Floodplain Delineation, Land Use Mapping, Constraint Mapping, and Aquifer Vulnerability

Transcription

1 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

2 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 km 2 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 km 2, and rural development land use covers 134 km 2. Agricultural land use was modified to reflect losses to urban and rural development; the net loss was 13 km 2 from i Executive Summary

3 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. ii Executive Summary

4 Table of Contents Executive Summary List of Figures List of Tables i vii xiv 1 Kings 2050 Project Overview 2 2 Floodplain Analysis and Delineation Introduction Study Area Coastal Flooding Fluvial flooding Literature Review Methods Lidar and DEM Topologic-Hydrological Data Processing Environmental Data Watershed Modelling Results Discussion...35 iii Executive Summary

5 2.4.1 Floodplain Mapping Storm Surge Mapping References Land Cover and Land Use Mapping Introduction Data Summary Methods Clear Cut Mapping Urban and Agricultural Land Use Updating Results Clear Cut Mapping Urban and Agricultural Land Use Updating Discussion Conclusions Development Constraint Mapping Introduction Methods Results...73 iv Executive Summary

6 m DEM m DEM Discussion and Conclusions References Groundwater and Surficial Geology Mapping Introduction Methods Study Area Hydrostratigraphic Units The DRASTIC Model DRASTIC Parameter Data Depth to Water Net Recharge Aquifer Media Soil Media Topography Impact of the Vadose Zone Hydraulic Conductivity of the Aquifer v Executive Summary

7 5.4 Results Modelled Vulnerability Scenarios Results by Hydrostratigraphic Unit Discussion Conclusions References Overall Conclusions, Discussion, Future Work 123 vi Executive Summary

8 List of Figures Figure 2.1: The watersheds and rivers being modelled in this study over a shaded relief elevation model Figure 2.2: Short Duration Rainfall Intensity-Duration-Frequency Data for Kentville. Source: ftp://ftp.tor.ec.gc.ca/pub/engineering_climate_dataset/idf/ Figure 2.3: Flooding of the Gaspereau River in Gaspereau on March 31, Figure 2.4: Flooding in Meadowview in November, 2010 (source: Kings County News, 12 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 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 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) 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 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 vii List of Figures

9 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 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) 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 = viii List of Figures

10 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 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 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 The yellow arrows indicate areas detected by the satellite analysis that have been clear cut since Step 5: The satellite image is used to verify that the areas indicated by the yellow arrows have been clear cut by 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 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). ix List of Figures

11 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 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 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 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 Figure 3.10: A total of 39 km 2 of clear cut land has been mapped since 2005, mainly on South Mountain x List of Figures

12 Figure 3.11: New development since 2002 in the Kentville-Wolfville Urban corridor Figure 3.12: Final land use map for Kings County 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) Figure 4.2: The soil names of the soils categorized as having poor and very poor drainage 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 xi List of Figures

13 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 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 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) 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) Figure 5.5: Depth to water data for bedrock (left panel) and surficial (right) aquifers Figure 5.6: Net Recharge Data obtained from GSC Figure 5.7: Aquifer Media data for Bedrock (top) and Surficial (bottom) geology Figure 5.8: Soil media from the 1960s soils report for Annapolis and Kings counties xii List of Figures

14 Figure 5.9: Data used for the topography parameter are Percent Slope, and were calculated from a DEM obtained from the NSGC 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 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) Figure 5.12:Bedrock Aquifers Results Figure 5.13: Surficial Aquifers Results 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) 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 Figure 5.18: New well log data downloaded from the Groundwater Information Network (GIN, and Nova Scotia Department of the Environment ( 118 Figure 5.19: Well log data in the bedrock and surficial deposits used in Blackmore (2006), and recently downloaded well log data (>2004) xiii List of Figures

15 List of Tables Table 3.1: Land use data layers Table 3.2: Summary of changes in land use areas Table 4.1: Statistics for the 2 m DEM for areas with steep slope and poor drainage Table 4.2 : Statistics for the 5 m DEM for areas with steep slope and poor drainage 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) Table 5.2: DRASTIC results ratings and descriptions of relative vulnerability (after Aller et al., 1987) Table 5.3: Groundwater vulnerability scenarios xiv List of Tables

16 1 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. 2 Kings 2050 Project Overview

17 This report contains four chapters containing the results of the research that has been done at the Applied Geomatics Research Group for Kings 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. 3 Kings 2050 Project Overview

