floodplain delineation, land use mapping,...

Floodplain Delineation, Land Use Mapping, Constraint Mapping, and Aquifer Vulnerability Kings County 2050 Report

Tim Webster, Ph.D. Kevin McGuigan Theresa Constantine Smith Kate Collins Applied Geomatics Research Group Submitted to: Monica Beaton David Poole Ben Sivak Scott Quinn Municipality of Kings County October 5, 2012

i Executive Summary

Executive Summary

The Kings 2050 project was undertaken to guide the long-term sustainable development of Kings County for future generations. The

project looks at how Kings County will change in the coming years and what challenges it will face: environmental, economic, and

cultural, and how it will deal with these challenges. This report provides the spatial information necessary to support municipal planners

as they face the challenges of climate change, and as they formulate a plan to find the right balance of land use, population density, and a

sustainable economy in Kings County.

Floodplain boundaries were delineated for Bass Creek, and the Fales, Annapolis, Canard, Cornwallis, Gaspereau, Habitant, and Peraux

Rivers, using a lidar-derived Digital Elevation Model and a cross-section-based hydrodynamic model. The model links meteorological,

hydrometric, and topographic data to generate water flow and stage predictions. Incorporation of tides and precipitation allows for the

generation of geomorphic floodplain boundaries, the natural plane of land that has resulted from past flooding, that would occur more

frequently under possible climate change conditions. Additionally, a GIS-based tool was developed to provide additional planning

capabilities, such that planners have the ability to generate flood plain boundaries in-house. The updated hydrodynamic boundaries,

along with the GIS tool, will allow municipal planners to both manage/minimize property damage from regular, seasonal flooding, and

plan for the extent of flooding in a climate change scenario. Areas vulnerable to storm surges have also been mapped.

A first step towards planning for future development in Kings County is to have an accurate, up-to-date inventory of land cover and land

use. An analysis of satellite images from 2005 and 2010 provided an updated map of forest clear cut areas in Kings County; adding these

new areas to the previous clear cut data provided by the Department of Natural Resources resulted in 135 km2 of clear cut regions. An

inventory of urban development was updated using 2008 orthophotos. Additionally, the urban land use was divided into urban and rural

development using town and village boundaries; urban land use covers 64 km2, and rural development land use covers 134 km2.

Agricultural land use was modified to reflect losses to urban and rural development; the net loss was 13 km2 from 2002.

ii Executive Summary

The flat, low-lying land in the center of the Annapolis Valley is bordered by the North and South Mountains; ridges and smaller valleys

also occur throughout the county. These steep slopes, along with areas classified as having poorly draining soils, are potentially at risk of

landslides. Such areas, defined as having slope >15 %, were identified using lidar data to aid municipal planners in site selection for

potential limiting of future development.

Proper and sustainable management of water resources in Kings County is an important part of growth and development. Aquifer

vulnerability was modelled and maps generated showing the extent of vulnerability of aquifers in the surficial sediments and in the

bedrock. The DRASTIC model used is an acronym for the seven hydrologic conditions used as parameters in the model: Depth to

groundwater, net Recharge by rainfall, Aquifer media, Soil media, Topography, Impact of the vadose zone, and hydraulic Conductivity

of the aquifer. The bedrock and surficial groundwater in the Annapolis Valley found to be most vulnerable to contamination is contained

within the highly productive aquifers located along the valley floor. The surficial aquifers are especially vulnerable due to the porous

soils on the valley floor. This is a concern due to the dense population in that area, which increases the risk of contamination.

iii Executive Summary

Table of Contents

Executive Summary i

List of Figures vii

List of Tables xiv

1 Kings 2050 Project Overview 2

2 Floodplain Analysis and Delineation 4

2.1 Introduction ...................................................................................................................................................................................4

2.1.1 Study Area .............................................................................................................................................................................5

2.1.2 Coastal Flooding ....................................................................................................................................................................7

2.1.3 Fluvial flooding ......................................................................................................................................................................9

2.1.4 Literature Review.................................................................................................................................................................14

2.2 Methods .......................................................................................................................................................................................15

2.2.1 Lidar and DEM ....................................................................................................................................................................15

2.2.2 Topologic-Hydrological Data Processing ............................................................................................................................15

2.2.3 Environmental Data .............................................................................................................................................................16

2.2.4 Watershed Modelling ...........................................................................................................................................................19

2.3 Results .........................................................................................................................................................................................24

2.4 Discussion ...................................................................................................................................................................................35

iv Executive Summary

2.4.1 Floodplain Mapping .............................................................................................................................................................35

2.4.2 Storm Surge Mapping ..........................................................................................................................................................42

2.5 References ...................................................................................................................................................................................44

3 Land Cover and Land Use Mapping 49

3.1 Introduction .................................................................................................................................................................................49

3.2 Data Summary .............................................................................................................................................................................50

3.3 Methods .......................................................................................................................................................................................50

3.3.1 Clear Cut Mapping ...............................................................................................................................................................50

3.3.2 Urban and Agricultural Land Use Updating ........................................................................................................................53

3.4 Results .........................................................................................................................................................................................56

3.4.1 Clear Cut Mapping ...............................................................................................................................................................56

3.4.2 Urban and Agricultural Land Use Updating ........................................................................................................................59

3.5 Discussion ...................................................................................................................................................................................64

3.6 Conclusions .................................................................................................................................................................................68

4 Development Constraint Mapping 70

4.1 Introduction .................................................................................................................................................................................70

4.2 Methods .......................................................................................................................................................................................71

4.3 Results .........................................................................................................................................................................................73

v Executive Summary

4.3.1 2 m DEM..............................................................................................................................................................................73

4.3.2 5 m DEM..............................................................................................................................................................................73

4.4 Discussion and Conclusions ........................................................................................................................................................85

4.5 References ...................................................................................................................................................................................86

5 Groundwater and Surficial Geology Mapping 87

5.1 Introduction .................................................................................................................................................................................87

5.2 Methods .......................................................................................................................................................................................89

5.2.1 Study Area ...........................................................................................................................................................................89

5.2.2 Hydrostratigraphic Units ......................................................................................................................................................91

5.2.3 The DRASTIC Model ..........................................................................................................................................................94

5.3 DRASTIC Parameter Data ..........................................................................................................................................................97

5.3.1 Depth to Water .....................................................................................................................................................................97

5.3.2 Net Recharge ........................................................................................................................................................................98

5.3.3 Aquifer Media ....................................................................................................................................................................100

5.3.4 Soil Media ..........................................................................................................................................................................102

5.3.5 Topography ........................................................................................................................................................................103

5.3.6 Impact of the Vadose Zone ................................................................................................................................................104

5.3.7 Hydraulic Conductivity of the Aquifer ..............................................................................................................................106

vi Executive Summary

5.4 Results .......................................................................................................................................................................................106

5.4.1 Modelled Vulnerability Scenarios .....................................................................................................................................106

5.4.2 Results by Hydrostratigraphic Unit ...................................................................................................................................111

5.5 Discussion .................................................................................................................................................................................113

5.6 Conclusions ...............................................................................................................................................................................117

5.7 References .................................................................................................................................................................................120

6 Overall Conclusions, Discussion, Future Work 123

vii List of Figures

List of Figures

Figure 2.1: The watersheds and rivers being modelled in this study over a shaded relief elevation model. ................................................... 6

Figure 2.2: Short Duration Rainfall Intensity-Duration-Frequency Data for Kentville. Source:

ftp://ftp.tor.ec.gc.ca/Pub/Engineering_Climate_Dataset/IDF/ ....................................................................................................................... 11

Figure 2.3: Flooding of the Gaspereau River in Gaspereau on March 31, 2003. .......................................................................................... 12

Figure 2.4: Flooding in Meadowview in November, 2010 (source: Kings County News,

http://www.kingscountynews.ca/media/photos/unis/2010/12/02/photo_1282047_resize.jpg). .................................................................... 12

Figure 2.5: The suite of collected environmental time series used to drive all the later discussed river flooding models. .......................... 17

Figure 2.6: The distribution of input and intermediate data including the position pertaining to each environmental time series. ............. 18

Figure 2.7: The results of the River Runoff Calibration of the Cornwallis River. River Discharge – Observed (black) and modelled (blue).

Accumulated River Discharge – Observed (red) and modelled (green). The accumulated underestimation of the modelled result, due to

real world discrepancies in Kejimkujik Station and Cornwallis River watershed rainfall events over time, is acceptable, as only model

stage maxima are used in deriving floodplains. ............................................................................................................................................. 19

Figure 2.8 (Left) An illustration of the mathematical model applied to each bed elevation taken from lidar to estimate floodplains. (Right)

An example of various models (linear model as red boxes, root model as gray crosses) being applied to real world elevation data (Blue

dots)................................................................................................................................................................................................................ 22

Figure 2.9 The original modelled tidal levels for the peak simulated flooding event of 2010, Nov 7 (left). A 2.46 m storm surge residual

as applied over 3 tide cycles (right). This data is used to simulate the effect of a significant storm surge to flood delineation in the western

portion of Kings County. ............................................................................................................................................................................... 23

Figure 2.10: The extent of the hydrodynamically derived (Mike 11, hatched lines) and GIS derived floodplains (yellow polygons) for all

river systems. A good correlation was achieved, specifically in downstream floodplain areas, though the GIS method tends to diverge

from the Mike 11 output in the upstream....................................................................................................................................................... 25

viii List of Figures

Figure 2.11: The extent of the Peraux River floodplain. The model bed elevations are displayed per cross-section along with a

comparison of Mike and GIS flood level. ...................................................................................................................................................... 26

Figure 2.12: The extent of the Annapolis River floodplain. The model bed elevations are displayed per cross-section along with a

comparison of Mike and GIS flood level. The orthophoto database does not extend to the full view of the map (grey box). ..................... 27

Figure 2.13: The extent of the Fales River floodplain. The model bed elevations are displayed per cross-section along with a comparison

of Mike and GIS flood level. ......................................................................................................................................................................... 28

Figure 2.14: The extent of the Bass Creek floodplain. The model bed elevations are displayed per cross-section along with a comparison

of Mike and GIS flood level. ......................................................................................................................................................................... 29

Figure 2.15: The extent of the Canard River floodplain. The model bed elevations are displayed per cross-section along with a


Figure 2.16: The extent of the Cornwallis River floodplain. The model bed elevations are displayed per cross-section along with a


Figure 2.17: The extent of the Gaspereau River floodplain. The model bed elevations are displayed per cross-section along with a


Figure 2.18: The extent of the Habitant River floodplain. The model bed elevations are displayed per cross-section along with a


Figure 2.19 The floodplain extent under predicted tide conditions (yellow) and the potential flooding extent in a worst case scenario

storm surge, 2 m on a HHWLT (red). ............................................................................................................................................................ 34

Figure 2.20: The correlation of GIS and Hydrodynamic flood levels is quite good. The divergence of correlation in the headwaters is of

note and does have some markable effect on the final floodplain extent upstream. For linear regression of the hydrodynamic derived

flood levels per bed depth, R² = 0.9838. ........................................................................................................................................................ 35

ix List of Figures

Figure 2.21: The Canard and Cornwallis River hydrodynamically derived floodplains (colored polygons) coincide generally well with the

existing floodplain zoning (hatched lines). .................................................................................................................................................... 37

Figure 2.22: The hydrodynamically derived floodplain (colored polygons) of the Habitant River does show a greater extent in the

headwaters than the existing zoning (hatched lines). ..................................................................................................................................... 38

Figure 2.23: The Grand Pre area and the mouth of the Gaspereau River showing hydrodynamically derived floodplains (colored

polygons) and existing zoning (hatched lines). .............................................................................................................................................. 39

Figure 2.24: Near the area of Caribou Bog, the hydrodynamic model (colored polygons) shows greater discrepancy to the zoning

(hatched lines). ............................................................................................................................................................................................... 40

Figure 2.25: The stability of the Annapolis River (and thus Fales) may have been affected by the Bog near the headwater. ..................... 42

Figure 2.26 Shown is the effect of model output water levels over time, per cross-section of the inclusion of a simulated storm surge

water level. Various cross-sections of the Cornwallis River are shown using an unaltered predicted tide (left) and with an added storm

surge of 2.46 m (right) Lower water levels are indicative of cross-sections which are further downstream. ............................................... 42

Figure 3.1: The steps used to generate a final clear cut layer. Step 1: The DNR layer with clear cuts between 2002 and 2005 was used as

the basis for the new clear cut layer. Step 2: The results of a change detection satellite analysis for Landsat 5 Band 5 images from 2005

and 2010 are added. Step 3: The 2008 orthophoto is added and used as a guide for smoothing out the satellite analysis result. Step 4: The

DNR clear cuts and edited satellite-derived clear cuts are merged to arrive at a final layer of clear cuts up to 2010. The yellow arrows

indicate areas detected by the satellite analysis that have been clear cut since 2008. Step 5: The satellite image is used to verify that the

areas indicated by the yellow arrows have been clear cut by 2010. Step 6: The addition of the ALIP agricultural layer shows some clear

cut ares adjacent to agricultural land. The roads that appear in the orthophotos are likely logging roads, as they are not included in the

provincial roads database. .............................................................................................................................................................................. 52

Figure 3.2: The steps used to generate an updated urban development land use code. Step 1: A visual scan of the DNR Forestry Urban

data from 2002, with the 2008 orthophotos beneath it, reveals an area that has been developed since 2002 (indicated by the yellow circle).

x List of Figures

Step 2: The addition of the DNR Agriculture land use code (2002) shows, in this example, that the new development occurred on land

previously classified as agriculture. Step 3: The new urban development is re-classified, and the urban and agriculture land use codes are

modified accordingly. Step 4: The urban land use code is divided into urban (orange) and rural (yellow) development by the village

boundary. (The yellow colour of the updated urban is used to differentiate the updated urban land use code from the 2002 land use code,

which is coloured blue.) ................................................................................................................................................................................. 54

Figure 3.3: The top left panel shows the final updated agricultural layer overlaying the orthophotos, showing the good fit between the

layer and the photos. The bottom left panel shows the ALIP layer, which includes agricultural crop uses, and shows the mismatch

(coloured dark blue) between the ALIP layer and our DNR-based layer. Assigning the agricultural use attributes from the ALIP layer to

the DNR-based agricultural layer would result in a final agricultural layer that includes awkwardly shaped polygons with no agricultural

use attribute, shown labelled MISMATCH in the right panel. ...................................................................................................................... 55

Figure 3.4: Final clear cut land use code. Includes DNR clear cuts and satellite analysis clear cuts. ........................................................... 57

Figure 3.5: Area of heavy clear cutting between Lake George and Gaspereau Lake. ................................................................................... 58

Figure 3.6: Kings County land classified as Urban and Rural Development showing the town and village boundaries used to separate

urban development from rural. ...................................................................................................................................................................... 60

Figure 3.7: New urban development within the Village of Cornwallis Square (on the left); new rural development (on the right). The blue

polygons represent DNR urban classifications from 2002. ........................................................................................................................... 61

Figure 3.8: DNR 2002 Agriculture land use code (top left), gains and losses to this data caused by updating (bottom left), and the updated

agricultural land use code for all of Kings County (right). ............................................................................................................................ 62

Figure 3.9: Agricultural land loss in the village of Port Williams due to new urban developments such as the subdivision near the center

of the figure, new or recently modified individual properties, or properties that existed but did not appear in the DNR 2002 Agriculture

land use code. ................................................................................................................................................................................................. 63

Figure 3.10: A total of 39 km2 of clear cut land has been mapped since 2005, mainly on South Mountain. ................................................ 66

xi List of Figures

Figure 3.11: New development since 2002 in the Kentville-Wolfville Urban corridor. ............................................................................... 67

Figure 3.12: Final land use map for Kings County. ....................................................................................................................................... 69

