HEC-HMS MODEL PARAMETERS – DORCHESTER WATERSHED ............................. 18

TABLE 8: MODEL PARAMETERS – SHEDIAC WATERSHED ....................................................... 19

TABLE 9: MODEL PARAMETERS –ABOUJAGANE WATERSHED ................................................ 20

TABLE 10: MODEL PARAMETERS –CARTER WATERSHED ....................................................... 20

LIST OF FIGURES

FIGURE 1 : LOCATION OF THE STUDIED WATERSHEDS ............................................................. 3

FIGURE 2 : TYPICAL HYDROGRAPH .......................................................................................... 5

FIGURE 3 : SCHEMATIC OF RUNOFF PROCESSES AT LOCAL SCALE ............................................ 7

FIGURE 4: CALIBRATED AND VALIDATED HYDROGRAPHS OF THE TURTLE WATERSHED ....... 14

FIGURE 5: CALIBRATED AND VALIDATED HYDROGRAPHS OF THE COAL BRANCH WATERSHED

...................................................................................................................................... 15

FIGURE 6: CALIBRATED AND VALIDATED HYDROGRAPHS OF THE PETITCODIAC WATERSHED

...................................................................................................................................... 16

FIGURE 7: RESULTS OF THE REGIONALISATION METHOD ....................................................... 17

FIGURE 8: HYDROGRAPH OBTAINED USING REGIONALIZED PARAMETERS –DORCHESTER ..... 18

FIGURE 9: FLOOD HYDROGRAPHS ACCORDING TO THE RCP SCENARIOS FOR DIFFERENT

RETURN PERIODS – SHEDIAC WATERSHED ..................................................................... 21

FIGURE 10: FLOOD HYDROGRAPHS FOR DIFFERENT RETURN PERIODS – SHÉDIAC WATERSHED

...................................................................................................................................... 22

1

INTRODUCTION

According to the New Brunswick’s Flood Risk Reduction Strategy (Province of New

Brunswick, 2014), for the past five years, the number of financial claims caused by floods

has tripled in New Brunswick compared to the previous five years. Moreover, according to

the Strategy, this trend does not seem to be reversed because, among other things, the

increasing frequency and severity of extreme weather events such as precipitation and floods.

One objective of the Strategy is to achieve accurate identification of the risks of future

flooding at the local scale. Indeed, most maps of existing flood risk in New Brunswick date

back several decades. For this reason, an update map information of flood areas of inland

areas is necessary to take into account the current reality, namely the development of

structures and infrastructure over the last thirty years, but also to integrate technologies and

data on extreme events related to climate change such as precipitation and floods.

The overall objective of this project is to develop a methodology for mapping inland flood

areas of ungauged watersheds in New Brunswick. The methodology is based on an approach

by hydrological and hydraulic modeling. Hydrological models simulate the processes related

to the water cycle, such as precipitation, runoff, groundwater flow, infiltration. Usually, these

models produce results, called output data, which represent the discharge, that is to say, the

volume of water per unit time at the outlet of a watershed. Hydraulic models, for their part,

simulate the discharges in rivers, streams or canals. The results obtained by these models

produce the change in water level in rivers. Some Canadian provinces and several countries

favor this approach to obtain more precise results of the demarcation of flood zones of inland

areas while taking into account the data available for these regions. This report presents the

results of the first phase of the project namely the determination of peak flows and runoff

volumes for ungauged watersheds using a hydrological model. The model was also used to

simulate several scenarios of rainfall.

First, the data used in this study are exposed. Subsequently, the methodology developed for

the flood hydrographs for the ungauged watersheds is presented. Finally, the results of the

simulations and the analysis of different scenarios of storm events complete the report.

2

MATERIALS AND METHODS

Data used

Three categories of data were used in this study. The first one is related to the geometry of

the watersheds. To do this, the Data Catalog of GeoNB platform (Government of New

Brunswick, 2015) was used to download the following vector layers: limits of the regional

service commissions, geology, forests, provincial limit, non-forest areas, New Brunswick

Hydrographic Network, New Brunswick road network, forest soils, wetlands and municipal

areas. The Natural Resources Canada GeoGratis portal (Government of Canada, 2016a) was

used to download the digital elevation model (DEM) at a 1/50k scale.

