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Flood Hazard and Risk Assessment Research at UNB, GGE Department

GIS Group, Geodesy and Geomatics Engineering

Latest News

Esri ECCE App Challenge 2017 submission.

this app was developed by a group of 3 graduate students to promote sustainable transportation. This app is designed to promote awareness of how your vehicle choice affects your CO2 emissions.

Update of ER2 Calculator

  • Revised calculation for structure and contents damage ($)

  • a) Elevation REST API data for use in Flood Risk Mapping

    Elevation data has historically been commissioned for a given study area and the resulting data (typically) proprietary. However, elevation data, accessible through open web services is becoming increasingly available at regional, national and global levels. This research aims to evaluate the accuracy of the elevation data available from REST API services and determine their suitability for use in floodplain modelling. Two locations across Eastern Canada have been selected for this research. The elevation data from these web services, including Natural Resources Canada CDEM, Google and Bing, will be rasterized and compared to local LiDAR DEMs to access their level of uncertainty.

    Checkout a short article describing our research on Elevation Services and Simplified Flood Models at GoGeomatics:

    Or, come to Geomatics Atlantic in Fredericton in Sept. where we'll be presenting some preliminary findings.

    b) DEM FUSION via novel method of Clustering and Inverse Distance Weighting

    A fused elevation grid from multiple open elevation sources (CDEM, Google and Bing Elevation APIs) is generated using a data-driven approach, to achieve the best-accuracy DEM possible. The proposed method incorporates the fundamentals of the DBSCAN algorithm: neighborhood (eps) and minimum points (MinPts) to form a cluster and identify outliers to exclude from an inverse distance weighted estimate of elevation.

    c) Development of a Framework for Flood Risk Assessment Scenarios and Estimates of Vulnerability and Exposure at the ‘Press of a Button’

    Web map application: a work in progress
    Abstract: Complex computer models exist for flood risk assessment. While technologically sophisticated, these programs are intended, first of all, for use by a small number of technical and scientific experts. Regardless of how comprehensive and sophisticated the existing tools are, they cannot be fully adapted for the Canadian hazard and exposure settings and, in particular, do not fit the needs of the local non-expert public safety community and population living in hazard prone areas. In such situation communicating flood risk to local stakeholders represents even bigger challenge then computing it. This research proposes to develop an analytical conceptual framework based on geospatial business intelligence model to perform riverine flood risk analysis via user friendly tools, essentially, at the press of a button. This framework will allow for creation of flood inundation maps, estimates of vulnerability and exposure, and analysis and reporting of spatio-temporal information for general users and decision makers. The proposed framework is based on Spatial On-Line Analytical Processing (SOLAP) technology which is based on the multi-dimensional paradigm (OLAP philosophy) of providing intuitive and rapid methods of interactively exploring and analyzing large volumes of multi-scale spatio-temporal data. The City of Fredericton is selected as a case-study area, yet through the proposed framework, the developed models may be adapted for any other region.

    If you have questions, comments, ideas, data to share, or wish to get updates on this, please contact Heather: Send E-Mail

    Prototype:

     prototype


    d) API for Flood Loss Estimation

    Online and available now: click here

    RAPID RISK EVALUATION (ER2)

    ONLINE! ER2 Flood.

    Microsoft Excel calculator for estimating risk/damage resulting from flood hazard.

    This calculator allows a user to input structure details for a single building type or multiple buildings and estimates losses. Download/View the user guide for ER2 Flood.

    Want to receive notification of updates to ER2 (*Updates are (roughly) quarterly)? Send us an email

    Basic Version Download:

    Version 2.05 of the RAPID RISK EVALUATION (ER2) calculator is available for download.