Figure 4.1: DEM showing the 2 m lidar coverage used in the first slope constraint mapping calculation (left). The 5 m DEM covers all of

Kings County and is the 2 m lidar-derived DEM averaged to 5 m resolution, merged with the NSTDB 20 m resolution data (right). ...... 71

Figure 4.2: The soil names of the soils categorized as having poor and very poor drainage. ....................................................................... 72

Figure 4.3: Constraint mapping showing percent slope within the lidar coverage. ....................................................................................... 74

Figure 4.4: Constraint mapping showing percent slope within the lidar coverage; town locations, civic points, and poorly drained soils for

the entire region. ............................................................................................................................................................................................ 75

Figure 4.5: Constraint mapping showing percent slope within lidar coverage, civic points, and poorly drained soils for the town of

Wolfville. ....................................................................................................................................................................................................... 76

Figure 4.6: Constraint mapping showing percent slope within lidar coverage, civic points, and poorly drained soils for the Town of

Kentville. ........................................................................................................................................................................................................ 77

Figure 4.7: Constraint mapping showing percent slope for the town of Centerville. .................................................................................... 78

Figure 4.8: Constraint mapping showing percent slope, civic points, and poorly drained soils for the town of Berwick. ........................... 79

Figure 4.9: Slope constraint map generated for the entire area of Kings County using the 5 m lidar and NSTDB merged DEM, shown

overlaying the province-wide hillshade map. ................................................................................................................................................ 80

Figure 4.10: Constraint mapping showing percent slope generated using the 5 m lidar and NSTDB merged DEM, town locations, civic

points, and poorly drained soils for all of Kings County. .............................................................................................................................. 81


points, and poorly drained soils for Kings County south of Wolfville and New Minas................................................................................ 82


points, and poorly drained soils for Kings County south of Kentville, extending west almost to Berwick. ................................................. 83

xii List of Figures


points, and poorly drained soils for Kings County south of Kingston, Greenwood and Aylseford, extending east almost to Berwick. ...... 84

Figure 5.1: The watersheds used in this study (black outline) differ slightly from those used in the Floodplain Mapping study (filled

polygons, see Chapter 2). This study includes only the Annapolis, Cornwallis, Canard, Peraux and Habitant Rivers; additionally, the

watersheds that border the shoreline have been divided near the shore. One of those sub-divided sections is known as the Bass Creek

watershed in the Floodplain Mapping Study. Additionally, the Fales and Gaspereau Rivers are not included in this study, and there is a

difference in the Annapolis and Cornwallis boundaries north of Berwick. ................................................................................................... 90

Figure 5.2: Bedrock hydrostratigraphic units (HSU) within the study area. Major HSUs of interest include the Triassic sandstone (Ss),

shale (Sh), and conglomerate (Cg) (Wolfville Formation) and the Triassic-Jurassic siltstone (Si), shale (Sh), and sandstone (Ss)

(Blomidon Formation). Other HSU include the locally productive Devonian-Carboniferous sandstone (Ss), siltstone (Si), and shale (Sh)

(Horton Group), the Jurassic North Mountain Basalt (NMB), the Cambrian-Early Devonian slate (Sl) and quartzite (Qz), and Late

Devonian South Mountain Batholith (SMB) (from Blackmore 2006). ......................................................................................................... 92

Figure 5.3: Hydrostratigraphic units (HSU) for Quaternary or surficial deposits. Major HSUs of interest include outwash, kame field and

esker, and alluvial deposits. Glacial lake, intertidal sediment, marine, organic, and till HSU generally yield significantly less water (from

Blackmore 2006). ........................................................................................................................................................................................... 93

Figure 5.4: Hydrologic and hydrogeologic processes involved in the model parameters (after Heath, 1987). The well on the left is

drawing from an unconfined surficial aquifer, and the well on the right is drawing from a confined bedrock aquifer. The parameter

involved in the process is indicated by the DRASTIC parameter letter (figure from Blackmore, 2006). .................................................... 95

Figure 5.5: Depth to water data for bedrock (left panel) and surficial (right) aquifers. ................................................................................ 98

Figure 5.6: Net Recharge Data obtained from GSC. ..................................................................................................................................... 99

Figure 5.7: Aquifer Media data for Bedrock (top) and Surficial (bottom) geology. ................................................................................... 101

Figure 5.8: Soil media from the 1960s soils report for Annapolis and Kings counties. .............................................................................. 103

xiii List of Figures

Figure 5.9: Data used for the topography parameter are Percent Slope, and were calculated from a DEM obtained from the NSGC. ..... 104

Figure 5.10: This illustration shows a simplified case of how the final impact of the vadose zone values for the bedrock were calculated.

In the case above, if the surficial deposit is sand and gravel (raing of 8), and the bedrock deposit is the Wolfville Formation (rating of 4),

the final impact of the vadose zone for the bedrock aquifer is calculated at abou 6.67. ............................................................................. 105

Figure 5.11: DRASTIC Ratings for the four different Depth to water scenarios: Scenario 1 (Moderate), Scenario 4 (only accurate and

recent (1995) data used, Scenario 5 (only accurate data used), and Scenario 6 (only recent (1995) data used). ........................................ 108

Figure 5.12:Bedrock Aquifers Results 1-6. ................................................................................................................................................. 110

Figure 5.13: Surficial Aquifers Results 1-6. ................................................................................................................................................ 110

Figure 5.14: Category distribution per hydrostratigraphic unit, for both bedrock (left) and surficial (right) model results. ...................... 112

Figure 5.15: Model Results by HSU, for both bedrock (left) and surficial aquifers (right). ....................................................................... 113

Figure 5.16: Maximum vulnerability of groundwater in the bedrock aquifers, where a DRASTIC Rating of 1 represents low vulnerability,

and a DRASTIC rating of 8 represents higher vulnerability to groundwater contamination. ..................................................................... 115

Figure 5.17: Minimum vulnerability of groundwater in the surficial aquifers, where a DRASTIC Rating of 1 represents low vulnerability,

and a DRASTIC rating of 8 represents higher vulnerability to groundwater contamination. ..................................................................... 116

Figure 5.18: New well log data downloaded from the Groundwater Information Network (GIN, http://gw-info.net/) and Nova Scotia

Department of the Environment (http://www.gov.ns.ca/nse/groundwater/welldatabase.asp). .................................................................... 118

Figure 5.19: Well log data in the bedrock and surficial deposits used in Blackmore (2006), and recently downloaded well log data

(>2004). ........................................................................................................................................................................................................ 119

xiv List of Tables

List of Tables

Table 3.1: Land use data layers...................................................................................................................................................................... 50

Table 3.2: Summary of changes in land use areas. ........................................................................................................................................ 65

Table 4.1: Statistics for the 2 m DEM for areas with steep slope and poor drainage. ................................................................................... 85

Table 4.2 : Statistics for the 5 m DEM for areas with steep slope and poor drainage. .................................................................................. 85

Table 5.1: DRASTIC index ratings and weights for the seven parameters of depth to water, net recharge, aquifer media, topography,

impact of the vadose zone, and hydraulic conductivity of the aquifer (Aller et al., 1987; Blackmore, 2006). ............................................. 96

Table 5.2: DRASTIC results ratings and descriptions of relative vulnerability (after Aller et al., 1987). .................................................... 97

Table 5.3: Groundwater vulnerability scenarios. ......................................................................................................................................... 106

2 Kings 2050 Project Overview


The Kings 2050 project is a comprehensive undertaking involving multiple stakeholders in Kings County, Nova Scotia. The goal of

Kings 2050 is to “guide the long-term sustainable development of Kings County so that future generations can enjoy a quality of life that

is equal or better than today”.

Kings 2050 is composed of implementing partners, which include the three towns within Kings County: Berwick, Kentville, and

Wolfville; the Municipality of Kings; and the Kings Regional Development Agency. The seven villages within Kings County (Aylesford,

Canning, Cornwallis Square, Kingston, Greenwood, New Minas, Port Williams), several provincial government departments (Service

Nova Scotia and Municipal Relations, Nova Scotia Department of Agriculture, Nova Scotia Department of Transportation and

Infrastructure Renewal, Nova Scotia Environment), and a number of additional agencies (Annapolis Valley Health, Kings Transit,

Atlantic Canada Opportunities Agency (ACOA)) comprise the supporting partners.


This report contains four chapters containing the results of the research that has been done at the Applied Geomatics Research Group for

Kings 2050. Each chapter is formatted as an individual study and includes introduction, methods, results, conclusions and references

sections.

Floodplain Analysis and Delineation is presented in Chapter 2. This research contributes to achieving the Kings 2050 goals of improving

the coordination of municipal land use planning initiatives, reducing long-term infrastructure costs, and coordinating responses to climate

change. Chapter 3 contains the study on Land cover and Land use Mapping, which will give Kings County planners an updated and

accurate map of how land is being used currently, and aid in the Kings 2050 goal of improving coordination of economic development

and land use planning initiatives. The study of Development and Constraint Mapping, which shows the population density located on

landslide-prone slopes and relates to long-term infrastructure and land-use planning initiatives, is presented in Chapter 4. The

vulnerability of aquifers in the Annapolis Valley is found in Chapter 5, the Groundwater and Surficial Geology Mapping study. The

results of this study will demonstrate how Kings County municipalities must cooperate to protect our shared groundwater resources.

4 Floodplain Analysis and Delineation


2.1 Introduction

Floodplain management is a controversial topic. In fact, even definitions of the floodplain can conflict regarding the frequency (or

infrequency) of flooding within a floodplain (Saroli and Story, 2010). The definition of the floodplain affects the livelihoods and safety

of citizens and businesses with existing properties on or near the floodplain, and it affects the future development potential and

sustainability of towns whose downtown cores are located on the floodplain. In Kings County, like many river valley regions, towns

were settled near the river for easy access to fresh water, transportation, and sewage disposal. As development continued near the river,

consideration of the floodplain boundary was not always a priority. The floodplain in Port Williams has recently been mapped

(Fredericks, 2007), but for the most part in Kings County the floodplain maps are dated; for example, in Kentville the Cornwallis River

floodplain has not been mapped in at least 30 years (Saroli and Story, 2010). The techniques used in the past, from which current town


planning policies were developed, are based on coarse and sometimes out of date elevation data. In this chapter a lidar-derived Digital

Elevation Model (DEM) is used to generate an updated floodplain boundary for major rivers in Kings County using a 1D hydrodynamic

model from the Danish Hydraulic Institute (DHI) that links environmental and topographic data to generate water flow and stage

predictions. Lidar technology is capable of generating highly detailed elevation models of large swaths of terrain. The raw lidar elevation

data used in this study consists of a ground height for every square metre of the coverage area which is accurate to +/- 30 cm. Additional

information on the lidar elevation model such as dates for data collection flights can be found at http://agrg.cogs.nscc.ca/Annapolis-

Valley-DEM-from-LiDAR).

This section contains a description of the study area (Section 2.1.1), a summary of coastal and fluvial flooding history in Kings County

and potential climate change scenarios (Sections 2.1.2 through 2.1.3), and a literature review (2.1.4). A description of the modelling

methodology is presented in Section 2.2, and the new floodplain areas are found in Section 2.3 and have been overlain with property

boundaries. Studies at the AGRG have been conducted previously to identify critical infrastructure at risk of coastal storm surge flooding

in the Kings County area (Webster, Smith, Collins, 2012).

2.1.1 Study Area

The Caribou Bog, a large peat bog west of Berwick, is the highest point in the Valley floor and the source of both the West-flowing

Annapolis River and East-flowing Cornwallis River (Figure 2.1Error! Reference source not found.). The Annapolis and Fales Rivers

discharge into the Annapolis Basin, while the Cornwallis and Gaspereau Rivers, as well as the smaller Bass Creek, Canard, Habitant, and

Peraux Rivers all flow into the Minas Basin. The Annapolis and Fales River pass through Greenwood, and then head westward to

Middleton and into Annapolis County. The Cornwallis River passes mainly through sparsely populated rural areas before reaching an

area outside of Kentville called Meadowview (Figure 2.1), a small community that is prone to flooding (CBCL, 2011; Starratt, 2010).

The river widens as it passes through downtown Kentville and opens onto the tidal marshes, wetlands, and dyked areas that lay between

Wolfville and Port Williams. The Gaspereau River passes mainly through farmland in the Gaspereau Valley; the Canard, Habitant, and

http://agrg.cogs.nscc.ca/Annapolis-Valley-DEM-from-LiDAR

http://agrg.cogs.nscc.ca/Annapolis-Valley-DEM-from-LiDAR


Peraux Rivers, and Bass Creek, are located north of Kentville in the Canning area and flow mainly through less populated areas of Kings

County.

Figure 2.1: The watersheds and rivers being modelled in this study over a shaded relief elevation model.


2.1.2 Coastal Flooding

The strong tides of the Bay of Fundy affect the Cornwallis River up to 5 km west of the town of Kentville (Saroli and Story, 2010),

making the towns of Kentville and Wolfville, and the villages of Port Williams and New Minas (Figure 2.1) vulnerable to coastal

flooding and storm surge (Webster, McGuigan, and MacDonald, 2012). Tidal range in the Minas Basin of the Bay of Fundy, Nova

Scotia is between 13 and 16 m, the highest in the world. Following a semi-diurnal pattern, there are two high tides and two low tides

every 24 hours and 50 minutes in the Bay of Fundy. When a high tide coincides with strong winds and low pressure of a storm, a storm

surge can occur. A storm surge is an increase in the ocean water level above what is expected from the normal tidal level that can be

predicted from astronomical observations. The strong tidal currents of the Minas Basin cause erosion of the fine glacial till sediments of

the coastline at a rapid rate, making the coastal communities in this region ever more vulnerable to storm and flood events.

Dykes provide some protection against coastal flooding due to storm surge, although their primary purpose is to create and protect

agricultural land. The salt marshes of the Cornwallis River were dyked by the Acadians in the 1700s to create fertile agriculture lands, as

an alternative to clearing forests. The Acadians built one-way culverts called aboiteaux into the dykes to prevent salt water from entering

farmland on a flood tide, while still allowing drainage of the land on ebb tides. The dykes in the Minas Basin area are generally between

8 and 9 m above mean sea-level and are maintained by the NS Department of Agriculture. The dykes were brought up to standard in the

late 1960s and by 2002 most of the original wooden aboiteaux were replaced or relined with high density polyethylene pipe (NS

Department of Agriculture, 2007). In many cases in the Minas Basin region the elevation of the land behind the dykes is lower than the

land seaward of the dykes. This is due to the hundreds of years of sediment deposition on the seaward side of the dykes, and has serious

implications when considering flood water inundation.


2.1.2.1 Historical Coastal Flooding

The dykes in the Minas Basin have been overtopped by storm surges in the past. The Saxby Gale of 1869 overtopped most, if not all, of

the Acadian dykes in the Minas Basin (Ruffman, 1999). Breaching has occurred more recently, in 1913, 1931, 1958, and the Groundhog

Day storm of 1976 (Bleakney, 2009). In Port Williams in 1977, the dyke was breached causing flooding of the downtown area. Dykes

can also cause flooding from freshwater events, when the aboiteaux are closed during high tides, and rainwater runoff is prevented from

draining (Lieske and Bornemann, 2011).