18 2 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 4 Floodplain Analysis and Delineation

19 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 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 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) 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 5 Floodplain Analysis and Delineation

20 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. 6 Floodplain Analysis and Delineation

21 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. 7 Floodplain Analysis and Delineation

22 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) 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 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.). 8 Floodplain Analysis and Delineation

23 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 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 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 9 Floodplain Analysis and Delineation

24 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. 10 Floodplain Analysis and Delineation

25 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 Floodplain Analysis and Delineation

26 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 km 2 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, Figure 2.4: Flooding in Meadowview in November, 2010 (source: Kings County News, 12 Floodplain Analysis and Delineation

27 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 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. 13 Floodplain Analysis and Delineation

28 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) 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. 14 Floodplain Analysis and Delineation

29 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 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 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 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 15 Floodplain Analysis and Delineation

30 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 m 2 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) 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). 16 Floodplain Analysis and Delineation

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

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

33 2.2.4 Watershed Modelling 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 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. 19 Floodplain Analysis and Delineation

34 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 m were set to 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 m to 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/ :00:00 PM and 12/1/ :00:00 PM at a fixed time step of 2 seconds. The Annapolis and Fales River models ran from 6/17/ :00:00 PM to 12/1/ :00:00 PM, due to the unavailability of bounding Annapolis River water level information for the first half of Floodplain Analysis and Delineation

35 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 interwatershed 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) 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 21 Floodplain Analysis and Delineation

36 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 crosssection nearby. 22 Floodplain Analysis and Delineation

37 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 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 on the closest high tide before the date of flood level maxima as derived from the previously run predicted tide driven flood models (Section ). 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 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 23 Floodplain Analysis and Delineation

38 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 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 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. 24 Floodplain Analysis and Delineation

39 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 Floodplain Analysis and Delineation

40 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 Floodplain Analysis and Delineation

41 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 Floodplain Analysis and Delineation

42 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 Floodplain Analysis and Delineation

43 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 Floodplain Analysis and Delineation

44 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. 30 Floodplain Analysis and Delineation

45 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. 31 Floodplain Analysis and Delineation

46 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. 32 Floodplain Analysis and Delineation

47 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. 33 Floodplain Analysis and Delineation

48 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 Floodplain Analysis and Delineation

49 2.4 Discussion 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 = Floodplain Analysis and Delineation

50 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. 36 Floodplain Analysis and Delineation

51 Figure 2.21: The Canard and Cornwallis River hydrodynamically derived floodplains (colored polygons) coincide generally well with the existing floodplain zoning (hatched lines). 37 Floodplain Analysis and Delineation

52 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 Floodplain Analysis and Delineation

53 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 Floodplain Analysis and Delineation

54 Figure 2.24: Near the area of Caribou Bog, the hydrodynamic model (colored polygons) shows greater discrepancy to the zoning (hatched lines). 40 Floodplain Analysis and Delineation

55 41 Floodplain Analysis and Delineation

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

57 43 Floodplain Analysis and Delineation

58 2.5 References Atlantic Climate Adaptation Solutions Association (ACASA) Themes: Inland. Retrieved from: Bleakney, S Sods, Soil, and Spades, The Acadians at Grand Pré and Their Dykeland Legacy. CBC News Climate change to bring fewer, stronger storms. Retrieved from: CBCL Fales River Flood Study: DRAFT Report. Report No CBCL Meadowview Flood Study: Draft Report. Report No Chisolm, Leanne Recommendations for the Redevelopment of the Port Williams Waterfront. Retrieved from: for%20the%20redevelopment%20of%20the%20port%20williams%20waterfront%20-%20december% 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 Clean Nova Scotia How is climate change affecting Nova Scotia? Retrieved from: Climate Change Nova Scotia The impacts of climate change in Nova Scotia. Retrieved from: Environment Canada Public Alerting Criteria. Retrieved from: 44 Floodplain Analysis and Delineation

59 Forbes, D.L., Manson, G.K., Charles, J., Thompson, K.R., and Taylor, R.B 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 Port Williams SPS mapping: Floodplain policy recommendation. Unpublished manuscript. Centre for Geographic Sciences: Lawrencetown, NS. Fullarton, Catherine, and Pente, Adam 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 Godin G 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 Kentville: Meadowview flood not town's fault. The Kings County Advertiser. Retrieved from: Leiske, D. L. and Borrnemann, J 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 Coastal Impacts of Climate Change and Sealevel Rise on Prince Edward Island. Geological Survey of Canada. Open File 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 Global climate projections. In Climate Change 2007: The Physical 45 Floodplain Analysis and Delineation