    Advanced Version (version 2.04):

    Added features in the Advanced Version include:

  • Metric water levels
  • View Map Buildings (based on Address or Coordinates) and colour/size symbols based on variety of attributes. (through compatibility with Esri Maps for Office)
  • User input of Building Value - to partially address regional variability
  • Over-ride the default damage curve selection
  • Users can Create their Own Depth-Damage curve
  • Please contact if you're interested in the advanced version; Email


    Click on thumbnails for a larger view of the application:


     Welcome  Single  Multi  Multi

    Example of map results using ER2 and Esri Maps for Office:

    To use the Esri Maps for Office you need an ArcGIS Online paid or trial subscription. Dowload Esri Maps for Office from Esri. Click on the image below to view a larger view of ER2 spreadsheet showing buildings with estimated losses from flood scenario. Users can spatially view data based on building address or coordinates. The user can choose from a variety of symbols, sizes and colours using any 'column' of data from ER2. The image below shows estimated Total Loss, through graduated colours of the same symbol.

     EsriMaps for Office



    Instructional Video & Tutorial

    Introduction to ER2. This short video provides an overview of the worksheets available in ER2: Single Building, Multi-building and Flooded Neighbourhood simulations. It shows how users can input building details in the worksheet, copy and paste from other rows, or from other files.

    (A) ER2 Introduction Video:

    (B) ER2 Advanced Features:

    (C) ER2 Maps!

    Simplified flood models

    Complex computer models exist for flood risk assessment and while technologically sophisticated, these programs are intended, first of all, for use by a small number of technical and scientific experts and require considerable processing time and extensive inputs. These existing solutions are generally not well suited for flood prediction in near real-time and often exceed the data available for any given community. This research developed standardized methods, adapted into user-friendly tools which accept limited user input and are based on hydrologic principles and processes and widely accepted risk computation methods, by leveraging open data. The developed flood mapping approaches access, and through a novel data fusion method, creates a better quality digital elevation model (DEM) from multiple open source elevation datasets. This fused DEM is combined with other open source data (e.g., IDF curves, river flow data, watershed boundaries, etc.) to generate a flood inundation surface through two methods: 0D bathtub model and hybrid 1D/2D raster cell storage approach. The 0D model ignores flow rates and changes over time, producing a grid of the maximum spatial extent and depth, calculated as the difference between the terrain elevation and the computed water surface. The hybrid model solves 1D kinematic wave approximation of shallow water equations in the channel and treats the floodplain as 2D flooding storage cells.

    0D bathtub model

    In this model, user input is limited to a number of points and an associated water depth - or data may be extracted from nearby river gauges. Estimated flood depth at a given number of points are inputs to ordinary kriging algorithm and an interpolated flood surface is generated. The DEM data is subtracted from the simulated flood surface to generate the flood inundation extent and water depths and then screened for hydrological connectivity: Bathtub model

    Preliminary results: DEM data obtained from two elevation providers were tested with the kriged water surface to evaluate the accuracy of the derived flood surface, CDEM and LiDAR. The resulting flood surfaces were compared to the observed flood boundary of the 2008 flood event to determine the goodness of fit of the simulated inundation extent. Three profile lines were generated along the northern shore of the Saint John River to compare the water depth for each of the surfaces. Bathtub model Results Bathtub Model Results Profile

    Using a Fit Measure, the observed inundated area of the historic flood is compared to that of the area predicted by the model and the overlapping area. A value of F = 1 indicates a perfect match between predicted and observed areas.

    CDEMLiDAR
    Estimate - Error0.77370.9895
    Estimate 0.78530.9996
    Estimate + Error0.79461.0
    Hybrid 1D/2D Model

    The second reduced complexity model implemented is based on the model popularized by Bates and De Roo [2000]. The authors approximated the channel flow by a 1D kinematic wave approximation and treated the floodplain as 2D flooding, where the spreading is simulated using cell storage reservoirs over a raster grid. This physically based raster model solves momentum and continuity equations for the kinematic wave. In this study, a linear scheme which uses the backward-difference method is used to derive the explicit finite-difference equations in terms of space (j) and time (i) [Chow et al., 1988]