2.1.2.2 Climate Change and Sea-level Rise

The global climate is changing due in part to the increase of greenhouse gas emissions, and the resulting warming trends will contribute

to an increase of global sea-level (Titus et al. 1991). Future projections of sea-level change depend on estimated future greenhouse gas

emissions and are predicted based on a number of scenarios (Raper et al. 2006). Global sea-level rise, as predicted by climate change

models, will increase the problem of flooding and erosion making more coastal areas vulnerable. The third assessment of the

Intergovernmental Panel on Climate Change (IPCC) indicates that there will be an increase in mean global sea-level from 1990 to 2100

between 0.09 m and 0.88 m (Church et al. 2001). The latest IPCC Assessment Report 4 (AR4) has projected global mean sea-level to rise

between 0.18 and 0.59 m from 1990 to 2095 (Meehl et al. 2007). However as Forbes et al. (2009) point out, these projections do not

account for the large ice sheets melting and measurements of actual global sea-level rise are higher than the previous predictions of the

third assessment report. Rhamstorf et al. (2007) compared observed global sea-level rise to that projected in the third assessment report

and found it exceeded the projections. They have suggested a rise between 0.5 and 1.4 m from 1990 to 2100. This projected increase in

global mean sea-level and the fact that many coastal areas of Maritime Canada have been deemed highly susceptible to sea-level rise

(Shaw et al. 1998) has led to various studies to produce detailed flood risk maps of coastal communities in PEI, NB, and NS (Webster et

al. 2004; Webster and Forbes, 2005; Webster et al. 2006; Webster et al. 2008). The most recent set of flood risk maps for coastal

communities in Nova Scotia has been produced during the Atlantic Climate Adaptation Solutions (ACAS) project (Webster, McGuigan,

and MacDonald, 2012; Webster, Smith, and Collins, 2012.).


In addition to global sea-level rise, local crustal dynamics also affect relative sea-level (RSL). The major influence on crustal motion for

this region is related to the last glaciation that ended ca. 10,000 years ago (Shaw et al., 1994; McCullough et al. 2002; Peltier, 2004). The

areas where the ice was thickest were depressed the most and peripheral regions where uplifted, termed the “peripheral bulge”. The ice

was thickest over Hudson Bay in central Canada, where the crust was most depressed, however today this area is still rebounding from

the removal of the ice load and continues to uplift. The Maritimes represent part of the peripheral bulge and southern New Brunswick

and Nova Scotia are subsiding (Peltier, 2004). Subsidence rates vary across the region with Nova Scotia having a rate of ~ 15 cm per

century (Forbes et al., 2009). The subsidence of the crust is important for coastal communities in that it compounds the problem of local

sea-level rise and must be considered when projecting future flood risk. The Bay of Fundy tidal range is expected to increase by ca. 10-

30 cm in the future with an increase in sea-level (Godin, 1992; Greenburg et al., in press). All of these factors must be combined; global

sea-level rise, crustal subsidence, and tidal amplitude, to produce a potential increase in RSL in the next century. This does not include

the possibility of increased storm intensity or frequency.

2.1.3 Fluvial flooding

Fluvial flooding is caused when high or intense precipitation, or snow and ice melt within the watershed flows into the river, causing it to

overtop its banks. High or intense precipitation can be defined using Environment Canada’s Rainfall Warning Criteria, wherein warnings

are issued when 25 mm of rain or more is expected in one hour, when 50 mm or more is expected within 24 hour or 75 mm or more

within 48 hours during the summer, or when 25 mm or more is expected within 24 hours during the winter (Environment Canada, 2011).

While flooding from snow and ice melt can be easy to predict, flash flooding from sudden downpours can be more of a challenge to

forecast (Royal Institute of British Architects, 2011).

The permeability of the land affects the ability of the land to absorb water and contributes to the severity of a fluvial flood. Frozen or

saturated land could have temporary low permeability, while developed land or rocks such as shale and unfractured granite have

permanently low permeability. Land cover such as pavement, ditched farmland, and deforested areas contribute to the amount of runoff


entering a river, and can worsen the severity of fluvial flooding. Evapotranspiration is the total amount of moisture removed from the

drainage basin by evaporation and plant transpiration.

Figure 2.2 shows a short duration rainfall Intensity-Duration-Frequency (IDF) graph for Kentville. The graph is produced by

Environment Canada from an extreme value statistical analysis of at least ten years of rate-of-rainfall observations. It includes the

frequency of extreme rainfall rates and amounts corresponding to the following durations: 5, 10, 15, 30 and 60 minutes, and 2, 6, 12, and

24 hours. Return periods are used as the measure of frequency of occurrence and are expressed in years. Estimates of the rates and

amounts for the durations noted above and their confidence intervals for the rates are provided for return periods of 2, 5, 10, 25, 50 and

100 years. More information on IDFs can be found here:

ftp://ftp.tor.ec.gc.ca/Pub/Engineering_Climate_Dataset/IDF/IDF_v_2.100_2011_05_17/Notes_on_EC_IDF.pdf.

ftp://ftp.tor.ec.gc.ca/Pub/Engineering_Climate_Dataset/IDF/IDF_v_2.100_2011_05_17/Notes_on_EC_IDF.pdf


Figure 2.2: Short Duration Rainfall Intensity-Duration-Frequency Data for Kentville. Source: ftp://ftp.tor.ec.gc.ca/Pub/Engineering_Climate_Dataset/IDF/

ftp://ftp.tor.ec.gc.ca/Pub/Engineering_Climate_Dataset/IDF/


2.1.3.1 Historical Floods

Many of the rivers in Kings County, including the Annapolis, Cornwallis, and Gaspereau Rivers experienced fluvial flooding when 70

mm of rain fell on frozen ground between March 30 and 31, 2003 (Figure 2.3). Temperatures soared to 14.0 C, melting the heavy winter

snow and ice and contributing to the rising water that caused sewage backups and widespread damage. Roughly 1000 buildings, 13,500

km2 of land, 200 roads and 47 bridges were affected by the flooding, and damages were estimated at around $10 million (Fullarton and

Pente, 2010). In their document on the History of the Kentville Floodplain, Saroli and Story (2010) present monthly flood frequency and

major flood frequency statistics for 1860 – present; their data show that major flood frequency has remained steady at 1.73 per decade

since 1860, and that the majority of flooding occurs in April. Major spring floods occurred in 1920, 1931, 1962, 1972, and 2003

(Bleakney, 2009).

Figure 2.3: Flooding of the Gaspereau River in Gaspereau on March 31, 2003.

Figure 2.4: Flooding in Meadowview in November, 2010 (source: Kings County News, http://www.kingscountynews.ca/media/photos/unis/2010/12/02/photo_1282047_resize.jpg).

http://www.kingscountynews.ca/media/photos/unis/2010/12/02/photo_1282047_resize.jpg


Meadowview is a low-elevation neighbourhood on the west side of Kentville that is located in the currently defined Cornwallis River

floodplain. The basements and backyards of houses in Meadowview are especially prone to flooding (Figure 2.4; CBCL, 2011) and have

experienced flooding numerous times over the past 80 years (Starratt, 2010). Controversy over the cause of the frequent flooding centers

around the effect of the Cornwallis Street Bridge, culverts and storm sewers that need repairs, and a recently built dyke that is meant to

protect Kentville during flooding (Hoegg, 2010). The issues surrounding the recurrent flooding in Meadowview demonstrate the complex

nature of the effects of flooding on development within the floodplain.

2.1.3.2 Climate Change Scenarios

As is the case with sea-level rise predictions, there are many different scenarios that influence how precipitation patterns will change with

climate change. The Atlantic Climate Adaptation Solutions Association (ACASA) (2012) states that precipitation will increase in

Atlantic Canada, while Clean Nova Scotia (2010) suggests that the amount of precipitation will likely remain about the same. Richards

and Daigle (2011) predict an annual increase in precipitation in Kentville; most of that increase is predicted to occur in the winter season,

with almost no increase in summer and fall precipitation, and very little in the spring. But while everyone may not agree on the amount

of annual precipitation Nova Scotia will receive with climate change, there is consensus that there will be much more variability and

frequency of intense rainfall (NRCAN, 2010; Climate Change NS, 2012; Clean Nova Scotia, 2010; ACASA, 2012). Climate Change

Nova Scotia (2012) warns that extreme rainfalls that happened only once every 50 years in the last century are likely to occur once every

10 years in this century, and precipitation is expected to vary more from season to season and from year to year. Some predict that the

time between rainfalls will likely grow longer, meaning that precipitation will arrive as single, intense storms instead of many small

showers spread throughout the year (Clean Nova Scotia, 2010). In addition, NRCAN (2010) predicts that Nova Scotians will see more

precipitation falling as rain, rather than snow.


Atlantic Canada is projected to see hotter and drier summers, and warmer winters, especially in the interior (NRCAN, 2010; Clean Nova

Scotia, 2010), and increased intensity of hurricanes impacting Atlantic Canada as ocean waters continue to warm (The Weather Network,

2011; CBC, 2010).

2.1.4 Literature Review

The flooding risks along the Cornwallis River in the vicinity of Meadowview were assessed by CBCL in a 2011 report to Kings County

(CBCL, 2011). The hydrology and hydraulic regime of the river system were assessed and a range of potential floodplain protection

measures were developed. The Storm Water Management Model Ver.5 (SWMM) was used for the analysis and developed to include the

various hydrologic characteristics of the tributary watersheds. The river floodplain and topography were modelled using nearly 200

cross-sections that were extracted from the lidar topographic data, which had been provided to Kings County by AGRG. Downstream

tidal boundary conditions were estimated using a tidal analysis that considered astronomical tides, storm surges, seiches, and sea-level

rise; upstream boundary conditions were estimated using flow gauge data; additional model inputs included field measurements of

surface roughness, channel depths, and bridge structure geometries. Model output was calibrated to results of a residential survey of the

Meadowview neighbourhood. The model was then used to estimate peak water levels for extreme water level scenarios, and a review of

options to reduce or prevent flooding risks was presented. Finally, the study recommended that the floodplain be defined and protected,

and that any further development within the delineated floodplain be restricted.

CBCL conducted a study of the Fales River in 2008 to address the Greenwood area’s concern over flooding in that area (CBCL, 2008).

The study evaluated flooding risks along the Fales River and identified potential options to protect the residents in the Fales Subdivision

against damage caused by high water levels. The authors employed the USEPA Storm Water Management hydraulic model, gauged

Annapolis River flow data, soils maps, and local flood extent knowledge to predict flooding for extreme precipitation events. The model,

together with information on the river topography and various bridge structures, produced estimates of peak water levels for the 1 in 20

year and 1 in 100 year events.


The Port Williams floodplain was defined in 2007 to be areas that are at or below the elevation of the surrounding dykes, as identified

using lidar data (Fredericks, 2007). The study determined that many areas along the Cornwallis River, including portions of the Port

Williams waterfront fall within a floodplain. A report on the redevelopment of the Port Williams water front suggested that the floodplain

lands should be zoned in such a way that no permanent structures would be permitted on these lands, but parks and public spaces would

be appropriate uses of the floodplain (Chisolm, 2007).

2.2 Methods

2.2.1 Lidar and DEM

Airborne lidar (Light Detection and Ranging) data for the Annapolis Valley was flown in the summer of 2000 and the spring of 2003 and

2004. The lidar data were separated into ‘ground’ and ‘non-ground’ points which have been used to construct Digital Elevation Models

(DEMs) and Digital Surface Models (DSMs) within the ArcGIS environment (Webster, 2004b). A DEM for Kings County was created

from this lidar data and used as the land elevation layer for the floodplain mapping done in this study.

2.2.2 Topologic-Hydrological Data Processing

The lidar DEM was integrated with elevation data from the Nova Scotia Topographic Database (NSTDB) to produce a cohesive

elevation model for the entirety of the catchment area of each of the river systems in question – where more precise and accurate lidar

data was given preference where available (the North Mountain, the valley floor and the north edge of the South Mountain) and NSTDB

data was employed for the more southern regions of the catchments on the South Mountain. Culvert information was collected similarly

from the NSTDB dataset under the roads layer with the coding RRCL. To further supplement culvert information, an intersection was

performed between the NSTDB roads and stream layers, and 10 m segments of intersecting streams were reassigned as culverts. The

combined sets of linear culvert information were then burned into the hybrid lidar-NSTB elevation model as to properly facilitate the

passage of surface water across the DEM. A standard fill sink procedure was then performed on the DEM to highlight and remove areas

of flow direction which would not accommodate proper flow accumulation toward outlets of the overall catchments (be it toward the


Minas Basin or the Annapolis River). The filled sink elevation model dataset was then subtracted from the original hybrid elevation

model which it was built from to highlight any potentially absent culvert locations. Missing culverts were digitized manually and the fill

sink subtraction procedure was iterated until deemed satisfactory. From the final fill sink DEM, flow direction and accumulation raster

data were derived using standard hydrological raster processing procedures. Main river channels lineation were then selected from a

threshold of flow accumulation area above 20 000 m2 and further pruned down manually to include only the main branches of each

network. Watershed polygons, and thus areas, for each network catchment were then calculated using the flow direction derived from the

hybrid DEM for each main river channel lineation. Due to processing errors found in the southern Gaspereau and Annapolis watersheds

derived from this method, existing 1:10,000 NS watershed boundaries (NS Environment) were joined with the derived watersheds

boundaries where necessary to create the final watershed boundary product. Only watershed regions in the far south the study are were

affected by data processing issues of this sort. Finally, cross-sections containing vertical elevation values for every 5 m laterally to the

river lineation were created throughout each of the river networks such that the entirety of the applicable catchment could be modelled in

the 1D hydrodynamic modelling program of DHI Mike 11. Cross-sections were spaced such to produce adequate results within the

timeline of the project (Figure 2.5 and Figure 2.6).

2.2.3 Environmental Data

Applicable environmental data was collected where possible. Data collected includes per hour river water level data for the Cornwallis

and Annapolis Rivers for the year of 2010 as logged by Environment Canada (EC) as well as daily river flow data dating back

significantly further (2000 in the case of the Cornwallis and 1963 in the case of the Annapolis). Unfortunately, good hourly precipitation

data for the area was not located and, given the scope of this project is in determining geomorphic floodplains as opposed to modelling a

particular rainfall event, using the precipitation record from the EC Kejimkujik weather station was considered to be sufficient. For

consistency, the daily maximum and minimum temperature readings of the Kejimkujik weather station were also used for the calculation

of daily evapotranspiration. The Department of Fisheries and Oceans (DFO) WebTide application was used to calculate the predicted tide

of the Minas Basin (Figure 2.5 and Figure 2.6).


Figure 2.5: The suite of collected environmental time series used to drive all the later discussed river flooding models.


Figure 2.6: The distribution of input and intermediate data including the position pertaining to each environmental time series.


2.2.4 Watershed Modelling

2.2.4.1 River Runoff Calibration

A river runoff calibration was performed for the Cornwallis river system using the Environment Canada hourly precipitation and daily

evapotranspiration records of the Kejimkujik station against the Environment Canada daily river discharge record of the Cornwallis River

(Figure 2.7). This step is essential in determining adequate watershed characteristic variables to best represent the storage capacity, and

overland runoff rates for the area. Ideally, a river runoff calibration model would be completed for each of the watersheds examined in

this study. However, due to a lack of stage or discharge data for all rivers except the Annapolis River, the calibrated Cornwallis

watershed coefficients were extended to each of the river systems examined in this study.

Figure 2.7: The results of the River Runoff Calibration of the Cornwallis River. River Discharge – Observed (black) and modelled (blue). Accumulated River Discharge – Observed (red) and modelled (green). The accumulated underestimation of the modelled result, due to real world discrepancies in Kejimkujik Station and Cornwallis River watershed rainfall events over time, is acceptable, as only model stage maxima are used in deriving floodplains.

2.2.4.2 Hydrodynamic Modelling

A similar but separate setup was used for the hydrodynamic model of each river system. Each model contained the appropriate and

unique cross-sectional elevation and river channel lineation information as well as applicable catchment area values.


All models employed the same river runoff coefficients as determined by the calibration of the Cornwallis River and utilized the

precipitation and evapotranspiration rates as recorded by the Kejimkujik EC weather station.

Kejimkujik EC weather station records used to drive each model were multiplied by a factor of three to ensure significant flooding was

achieved and the geomorphic floodplain could be established.