For data related to the hydrological processes, total daily precipitations were downloaded

from the Environment Canada database for rainfall stations in Moncton, Turtle Creek,

Sussex, Bouctouche, Miramichi and Sackville (Government Canada, 2016b). The HYDAT

database of Environment and Climate Change Canada (Government of Canada, 2016c) was

used to select the daily discharge at the outlet of the studied watersheds. A spatial

extrapolation technic, inverse distance method, was applied to interpolate the total

precipitation on the different territories.

Watersheds studied

The Southeast region of New Brunswick was selected for this study. Specifically, the territory

that is contained within the Southeast Regional Service Commission's limits. Four gauged

watersheds (Turtle Creek, Petitcodiac, Dorchester, Coal Branch) and three ungauged

watersheds (Shediac, Aboujagane, Carter) were selected. Figure 1 shows the studied

watershed boundaries. It should be noted that these watershed boundaries were calculated

using the location of the gauging station for the gauged watersheds and from the edge of the

tide for the ungauged watersheds.

Geographical data

This section presents the geographical data that were collected for the studied watersheds.

They were divided into two categories: geometrical or topographic data and data related to

the river system. The geometrical characteristics that differentiate one watershed to another

are the size, the shape and the relief.

3

Figure 1 : Location of the studied watersheds

The area of a watershed is characterized by its total area while the perimeter is the length of

the contour of the latter. The length of the watershed is a curved distance from the outlet to

the center of gravity of the watershed. The main length of the watershed is a bent distance

from the outlet to the watershed water divide limit. These values were obtained from the

digital elevation model of each watershed.

The shape of a watershed influences the look of the hydrograph at the outlet. There are several

morphological indices that exist to characterize the flow, but also to compare watersheds. In

this study, the Gravelius index of compactness (KG) was used, as noted in equation (1):

𝐾𝐺 =𝑃

2√𝜋∙𝐴 (1)

where : KG : Gravelius index of compactness [-]

P : perimeter of the watershed [km]

A : area of the watershed [km2].

Table 1 summarizes the physiographic characteristics of the studied catchments.

4

Table 1 : Physiographic characteristics of studied watersheds

Watershed Aa

(km2)

Pb

(km)

Lbvc

(km)

Lcd

(km)

KGe

(-)

Turtle 129 74 23 13 1.8

Petitcodiac 413 208 48 40 2.9

Coal Branch 185 100 27 16 2.1

Canaan 667 202 176 199 2.2

Shédiac 158 85 25 12 1.9

Aboujagane 51 48 13 4 1.9

Carter 41 39 13 3 1.7

Dorchester 32 38 9 1 1.8

a : area

b : perimeter

c : watershed length

d : main water course length

e : Gravelius index of compactness

Hydrological modelling

Hydrological modelling is a simplified representation of the hydrological response of a

territory to a precipitating event. In hydrology, this response is represented by a hydrograph,

a graph of flow rate versus time. Typically, it consists of four different parts (Figure 2):

- Groundwater recession curve, slow depletion;

- Rising limb;

- Peak flow;

- Recession curve, rapid depletion.

5

Figure 2 : Typical hydrograph

The hydrograph is obtained from flow rates measured in the field with a gauging station.

These stations allow us to record historical information about the volume of water flowing at

a given location on the river at different times of the year. The hydrograph is an essential tool

to predict the behavior of a watershed during rainfall events by providing peak flow and

volume runoff.

There are several hydrological models that simulate peak flows in watersheds. These models

can be deterministic (relation between input and output data) or stochastic (random-

dependent). These models are based on different mathematical equations that require a lot of

parameters to adjust the hydrograph to the measured values.

There are three major categories of deterministic hydrological models to simulate

hydrographs in watersheds (Kuchement 1971, Beven 1989, Spence et al. 2004, Kampf et al.

2007).

- Empirical models or black box models are based on mathematical equations

establishing relationships between observed and simulated data. No

Dis

char

ge

Time

Recession curveRisinglimbRecession curve

Peak flow

6

information is provided on the equation that the model uses to which the

concept of black box.

- Conceptual models represent the rainfall-runoff relationships using transfers

between reservoirs, regardless of the physical properties of the watershed.

This type of model requires a significant amount of observed and measured

data.