60 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 Natural Resources Canada (NRCAN) Climate Change Impacts and Adaptation in Atlantic Canada. Nova Scotia Department of Agriculture Dykeland History Archive. Retrieved from Peltier, W.R 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, Raper, S. C., & Braithwaite, R. J Low Sea-level Rise Projections from Mountain Glaciers and Icecaps Under Global Warming. Nature, 439, Richards, William and Daigle, Real Scenarios and Guidance for Adaptation to Climate Change and Sea Level Rise- NS and PEI Municipalities. Atlantic Climate Adaptation Solutions Association. Retrieved from Rhamstorf, S.; Cazenave, A.; Church, J.A.; Hansen, J.E.; Keeling, R.F.; Parker, D.E.; Somerville, R.C.J Recent climate observations compared to projections. Science, 316, 709. Royal Institute of British Architects Flooding Explained. Retrieved from: 46 Floodplain Analysis and Delineation

61 Ruffman, Alan A Multi-disciplinary and Inter-scientific Study of The Saxby Gale: an October 4-5, 1869 hybrid hurricane and record storm surge. Retrieved from Saroli and Story, Saroli, Miranda and Story, Chris 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 Shaw, J., Taylor, R., Forbes, D., Ruz, M., & Solomon, S Sensitivity of the Coasts of Canada to Sea-level Rise. Geological Survey of Canada (Bulletin 505), Natural Resources Canada, Shaw, J., Taylor, R.B., Forbes, D.L., Ruz, M-H., and Solomon, S Sensitivity of the Canadian coast to sea-level rise. Geological Survey of Canada Open File Report, No. 2825, 114 p. Starratt, Kirk Meadowview bearing brunt of flooding? The Kings County Advertiser. Retrieved from: The Weather Network Is Climate Change Causing More Hurricanes? Retrieved from: Titus, J. G., Park, R. A., Leatherman, S. P., Weggel, J. R., Greene, M. S., Mausel, P. W., et al Greenhouse Effect and Sea-level Rise: The Cost of Holding Back the Sea. Coastal Management, 19, Webster, T.L., Christian, M., Sangster, C. and Kingston, D 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. 47 Floodplain Analysis and Delineation

62 Webster, T., Forbes, D., Dickie, S., & Shreenan, R Using Topographic Lidar to Map Risk from Storm-surge Events for Charlottetown, Prince Edward Island, Canada. Canadian Journal of Remote Sensing, 30 (1), Webster, T.L. and Forbes, D.L Using Airborne lidar to map exposure of coastal areas in Maritime Canada to flooding from stormsurge events: A review of recent experience. Canadian Coastal Conference. Webster, T.L., Forbes, D.L., MacKinnon, E. and Roberts, D Floodrisk mapping for storm-surge events and sea-level rise in Southeast New Brunswick. Canadian Journal of Remote Sensing, Vol. 32, No. 2, pp Webster, T.L., McGuigan, K., MacDonald, C 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 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 Webster, T.L., Smith, T., Collins, K 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. 48 Floodplain Analysis and Delineation

63 3 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. 49 Land Cover and Land Use Mapping

64 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, Land use base information 2002, last updated in 2006 for treated trees only alders, clear cut, as well as non-forest land uses such as urban and agriculture. NS Department of Agriculture Lands identified as agricultural. Includes 1998 Agricultural Land Use Project (ALIP) types of crops, e.g. rotational. NS Geomatics Center Orthophotos Black and white aerial photograph 2008 mosaics Kings County Land Use Agricultural land classified into subcategories Continually updated based on parcels, % land use from assessment 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 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 50 Land Cover and Land Use Mapping

65 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 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 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. 51 Land Cover and Land Use Mapping

66 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 The yellow arrows indicate areas detected by the satellite analysis that have been clear cut since Step 5: The satellite image is used to verify that the areas indicated by the yellow arrows have been clear cut by 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 Land Cover and Land Use Mapping

67 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 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. 53 Land Cover and Land Use Mapping

68 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.) 54 Land Cover and Land Use Mapping