Each river system of which the outlet drains into the Minas Basin were bound by the predicted tidal elevation record as calculated from

DFO WebTide, including the Cornwallis, the Habitant, the Gaspereau, the Peraux, and the Canard as well as Bass Creek. The Annapolis

River, as well as the attributing Fales River, was bound by the hourly observed river stage record provided by EC, located at Wilmot.

Each bounding time series was prepared such that water elevations would not fall below that of the applicable lowest point of the outlet

cross-section as derived from lidar. This was performed in the interest of the stability of each model. Thus, all DFO WebTide predicted

elevations in the tidal boundary time series which fell below 5.0 m were set to 5.0 m - as per the maxima tidal elevation observed during

the time of lidar acquisition. Similarly, all EC Annapolis River stage records which fell below 10.29 m were set to 10.29 m - as per the

water level observed at the location of the gauge during lidar acquisition.

To further ensure model stability, the lowest cross-sectional elevation value of the Fales River outlet was set to match the interpolated

low value between cross-sections of the Annapolis River at the point of intersection of the Fales. This resulted in a change of river

elevation at the outlet of the Fales River from 13.47 m to 13.81 m.

Each model, with the exception of the Annapolis and Fales River models, was run for a time period of one year between 1/2/2010

12:00:00 PM and 12/1/2010 12:00:00 PM at a fixed time step of 2 seconds. The Annapolis and Fales River models ran from 6/17/2010

12:00:00 PM to 12/1/2010 12:00:00 PM, due to the unavailability of bounding Annapolis River water level information for the first half

of 2010.


Results for each model were output for the entirety of the modeled time periods as a maximum value per hour for each cross-section. For

analytical purposes, the first month of each model output was discarded to eliminate erroneously high water levels recorded during model

initialization.

After the hydrodynamic model runs for each river system were complete, the maximum water level for each cross-section over the

simulation period was interpolated between cross-sections and intersected with the hydrologically corrected elevation model. Areas

below 7.35 m in resultant floodplain models were automatically added to the final floodplain extents, regardless of hydrodynamic model

output, to ensure all coastal floodplains were not to be underrepresented with regard to coastal waters.

Each river and tributary floodplain extent were clipped to within the extent of the corresponding watershed layer to eliminate inter-

watershed flooding artifacts which occasionally result from the interpolation between cross-sections of model output, due to the

complexity of some watershed shapes.

All floodplain extent products were buffered by 5 m (the processing cell size) to account for sampling errors through raster processing

with the bias of overestimation. Additionally, river reach polylines for each of the studied systems were buffered by 5 m and incorporated

seamlessly into the final floodplain extents to alleviate any drawing errors which arose in the model output in areas where the river

channels were quite thin (<10 m).

2.2.4.3 GIS Modelling

A suitable rapid approach to determine maximum water for floodplain delineation was also developed to be run in the GIS environment

for future work when Mike 11 may not be readily available. This model runs with similar inputs to the Mike 11 method. In this method,

each river system was processed individually. River bed levels were first determined from the intersection of river cross-sections, similar

to those digitized in the Mike 11 method, and river reach lines were derived from the earlier flow accumulation process. Maximum and

minimum bed levels for each river dataset were then used to determine a logarithmic function such that the highest river bed level was

assigned a water depth of 0 m (dry) and the lowest bed level (the river mouth) could be assigned a water depth of some float value input


determined by inspecting the DEM (Figure 2.8). All other river bed values where then assigned a suitable water depth based on the

derived stretch function.

Figure 2.8 (Left) An illustration of the mathematical model applied to each bed elevation taken from lidar to estimate floodplains. (Right) An example of various models (linear model as red boxes, root model as gray crosses) being applied to real world elevation data (Blue dots).

This methodology was then built into a python script whereby water depths at each intersection point were converted to water levels

(CGVD28), interpolated between river cross-sections and intersected with the hydrologically prepared DEM to produce cohesive

floodplain polygons. The script was then run for each of the river systems to compare the floodplain output to that of Mike 11. Outlet

water depth inputs for each of the systems draining into the Minas Basin were set as 2.35 m based on tidal data. The water depth input of

the Annapolis River GIS model was set to 2.60 m which was the maximum water depth of the Annapolis River Mike 11 output. The

validity any floodplain derived using the GIS method depends heavily upon the input depth of the outlet (minimum bed elevation). The

Fales River outlet water depth was set to 1.63 m based on the Annapolis River GIS floodplain model output values taken from a cross-

section nearby.


Similarly as in the Mike 11 flood model methods, each GIS floodplain output was clipped per river to within the appropriate river

watershed boundary to remove the inter-watershed flood level interpolation errors.

2.2.4.4 Hydrodynamic Modelling with Simulated Storm Surge

To implement the simulated effect of a significant storm surge occurring in the coastal river models located in the east of Kings County,

the predicted tidal level was artificially raised by a residual water level of 2.46 m smoothly over three tide cycles, peaking November 7

2010 on the closest high tide before the date of flood level maxima as derived from the previously run predicted tide driven flood models

(Section 2.2.4.2). A residual of 2.46 m was used such that a 2 m storm surge could be simulated on a HHWLT, and whereas the tide at on

November 7, 2010 was approximately 0.46 m below at peak tide.

Figure 2.9 The original modelled tidal levels for the peak simulated flooding event of 2010, Nov 7 (left). A 2.46 m storm surge residual as applied over 3 tide cycles (right). This data is used to simulate the effect of a significant storm surge to flood delineation in the western portion of Kings County.

Storm surge simulations were run in the Mike 11 environment with otherwise the same parameters as the simulations used to derive

floodplains in Section 2.2.4.2. Model water level maxima outputs were similarly interpolated between model cross-sections to generate a

water surface to delineate the extent of flooding. Areas where the elevation model fell below the total storm surge level (9.34 m

CGVD28) were deemed to be at risk of flooding for this scenario and thus added to the flooding extent where vacant. Delineated


flooding extents were then clipped to each appropriate watershed to eliminate interpolation errors between watersheds. Smaller coastal

watersheds which were not focused on in this study were included in the final flood extent polygon where flooding was based on DEM

elevations relative to 9.34 m CGVD28. Such additional watersheds were classified as ‘other’ in the final flooding extent output.

2.3 Results

Results of the hydrodynamic (Mike 11) and GIS floodplain outputs across the entirety of the Kings County area are as shown in Figure

2.10.

The extent of each river floodplain individually, as derived from the Mike 11 hydrodynamic process, and compared to those derived from

the GIS method are displayed per cross-section in Figure 2.10 through Figure 2.18. Water depth values for each model method are also

compared.

The extent of the hydrodynamic derived floodplains are also compared to the flooding extent in a significant storm surge event (Figure

2.20).

Each relevant data set as been provided to accompany this report.


Figure 2.10: The extent of the hydrodynamically derived (Mike 11, hatched lines) and GIS derived floodplains (yellow polygons) for all river systems. A good correlation was achieved, specifically in downstream floodplain areas, though the GIS method tends to diverge from the Mike 11 output in the upstream.


Figure 2.11: The extent of the Peraux River floodplain. The model bed elevations are displayed per cross-section along with a comparison of Mike and GIS flood level.


Figure 2.12: The extent of the Annapolis River floodplain. The model bed elevations are displayed per cross-section along with a comparison of Mike and GIS flood level. The orthophoto database does not extend to the full view of the map (grey box).


Figure 2.13: The extent of the Fales River floodplain. The model bed elevations are displayed per cross-section along with a comparison of Mike and GIS flood level.


Figure 2.14: The extent of the Bass Creek floodplain. The model bed elevations are displayed per cross-section along with a comparison of Mike and GIS flood level.


Figure 2.15: The extent of the Canard River floodplain. The model bed elevations are displayed per cross-section along with a comparison of Mike and GIS flood level.


Figure 2.16: The extent of the Cornwallis River floodplain. The model bed elevations are displayed per cross-section along with a comparison of Mike and GIS flood level.


7.77

Figure 2.17: The extent of the Gaspereau River floodplain. The model bed elevations are displayed per cross-section along with a comparison of Mike and GIS flood level.


Figure 2.18: The extent of the Habitant River floodplain. The model bed elevations are displayed per cross-section along with a comparison of Mike and GIS flood level.


Figure 2.19 The floodplain extent under predicted tide conditions (yellow) and the potential flooding extent in a worst case scenario storm surge, 2 m on a HHWLT (red).


2.4 Discussion

2.4.1 Floodplain Mapping

Overall, the accuracy of the GIS approach can be deemed quite good with regard to the general adherence spatially to the Mike 11

approach whereby water level maxima were output from the hydrodynamic model with a simulation period of one year. The accuracy of

the GIS method does, however, hinge greatly upon the choice of input water depth for the outlet, which must be estimated by the user.

This approach, however, does not replace or imitate in any way the hydrodynamic systems ability to model and replicate the flooding

effect of a particular storm event but rather delineates the natural geomorphic floodplain.

Figure 2.20: The correlation of GIS and Hydrodynamic flood levels is quite good. The divergence of correlation in the headwaters is of note and does have some markable effect on the final floodplain extent upstream. For linear regression of the hydrodynamic derived flood levels per bed depth, R² = 0.98.


Generally, hydrodynamic modelling requires a great deal of input and validation data such that a particular event or scenario may be

modelled for a given river system, each having unique hydrological characteristics, such as ground water penetration, root zone storage

and surface runoff deflection. In the case of this project, all of the river systems were modelled based on the calibration of the Cornwallis

River system alone using a single point rain gauge (EC Kejimkujik station located approx. 85 km away) . Given the nature of this

analysis however, in that the stated goal is the derivation of physical geomorphic floodplain extents, issues such as timing, be it of a

particular rain event or the rate at which the river system responds to high rainfall events, be it flow constraints or validation water

records, need not be thoroughly accounted for. For this goal, simply a high rate of precipitation, coupled with the general elevation trend

of each river system and taking into account the typical impedance of surface water flow for a given cross-sectional conveyance, will, at

a variety of specific precipitation rates, culminate into a geomorphic floodplain extent when high water levels are interpolated and

intersected with the ground surface – as was done in this study. Though no explicit validation data exists for the water levels recorded by

the model outputs, it is notable that the floodplain extents independently derived for this study based on topographic and environmental

factors alone does tend to coincide well to the existing zoning of floodplains Kings County provided (zone O1) – which were manually

digitized based on both topographic data and historical accounts (Figure 2.21). It is also of note, however, that the general agreement

between the existing zoning and the derived floodplains does deteriorate in the area of the Caribou Bog – where the Cornwallis and

Annapolis Rivers meet. The effect of the Bog is most severe on the Annapolis River side as the large area with no significant channel has

affected the stability of the Annapolis River HD model as a whole, causing high rates of lateral flow and some mass balance errors which

in turn effect the resultant high water levels and thus the efficacy of the derived floodplain (Figure 2.25).

If further flooding analysis of specific events are required then precipitation needs to be better accounted for (perhaps via radar).

Furthermore, in situ water level loggers should be deployed so that flow to stage rating curves can be developed and calibrated river

runoff models could be established for each river system.


Figure 2.21: The Canard and Cornwallis River hydrodynamically derived floodplains (colored polygons) coincide generally well with the existing floodplain zoning (hatched lines).


Figure 2.22: The hydrodynamically derived floodplain (colored polygons) of the Habitant River does show a greater extent in the headwaters than the existing zoning (hatched lines).


Figure 2.23: The Grand Pre area and the mouth of the Gaspereau River showing hydrodynamically derived floodplains (colored polygons) and existing zoning (hatched lines).


Figure 2.24: Near the area of Caribou Bog, the hydrodynamic model (colored polygons) shows greater discrepancy to the zoning (hatched lines).


Figure 2.25: The stability of the Annapolis River (and thus Fales) may have been affected by the Bog near the headwater.

2.4.2 Storm Surge Mapping

Our approach to storm surge mapping is a synthesis of a static and hydrodynamic approach. Due to the nature of the predicted tidal water

level boundary, as modified to accommodate a significant storm surge water level, the hydrodynamic input of a storm surge into the

fluvial system are not properly accounted for in terms of flux. As a result, output water levels are underestimated upstream. This

underestimation is accounted for by areas with a DEM elevation below 9.46 m being added to the flooding output in a static GIS

approach. A fully hydrodynamic storm surge simulation may be accomplished by means of simulating the hydrodynamics of the Minus

Basin in a 2-D dynamic model whereby both water level and flux are accounted for. This simulation may then be linked to models of a 1-

D river cross-section approach, such as those used to derive fluvial flooding extents in this report, such that water flux may be accounted

for.

Figure 2.26 Shown is the effect of model output water levels over time, per cross-section of the inclusion of a simulated storm surge water level. Various cross-sections of the Cornwallis River are shown using an unaltered predicted tide (left) and with an added storm surge of 2.46 m (right). Lower water levels are indicative of cross-sections which are further downstream.


2.5 References

Atlantic Climate Adaptation Solutions Association (ACASA). 2012. Themes: Inland. Retrieved from: http://atlanticadaptation.ca/inland

Bleakney, S. 2009. Sods, Soil, and Spades, The Acadians at Grand Pré and Their Dykeland Legacy.

CBC News. 2010. Climate change to bring fewer, stronger storms. Retrieved from: http://www.cbc.ca/news/technology/story/2010/02/22/tech-climate-change-storm.html

CBCL. 2008. Fales River Flood Study: DRAFT Report. Report No. 081059.

CBCL. 2011. Meadowview Flood Study: Draft Report. Report No. 111013.0000.

Chisolm, Leanne. 2007. Recommendations for the Redevelopment of the Port Williams Waterfront. Retrieved from: http://www.county.kings.ns.ca/upload/All_Uploads/SPS/Port%20Williams/Folders/4%20Reference%20Materials/Recommendations%20for%20the%20Redevelopment%20of%20the%20Port%20Williams%20Waterfront%20-%20December%202007.pdf

Church, J.A.; Gregory, J.M.; Huybrechts, P.; Kuhn, M.; Lambeck, K.; Nhuan, M.T.; Qin, D.; Woodworth, P.L. Changes in sea-level. In

Climate Change 2001: The Scientific Basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental

Panel on Climate Change; Cambridge University Press: Cambridge, UK, 2001; pp. 639-693.

Clean Nova Scotia. 2010. How is climate change affecting Nova Scotia? Retrieved from: http://www.nsclimate.ca/impacts.htm.

Climate Change Nova Scotia. 2012. The impacts of climate change in Nova Scotia. Retrieved from:

http://www.climatechange.gov.ns.ca/content/adapt

Environment Canada. 2011. Public Alerting Criteria. Retrieved from: http://www.ec.gc.ca/meteo-

weather/default.asp?lang=En&n=D9553AB5-1#rainfall

http://atlanticadaptation.ca/inland

http://www.cbc.ca/news/technology/story/2010/02/22/tech-climate-change-storm.html

http://www.county.kings.ns.ca/upload/All_Uploads/SPS/Port%20Williams/Folders/4%20Reference%20Materials/Recommendations%20for%20the%20Redevelopment%20of%20the%20Port%20Williams%20Waterfront%20-%20December%202007.pdf

http://www.county.kings.ns.ca/upload/All_Uploads/SPS/Port%20Williams/Folders/4%20Reference%20Materials/Recommendations%20for%20the%20Redevelopment%20of%20the%20Port%20Williams%20Waterfront%20-%20December%202007.pdf

http://www.nsclimate.ca/impacts.htm

http://www.climatechange.gov.ns.ca/content/adapt

http://www.ec.gc.ca/meteo-weather/default.asp?lang=En&n=D9553AB5-1#rainfall

http://www.ec.gc.ca/meteo-weather/default.asp?lang=En&n=D9553AB5-1#rainfall


Forbes, D.L., Manson, G.K., Charles, J., Thompson, K.R., and Taylor, R.B. 2009. Halifax Harbour Extreme Water levels in the Context

of Climate Change: Scenarios for a 100-Year Planning Horizon. Geological Survey of Canada, Open File 6346, 21 p.