- Physically-based models are based, mostly, on the physical properties of

watersheds and hydrological processes. These models apply mass and energy

transfer equations in watersheds and are calibrated using measurable

characteristics of the watersheds (model parameters).

Based on the description of these types of models and taking into account the objective of

this study, the HEC-HMS model (US Army Corps of Engineers, 2015a) was selected in this

study. It adequately simulates the rainfall-runoff relationship between different types of

watersheds. Moreover, it is a widely used model for floodplain mapping studies. The model

HEC-HMS uses two types of representation of hydrological processes: the empirical model

and the conceptual model. In this study, we turned to the empirical model based upon the

Unit Hydrograph principle. The Unit Hydrograph can determine the rainfall-runoff

relationship without having to consider all the hydrological processes at the watershed scale.

The Unit Hydrograph is defined as the hydrograph resulting from one unit of excess rainfall

generated uniformly over the watershed at a constant rate during a specific period of time.

First of all, watersheds have been delineated using a Geographic Information System

(ArcGIS) (ESRI, 2015) using the extension HEC-GeoHMS (US Army Corps of Engineers,

2015b). This extension prepares the different inputs files needed by HEC-HMS to compute

simulation. Watersheds are represented using sub-watersheds, reaches, junctions and an

outlet. To simulate hydrographs, HEC-HMS calculates hydrological processes using

different methods. For sub-watersheds and reaches, the following methods were selected:

1. Losses: SCS Curve Number method

2. Transform (rainfall-runoff): SCS Unit Hydrograph method

3. Baseflow: Recession method

4. Reach: Muskingum method.

Figure 3 shows, in schematic form, runoff-related processes that are simulated by the HEC-

HMS model. Only part of the precipitation that reaches the study area surface generates

runoff; it is called the rainfall excess. Losses are due to the storage in the depressions of the

soil, to foliage of plants. Subsequently, the model turns this rainfall excess in surface runoff

that will reach streams. Baseflow that comes from groundwater movement also feeds

7

streams. The river discharge is calculated using a routing technic to take into account the

effect of channel storage in a reach section.

Figure 3 : Schematic of runoff processes at local scale

For the SCS Curve Number Loss method, the model requires three parameters, the initial

abstraction, the Curve Number and the percentage of impervious surface. These last two

elements were calculated from the land used data and soil type in the Geographical

Information System. The land used layer (forestry, agriculture, urban, etc.) was obtained from

Natural Resources Canada (Government of Canada, 2016a) while that of soil type comes

from Agriculture Canada (Government of Canada, 2016d). Each curve number is associated

with a use of different soil type. A high value of the Curve Number means a higher runoff

volume. Curve Numbers are divided among four categories base with the soil infiltration

potential. The Curve Numbers were calculated using the soil infiltration potential of each

watersheds associated with land use. The model then calculates the excess rainfall (Equation

2), that is to say, the amount of precipitation that contributes to runoff. This value is

proportional to the maximum retention potential of the subwatershed and the initial

abstraction (water retained in depressions, etc.).

S = 25400−254 CN

CN

Ia = 0.2 S (2)

Precipitation

Territory Ruissèlement

Baseflow

Groundwater

River

Runoff

8

Pe =(P−Ia)2

P−Ia+S

where : S : maximal retention potentiel;

CN : curve number;

Ia : initial abstraction;

P : total precipitation;

Pe : excess rainfall.

The Transfrom function of precipitation into runoff is computed using the SCS Unit

Hydrograph. This function converts the excess rainfall into direct runoff. The only parameter

is the time lag, i.e. the time between the center of mass of excess rainfall and the peak flow

hydrograph. It is calculated using Equation 3 based on the physical characteristics of the sub-

watersheds. This parameter was determined using the Geographic Information System:

𝑇𝐿 =(𝐿𝑓𝑙𝑝∗3280)0,8(

1000

𝐶𝑁−9)0,7

(1900 𝑌)0,5 (3)

where : TL: time lag;

Lflp: longest flow path;

CN: Curve Number;

Y: mean slope of the watershed.

Baseflow was calculated using the exponential recession model indicated by Equation 4:

𝑄𝑡 = 𝑄0𝑘𝑡 (4)

where : Qt : discharge at time t;

Q0 : initial discharge;

k : recession constant;

t: time.