    User input is described in three categories: (a) geographic location, (b) rain event, (c) local environmental conditions. The geographic location is determined as the watershed limit provided by GeoGratis (ftp.geogratis.gc.ca/pub/nrcan_rncan/vector/geobase_nhn_rhn/), based on the bounding box of the map extents or may be manually selected by drawing graphics. The rain event is selected by the user as a time period of rain, (e.g.: 5m, 1h, etc.) over a specified recurrence interval from a series of drop-down menus. The local environmental conditions include: condition (level of saturation), soil group (clays, loam, etc.) and land use category (e.g.: residential, forest, farmland, etc.), which allow the program to select of appropriate Soil Conservation Service (SCS) Curve Number (CN).

    Analysis in progress.

    References:

    Bates, P., & De Roo, A. P. J. (2000). A simple raster-based model for flood inundation simulation. Journal of Hydrology, 236(1-2), 54-77.

    Chow, V. T., Maidment, D. R., & Mays, L. W. (1988). Applied hydrology. New York: McGraw-Hill.

    Presentations & Conference Papers



    Upcoming:

    (A) Geomatics Atlantic

    here to link directly to Conference Site

    Geomatics Atlantic 2016,

    26 - 28 September, Fredericton, New Brunswick

    Past Presentations/Conferences:

    (A) ISPRS Prague

    here to link directly to INTERNATIONAL SOCIETY FOR PHOTOGRAMMETRY AND REMOTE SENSING (ISPRS), XXIII CONGRESS web site. The conference runs from July 12 - 19th.

    As part of the Technical Commission VIII(/1) Disaster Risk Reduction, Presented research and results from our paper: RAPID RISK EVALUATION (ER2) USING MS EXCEL SPREADSHEET: A CASE STUDY OF FREDERICTON (NEW BRUNSWICK), which you'll be able to find in the ISPRS Annals: here

    Past papers and Conferences:

    (A) CSCE 22nd Canadian Hydrotechnical Conference, April 29 May 2, 2015

    here to link directly to CSCE 22nd Canadian Hydrotechnical Conference website

    a) Presentation on reserach proposal into use of simplified flood models in risk analysis. For copy of presentation, click here and for extended abstract, click here

    b) Poster presented representing research into using MS Excel to compute flood risk estimates:
     front page

    (B) Hazus Canada - January call

    Canadian Risk and Hazards Network (CRHNet) Symposium, Toronto, ON. October 2014. Click here to link directly to CanHUG January call information page.

    Title: Flood risk assessment in Fredericton using MS Excel spreadsheet .

    Authors: H. McGrath, E.Stefanakis, M. Nastev

    Department of Geodesy and Geomatics Engineering, University of New Brunswick, Natural Resources Canada


    (C) Sensitivity Analysis of Flood Damage Estimates @ CRHNet, 2014

    Canadian Risk and Hazards Network (CRHNet) Symposium, Toronto, ON. October 2014. Click here to link directly to CRHNet site.

    Title: Sensitivity Analysis of Flood Damage Estimates.

    Authors: H. McGrath, E.Stefanakis, M. Nastev

    Department of Geodesy and Geomatics Engineering, University of New Brunswick, Natural Resources Canada
    Abstract:
    Recently, the U.S. FEMA’s standardized best-practice methodology Hazus for estimating potential losses from common natural hazards, including earthquakes, flood, and hurricanes has been adopted for use in Canada. Flood loss estimation relies on the combination of three components: flood level, inventory of the built environment, and pre-selected vulnerability parameters such as depth-damage functions, all of which have large associated uncertainties. Some of these parameters, such as occupancy schemes and vulnerabilities, have been carried over from the U.S. version on the presumption of regional similarities between Canadian provinces and states south of the border. Many of the uncertainties can be reduced by acquiring additional data or by improving the understanding of the physical processes. This paper presents results from a series of flood model analyses to illustrate the sensitivity that can be associated to the depth-damage function, flood level, and restoration duration and to identify their relative impacts on the resulting losses. The city of Fredericton is chosen as the test case as it was subjected in 2008 to flood water levels breaching 1.86 m above flood stage resulting in more than 680 residents evacuated from their homes, and economic costs of more than $23 million. The loss results are expressed by the number of flooded residential buildings which varied between 579 and 623 and the range of replacement cost is $21 million. These results highlight the importance of proper selection of input parameters customized to the study area under consideration.