69 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 Land Cover and Land Use Mapping

70 3.4 Results Clear Cut Mapping Figure 3.4 shows land in Kings County that has been clear cut since 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 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 The largest clear cut (~1.5 km 2 ) was located in a heavily clear cut area between Gaspereau Lake and Lake George (Figure 3.5). 56 Land Cover and Land Use Mapping

71 Figure 3.4: Final clear cut land use code. Includes DNR clear cuts and satellite analysis clear cuts. 57 Land Cover and Land Use Mapping

72 Figure 3.5: Area of heavy clear cutting between Lake George and Gaspereau Lake. 58 Land Cover and Land Use Mapping

73 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 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. 59 Land Cover and Land Use Mapping

74 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 Land Cover and Land Use Mapping

75 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 Land Cover and Land Use Mapping

76 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 Land Cover and Land Use Mapping

77 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 Land Cover and Land Use Mapping

78 3.5 Discussion The satellite analysis resulted in ~39 km 2 of new clear cuts being added to the existing DNR clear cut data, resulting in a total of 135 km 2 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 km 2 to 198 km 2. 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 km 2 of agricultural land, and 6 km 2 of new agricultural land was gained, resulting in a net loss of 13 km 2 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 km 2 of agricultural land lost, but 102 km 2 of urban development and 39 km 2 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. 64 Land Cover and Land Use Mapping

79 Land Use Previous Area (km 2 ) New Area (km 2 ) Change (km 2 ) Clear Cut Urban Urban Rural = Agriculture (Total ag. land gained = 6, total ag. land lost = 19) Table 3.2: Summary of changes in land use areas. 65 Land Cover and Land Use Mapping

80 Figure 3.10: A total of 39 km 2 of clear cut land has been mapped since 2005, mainly on South Mountain. 66 Land Cover and Land Use Mapping

81 Figure 3.11: New development since 2002 in the Kentville-Wolfville Urban corridor. 67 Land Cover and Land Use Mapping

82 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 km 2 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 km 2 of Kings County, and rural land use covered 134 km 2 ; overall urban land classifications increased by 102 km 2 since Agricultural land use classification decreased by 13 km 2 ; 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 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. 68 Land Cover and Land Use Mapping

83 Figure 3.12: Final land use map for Kings County. 69 Land Cover and Land Use Mapping

84 4 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. 70 Development Constraint Mapping

85 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 lidarderived DEM averaged to 5 m resolution, merged with the NSTDB 20 m resolution data (right). 71 Development Constraint Mapping

86 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. 72 Development Constraint Mapping

87 4.3 Results 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) 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). 73 Development Constraint Mapping

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

89 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 Development Constraint Mapping

90 Figure 4.5: Constraint mapping showing percent slope within lidar coverage, civic points, and poorly drained soils for the town of Wolfville. 76 Development Constraint Mapping

91 Figure 4.6: Constraint mapping showing percent slope within lidar coverage, civic points, and poorly drained soils for the Town of Kentville. 77 Development Constraint Mapping

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

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

94 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 Development Constraint Mapping

95 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 Development Constraint Mapping

96 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. 82 Development Constraint Mapping

97 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. 83 Development Constraint Mapping

98 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. 84 Development Constraint Mapping

99 4.4 Discussion and Conclusions 2 The development constraint mapping results show that 60 km of Kings County, or 5% of the area of the 2 m DEM, has a slope between 15 and 20%; and 115 km 2, or 9.6% of the area of the 2 m DEM, is classified as having a slope greater than 20% (Table 4.1). 14 km 2 is classified as having 15-20% slope and poorly drained soil (1.2% of the area of the 2 m DEM); 25 km 2 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 km 2. For the 5 m resolution DEM, which covers all of Kings County (2213 km 2 ), 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 (km 2 ) Percent area of 2 m DEM 15-20% slope % slope and poor drainage >20% slope >20% slope and poor drainage Table 4.1: Statistics for the 2 m DEM for areas with steep slope and poor drainage. Kings County 5 m DEM Area (km 2 ) Percent area of 5 m DEM 15-20% slope % slope and poor drainage >20% slope >20% slope and poor drainage Table 4.2 : Statistics for the 5 m DEM for areas with steep slope and poor drainage. 85 Development Constraint Mapping

100 4.5 References District of North Vancouver Guide to Living Near Steep Slopes. Retrieved from: 86 Development Constraint Mapping