This method requires three input parameters, initial baseflow, constant recession and Ratio

to Peak. Initial baseflow is the total observed discharge before precipitation begins. The Ratio

to Peak represents the ratio of the peak flow to the return to the baseflow. The third parameter

is the constant of recession; it expresses the constant decrease in the baseflow in time.

9

Generally, the baseflow parameters are obtained from historical observations of the

hydrological behavior of the watershed.

The flow in the reach was calculated according to the model of Muskingum. The model

calculates the water storage in a reach by a finite difference method, as shown in Equation 5.

The model computes a volume of water coming from the flood wave by calculating the

storage in the reach.

𝑆 = 𝐾𝑂𝑡 + 𝐾𝑋(𝐼𝑡 − 𝑂𝑡) (5)

où : S : storage in the reach;

K : time of travel;

X : constant;

I et O : inflow and outflow;

t : time.

For each of the presented methods, the model requires parameters that had to be calibrated,

that is to say, adjusted to reproducing the observed hydrograph. Some of the parameters were

calculated from the digital terrain model (DTM) and land use layers using the Geographical

Information System. One parameter has been fixed to a value, due to the nature of watersheds

that are mostly rural watersheds and rivers that have not undergone any human disturbance;

there watercourse is still natural. Table 1 shows all the parameters that were used, and the

initial values set for each of them. The last column within the table shows the parameter

estimation method, whether it was calibrated, measured (MNT) or fixed.

Table 2 : Calibration parameters for the HEC-HMS model

Method Parameter Initial value Unit Estimation

Loss

Ia 20 mm Calibration

CN 70 - MNT

Impervious 5 % MNT

Transform TL 1440 min MNT

Baseflow

Id 0.2 m3/s Calibration

Rc 0.8 - Calibration

Ratio to Peak 0.2 - Calibration

Reach K 0.5 h Calibration

X 0.2 - Fixed

10

Calibration and validation

The HEC-HMS model was calibrated and validated on three gauged watersheds to determine

the best combination of parameters to reproduce the observed hydrograph. All simulations

were performed with a time step of one day. To start the calibration of a watershed, at least

two rainfall-runoff events were selected in the respective databases. The parameters have

been optimized to minimize the error between the simulated and observed volume of runoff

and the difference between the simulated and observed peak flow. These optimizations allow

to calibrate the settings of the parameters so that the volume runoff, and the peak flow of the

simulated hydrograph are as similar as possible to that of the observed hydrograph. The

calibrated parameters were then used on other rainfall-runoff events to validate the

simulations to reflect the observed hydrograph. To ensure that the simulation results are

adequate, performance parameters were applied for each simulation.

The choice of the rainfall-runoff events was made according to the assumptions associated

with the application of synthetic Unit Hydrograph. All rainfall-runoff events used throughout

this study meet the following criteria:

- the excess rainfall is evenly distributed in space and is of constant intensity during

the simulation time interval;

- ordinates of the direct runoff hydrograph are directly proportional to the excess

rainfall;

- equal duration of excess rainfalls produces equivalent hydrograph, regardless of

the intensity of the precipitation.

Table 3 summarizes the rainfall-runoff occurrences that were used during this study. As

mentioned, these events meet the simulation criteria of the selected method.

11

Table 3 : Rainfall-runoff events used for calibration and validation

Watershed Date Total precipitation

(mm)

Peak flow

(m3/s)

Turtle 1993-10-21 to 1993-10-29 72 31.4

1972-10-01 to 1972-10-12 76 19.9

Petitcodiac 1970-07-03 to 1970-07-17 49 19.8

2009-08-25 to 2009-10-03 92 77.0

Coal Branch 1989-08-01 to 1989-08-15 167 83.5

2009-08-27 to 2009-09-07 75 20.8

Dorchester 1983-07-18 to 1983-07-25 82 11.4

1976-07-01 to 1976-07-15 98 13.7

Performance parameters

To assess the performance on the simulation, statistics and graphics parameters were used.

These methods compare the observed to the simulated values computed by the model. The

Nash-Sutcliffe Efficiency coefficient (NSE), the Percent Error in Volume (PEV) and the

Percent Error in Peak flow (PEP) were used to validate the performance of the model HEC

HMS.

The Nash-Sutcliffe Efficiency coefficient is the most used to characterize the performance

of a hydrological model. This method takes into account the average of the observed rates

and the difference between observed and simulated discharges. The optimal value of this

coefficient is “1”, which means that the model perfectly reproduces the observed data.