    Click here to view/download the presentation, or on the images below to preview select slides.
     front page  methodology  base base


    (D) Sensitivity Analysis of Flood Damage Estimates @ UNB GGE Fall 2014 Graduate Seminar

    University of New Brunswick, Geodesy and Geomatics Engineering Fall Graduate Seminar, October, 2014.

    A modified version of the CRHNet presentation was delivered to the faculty and graduate students in the department. Included in this presentation are images of the spatial comparison of the sensitivity of selection of depth-damage curve and water depth.

    Click here to view the presentation pdf or download.
     parameters  changes across study area  base case losses


    (E) I3R2 Conference

    Presented at the INTERNATIONAL INSTITUTE FOR INFRASTRUCTURE RESILIENCE AND RECONSTRUCTION (I3R2) CONFERENCE, at Purdue University, May, 2014. I3R2 website link

    Link to the paper and presentation: Purdue e-Pubs

    Title: A comparison of the HAZUS Flood Mapping Tool against Flood Records from a New Brunswick Municipality

    Authors: H. McGrath, E.Stefanakis, M. McCarthy, M. Nastev

    Department of Geodesy and Geomatics Engineering, University of New Brunswick , Public Safety Canada , Natural Resources Canada
    Abstract
    As our climate is changing the occurrence of extreme weather events and heavier rainfall is becoming more common. This change in weather patterns and precipitation is resulting in greater number of recorded flood events and larger magnitude of flood events. Canadian municipalities are therefore facing a pressing need to perform risk assessments to identify and measure communities at risk of potential economic and societal losses due to flood events. Federal Emergency Management Agency (FEMA) developed a standardized tool, Hazus-MH for loss estimation from natural disasters for use in the USA. This research aims to compare the results from the Hazus model against historical flood records from a New Brunswick municipality. The analysis will include a comparison of the delineation and the depth of the flood across the region. Additionally, a comparison of the loss estimation results from the model will be compared against the recorded economic losses and the rebuilding costs as reported by the municipality.
     Hazus Adaption  fredericton  replacement values


    (F) ISPRS Conference

    Presented at the Joint International Conference on Geospatial Theory, Processing, Modelling and Applications in Toronto, October, 2014 ISPRS website link

    Link to the presentation

    Link to the paper

    Title: Development of a Data Warehouse for Riverine and Coastal Flood Risk Assessment

    Authors: H. McGrath, E.Stefanakis

    Abstract
    In New Brunswick flooding occurs typically during the spring freshet, though, in recent years, midwinter thaws have led to flooding in January or February. Municipalities are therefore facing a pressing need to perform risk assessments in order to identify communities at risk of flooding. In addition to the identification of communities at risk, quantitative measures of potential structural damage and societal losses are necessary for these identified communities. Furthermore, tools which allow for analysis and processing of possible mitigation plans are needed. Natural Resources Canada is in the process of adapting Hazus-MH to respond to the need for risk management. This requires extensive data from a variety of municipal, provincial, and national agencies in order to provide valid estimates. The aim is to establish a data warehouse to store relevant flood prediction data which may be accessed thru Hazus. Additionally, this data warehouse will contain tools for On-Line Analytical Processing (OLAP) and knowledge discovery to quantitatively determine areas at risk and discover unexpected dependencies between. The third application of the data warehouse is to provide data for online visualization capabilities: web-based thematic maps of Hazus results, historical flood visualizations, and mitigation tools; thus making flood hazard information and tools more accessible to emergency responders, planners, and residents. This paper represents the first step of the process: locating and collecting the appropriate datasets.
     Hazus Adaption  fredericton  data flow