Fredericks, M. 2007. Port Williams SPS mapping: Floodplain policy recommendation. Unpublished manuscript. Centre for Geographic

Sciences: Lawrencetown, NS.

Fullarton, Catherine, and Pente, Adam. 2010. “Chapter 8: Severe Weather and Kentville; a History”. An Examination of Kentville’s Environmental History. Eds. David Duke and Laura Churchill Duke. Acadia University. Retrieved from http://www.kentville.ca/documents/icsp/icspfinalreport.pdf.

Godin G. 1992. Possibility of rapid changes in the tide of the Bay of Fundy, based on a scrutiny of the records from Saint John.

Continental Shelf Research, 12

Greenburg, D., Blanchard, W., Smith, B. and Barrow, E. (in press). Climate Change, mean Sea-level and High Tides in the Bay of

Fundy.

Hoegg, Jennifer. 2010. Kentville: Meadowview flood not town's fault. The Kings County Advertiser. Retrieved from:

http://www.kingscountynews.ca/News/2010-12-02/article-2011761/Kentville%3A-Meadowview-flood-not-towns-fault--/1

Leiske, D. L. and Borrnemann, J. 2011. Coastal Dykelands in Tantramar Area: Impact the Climate Change on Dyke Erosion and Flood

Risk. Atlantic Climate Adaptations Solutions Association unpublished report.

McCulloch, M.M., D.L. Forbes, R.W. Shaw and the CCAF A041 Scientific Team. 2002. Coastal Impacts of Climate Change and Sea-

level Rise on Prince Edward Island. Geological Survey of Canada. Open File 4261.

Meehl, G.A.; Stocker, T.F.; Collins, W.D.; Friedlingstein, P.; Gaye, A.T.; Gregory, J.M.; Kitoh, A.; Knutti, R.; Murphy, J.M.; Noda, A.;

Raper, S.C.B.; Watterson, I.G.; Weaver, A.J.; Zhao, Z.-C. 2007. Global climate projections. In Climate Change 2007: The Physical

http://www.kentville.ca/documents/icsp/icspfinalreport.pdf

http://www.kingscountynews.ca/News/2010-12-02/article-2011761/Kentville%3A-Meadowview-flood-not-towns-fault--/1


Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change;

Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K.B., Tignor, M., Miller, H.L., Eds.; Cambridge University Press:

Cambridge, UK and New York, NY, USA, pp. 1-844.

Natural Resources Canada (NRCAN). 2010. Climate Change Impacts and Adaptation in Atlantic Canada. http://www.nrcan.gc.ca/earth-

sciences/climate-change/community-adaptation/830

Nova Scotia Department of Agriculture. 2007. Dykeland History Archive. Retrieved from

http://www.gov.ns.ca/agri/rs/marsh/history.shtml.

Peltier, W.R. 2004. Global glacial isostasy and the surface of the ice-age earth: The ice-5G (VM2) model and Grace. Annual Review of

Earth and Planetary Sciences, 32, 111–149.

Raper, S. C., & Braithwaite, R. J. 2006. Low Sea-level Rise Projections from Mountain Glaciers and Icecaps Under Global Warming.

Nature , 439, 311-313.

Richards, William and Daigle, Real. 2011. Scenarios and Guidance for Adaptation to Climate Change and Sea Level Rise- NS and PEI

Municipalities. Atlantic Climate Adaptation Solutions Association. Retrieved from

http://www.gov.pe.ca/photos/original/ccscenarios.pdf.

Rhamstorf, S.; Cazenave, A.; Church, J.A.; Hansen, J.E.; Keeling, R.F.; Parker, D.E.; Somerville, R.C.J. 2007. Recent climate

observations compared to projections. Science, 316, 709.

Royal Institute of British Architects. 2011. Flooding Explained. Retrieved from:

http://www.architecture.com/FindOutAbout/Sustainabilityandclimatechange/Flooding/FloodingExplained.aspx.

http://www.nrcan.gc.ca/earth-sciences/climate-change/community-adaptation/830

http://www.nrcan.gc.ca/earth-sciences/climate-change/community-adaptation/830

http://www.gov.ns.ca/agri/rs/marsh/history.shtml

http://www.gov.pe.ca/photos/original/ccscenarios.pdf

http://www.architecture.com/FindOutAbout/Sustainabilityandclimatechange/Flooding/FloodingExplained.aspx


Ruffman, Alan. 1999. A Multi-disciplinary and Inter-scientific Study of The Saxby Gale: an October 4-5, 1869 hybrid hurricane and

record storm surge. Retrieved from http://www.shunpiking.com/ol0103/03SaxbyGale11869RUFFMAN.htm

Saroli and Story, 2010. http://www.kentville.ca/documents/icsp/icspfinalreport.pdf.

Saroli, Miranda and Story, Chris. 2010. “Chapter 9:The History of the Kentville Floodplain”. An Examination of Kentville’s

Environmental History. Eds. David Duke and Laura Churchill Duke. Acadia University. Retrieved from

http://www.kentville.ca/documents/icsp/icspfinalreport.pdf.

Shaw, J., Taylor, R., Forbes, D., Ruz, M., & Solomon, S. 1998. Sensitivity of the Coasts of Canada to Sea-level Rise. Geological Survey

of Canada (Bulletin 505), Natural Resources Canada , 1-79.

Shaw, J., Taylor, R.B., Forbes, D.L., Ruz, M-H., and Solomon, S. 1994. Sensitivity of the Canadian coast to sea-level rise. Geological

Survey of Canada Open File Report, No. 2825, 114 p.

Starratt, Kirk. 2010. Meadowview bearing brunt of flooding? The Kings County Advertiser. Retrieved from:

http://www.kingscountynews.ca/News/2010-11-09/article-1937584/Meadowview-bearing-brunt-of-flooding/1.

The Weather Network. 2011. Is Climate Change Causing More Hurricanes? Retrieved from: http://www.theweathernetwork.com/news/storm_watch_stories3&stormfile=is_climate_change_causing_mo_240611

Titus, J. G., Park, R. A., Leatherman, S. P., Weggel, J. R., Greene, M. S., Mausel, P. W., et al. 1991. Greenhouse Effect and Sea-level

Rise: The Cost of Holding Back the Sea. Coastal Management , 19, 171-204.

Webster, T.L., Christian, M., Sangster, C. and Kingston, D. 2004. HighResolution Elevation and Image Data Within the Bay of Fundy

Coastal Zone, Nova Scotia, Canada. In GIS for Coastal Zone Management. Edited by Bartlett, D. and Smith, J., CRC Press.

http://www.shunpiking.com/ol0103/03SaxbyGale11869RUFFMAN.htm



http://www.kingscountynews.ca/News/2010-11-09/article-1937584/Meadowview-bearing-brunt-of-flooding/1

http://www.theweathernetwork.com/news/storm_watch_stories3&stormfile=is_climate_change_causing_mo_240611


Webster, T., Forbes, D., Dickie, S., & Shreenan, R. 2004. Using Topographic Lidar to Map Risk from Storm-surge Events for

Charlottetown, Prince Edward Island, Canada. Canadian Journal of Remote Sensing , 30 (1), 64-76.

Webster, T.L. and Forbes, D.L. 2005. Using Airborne lidar to map exposure of coastal areas in Maritime Canada to flooding from storm-

surge events: A review of recent experience. Canadian Coastal Conference.

Webster, T.L., Forbes, D.L., MacKinnon, E. and Roberts, D. 2006. Floodrisk mapping for storm-surge events and sea-level rise in

Southeast New Brunswick. Canadian Journal of Remote Sensing, Vol. 32, No. 2, pp. 194-211.

Webster, T.L., McGuigan, K., MacDonald, C. 2012. Lidar processing and Flood Risk Mapping for the Communities of the District of

Lunenburg, Oxford-Port Howe, Town and District of Yarmouth, Chignecto Isthmus and Minas Basin. Atlantic Climate Adaptations

Solutions Association unpublished report.

Webster, T.L., Mosher, R., Pearson, M. 2008. Water Modeler: A component of a Coastal Zone Decision Support System to generate

Flood-Risk Maps from Storm Surge Events and Sea-level Rise. Geomatica. Vol. 62, No. 4, pp. 393-406.

Webster, T.L., Smith, T., Collins, K. 2012. Inventory of Physical Wastewater Infrastructure at Risk of Flooding to Climate Change

induced Sea-level Incursions in the Minas Basin Area. Atlantic Climate Adaptations Solutions Association unpublished report.

49 Land Cover and Land Use Mapping


3.1 Introduction

As Kings County continues to grow and residential areas continue to expand, it is necessary for planners to have the most recent

information on land use within the county. Having access to these data will enable municipal planners to balance the preservation of

agricultural land with residential and industrial growth.

In this chapter land use data from a variety of sources (listed in Table 3.1) are used in conjunction with 2008 regional orthophotos to map

recent areas of clear cutting, new urban and rural developments since 2002, and the resulting changes to agricultural land use.


3.2 Data Summary

Name Data Type Date

NS Department of Natural Resources

(DNR) Forestry Data

ArcGIS layer classified into forest-related

land use, e.g. Christmas trees, brush,

alders, clear cut, as well as non-forest

land uses such as urban and agriculture.

Land use base information 2002, last updated in 2006 for

treated trees only

NS Department of Agriculture

Agricultural Land Use Project (ALIP)

Lands identified as agricultural. Includes

types of crops, e.g. rotational.

1998

NS Geomatics Center Orthophotos Black and white aerial photograph

mosaics

2008

Kings County Land Use Agricultural land classified into sub-

categories based on parcels, % land use

from assessment

Continually updated

Landsat Satellite Images Band 5 satellite images 2005, 2010

Table 3.1: Land use data layers

3.3 Methods

The best thematic data layer available to produce an up to date land use map of Kings County was the DNR Forest Layer, which, in

addition to forest type, also captured non-forest features such as urban and agricultural. A satellite analysis was the primary method used

to update the clear cut layer and is described below. Orthophotos were used to update urban development and agricultural land use, as

described in Section 3.3.2.

3.3.1 Clear Cut Mapping

The clear cut layer (land use code 60) was generated using satellite imagery from 2002 to 2005 (Figure 3.1, Step 1). To add the areas that

have been clear cut since 2005, a similar analysis of satellite images was done. The difference between a pair of multi-temporal Band 5

Landsat images from 2005 and 2010 show the clear cuts during that timeframe (Figure 3.1, Step 2). Band 5 is the best band to use for this


type of analysis because it captures the mid-Infrared range of wavelengths and shows a sharp contrast between vegetation and soil, or

post-clear cut residue. The satellite analysis is an efficient method to show clear cuts over a large area, but the resulting polygons are only

as precise as the satellite resolution. The orthophotos (Table 3.1, 2008) were used to visually locate the clear cuts that occurred before

2008. This enabled the modification of pixelated clear cut areas resulting from the satellite analysis (Figure 3.1, Step 3). It also allowed

the rejection of falsely detected areas, e.g. cloud cover, lakes, or harvested agricultural areas. The final product is a layer with DNR clear

cuts and satellite clear cuts that have been modified according to the orthophotos where possible (Figure 3.1, Step 4). The yellow arrows

point to an area of clear cut detected by the satellite analysis that appears in the orthophotos as forest, indicating that that piece of land

must have been clear cut between 2008 and 2010. The satellite image was used to verify this assumption in Step 5. The addition of the

ALIP agricultural layer in Step 6 (which has been shifted slightly towards the north) reveals some areas of clear cut adjacent to

agricultural land. No assumptions were made regarding the possible purpose of clear cuts, i.e. it was not assumed that clear cuts adjacent

to agricultural land, as in this example, were cleared and then converted to agricultural land.


Figure 3.1: The steps used to generate a final clear cut layer. Step 1: The DNR layer with clear cuts between 2002 and 2005 was used as the basis for the new clear cut layer. Step 2: The results of a change detection satellite analysis for Landsat 5 Band 5 images from 2005 and 2010 are added. Step 3: The 2008 orthophoto is added and used as a guide for smoothing out the satellite analysis result. Step 4: The DNR clear cuts and edited satellite-derived clear cuts are merged to arrive at a final layer of clear cuts up to 2010. The yellow arrows indicate areas detected by the satellite analysis that have been clear cut since 2008. Step 5: The satellite image is used to verify that the areas indicated by the yellow arrows have been clear cut by 2010. Step 6: The addition of the ALIP agricultural layer shows some clear cut ares adjacent to agricultural land. The roads that appear in the orthophotos are likely logging roads, as they are not included in the provincial roads database.


3.3.2 Urban and Agricultural Land Use Updating

The DNR Forestry layer includes an urban land use code (87) and an agriculture land use code (86). The urban land use code was

updated and divided into urban and rural development, which automatically updated the agricultural land use code.

The DNR Urban land use code was most recently updated in 2002. The 2008 orthophotos were used to add recent urban development to

this land use code, and to produce a rural development land use code to accommodate new and existing homes and buildings outside of

town and village limits. The first step in accomplishing this was to locate new urban or rural developments. The orthophotos were

visually scanned for development outside of the DNR Urban land use code (Figure 3.2, Step 1). The civic points were also helpful in

locating properties that fell outside of the DNR Urban land use code. Once a new property had been located, the previous land

classification was identified. In this example, Figure 3.2, Step 2 shows a new residential development that was built on land previously

classified as agriculture. In that case, the new development was subtracted from the agriculture and added to the new urban development

(Figure 3.2, Step 3). If the new property was located on land classified as forest, the new development was subtracted from the forestry

land use code and added to the new urban development. Once all new development was located and the urban land use code was updated,

town and village boundaries were used, based on discussions with county planning officials, to divide the updated urban land use code

into rural and urban development such that new developments found outside of town or village boundaries were classified as rural

development (Figure 3.2, Step 4).

Ideally, the agricultural layer would have agricultural use attributes, such as ‘rotational’ or ‘in transition’. The Agriculture Land

Identification Project (ALIP) data available from the NS Department of Agriculture contains features with these attributes, but as Figure

3.3 illustrates, it was not possible to assign these attributes to our DNR-based agriculture layer due to a spatial mismatch.


Figure 3.2: The steps used to generate an updated urban development land use code. Step 1: A visual scan of the DNR Forestry Urban data from 2002, with the 2008 orthophotos beneath it, reveals an area that has been developed since 2002 (indicated by the yellow circle). Step 2: The addition of the DNR Agriculture land use code (2002) shows, in this example, that the new development occurred on land previously classified as agriculture. Step 3: The new urban development is re-classified, and the urban and agriculture land use codes are modified accordingly. Step 4: The urban land use code is divided into urban (orange) and rural (yellow) development by the village boundary. (The yellow colour of the updated urban is used to differentiate the updated urban land use code from the 2002 land use code, which is coloured blue.)


Figure 3.3: The top left panel shows the final updated agricultural layer overlaying the orthophotos, showing the good fit between the layer and the photos. The bottom left panel shows the ALIP layer, which includes agricultural crop uses, and shows the mismatch (coloured dark blue) between the ALIP layer and our DNR-based layer. Assigning the agricultural use attributes from the ALIP layer to the DNR-based agricultural layer would result in a final agricultural layer that includes awkwardly shaped polygons with no agricultural use attribute, shown labelled MISMATCH in the right panel.


3.4 Results

3.4.1 Clear Cut Mapping

Figure 3.4 shows land in Kings County that has been clear cut since 2002. It contains the DNR satellite analysis of clear cuts between

2002 and 2005 and the satellite analysis from this study for clear cuts between 2005 and 2010. The largest areas of clear cut land are

located in the southern part of the county; North Mountain contains several smaller clear cuts, but the valley floor did not undergo any

significant clear cutting since 2002. The largest clear cut (~1.5 km2) was located in a heavily clear cut area between Gaspereau Lake and

Lake George (Figure 3.5).