Knowing that, it is not always easy to get this value with hydrological modelling, scientists

have established criteria based on the value for the Nash-Sutcliffe Efficiency coefficient:

- very good performance: 0.75˂NSE≤1.0;

- good performance: 0.65˂NSE≤0.75;

- satisfactory performance: 0.5˂NSE≤0.65;

- unsatisfactory performance: 0.65˂NSE≤0.5.

Equation 6 shows the mathematical expression of the Nash-Sutcliffe Efficiency coefficient.

𝑁𝑆𝐸 = 1 −∑ (𝑄𝑂(𝑖)−𝑄𝑆(𝑖))

2𝑛𝑖=1

∑ (𝑄𝑂(𝑖)−𝑄𝑂̅̅ ̅̅ )2𝑛𝑖=1

(6)

12

where: NSE : Nash-Sutcliffe Efficiency coefficient;

QO : observed discharge;

QS : simulated discharge;

𝑄𝑂̅̅ ̅̅ : mean observed discharge.

The Percent Error in Volume measures the difference between the observed volume and

simulated volume. The closer the value is to 0, the better the simulation. Equation 7 shows

the mathematical expression of the Percent Error in Volume.

𝑃𝐸𝑉 = ∑(𝑉𝑜(𝑖)−𝑉𝑠(𝑖))

𝑉𝑜(𝑖)𝑛𝑖=1 (7)

where: PEV : Percent Error in Volume;

VO : observed volume;

VS : simulated volume.

The Percent Error in Peak is the difference between the observed peak flows and the

simulated peak flows. For event simulations, it is recommended to use this function (Dawson

et al., 2007). Smaller is the error, higher is the fit between observed and simulated peak flows.

It indicates a good correlation between observed and simulated peak flows. Equation 8 is the

mathematical expression of the Percent Error in Peak.

𝑃𝐸𝑃 =𝑄𝑃𝑆−𝑄𝑃𝑂

𝑄𝑃𝑂 (8)

where: PEP : Percent Error in Peak;

QPS : simulated peak flow;

QPO : observed peak flow.

Methods of regionalization

One of the great difficulties in hydrology is to determine the flood hydrograph in ungauged

watersheds. In order to simulate those hydrographs, a regionalization method was used. This

kind of method assigns values to the parameters of the hydrological model while respecting

flow processes associated with watersheds. To find the best method of regionalization

(inverse distance or linear regression), a correlation analysis was performed to determine

which physical or geometrical characteristic most affects the parameter, i.e. the area of the

sub-watershed, the slope of the watershed, the longest flowpath or the inverse of the distance

between the centroid of watersheds. Thereafter, it was possible to determine the parameter

13

value (Ia, Id, R, Ratio to Peak, K) from the relationships established by the dominant

physiographic or geometric feature.

In order to ensure that the developed method gives reliable results, the regionalization method

was applied to a calibrated watershed used as ungauged watershed. The Dorchester watershed

was selected as the ungauged catchment in order to compare the simulated hydrograph with

the regionalized parameters to the observed hydrograph.

RESULTS AND DISCUSSION

Simulated Hydrographs – Gauged Watersheds

The results obtained from the simulations of the gauged watersheds are presented throughout

this section. Each of the tables (Tables 4, 5, 6) indicates the parameter values for each of the

methods of simulation for the calibration and validation periods. The tables also show the

values of the performance parameters. The Figures 4, 5 and 6 fulfil the presentation of the

results.

For simulations associated with the hydrological processes at the Turtle watershed (Table 4),

the NSE is equal to 97.9% for the period of calibration and 95.2% for the validation one. For

the Coal Branch watershed (Table 5), the NSE corresponds to 95.1% for the period of

calibration and 81.8% for the validation. Finally, for the Petitcodiac watershed (Table 6), the

NSE is equal to 96.4% for the period of calibration and 86.5% for the validation. These values

of NSE indicate that simulations of flood hydrographs by the HEC-HMS model are highly

efficient (Moriasi et al. 2007, Bennett et al. 2013, Chatterjee et al. 2014).