    Peer Reviewed Research

    Title: RAPID RISK EVALUATION (ER2) USING MS EXCEL SPREADSHEET: A CASE STUDY OF FREDERICTON (NEW BRUNSWICK)

    Authors: H. McGrath, , E. Stefanakis, M. Nastev

    Published: ISPRS Annals, 2016 XXIII ISPRS Congress, Commission VIII (Volume III-8)

    ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-8, 27-34, doi:10.5194/isprs-annals-III-8-27-2016, 2016

    Abstract: Conventional knowledge of the flood hazard alone (extent and frequency) is not sufficient for informed decision-making. The public safety community needs tools and guidance to adequately undertake flood hazard risk assessment in order to estimate respective damages and social and economic losses. While many complex computer models have been developed for flood risk assessment, they require highly trained personnel to prepare the necessary input (hazard, inventory of the built environment, and vulnerabilities) and analyze model outputs. As such, tools which utilize open-source software or are built within popular desktop software programs are appealing alternatives. The recently developed Rapid Risk Evaluation (ER2) application runs scenario based loss assessment analyses in a Microsoft Excel spreadsheet. User input is limited to a handful of intuitive drop-down menus utilized to describe the building type, age, occupancy and the expected water level. In anticipation of local depth damage curves and other needed vulnerability parameters, those from the U.S. FEMA’s Hazus-Flood software have been imported and temporarily accessed in conjunction with user input to display exposure and estimated economic losses related to the structure and the content of the building. Building types and occupancies representative of those most exposed to flooding in Fredericton (New Brunswick) were introduced and test flood scenarios were run. The algorithm was successfully validated against results from the Hazus-Flood model for the same building types and flood depths


    Title: Sensitivity analysis of flood damage estimates: A case study in Fredericton, New Brunswick

    Authors: H. McGrath, , E. Stefanakis, M. Nastev

    Published: International Journal of Disaster Risk Reduction Volume 14, Part 4, December 2015, Pages 379–387

    Abstract: Recently, the U.S. FEMA's standardized best-practice methodology Hazus for estimating potential losses from common natural hazards, including earthquakes, flood, and hurricanes has been adopted for use in Canada. Flood loss estimation relies on the combination of three components: flood level, inventory of the built environment, and pre-selected vulnerability parameters such as depth-damage functions, all of which have large associated uncertainties. Some of these parameters, such as occupancy schemes and vulnerabilities, have been carried over from the U.S. version on the presumption of regional similarities between Canadian provinces and states south of the border. Many of the uncertainties can be reduced by acquiring additional data or by improving the understanding of the physical processes. This paper presents results from a series of flood risk analyses to illustrate the sensitivity that can be associated to the depth-damage function, flood level, and restoration duration and to identify their relative impacts on the resulting losses. The city of Fredericton is chosen as the test case as it was subjected in 2008 to flood water levels breaching 1.86 m above flood stage resulting in more than 680 residents evacuated from their homes, and economic costs of more than $23 million. The loss results are expressed by the number of flooded residential buildings which varied between 579 and 623 and the range of replacement cost is $21 million. These results highlight the importance of proper selection of input parameters customized to the study area under consideration.


    Title: Development of methods and tools for natural hazard risk analysis in Eastern Canada – Use of knowledge to understand vulnerability and implement mitigation measures

    Authors: Nastev, M., Nollet, M., Abo El Ezz, A., Smirnoff, A., Ploeger, S., McGrath, H., Sawada, M., Stefanakis, E., and Parent, M. (2015).

    Published: ASCE, Nat. Hazards Rev. , 10.1061/(ASCE)NH.1527-6996.0000209 , B4015002.