Figure 3.4: Final clear cut land use code. Includes DNR clear cuts and satellite analysis clear cuts.


Figure 3.5: Area of heavy clear cutting between Lake George and Gaspereau Lake.


3.4.2 Urban and Agricultural Land Use Updating

Figure 3.6 shows land in Kings County classified as urban and rural development, and the town and village boundaries used to separate

rural from urban. The majority of total development is centered along the valley floor, and is mainly urban; the Village of Canning and

the northern part of Cornwallis Square are the only places to fall outside this urban corridor. The federal land of 14 Wing Greenwood is

not included in the Greenwood Village boundary and is therefore not classified as urban development. Rural development outside of the

valley floor region tends to be clustered along roads, lake shores and the coast. Figure 3.7 shows an area on the boundary of the Village

of Cornwallis Square where several properties have been added to the previous land classification (the DNR urban land use code from

2002). Figure 3.7 also shows how the Cornwallis Square village boundary was used to classify development as urban or rural.

Figure 3.8 shows land classified as agricultural in Kings County both before and after updating, as well as a panel showing gains and

losses to agricultural land. Figure 3.9 shows how the addition of properties to the urban and rural development land use codes decreased

the area of the agricultural land in Port Williams. In some cases the developments were new subdivisions, such as the one near the center

of Figure 3.9; in other cases the properties were not new, but may have been modified since 2002. Often the properties added to the urban

and rural development land use codes were simply not included in the DNR 2002 urban land use code.


Figure 3.6: Kings County land classified as Urban and Rural Development showing the town and village boundaries used to separate urban development from rural.


Figure 3.7: New urban development within the Village of Cornwallis Square (on the left); new rural development (on the right). The blue polygons represent DNR urban classifications from 2002.


Figure 3.8: DNR 2002 Agriculture land use code (top left), gains and losses to this data caused by updating (bottom left), and the updated agricultural land use code for all of Kings County (right).


Figure 3.9: Agricultural land loss in the village of Port Williams due to new urban developments such as the subdivision near the center of the figure, new or recently modified individual properties, or properties that existed but did not appear in the DNR 2002 Agriculture land use code.


3.5 Discussion

The satellite analysis resulted in ~39 km2 of new clear cuts being added to the existing DNR clear cut data, resulting in a total of 135 km2

of land in Kings County that has been clear cut since 2002 (Table 3.2). Most new clear cuts in Kings County (newer than 2005) are

located on the South Mountain (Figure 3.10).

Updating of the land use codes resulted in a decrease in land previously classified as urban, due to the introduction of the rural

development class (Table 3.2). Overall, however, the sum of the new urban and rural development (the equivalent of the old urban

classification) increased by 102 km2 to 198 km2. The addition of small, individual properties in rural areas was the most common type of

updating required for the land use code. Since these small properties do not show up well on an overview of the entire area of Kings

County, Figure 3.11 shows new rural and urban development in the Kentville-Wolfville corridor rather than a whole county map. This

populated area of Kings County contains the largest sections of new development, and new and expanded subdivisions in this area

account for a large part of the urban growth in Kings County.

The development of urban and rural properties accounted for the loss of 19 km2 of agricultural land, and 6 km2 of new agricultural land

was gained, resulting in a net loss of 13 km2 of agricultural land. There is the possibility that clear cut areas were transitioned into

agricultural land by the landowner, but that assumption was not made in this study, so the only additions to the agricultural land class

were due to a correction to a classification (e.g. from urban to agriculture based on orthophotos). Since there was only 19 km2 of

agricultural land lost, but 102 km2 of urban development and 39 km2 of new clear cuts, this implies that most of the new development

and clear cutting did not take place on agricultural land, but rather on land classed as something else; e.g. old field, brush, barren,

miscellaneous, or unclassified.


Land Use Previous Area (km2) New Area (km2) Change (km2)

Clear Cut 96 135 39

Urban 96 64 Urban + 134 Rural = 198 102

Agriculture 405 392 -13 (Total ag. land gained = 6,

total ag. land lost = 19) Table 3.2: Summary of changes in land use areas.


Figure 3.10: A total of 39 km2 of clear cut land has been mapped since 2005, mainly on South Mountain.


Figure 3.11: New development since 2002 in the Kentville-Wolfville Urban corridor.


3.6 Conclusions

Areas of land in Kings County that have been clear cut since 2005 were mapped using satellite change detection analysis and added to

the existing DNR clear cut data. Approximately 40 km2 of clear cut was detected, mainly on the North and South Mountains. Urban land

use was updated to include developments built after 2005, as well as developments that were not included in the previous dataset. Urban

land use was separated into urban and rural development using town and village boundaries. Urban land use made up 64 km2 of Kings

County, and rural land use covered 134 km2; overall urban land classifications increased by 102 km2 since 2005. Agricultural land use

classification decreased by 13 km2; urban and rural developments (both new and un-mapped) accounted for this loss.

The final land use map for Kings County is shown in Figure 3.12. It is clear that agriculture remains the primary land use along the

valley floor, with urban and rural land use taking up most of the remaining land on the valley floor. Camp Aldershot accounts for the

large un-classified area north of Kentville. Rural properties not located adjacent to towns and villages in the valley floor tend to follow

coastlines, lakeshores, and roads. Clear cuts are mostly restricted to the North and South Mountains.


Figure 3.12: Final land use map for Kings County.

70 Development Constraint Mapping


4.1 Introduction

The slope grade of a parcel of land is an important factor to consider when it is being assessed as a site for development. The drainage

characteristics of the soil also contribute to the stability of the land. Slope failures or landslides may be caused by any combination of

water saturation and flow, weak earth materials, and steep slopes (District of North Vancouver, 2012).

In this section, the steep nature of the terrain in Kings County and areas with soils classified as poorly draining have been mapped to

identify areas that are potentially at risk of landslide. Maps are presented without civic points to aid municipal planners in site selection

for urban development, and with civic points to identify properties that are currently located in the areas most susceptible to landslide.


4.2 Methods

ArcGIS was used to calculate the slope of the land. The calculation was completed twice using two different DEMs: (1) the lidar-based

2 m resolution DEM, which extends only to the top of South Mountain, and (2) the 2 m lidar data and 20 m NSTDB data averaged and

merged into a 5 m resolution DEM that covers all of Kings County (Figure 4.1). Using these two different DEMS makes it possible to

provide results using the high-resolution lidar data where it was available, while still providing slope constraint maps for the entire region

of Kings County as required. Results for both calculations were categorized using a conditional statement in ArcMap into slopes between

15 and 20% (Category 2), and slopes greater than 20% (Category 3).

Figure 4.1: DEM showing the 2 m lidar coverage used in the first slope constraint mapping calculation (left). The 5 m DEM covers all of Kings County and is the 2 m lidar-derived DEM averaged to 5 m resolution, merged with the NSTDB 20 m resolution data (right).


The Poorly Drained Soils data were selected from the Agriculture Canada Canadian Soil Information Service GIS layer. Those with

“Drainage” attribute “Poor” or “Very Poor” were grouped together for this analysis. The Castley and Millar groups combined make up

two thirds of the total Kings County area covered by Poorly Drained Soils (Figure 4.2).

Figure 4.2: The soil names of the soils categorized as having poor and very poor drainage.


4.3 Results

4.3.1 2 m DEM

Within Kings County, the entire south side of North Mountain has a slope steeper than 15%, and most of the area is steeper than 20%

(Figure 4.3). Steep-sided ravines cut through South Mountain south of Coldbrook, Kentville and Wolfville, and the south faces of South

Mountain and Wolfville Ridge are very steep-sloped. While the steep south face of North Mountain has relatively low population

density, the steep areas along South Mountain contain far more civic points (Figure 4.4). The Wolfville Ridge area does not contain

poorly draining soils, but contains a high number of civic points along the steep-sided south face (Figure 4.5). Many of the highly sloped

areas of South Mountain are located on regions of poor soil drainage (Figure 4.6); the area at the base of North Mountain north of

Centerville is also an area with poorly drained soils and many steep-sided ravines (Figure 4.7). As the valley widens to the west towards

Berwick, there are few areas where steep sloped, poorly drained land exist (Figure 4.8).

4.3.2 5 m DEM

The slope constraint analysis for the 5 m DEM produced results that agree with the results generated using the 2 m DEM, although

results are coarser, as expected. The effect of using a lower resolution DEM is that some steep areas become smoothed out, so there are

fewer areas that are classified as steep. Therefore, the 2 m results should always be used where available. The 5 m DEM results for the

southern part of Kings County fall mostly into the 15-20% slope category (Figure 4.9), and there is minimal overlap with poorly drained

soils and civic points (Figure 4.10). Rivers or gorges cutting across South Mountain south of Wolfville have steep sides, but contain

small incidence of poorly drained soils and civic points (Figure 4.11). The southern part of Kings County, south of Kentville, Aylesford

and Greenwood, is fairly flat and dotted with lakes (Figure 4.12, Figure 4.13). Steep slopes in this region are found mainly along

lakeshores. South of Greenwood there is some overlap with poorly drained soils (Figure 4.13).


Figure 4.3: Constraint mapping showing percent slope within the lidar coverage.


Figure 4.4: Constraint mapping showing percent slope within the lidar coverage; town locations, civic points, and poorly drained soils for the entire region.


Figure 4.5: Constraint mapping showing percent slope within lidar coverage, civic points, and poorly drained soils for the town of Wolfville.


Figure 4.6: Constraint mapping showing percent slope within lidar coverage, civic points, and poorly drained soils for the Town of Kentville.


Figure 4.7: Constraint mapping showing percent slope for the town of Centerville.


Figure 4.8: Constraint mapping showing percent slope, civic points, and poorly drained soils for the town of Berwick.


Figure 4.9: Slope constraint map generated for the entire area of Kings County using the 5 m lidar and NSTDB merged DEM, shown overlaying the province-wide hillshade map.


Figure 4.10: Constraint mapping showing percent slope generated using the 5 m lidar and NSTDB merged DEM, town locations, civic points, and poorly drained soils for all of Kings County.


Figure 4.11: Constraint mapping showing percent slope generated using the 5 m lidar and NSTDB merged DEM, town locations, civic points, and poorly drained soils for Kings County south of Wolfville and New Minas.


Figure 4.12: Constraint mapping showing percent slope generated using the 5 m lidar and NSTDB merged DEM, town locations, civic points, and poorly drained soils for Kings County south of Kentville, extending west almost to Berwick.


Figure 4.13: Constraint mapping showing percent slope generated using the 5 m lidar and NSTDB merged DEM, town locations, civic points, and poorly drained soils for Kings County south of Kingston, Greenwood and Aylseford, extending east almost to Berwick.


4.4 Discussion and Conclusions

The development constraint mapping results show that 60 km2 of Kings County, or 5% of the area of the 2 m DEM, has a slope between 15 and

20%; and 115 km2, or 9.6% of the area of the 2 m DEM, is classified as having a slope greater than 20% (Table 4.1). 14 km2 is classified as

having 15-20% slope and poorly drained soil (1.2% of the area of the 2 m DEM); 25 km2 of the land in Kings County (2.1% of the area of the

2 m DEM) has a slope greater than 20% and poorly drained soil. The 2 m resolution DEM covers 1192 km2.

For the 5 m resolution DEM, which covers all of Kings County (2213 km2), the relative amounts of land that are classified as having

slopes between 15 and 20% are similar to that of the 2 m resolution DEM: 3.9 % and 1.0% for slope and slope + poor drainage,

respectively (Table 4.2). The addition of the flatter southern part of the county results in less overall area being classified as having >20%

slopes and poor drainage compared to just looking at the 2 m DEM.

Kings County 2 m DEM Area (km2) Percent area of 2 m DEM

15-20% slope 60 5.0

15-20% slope and poor drainage 14 1.2

>20% slope 115 9.6

>20% slope and poor drainage 25 2.1

Table 4.1: Statistics for the 2 m DEM for areas with steep slope and poor drainage.

Kings County 5 m DEM Area (km2) Percent area of 5 m DEM

15-20% slope 87 3.9

15-20% slope and poor drainage 22 1.0

>20% slope 102 4.6

>20% slope and poor drainage 21 0.9 Table 4.2 : Statistics for the 5 m DEM for areas with steep slope and poor drainage.


4.5 References

District of North Vancouver. 2012. Guide to Living Near Steep Slopes. Retrieved from: http://www.dnv.org/article.asp?c=1030.

http://www.dnv.org/article.asp?c=1030

87 Groundwater and Surficial Geology Mapping


5.1 Introduction

As Kings County continues to grow, the proper and sustainable management of water resources will be a critical component of that

growth. The World Bank Group (WBG) states that water is essential for life, socio-economic development and for maintaining healthy

ecosystems, and places water at the forefront of its mandate for global sustainable development in a changing climate (WBG, 2012). In

Kings County, the surface water and groundwater resources we depend on for drinking, irrigation and recreational activities are currently

threatened by contamination that is likely to continue and even increase in frequency in the future (Rivard et al., 2004). In fact, a shift

away from surface water aquifers has already occurred, sparked by problems with the quality and quantity of surface water aquifers

(Timmer et al., 2005); currently 90% of residents depend on groundwater resources alone for their drinking water (Blackmore, 2006).

Groundwater resources in Kings County face a threat of pollution from contamination by nitrates, bacteria, and pesticides from


agricultural activities, accidental spills or human error; poorly constructed or maintained septic systems; a lack or malfunction of

municipal sewage treatment plants, and the removal of riparian vegetation for increased agricultural space, industry, and military

operations (Timmer, 2003). In order to best follow the example of the WBG in prioritizing water resource management, this chapter of

the report provides a vulnerability assessment of Kings County ground water aquifers so that their protection and management can be

included as one of the key points in the Kings 2050 Project. Vulnerability mapping can be used to evaluate land-use activity with respect

to sources of potential pollution, and can be of assistance in decision-making.

The concept of aquifer vulnerability involves the idea that strata containing water can be influenced by impacts occurring above, below,

or laterally adjacent to them. Researchers have recognized that overlying strata can provide groundwater sources a degree of protection

from potential contamination occurring at the ground surface (Foster, 1998; Fredrick et al., 2004), and that the concept of vulnerability

can be used for delineating land areas that are more vulnerable than others to potential contamination (Gogu and Dassargues, 2000).

Here, a model called DRASTIC has been used to examine groundwater vulnerability of potential bedrock and surficial aquifers in the

Annapolis Valley of southwestern Nova Scotia, Canada.

The vulnerability maps presented in this chapter are part of the results of a regional hydrogeological study of the Annapolis Valley

conducted by the Geological Survey of Canada between 2003 and 2006. Major results are found in Blackmore (2006) and Blackmore et

al (in prep.). This chapter outlines the methodology of the DRASTIC model used to produce the vulnerability maps in Section 5.2, and

describes data sources and modelling procedures in Section 5.3. Results are found in Section 5.4; Discussion and Conclusions are found

in Sections 5.5 and 5.6, respectively.


5.2 Methods

5.2.1 Study Area

The study area covers 2100 km2 and includes five watersheds in the Annapolis Valley, in southwestern Nova Scotia (Figure 5.1). Kings

County includes the entire area of the four watersheds included in this study that flow east into the Minas Basin, including the largest, the

Cornwallis River; about a quarter of the area of the watershed of the southwest-flowing Annapolis River is also contained within Kings

County. The Annapolis watershed drains almost 1600 km2, while the other four watersheds together drain about 500 km2 (Rivard et al.,

2004; Trescott, 1968; Neily et al., 2003).

The main bedrock aquifers of the Valley are located in the Wolfville and Blomidon formations and, to a lesser extent, in the North

Mountain basalts. The Wolfville and Blomidon formations are composed of lenticular bodies of sandstone, conglomerate, shale and

siltstone, in variable proportions. The Wolfville Formation is dominated by coarser-grained facies and the Blomidon Formation is

characterized by more fine-grained strata. The North Mountain basalts contain mainly vertical fractures that can provide good yields on a

local basis only (Blackmore, 2006).