101 5 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 87 Groundwater and Surficial Geology Mapping

102 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 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. 88 Groundwater and Surficial Geology Mapping

103 5.2 Methods Study Area The study area covers 2100 km 2 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 km 2, while the other four watersheds together drain about 500 km 2 (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). 89 Groundwater and Surficial Geology Mapping

104 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 Groundwater and Surficial Geology Mapping

105 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. 91 Groundwater and Surficial Geology Mapping

106 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 Groundwater and Surficial Geology Mapping

107 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 Groundwater and Surficial Geology Mapping

108 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). 94 Groundwater and Surficial Geology Mapping

109 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 Groundwater and Surficial Geology Mapping

110 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/ft 2 ) (30.5 m+) 0-2 (0-50 mm) Nonshrink-ing & Nonaggre-gated Clay 18 + Confining Aquifer (4.72E E-05 m/s) ( m) Massive Shale (1 to 3) Muck (4.72E E-04 m/s) ( m) 2-4 Metamorph- Clay Loam Silt/Clay (2 to 6) ic/igneous (2 to 5) Silty Loam ( mm) 4 Weathered Metamorphic/Igneous (3 to 5) ( m) Glacial Till (4 to 6) Loam ( mm) Bedded Sandstone, Limestone & Shale Sequences (5 to 9) Massive Sandstone (4 to 9) Massive Limestone (4 to 9) Shale (2 to 5) Metamorphic /Igneous (2 to 8) 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) (1.41E E-04 m/s) (3.30E-04 to 4.72E-04 m/s) ( m) Shrinking and/or Aggregat-ed Clay ( mm) Sand and Gravel (4 to 9) Peat Sand & Gravel (6 to 9) (4.72E E-04 m/s) ( m) Basalt (2 to 10) Sand 2-6 Basalt (2 to 10) (0-1.5 m) 10 + (254 mm+) Karst Limestone (9 to 10) Thin or absent Gravel 0-2 Karst Limestone (8 to 10) (9.43E-04 m/s +) Weight 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 Groundwater and Surficial Geology Mapping

111 Model Index Value Result Vulnerability Category Description of Relative Vulnerability Index Rating 1 to 79 1 Extremely low 80 to 99 2 Very low 100 to Low 120 to Moderate 140 to Moderately high 160 to High 180 to Very high 200 to 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) 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. 97 Groundwater and Surficial Geology Mapping

112 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 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 98 Groundwater and Surficial Geology Mapping

113 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 for the low vulnerability scenario and by for the high vulnerability scenario. Figure 5.6: Net Recharge Data obtained from GSC. 99 Groundwater and Surficial Geology Mapping

114 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. 100 Groundwater and Surficial Geology Mapping

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

116 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. 102 Groundwater and Surficial Geology Mapping

117 Figure 5.8: Soil media from the 1960s soils report for Annapolis and Kings counties 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 103 Groundwater and Surficial Geology Mapping

118 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 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 104 Groundwater and Surficial Geology Mapping

119 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 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 Groundwater and Surficial Geology Mapping

120 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 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. 106 Groundwater and Surficial Geology Mapping

121 In Blackmore (2006) a seventh scenario was run using New Quaternary mapping (1: ) 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. 107 Groundwater and Surficial Geology Mapping

122 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 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. 108 Groundwater and Surficial Geology Mapping

123 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. 109 Groundwater and Surficial Geology Mapping

124 Figure 5.12:Bedrock Aquifers Results 1-6. Figure 5.13: Surficial Aquifers Results Groundwater and Surficial Geology Mapping

125 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). 111 Groundwater and Surficial Geology Mapping

126 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. 112 Groundwater and Surficial Geology Mapping

127 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 113 Groundwater and Surficial Geology Mapping

128 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 114 Groundwater and Surficial Geology Mapping

129 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 Groundwater and Surficial Geology Mapping

130 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 Groundwater and Surficial Geology Mapping

131 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 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). 117 Groundwater and Surficial Geology Mapping

132 Figure 5.18: New well log data downloaded from the Groundwater Information Network (GIN, and Nova Scotia Department of the Environment ( 118 Groundwater and Surficial Geology Mapping

133 Figure 5.19: Well log data in the bedrock and surficial deposits used in Blackmore (2006), and recently downloaded well log data (>2004). 119 Groundwater and Surficial Geology Mapping