    Abstract: While Canada is exposed to a variety of natural hazards, most risk and emergency managers currently lack the necessary tools and guidance to adequately undertake rigorous risk assessments. Unlike the complex computer models for natural hazard risk assessment intended for use by a small number of technical experts, user-friendly rapid risk-assessment tools are being developed to allow nonexpert users from the public-safety community to run otherwise complex risk scenarios at a so-called press-of-a-button. This paper reports on the roles and responsibilities of different levels of government in Canada. Part of the ongoing activities carried out jointly by the government and academia in eastern Canada on the development of inventory and seismic and flood risk-analysis tools is discussed, and examples at urban scales for Ottawa, Gatineau, Quebec City, and Fredericton are given.

    Hazus

    For general details about Hazus, visit Hazus Canada

    Hazus flood loss scenario, using nationally supplied general building stock. The nationally supplied general building stock is aggregated data, at the dissemination block (census block level). The building stock was derived from 2011 Census data from Statistics Canada and Dun & Bradstreet. Additional data, representing emergency facilities (hospitals, police & fire stations, and schools), transportation network (bridges and roads), utilities (wastewater and potable facilities) were imported.

    Hazus Validation Study:

    1. Data collection: located datasets for Hazus from local and provincial government for Inventory data (buildings, transporation network, essential facilities, etc.) and flood hazard (flood depth grid) for the case study site of Fredericton, NB
    2. Pre-Processing: reclassify data into acceptable 'Hazus' structure. This involved using field calculators to compute values, reclassifying fields, and copying data into new fields. Also used Google Maps Street view to populate data fields which were not found in government datasets, including: presence/absence of basement, presence/absence of garage, height of first floor, foundation type. These details were added for structures within 100m of the floodplain boundary. The result is Hazus compatible database of existing buildings.
    3. Results of Site Specific (User Defined Facilities). Successfully imported individual buildings to Hazus, and ran analysis on individual structures. Comparison of the results of aggregated (nationally supplied) to site specific, showed an over estimation of losses. Aggregate, residential losses: $68million, site specific losses estimated to be $8 million. The Hazus assumption of 'equal distribution of structures' in a census block (in this study area) overestimates losses in many census blocks.
    4. Calibration of Vulnerability Curves: to date, have not been able to validate the model and determine most appropriate vulnerability curves for the study area. In lieu of this, a sensitivity analysis was performed (see: (a) Sensitivity Analysis of Flood Damage Estimates @ CRHNet, 2014 in Presentations & Papers above) to identify the influence the choice of vulnerability function, water level and restoration duration have on the results of Hazus analysis.

    Hazus program menu

     View

    Inventory menu items, to view/display data

     View

    Hazard menu items, to set/load flood hazard data and scenario

     View

    Analysis menu items, to view/change parameters and select analysis to run

     View

    Results menu items, view reports or thematic data of results

     View

    Tips, Tricks and Templates for Hazus

    Links to documents outlining Hazus data structure, templates for you to merge your datasets to & use ArcMap Field Calculator to populate data, etc.

    1. Files used in Hazus Canada need to be in Latitude/Longitude, referenced to NAD83 datum
    2.  data flow
    3. The Comprehensive Data Management System (CDMS) guide details all the fields, types, domains, defaults, etc. which are either required or optional for risk assessment in the earthquake, flood and wind models. View/Download the CDMS documents directly from FEMA. Or, Click here to download an Excel spreadsheet made from CDMS v2.0 User Guide
    4. CDMS - is very fickle about data fields, lengths, and types!
    5. Hazus comes with demographics, based on the 2011 Statistics Canada Census data.
    6. Hazus comes with general building based on the 2011 Statistics Canada Census data (residential) and Dun and Bradstreet data (Commercial & industrial).
      1. *It's recommended to use your own local level datasets for greater accuracy in the model results
    7. Results can be computed for aggregate (census block (dissemination block)) data or for individual buildings.
    8. When using aggregated data, the assumption is that the structures are equally distributed throughout the census block. For individual blocks, this may lead to over (or under) estimates. It's assumed that the over/under estimations average out over the study area.
       data flow
      image adapted from FEMA Hazus training manual
    9. Hazus estimates debris generated when running analysis on aggregate data.