The Percent Error in Volume for the three gauged watersheds ranges between 4% and 10%

for the calibration periods and between -7% and -25% for the validation periods. Negative

values indicate that the model underestimates the volumes while positive values are an

indication of an overestimation of the volumes. However, these performances are satisfactory

when the object to the study is to simulate flood hydrograph (Moriasi et al., 2007).

The PEP (Percent Error in Peak) for the three gauged watersheds varied between 6% and

14% for the calibration period and between -7% and -21% for the validation one. Again, a

negative value indicates an underestimation of peak flows by the model and a positive value

an overestimation of peak flows. Moriasi et al. (2007) report that a performance of ± 25% of

the error (volume or peak flow) is an indication of a satisfactory simulation of the peak flow

and volume.

14

Table 4: Calibrated parameters of the HEC-HMS model – Turtle watershed

Method Parameter Calibration

(October 21 to 29)

Validation (October 01 to 12 1972)

Loss

Ia 10 10

CN 75.48 75.48

Impervious 0.391 0.391

Transform

TL (W40) 212.18 212.18

TL (W50) 450.99 450.99

TL (W60) 434.59 434.59

Baseflow

Id 0.72 0.392

Rc 0.89 0.89

Ratio to Peak 0.318 0.294

Reach K 1 1

X 0.2 0.2

Nash-Sutcliffe (NSE) 0.979 0.952

PEP (peak flow) 0.09 -0.08

PEV (volume) 0.04 -0.07

Figure 4: Calibrated and Validated Hydrographs of the Turtle watershed

15

Table 5: Calibrated parameters of the HEC-HMS model – Coal Branch watershed

Method Parameter Calibration

(August 08 to 15 1989)

Validation (August 27 to September

07 2009)

Loss

Ia 18.5 18.5

CN 74.277 74.277

Impervious 1.5 1.5

Transform

TL (W100) 538.2 538.2

TL (W80) 599.4 599.4

TL (W60) 348.42 348.42

Baseflow

Id 0.22 0.12

Rc 0.8 0.8

Ratio to Peak 0.136 0.152

Reach K 0.9 0.9

X 0.2 0.2

Nash-Sutcliffe (NSE) 0.951 0.818

PEP (peak flow) 0.14 -0.21

PEV (volume) 0.04 -0.20

Figure 5: Calibrated and Validated Hydrographs of the Coal Branch watershed

It is worth mentioning that the review of the flood hydrograph (Figures 4, 5, 6) shows that

the simulated peak flow always occurs at the same moment of the observed peak flow. This

factor is very important when estimating flood zone (Ramirez, 2000).

16

Tableau 6: Calibrated parameters of the HEC-HMS model – Petitcodiac watershed

Method Parameter Calibration

(July 03 to 17 1970)

Validation (August 25 to September

03 2009)

Loss

Ia 14.5 14.5

CN 75.935 75.935

Impervious 1.407 1.407

Transform

TL (W170) 925.15 925.15

TL (W210) 725.95 725.95

TL (W180) 419.72 419.72

Baseflow

Id 0.50 0.67

Rc 0.85 0.85

Ratio to Peak 0.206 0.239

Reach K 0.922 0.922

X 0.2 0.2

Nash-Sutcliffe (NSE) 0.964 0.865

PEP (peak flow) 0.06 -0.07

PEV (volume) 0.10 -0.25

Figure 6: Calibrated and Validated Hydrographs of the Petitcodiac watershed

In light of these results, it is possible to conclude that the HEC-HMS model could reproduce

very satisfactorily flood hydrograph for daily storm events for the three gauged watersheds.

Moreover, despite an underestimation of peak flows and volumes by the model, the results

show good performance. The HEC-HMS model can be used to simulate flood hydrograph

for daily storm events.

17

Regionalization of the calibration parameters

The linear regression method related to the physical characteristics of the watershed is the

one that had the best correlation. Using linear regression equations and the physical

characteristics of the ungauged watersheds, it was possible to determine each input of the

HEC-HMS model parameter. Figure 7 shows the relationship between the parameters (initial

abstraction (Ia), initial baseflow (Rd), recession constant (Rc), travel time (K)) and the most

sensitive physical characteristic (slope, longest flowpath). Table 7 shows the model values

of the parameter for Dorchester watershed.