The Quaternary sediments in the study area consist mostly of tills, ice-contact glaciofluvial sands and gravels, as well as glaciomarine

and/or glaciolacustrine clays of variable thickness. Generally, sediment units are thicker and coarser in the eastern and central parts of the

valley. Till is the most widespread glacial deposit, and is almost the only sediment present on both mountains, with differences in

composition due to changes in glacial deposition and underlying bedrock lithology (Blackmore, 2006).


Figure 5.1: The watersheds used in this study (black outline) differ slightly from those used in the Floodplain Mapping study (filled polygons, see Chapter 2). This study includes only the Annapolis, Cornwallis, Canard, Peraux and Habitant Rivers; additionally, the watersheds that border the shoreline have been divided near the shore. One of those sub-divided sections is known as the Bass Creek watershed in the Floodplain Mapping Study. Additionally, the Fales and Gaspereau Rivers are not included in this study, and there is a difference in the Annapolis and Cornwallis boundaries north of Berwick.


5.2.2 Hydrostratigraphic Units

Hydrostratigraphic units (HSUs) are layers of rock with similar water-bearing properties. Groundwater vulnerability was studied for the

HSUs of bedrock and surficial aquifers. Bedrock HSUs in the region include Wolfville, Blomidon, and North Mountain Basalt,

carboniferous rocks, slates and quartzites, the South Mountain Batholith (Figure 5.2). The main bedrock HSU in the study area is the

Wolfville Formation, which is comprised of water-bearing sandstone and/or conglomerate that can be penetrated almost anywhere in the

formation. There is variable aquifer potential and water quality throughout the Triassic Wolfville Formation. To some extent the Triassic

Blomidon Formation can be considered a variable aquifer, yielding a varying amount of water depending on the location. The lower beds

of the Devonian-Carboniferous Horton Group are composed of sandstone and conglomerate and can generally transmit water well

through original pores as well as joints, although water quantity available within this HSU is limited by the relatively small area it

comprises (Rivard et al., 2004; Trescott, 1968). Horton Group shale and siltstone and the Jurassic North Mountain Formation basalt can

provide high yields only on a local basis, depending on the fracturing, jointing, and weathering (Rivard et al., 2004; Trescott, 1968).

Other HSU units include the slate and quartzite of the Cambrian-Early Devonian Goldenville and Torbrook formations, in which the

permeability is associated with locally fractured systems and the granites of the Late Devonian South Mountain Batholith, where

permeability is almost entirely dependent on jointing (Trescott, 1968).

Surficial HSUs include alluvial, colluvial, glacial lake, intertidal sediment, kame field and esker, marine, organic, outwash and till

deposits (Figure 5.3). Sand and gravel surficial deposits in the Annapolis Valley, especially the outwash HSU and kame and esker HSU,

are productive aquifers at many sites, depending on the permeability and saturation thickness of the unit. The alluvial, or stream deposit

HSU, composed of well-sorted sand and gravel, thus having high permeability, can produce high water yields where the deposits are both

saturated and thick (Trescott, 1968; Rivard et al., 2004; Schwartz and Zhang, 2003). Till units, varying in composition depending on the

underlying bedrock lithology, generally cannot provide significant water yields as they are composed of unsorted and unstratified

sediments, thus having poor hydraulic conductivity.


Figure 5.2: Bedrock hydrostratigraphic units (HSU) within the study area. Major HSUs of interest include the Triassic sandstone (Ss), shale (Sh), and conglomerate (Cg) (Wolfville Formation) and the Triassic-Jurassic siltstone (Si), shale (Sh), and sandstone (Ss) (Blomidon Formation). Other HSU include the locally productive Devonian-Carboniferous sandstone (Ss), siltstone (Si), and shale (Sh) (Horton Group), the Jurassic North Mountain Basalt (NMB), the Cambrian-Early Devonian slate (Sl) and quartzite (Qz), and Late Devonian South Mountain Batholith (SMB) (from Blackmore 2006).


Figure 5.3: Hydrostratigraphic units (HSU) for Quaternary or surficial deposits. Major HSUs of interest include outwash, kame field and esker, and alluvial deposits. Glacial lake, intertidal sediment, marine, organic, and till HSU generally yield significantly less water (from Blackmore 2006).


5.2.3 The DRASTIC Model

The model used for groundwater vulnerability mapping was developed by the U.S. Environmental Protection Agency in 1987 (Aller et

al., 1987). DRASTIC is an acronym for the seven hydrologic conditions used as parameters in the model: Depth to groundwater, net

Recharge by rainfall, Aquifer media, Soil media, Topography, Impact of the vadose zone, and hydraulic Conductivity of the aquifer.

Figure 5.4 illustrates the role each of these parameters play in determining groundwater vulnerability. Each DRASTIC parameter is

classified into ranges (for continuous variables) or significant media types (for thematic data), according to the assessed impact on

pollution potential, where the ratings range for each parameter is from 1 to 10. Each parameter is assigned a weighting factor according

to its relative importance to the equation, as determined by Aller et al. (1987) in Table 5.1. The final vulnerability index rating is a

weighted sum of these seven parameters, as follows:

where R=rating and W=weight. The final DRASTIC index results (Pollution Potential values) are classified into relative vulnerability

categories that can be used as a tool for a broad assessment of groundwater vulnerability (Table 5.2). The higher the DRASTIC index

value, the higher vulnerability category and the greater the perceived groundwater contamination potential. The assumptions of this

model method include a general contaminant having the mobility of water, which is introduced at the ground surface and carried

vertically downwards into the aquifer by precipitation from recharge. This system also does not easily address certain conditions, such as

semi-confined or leaky aquifers, and the user must adjust the model taking such conditions into account (Aller et al., 1987).


Figure 5.4: Hydrologic and hydrogeologic processes involved in the model parameters (after Heath, 1987). The well on the left is drawing from an unconfined surficial aquifer, and the well on the right is drawing from a confined bedrock aquifer. The parameter involved in the process is indicated by the DRASTIC parameter letter (figure from Blackmore, 2006).


Rating Depth to Water Net Recharge Aquifer Media Soil Media Topography Impact of the Vadose Zone

Hydraulic Conductivity of the Aquifer

(feet) (inches) (% slope) (gpd/ft2) 1 100 +

(30.5 m+) 0-2 (0-50 mm)

Nonshrink-ing & Nonaggre-gated Clay

18 + Confining Aquifer 1-100 (4.72E-07 – 4.72E-05 m/s)

2 75-100 (22.8-30.5 m) Massive Shale (1 to 3)

Muck 100-300 (4.72E-05 – 1.41E-04 m/s)

3 50-75 (15.2-22.8 m) 2-4 (50-102 mm)

Metamorph-ic/Igneous (2 to 5)

Clay Loam 12-18 Silt/Clay (2 to 6) Shale (2 to 5)

4 Weathered Metamorph-ic/Igneous (3 to 5)

Silty Loam Metamorphic /Igneous (2 to 8)

300-700 (1.41E-04 - 3.30E-04 m/s)

5 30-50 (9.1-15.2 m) Glacial Till (4 to 6) Loam 6-12 6 4-7

(102-178 mm) Bedded Sandstone, Limestone & Shale Sequences (5 to 9) Massive Sandstone (4 to 9) Massive Limestone (4 to 9)

Sandy Loam Limestone (2 to 7) Sandstone (4 to 8) Bedded Limestone, Sandstone, Shale (4 to 8) Sand & Gravel with significant Silt & Clay (4 to 8)

700-1000 (3.30E-04 to 4.72E-04 m/s)

7 15-30 (4.6-9.1 m) Shrinking and/or Aggregat-ed Clay

8 7-10 (178-254 mm)

Sand and Gravel (4 to 9)

Peat Sand & Gravel (6 to 9) 1000-2000 (4.72E-04 - 9.43E-04 m/s)

9 5-15 (1.5-4.6 m) Basalt (2 to 10) Sand 2-6 Basalt (2 to 10) 10 0-5

(0-1.5 m) 10 + (254 mm+)

Karst Limestone (9 to 10)

Thin or absent Gravel

0-2 Karst Limestone (8 to 10)

2000 + (9.43E-04 m/s +)

Weight 5 4 3 2 1 5 3 Table 5.1: DRASTIC index ratings and weights for the seven parameters of depth to water, net recharge, aquifer media, topography, impact of the vadose zone, and hydraulic conductivity of the aquifer (Aller et al., 1987; Blackmore, 2006).


Model Index Value Result Vulnerability Category Index Rating

Description of Relative Vulnerability

1 to 79 1 Extremely low

80 to 99 2 Very low

100 to 119 3 Low

120 to 139 4 Moderate

140 to 159 5 Moderately high

160 to 179 6 High

180 to 199 7 Very high

200 to 230 8 Extremely high

Table 5.2: DRASTIC results ratings and descriptions of relative vulnerability (after Aller et al., 1987).

5.3 DRASTIC Parameter Data

One factor considered when selecting the DRASTIC model for use in the Annapolis Valley was that the data required to run the model

were either available or derivable from available data. For each parameter (D,R,A,S,T,I,C) data were obtained, processed, and assigned a

value between zero and ten according to the DRASTIC rating rules (Table 5.1).

5.3.1 Depth to Water

The depth to water is the vertical distance a contaminant would travel before reaching the top of a confined aquifer, or the base of the

confining layer. The depth to water parameter data were generated from interpolations of water level point data available from the well

log database compiled by the Geological Survey of Canada (GSC) from various sources (Rivard et al., 2006). Various sources of well

records (Nova Scotia Department of Natural Resources (NSDNR), Nova Scotia Department of Environment and Labour (NSDOEL))

were merged to construct a depth to bedrock database by statistically summarizing the well records for each location, based on the casing

depth (which can be thought of as an approximation of the depth to bedrock). The depth to water for the bedrock and surficial aquifers

(Figure 5.5) were divided into their respective datasets using the interpolated depth to bedrock surface, such that points with well depths

deeper than the bedrock depth were classified as being in the bedrock aquifer, and points with well depths less than the bedrock depth

were classified as being in the surficial aquifer.


Summary statistics calculated from the monitoring well water level data assisted in determining a general variability between the water

level depth and the calculated mean depth, which were useful in selecting guidelines for the low and high vulnerability scenarios. The

measured average ranges for both the bedrock and the surficial aquifers are slightly less than 6 ft., so 6 ft. was added to the baseline depth

to water to represent low vulnerability, and 6 ft. was subtracted from the baseline scenario to represent high vulnerability. This accounted

for seasonal variations in water level data.

Figure 5.5: Depth to water data for bedrock (left panel) and surficial (right) aquifers.

5.3.2 Net Recharge

Net Recharge is the annual average amount of water that infiltrates the vadose zone and reaches the water table. Recharge is a significant

controlling factor for the quantity of water available for contaminant dispersion and dilution in the vadose zone, and is given a weight of


4 in the pollution potential equation. Recharge data were calculated by the GSC using a water balance model at a resolution of 500 m, the

coarsest data of any parameter.

A statistical summary of the final recharge data was provided, by watershed and sub-watershed, and an average variability of the results

per each area was determined to be 33%. The recharge data were multiplied by 1-0.33 for the low vulnerability scenario and by 1+0.33

for the high vulnerability scenario.

Figure 5.6: Net Recharge Data obtained from GSC.


5.3.3 Aquifer Media

The aquifer media refers to the type and characteristics of the comprising rock or sediment serving as the aquifer. Bedrock and surficial

geology data were digitized from various sources (Blackmore, 2004), compiled and manipulated, and integrated digitally into respective

bedrock and surficial datasets covering the study area. These data were used for the Impact of the vadose zone and hydraulic

Conductivity parameters as well.

Vulnerability index values for all scenarios, were assigned for each aquifer media unit, based on the range of vulnerability values deemed

most appropriate for each type of media.


Figure 5.7: Aquifer Media data for Bedrock (top) and Surficial (bottom) geology.


5.3.4 Soil Media

The term soil media generally refers to the characteristic biological and chemical activity of this uppermost part of the vadose or

unsaturated zone. The DRASTIC methodology provides an index scheme for contaminant potential based on soil types (Aller et al.,

1987). These rating values, applied to the soil data according to the soil description, take into account the dominant soil type affecting

infiltration, composition, texture, and soil depth or thickness. The soil data originally digitized from the 1960’s soil reports for both

Annapolis County (MacDougall et al., 1969) and Kings County (Cann et al., 1965) were used for the soil media parameter. These data

were further processed to correct polygon topology and attributes according to the original maps.

The soil types were assigned moderate, low and high vulnerability index values based on soil composition. The ratings depended on the

soil characteristics such as composition and texture. For example, the Acadia soil group is made up of a combination of loam, silty loam,

and clay loam; these have vulnerability indexes of 3, 4 and 5, respectively (Table 5.1). Therefore, the Acadia unit would be assigned a

DRASTIC rating of 4 for the moderate scenario, 3 for the low, and 5 for the high scenario.


Figure 5.8: Soil media from the 1960s soils report for Annapolis and Kings counties.

5.3.5 Topography

Topography is the slope or slope variability of the ground surface; in general, the possibility of contamination is higher in flat areas than

in steep-sloped areas. The most accurate and precise dataset was the Topography data, which was generated from 20 m resolution Digital


Elevation Model (DEM) data acquired from the Nova Scotia Geomatics Centre (NSGC). Due to the lack of concern over inherent

variability of this dataset, this was the only parameter that remained unchanged throughout all seven vulnerability scenarios.

Figure 5.9: Data used for the topography parameter are Percent Slope, and were calculated from a DEM obtained from the NSGC.

5.3.6 Impact of the Vadose Zone

The vadose zone is the unsaturated or discontinuously saturated zone above the water table. Characteristics of the vadose media

determine attenuation, path length, and route of water and potential contaminants in the zone below the soil horizon and above the water

table (Aller et al. 1987). The impact of the vadose zone media ratings in DRASTIC methodology were designated by descriptive names

and their associated characteristics, which were applied to both the bedrock and surficial media (Aller et al., 1987). For surficial deposits


aquifers, the impact of the vadose zone was directly joined to surficial geology using a table provided by Aller et al. (1987) that

prescribes each surficial deposit a low, moderate or high DRASTIC rating for Impact of the Vadose Zone based on permeability (higher

DRASTIC rating for higher permeability).

For bedrock aquifers, a similar table was used to assign DRASTIC rating values to each Bedrock Unit (e.g. Blomidon Formation, South

Mountain Batholith, etc.). Units with fine-grained sedimentary rocks, which provide some protection to groundwater, were rated as less

vulnerable than units with high potential for fractures. The impact of the overlying surficial sediment or overburden above the bedrock

was calculated using the depth to bedrock data and depth to water data, as illustrated in Figure 5.10.

Figure 5.10: This illustration shows a simplified case of how the final impact of the vadose zone values for the bedrock were calculated. In the case above, if the surficial deposit is sand and gravel (raing of 8), and the bedrock deposit is the Wolfville Formation (rating of 4), the final impact of the vadose zone for the bedrock aquifer is calculated at abou 6.67.


5.3.7 Hydraulic Conductivity of the Aquifer

Hydraulic conductivity can be defined as the ease with which fluid flows through a porous medium, in units of length over time.

Hydraulic conductivity tends to be relatively large for permeable media such as sand and gravel, and relatively small for relatively

impermeable media such as clay and shale (Schwartz and Zhang, 2003; Aller et al., 1987). Sources of hydraulic conductivity data

included aquifer pumping tests provided by the GSC (Rivard et al., 2006), personal communication with hydrogeologists familiar with

the area, and estimations based on knowledge of the rocks or deposit materials. Index values for the moderate, low and high vulnerability

scenarios were assigned using Table 5.1 using the mean, minimum and maximum hydraulic conductivity values.