    more to come!

    temp

    Grand Lake Meadows is the largest freshwater marsh/wetland in the province of New Brunswick. The region consists primarily of a broad flat floodplain and wetland meadow, with elevations ranging from sea level up to ~16 meters. Grand Lake Meadows is classified as a Class II Protected Natural Area by the government of New Brunswick. This classification protects the area from development and allows ‘low impact’ recreational activities.

    Grand Lake Meadows is known for its diverse ecology and its abundant wildlife. This diversity is due to three factors:

  • The presence of Grand Lake
  • Extensive floodplains
  • The presiding water levels over the growing season [GNB, 2013].
  • Grand Lake is the largest open body of water in New Brunswick being a total of 20 miles long and 7 miles wide [Nason, R. 2013]. This large body of water acts as a heat sink: moderating local temperatures, creating the warmest climate in the province, and extending the growing season [D’Arcy, M, 2008].It is considered a floodplain wetland because on any given year approximately 85% of the area is inundated by seasonal floodwaters [Dickinson, P.J, 2008]. In addition to the seasonal flooding, tides displace water and may cause flooding in Grand Lake Meadows.

    Current Research Projects:

    (1) Impact of Flood Waters on Grand Lake Meadows Ecosystem. Past and Present

    Flood waters from both the Saint John River and Grand Lake have the potential to make a dramatic impact on the meadows ecosystem. Although freshet is seasonal, with peaks each spring, it may occur at an time of the year. It is most often caused by heavy rainfall, rapid melting of a thick snow pack, ice jams or failure of a natural or artificial dam. Major flooding was recorded in the Grand Lake Meadows recently (2008) and several times over the last decades (2005, 1993, 1979 and 1973).

    The overflow of water that occurs during a flood can have consequences, both positive and negative, for the physical environment as well as the flora and fauna of the Meadows ecosystem. It therefore becomes a necessity to monitor water levels and map the dry and wet areas in the Meadows on a regular temporal basis. From these records, flood prone areas in the Meadows will be apparent. Additionally, analysis of these spatial-temporal records will lead to a better understanding of the flood waters impact.

    The overall objective, is to create a repository of flood related data and enhanced map visualizations. Advanced tools will be developed to assist the online analytical processing of the content, while both source data and results of the analysis will become accessible through advanced geospatial web services.

    (2) Historical Flooding

    This project is divided into two components: (a) data collection and (b) preparation and visualization.

    (a) Data Collection and Preparation

    Data collection and preparation is gathered from a variety of sources. Much of this data was acquired from government and public records. Many of these records had previously been digitized and available through electronic means, however the bulk of this data is not. An extensive search of four different library systems in the Fredericton area (UNB Libraries, Provincial Libraries, Provincial Archives, and the Legislative Library). These resources provided valuable data into the history of flooding in the region, including details about how flooding impacts the region.

    (b) Visualization of Historical Flood Data

    From the compiled repository from above, the development of meaningful map visualizations and animations for a better understanding of flooding phenomenon in the area and their impact to the ecosystem are the focus of this project.

    (3) Evaluation and Fusion of Elevation Web Services for Flood Mapping

    In New Brunswick, the frequency and magnitude of flood have been increasing in recent years. Providing tools for emergency management response, public awareness and mitigation will help decision makers, emergency responders and local residents. This research team has been working towards this direction in close collaboration with Natural Resources Canada (NRCan), Public Safety Canada and the Hazus Canada initiative since 2013.