Figure 7: Results of the regionalization method

After estimating the parameters, the model was tested using two rainfall-runoff events,

namely from July 18th to July 25th 1983 and from July 1st to July 15th 1976. It was impossible

to test the model with more recent events because the gauging station is no longer in service

since 1985. The Nash-Sutcliffe Efficiency coefficient varied between 83% and 95%,

indicating a very good performance from the model.

0

5

10

15

20

0 5 10

Ia

Slope

00.10.20.30.40.50.6

0 10 20 30 40

Id

Longest flowpath

0.880.900.920.940.960.981.001.02

0 5 10

K

Slope

0.78

0.80

0.82

0.84

0.86

0.88

0.90

0 5 10

Rc

Slope

18

Table 7: HEC-HMS model parameters – Dorchester watershed

Method Parameter Value

Loss

Ia 11.22

CN 75.749

Impervious 2.875

Transform TL (W20) 208

Baseflow

Id 0.07

Rc 0.813

Ratio to Peak 0.188

Figure 8: Hydrograph obtained using regionalized parameters –Dorchester

For the Percent Error in Volume, the value ranged between 2% and 23%, which is an

indication of an underestimation of the simulated volumes of water compared to those

observed. However, these results are similar to those obtained from the simulations of the

gauged watersheds. This suggests that the model can reproduce hydrograph with some

confidence.

The results related to the Percent Error in Peak flows showed a variation between -11% and

-25%. Again, as mentioned above, the results of the gauged watershed simulations, the model

performs well to reproduce the peak flows.

Figure 8 shows the results associated with the simulation for 1983 where the black curve

represents the observed flow and the blue one the simulated flow. Note that the simulated

peak flow occurs at the same time as the peak flow observed. This is important when studying

flood zones.

19

Simulated Hydrographs – Ungauged Watersheds

The Tables 8, 9 and 10 show the values of the calculated and regionalized parameters for the

three ungauged watersheds. No simulated hydrograph is presented throughout this section or

any performance parameter. As the three watersheds contain no measured data, it is difficult

to compare simulated and observed values. However, given the results obtained from the

simulations on the three watersheds gauged, as well as those obtained when testing on the

Dorchester watershed, it is possible to rule that the model HEC-HMS manages to produce

flood hydrograph with good to very good confidence (Moriasi et al., 2007). Indeed, the model

could justify, on average, 87% of the simulated hydrograph (see Nash-Sutcliffe Efficiency

coefficient). For the Percent Error in Volume and in Peak, the model tends to underestimate

the volume by 19% and the peak flow by 18%, on average. However, it should be noted that

the shape of the simulated hydrograph is comparable to the observed hydrograph. In addition,

the simulated peak flow occurs at the same time in all the cases studied; two important factors

when studying floodplains (Ramirez, 2000).

Table 8: Model parameters – Shediac watershed

Method Parameter Value

Loss

Ia 10.57

CN 76.133

Impervious 1.77

Transform

TL (W80) 212.18

TL (W100) 307.62

TL (W130) 308.40

TL (W140) 333.00

Baseflow

Id 0.278

Rc 0.81

Ratio to Peak 0.222

Reach K 0.896

X 0.2

20

Table 9: Model parameters –Aboujagane watershed

Method Parameter Value

Loss

Ia 10.058

CN 77.149

Impervious 3.56

Transform TL (W50) 212.18

TL (W40) 250.00

Baseflow

Id 0.188

Rc 0.801

Ratio to Peak 0.213

Reach K 0.89

X 0.2

Table 10: Model parameters –Carter watershed

Method Parameter Value

Loss

Ia 11.87

CN 73.78

Impervious 1.598

Transform TL (W20) 212.18

Baseflow

Id 0.244

Rc 0.820

Ratio to Peak 0.331

Simulation Scenarios

This section presents various rainfall intensity scenarios to be used on the three ungauged

watersheds. The goal is to use the HEC-HMS model to obtain peak flows and volumes of

water for different rainfall intensities. The model was tested using the values of the rainfall

intensities based on scenarios of Representative Concentration Pathways (RCP) established

by scientists of the Intergovernmental Panel on Climate Change (IPCC, 2008). In 2007, the

IPCC has proposed new baseline scenarios of changes in radiative forcing. These scenarios

are based upon the trajectory of changes in concentrations of various greenhouse gases and

land use. The most favorable scenario, RCP 2.6, describes a trajectory that will peak in 2100

and decline thereafter. The RCP 4.5 scenario describes a stabilization of the concentration of

greenhouse gases in 2100. The RCP 8.5 scenario, the worst one, provides a constant growth

of greenhouse gas until 2300. In order to supply the HEC -HMS model, the IDF CC tool

(Srivastav, 2015) was used to generate rainfall intensity associated with the different RCP

scenarios.