5.4 Results

5.4.1 Modelled Vulnerability Scenarios

To take into account issues such as data quality, data quantity, and potential variability among the hydrogeologic conditions considered

in the model, five groundwater vulnerability scenarios were run in addition to the moderate (baseline) scenarios. Each scenario was

examined for both bedrock and surficial aquifers throughout the Annapolis Valley (Table 5.3). The moderate vulnerability scenario,

Scenario 1, is used as a baseline for comparing the results of the other five scenarios.

Scenario Description

1 Moderate (baseline) vulnerability

2 Low vulnerability

3 High vulnerability

4 Only accurate and recent (1995) data used for Depth to water parameter

5 Only accurate data used for Depth to water parameter

6 Only recent (1995) data used for Depth to water parameter

Table 5.3: Groundwater vulnerability scenarios.


In Blackmore (2006) a seventh scenario was run using New Quaternary mapping (1:100 000) by the GSC (Paradis et al., 2005) for new

A, I, and C parameters where appropriate, with all else remaining the same as in the moderate vulnerability scenario (1). Results of that

scenario are not presented here as they were very similar to the moderate (baseline) scenario due to the similarity of ratings used.

Depth to water level data were variable in spatial accuracy and questionable in actual depth measurement provided, depending on the

original source dataset of the well data. For the moderate vulnerability scenario (1), all the well data were used. Scenarios 4, 5 and 6 each

contains a different subset of the Depth to water data (Figure 5.11). The different scenarios were determined to separate the effects of

spatial accuracy of the well location and time.


Figure 5.11: DRASTIC Ratings for the four different Depth to water scenarios: Scenario 1 (Moderate), Scenario 4 (only accurate and recent (1995) data used, Scenario 5 (only accurate data used), and Scenario 6 (only recent (1995) data used).

The results of scenarios 1 through 6 are shown for Bedrock Aquifers in Figure 5.12 and for Surficial Aquifers in Figure 5.13. Table 5.2

defines how the Index Ratings shown on the figures were derived from the final DRASTIC vulnerability index results (the result of the

DRASTIC equation). A comparison of the moderate scenario results indicates that bedrock aquifers are less vulnerable than surficial

aquifers. In both bedrock and surficial aquifers, the greatest variability occurs along the valley floor. Variability is contained within one

vulnerability category of the moderate scenario for both bedrock and surficial aquifers. Additionally, there are the expected differences

between the minimum, maximum and moderate scenarios for both bedrock and surficial aquifers.


For bedrock aquifers, the additional vulnerability scenarios showed that scenarios 4 and 5 (only accurate and recent depth to water data

and only accurate depth to water data used, respectively) were nearly as high as the maximum vulnerability scenario (3). This suggests

that the inclusion of the inaccurate depth to water data in the other cases introduced a strong bias toward lower vulnerability in the

bedrock aquifer.

In the surficial aquifers results the scenarios with only accurate depth to water data (4, 5) resemble the minimum scenario (2) most

closely, suggesting that the inclusion of the inaccurate depth to water data in the other modelled scenarios introduces a bias towards

higher vulnerability in the surficial aquifer.


Figure 5.12:Bedrock Aquifers Results 1-6. Figure 5.13: Surficial Aquifers Results 1-6.

1 2

3 4

5 6

1 2

3 4

5 6


5.4.2 Results by Hydrostratigraphic Unit

The primary goal of this study was to perform a potential vulnerability assessment of both bedrock and surficial aquifers in the Annapolis

Valley. Of key interest are areas most highly populated and aquifers considered most productive. Such aquifers include the Wolfville and

Blomidon formations, as bedrock aquifers, and the sand and gravel units of the valley floor, as surficial aquifers.

Figure 5.14 graphs the complete range of values within vulnerability categories, even though for some categories the actual percent of the

study area covered may be less than 1%. Figure 5.15 illustrates the distribution of vulnerability categories by HSU, showing the percent

of the study area covered. Those units with highest vulnerability will be of greatest concern.

The bedrock results reveal that the valley floor region is the most vulnerable region of the bedrock aquifer. This indicates that the

bedrock aquifer of highest vulnerability was the Wolfville sandstone and conglomerate (30.0% of the study area), where the index values

ranged from very low to moderately high (2 to 5), the majority of the results falling within the moderate vulnerability category (4).

Vulnerability values in the Blomidon Formation ranges from extremely low to low (1 to 3), with the majority of the results being very

low (2) in terms of vulnerability to contamination. Vulnerability in the Horton Group ranges from very low to moderate (2 to 4), with the

majority of the results being low (3).

The highest vulnerability occurred in the Wolfville Formation, due to the coarse-grained rocks (sandstone/conglomerate) and the

overlying coarse-grained sediments. The Blomidon Formation is less vulnerable, due to its laterally extensive shale and siltstone beds,

which provide protection from potential contamination. The Horton Group is slightly less vulnerable than the Wolfville Formation, and

more vulnerable than the Blomidon Formation, due to its composition of coarse rocks (sandstone) that increase vulnerability, and to its

composition of fine-grained rocks (siltstone and shale) that would provide some protection.

The surficial aquifer units of particular concern were those comprised of sand and gravel (alluvial, kame field and esker, and outwash

deposits), which had the highest vulnerability values ranging from moderately high or high to extremely high (5 or 6 to 8, respectively).


Generally, these deposits comprise the most productive aquifers in the Annapolis Valley, especially in comparison to other deposits such

as the till, which covered most of the study area and had lower vulnerability (low to moderately high, or 2 to 5).

Contributing factors for the high vulnerability of the sand and gravel deposits included the shallow depth to water levels, great amounts

of net recharge in the valley floor where these sediments were deposited, the intrinsic characteristics of the deposit (high permeability

and hydraulic conductivity), the properties of the soil cover (coarse loamy and sandy), and the very flat slopes of the valley floor.

Figure 5.14: Category distribution per hydrostratigraphic unit, for both bedrock (left) and surficial (right) model results.


Figure 5.15: Model Results by HSU, for both bedrock (left) and surficial aquifers (right).

5.5 Discussion

The results of modelling different cases with different depth to water data points to the conclusion that the moderate scenarios (1) are

perhaps not the best representation of aquifer vulnerability in the Annapolis Valley. The results indicated that, for bedrock aquifers, the

moderate scenario was falsely low due to the inclusion of inaccurate depth to water data. For surficial aquifers, the opposite conclusion

was reached: the inaccurate depth to water data caused the moderate scenario to be falsely high. Therefore, the authors recommend that

the most conservative map to be used as an assessment of groundwater vulnerability in the bedrock aquifers is the maximum

vulnerability map (Figure 5.16). For surficial aquifers, the best representation of actual groundwater vulnerability is the minimum

vulnerability scenario (Figure 5.17).

The vulnerability model produced by DRASTIC can be significantly altered by minor variations in data precision and accuracy


Data used for model parameters need improvement: the input data should be of sufficient resolution for the final mapping in the most

populated (and thus more subject to contamination) areas


Figure 5.16: Maximum vulnerability of groundwater in the bedrock aquifers, where a DRASTIC Rating of 1 represents low vulnerability, and a DRASTIC rating of 8 represents higher vulnerability to groundwater contamination.


Figure 5.17: Minimum vulnerability of groundwater in the surficial aquifers, where a DRASTIC Rating of 1 represents low vulnerability, and a DRASTIC rating of 8 represents higher vulnerability to groundwater contamination.


5.6 Conclusions

The groundwater in the Annapolis Valley found to be most vulnerable to contamination is contained within the highly productive

surficial HSU along the valley floor (outwash, kame field and esker, and alluvial deposits). This is due to the high permeability of those

sediments, the flat topography, and elevated values of recharge. The bedrock aquifers most vulnerable to contamination were also located

within the valley floor, in the productive Wolfville Formation, Blomidon Formation, and Horton Group HSU. Overall, bedrock HSU

were less vulnerable than surficial HSU. North and South Mountain were less variable and less vulnerable for both bedrock and surficial

HSU. The high vulnerability in the valley floor is a concern due to the dense population in that area, which increases the risk of surface

contamination from agriculture, industrial and wastewater sources.

These results highlight general regions of high vulnerability that will require attention as Kings County continues to grow, putting

pressure on aquifers for water, while simultaneously increasing risk of contamination. Higher quality and more regularly distributed

spatial data (such as for the depth to water point data) would further refine vulnerability results. The latest well log data (between 2004

and up to September 2011) have been downloaded (Figure 5.18) for Kings County only. These data represent 4153 new well log data

points, while the well log data used in Blackmore (2006) within Kings County represent 10105 well logs. The new data have not been

assessed for quality control, precision or accuracy, but more significantly, they do not represent very much new spatial information

(Figure 5.19).


Figure 5.18: New well log data downloaded from the Groundwater Information Network (GIN, http://gw-info.net/) and Nova Scotia Department of the Environment (http://www.gov.ns.ca/nse/groundwater/welldatabase.asp).

http://gw-info.net/

http://www.gov.ns.ca/nse/groundwater/welldatabase.asp


Figure 5.19: Well log data in the bedrock and surficial deposits used in Blackmore (2006), and recently downloaded well log data (>2004).


5.7 References

Aller, L., Bennett, T., Lehr, J. H., Petty, R. and Hackett, G. 1987, DRASTIC: A Standardized System for Evaluating Ground Water

Pollution Potential Using Hydrogeologic Settings. National Water Well Association, Dublin, OH.

Aller, L., Lehr, J., Petty, R., and Bennett, T. 1991. DRASTIC: A Standardized System to Evaluate Ground Water Pollution Potential

Using Hydrogeological Settings (In Proceedings of the NWWA/API Conference on Petroleum Hydrocarbons and Organic Chemicals in

Ground Water, November 1986). In Understanding DRASTIC: An Anthology. National Ground Water Association, Dublin, OH. pp.

38-57.

Al-Zabet, T. 2002. Evaluation of aquifer vulnerability to contamination potential using the DRASTIC method. Environmental

Geology, 43: 203-208.

Belousova, A. P. 2003. The Basic Principles and Recommendations for the Assessment and Mapping of the Degree of Groundwater

Protection against Pollution. Water Resources, 30: 613-622.

Blackmore, A. 2004. AVCAS Data Compilation. Applied Geomatics Research Group, Centre of Geographic Sciences, Nova Scotia

Community College, Middleton, NS.

Blackmore, A. 2006. Groundwater Vulnerability to Potential Contamination in the Annapolis Valley, Nova Scotia. M.Sc. Thesis,

Acadia University Geology Department, Wolfville, NS.

Cann, D.B., MacDougall, J.I., and Hilchey, J.D. 1965. Soil Survey of Kings County, Report No. 15. Canada Department of Agriculture

and Nova Scotia Department of Agriculture and Marketing.

Foster, S. S. D. 1998. Groundwater recharge and pollution vulnerability of British aquifers: a critical overview. In Groundwater

Pollution, Aquifer and Vulnerability. Edited by N.S. Robins. Geological Society, London, Special Publications, 130: 7-22.


Fredrick, K. C., Becker, M. W., Flewelling, D. M., Silavisesrith, W., and Hart, E. R. 2004. Enhancement of aquifer vulnerability

indexing using the analytic-element method. Environmental Geology, 45: 1054-1061.

Gogu, R. C. and Dassargues, A. 2000. Current trends and future challenges in groundwater vulnerability assessment using overlay and

index methods. Environmental Geology, 39: 549-559.

Health Canada. 2006. Guidelines for Canadian Drinking Water Quality. Federal-Provincial-Territorial Committee on Health and the

Environment. Health Canada [online]. Available from http://www.hc-sc.gc.ca/ewh-semt/water-eau/index_e.html [cited June 1, 2006].

Heath, R. C. 1987. Basic Ground-Water Hydrology. U.S. Geological Survey Water-Supply Paper 2220, U.S. Department of the

Interior, U.S. Geological Survey.

MacDougall, J.I., Nowland, J.L., and Hilchey, J.D. 1969. Soil Survey of Annapolis County, Report No. 16. Canada Department of

Agriculture and Nova Scotia Department of Agriculture and Marketing.

Neily, P.D., Quigley, E., Benjamin, L., Stewart, B., and Duke, T. 2003. Ecological Land Classification for Nova Scotia, Volume 1 –

Mapping Nova Scotia’s Terrestrial Ecosystems. Report DNR 2003-2. Nova Scotia Department of Natural Resources, Renewable

Resources Branch.

Palmer, R. C. and Lewis, M. A. 1998. Assessment of groundwater vulnerability in England and Wales. In Groundwater Polllution,

Aquifer and Vulnerability. Edited by N. S. Robins. Geological Society, London, Special Publications, 130: 191-198.

Paradis, S.J., Bolduc, A.M., and Stea, R.R. 2005. Surficial geology, Annapols Valley, Nova Scotia. Geological Survey of Canada, Open

File 5276, Scale 1:100 000.


Rivard, C., Paradis, D., Paradis, S., Bolduc, A., Morin, R. H., Lioa, S., Pullan, S., Gauthier M. J., Trepanier, S., Blackmore, A., Spooner,

I., Deblonde, C., Fernandes, R., Castonguay, S., Hamblin, T., Michaud, Y., Drage, J., and Paniconi, C. 2006. Canadian Groundwater

Inventory: Regional Hydrogeological Characterization of the Annapolis-Cornwallis Valley Aquifer, In press.

Rivard, C., Ross, M., Michaud, Y., Hamblin, T., Drage, J., Blackmore, A., Webster, T., Paniconi, C., and Deblonde, C. 2004.

Preliminary Hydrogeological Characterization of the Annapolis Valley, Nova Scotia. [online]. Available from

http://cgcq.rncan.gc.ca/Annapolis/projet/documents/articles/AIH_2004_93585.pdf.

Schwartz, F.W. and Zhang, H. 2003. Fundamentals of Ground Water. John Wiley and Sons, Inc., New York, NY.

The World Bank. 2012. Water Resource Management: Challenges. Retrieved from: http://water.worldbank.org/topics/water-resources-

management

Timmer, D. 2003. Watershed Characterization Project, Guelph Water Management Group, University of Guelph [online]. Available

from http://www.uoguelph.ca/gwmg/wcp_home/Pages/A_home.htm [November 24, 2005].

Timmer, D. K., de Loë, R. C., and Kreutzwiser, R. D. 2005. Source Water Protection in the Annapolis Valley, Nova Scotia: Lessons

for Building Local Capacity. Land Use Policy, In press.

Trescott, P. C. 1967. An Investigation of the Groundwater Resources of the Annapolis-Cornwallis Valley, Nova Scotia. Ph.D. Thesis,

Graduate College, University of Illinois, Urbana, IL.

Trescott, P. C. 1968. Groundwater Resources and Hydrogeology of the Annapolis-Cornwallis Valley, Memoir 6. Nova Scotia, Province

of Nova Scotia Department of Mines, Halifax, NS.

http://water.worldbank.org/topics/water-resources-management

http://water.worldbank.org/topics/water-resources-management

123 Overall Conclusions, Discussion, Future Work

6 Overall Conclusions, Discussion, Future Work

The Kings 2050 project is about looking forward and planning a future for Kings County that considers environment, demographics, land

use, and the economy. Before one can plan ahead, however, one must have an accurate picture of the present. To this end, the Kings 2050

team has gathered reports from partners and stakeholders, each an expert in their field, and will put them all together to form a

comprehensive picture of Kings County today.

The research that has been done at the AGRG has provided that background information for four main areas: floodplain mapping in a

climate change scenario, land use and land cover mapping, slope constraint mapping, and aquifer vulnerability mapping. The high-

resolution lidar-based digital elevation models, georeferenced high-quality orthophoto mosaics, powerful geomatics software and

hydrodynamic models used to carry out this research all contribute to the most accurate results possible, and enable Kings 2050 to move

forward in achieving its goals.

floodplain delineation, land use mapping,...

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