21

Only the results for the Shediac watershed are presented within the body of the report. The

reader is referred to the Appendix for cases associated with the Aboujagane and Carter

watersheds. Figure 9 shows flood hydrograph according to the RCP scenarios for different

return periods while Figure 10 shows the flood hydrograph for RCP scenarios, but divided

by return periods. Both figures also show the simulated hydrograph with historical data, that

is to say, the rainfall intensity based upon actual events that have already occurred.

Figure 9: Flood hydrographs according to the RCP scenarios for different return periods –

Shediac watershed

As the overall objective of this project is to map flood zones using mathematical models, we

will remain in a succinct analysis of scenarios related to climate change. However, it is still

possible to briefly analyze the information in Figures 9 and 10. At first glance, the peak flows

and volumes simulated for scenarios for RCP 2.6 and RCP 4.5 change very little to one

another. For each return period, a difference of about 2% occurs much for the value of the

peak flow than that of the volume. For a 100-year return period, simulated peak flow varies

between 125 and 135 m3/s, while the centenary flow simulated using historical data is only

RCP 2.6

RCP 4.5 RCP 8.5

22

94 m3/s, a potential increase of more than 33 %. So one should see, on a map, what this

increase produces as changes within the floodplain zone limit.

Figure 10: Flood hydrographs for different return periods – Shédiac Watershed

In these conditions, this information and analysis remain partial concerning the mapping of

flood zones. Indeed, this work is only the development of the first stage of the proposed

methodology. The aim is to produce peak flow hydrographs and volumes of water using a

hydrological model for ungauged watersheds, taking into account the availability of spatial

23

data, hydrometric data and weather data from the southeast region of New Brunswick. The

results obtained using the HEC-HMS model shows that the project has a great potential in

achieving the creation of flood hazard maps of inland waters since the model performance

indicated results varying between acceptable to very satisfactory for the simulation of flood

hydrograph for both gauged and ungauged watersheds.

24

CONCLUSION

The objective of this study was to develop a method to simulate peak flows and volumes of

water from storm events for ungauged watersheds in the southeastern region of New

Brunswick. The proposed approach consists in the calibration of a hydrological model, HEC-

HMS, on gauged watersheds in order to regionalize the simulation parameters to ungauged

watersheds.

The method presented in this report has achieved this goal for daily precipitation events. It

was also possible to simulate future rainfall scenarios to determine peak flows and volumes

of water generated in a context of climate change. To continue this analysis, other rainfall-

runoff events should be tested for this region to make the model more versatile. The method

could be transposed on an hourly temporal basis, but also on a continuous basis, i.e. simulate

the evolution of rates over several years using an hourly time step. Of course, other

watersheds in the region could benefit from this method to generate flood hydrographs.

The model can also be used to integrate Water Resource Management scenarios such as soil

use change to produce peak flows and associated water volumes. This information becomes

relevant when making decisions about the planning of future land use, such as the

development of new neighborhoods or new roads.

The results obtained from this project will help bridge a lack of information on the

hydrological and climate variability process (rainfall and peak flow) of inland areas within

the province. This information will serve as input data to the hydraulic model to simulate the

change in the water level in a river during a storm event. The generation of these water levels

may subsequently be transposed into flood-prone areas using a Geographical Information

System. In addition, the information generated by the method may be useful for other groups,

such as municipalities’ development services, entrepreneurs or even citizens who are

interested in these issues. In another vein, it will be possible also to establish vulnerability

maps or risk maps related to flooding from the flood zone maps.

25

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27

28

APPENDIX

29

Figure A1: Flood hydrographs according to the RCP scenarios for different return periods –

Aboujagane Watershed

30

Figure A2: Flood hydrographs for different return periods – Aboujagane Watershed

31

Figure A3: Flood hydrographs according to the RCP scenarios for different return periods –

Carter Watershed

32

Figure A4: Flood hydrographs for different return periods – Carter Watershed

33