    Digital Elevation models (DEM) are an integral part of flood modelling. High resolution DEM data is not always available or affordable for communities, thus other elevation data sources are explored. While the accuracy of some of these sources has been rigorously tested, (e..g.: SRTM and ASTER), others, such as NRCan's Canadian Digital Elevation Model (CDEM) and Google, Esri and Bings' Elevation REST APIs' have not yet been properly evaluated. There are several advantages to using these products, including: Canada wide coverage and machine readable data acquisition; however, limitations include lack of metadata detailing acquisition source and unreported accuracy.

    The scope of this research project is two-fold: (a) evaluate the accuracy of these resources and (b) test a recently developed, novel DEM fusion method. The individual DEMs and fusion DEMs will be compared to a high-resolution Light Detection and Ranging (LidAR) surface, while flood inundation maps will be generated for a study area of high ecological significance - the Grand Lake Meadows.

    Click on the image below to launch the Esri Story Map, created by undergraduate student: Patrick McNeil. This story map introduces describes the history of flood events in this area, including years of flooding, water depths and spatial extents in GLM. Additionally on this site the progression of flooding, from low, to moderate and high flood levels is described and visualized though both text and animation.


     Story Map

    Existing Resources:

    Document Title Author(s) Availability Reference
    The March 1936 Ice Jam Flood Lebrun-Salonen, Melanie L. Electronic Lebrun-Salonen. (1983). The March 2936 Ice Jam Flood in New Brunswick. The New Employment Expansion Development Program. Fredericton: J.E. Peters Managment Limited. link to source
    Hydrotechnical Studies of the Saint John River: McKinley Ferry to Lower Jemseg MacLaren Atlantic Ltd. Government Records (UNB Libraries) MacLaren Atlantic Ltd. (1979). Hydrotechnical Studies of the Saint John River: From McKinley Ferry to Lower Jemseg. Moncton: Canada-New Brunswick Flood Damage Reduction Program.
    New Brunswick's Flood Risk reduction Strategy Province of New Brunswick Electronic Province of New Brunswick. (2014). New Brunswick's Flood Risk Reduction Strategy. Environment and Local Government. Fredericton: Province of New Brunswick. link to source
    Is it Possible for the Mactaquac Dam to Mitigate the Flooding in the Maugerville/Sheffield Area Ismail, Sayed; Harriman, Fred Government Records (NB Legislative Library) Ismail, S., & Harriman, F. (1997). Is it Possible for the Mactaquac Dam to Mitigate the Flooding in the Maugerville/Sheffield Area. Generation Buisness Unit. Fredericton: NB Power Hydro Region.
    New Brunswick River Ice Manual The New Brunswick Subcommittee on River Ice. Electronic The New Brunswick Subcommittee on River Ice. (1989). New Brunswick River Ice Manual. Environment Canada, Inland Waters Directorate. Fredericton: Communication New-Brunswick. link to source
    The Flooding Problem in the Saint John River Valley Inland Waters Directorate Government Records (UNB Libraries) Inland Waters Directorate. (1973). The Flooding Problem in the Saint John River Valley. Department of the Environment. Halifax: Saint John River Basin Board.
    New Brunswick Flood History Database Electronic Database NB Flood History Database, Environment and Local Government:
    Floods of 1961 in New Brunswick Inland Waters Branch Government Records (UNB Libraries) Inland Waters Branch. (1967). Floods of 1961 in New Brunswick. Halifax: Government of Canada: Department of Energy, Mines and Resources.
    Floods in the Saint John River Basin Inland Waters Directorate Government Records (UNB Libraries) Inland Waters Directorate. (1972). Floods in the Saint John River Basin. Department of the Environment. Fredericton: Saint John River Basin Board.
    New Brunswick Flood, April-May 1973 Inland Waters Directorate Government Records (UNB Libraries) Inland Waters Directorate. (1974). New Brunswick Flood April-May 1973. Environment Canada. Ottawa: Information Canada.
    New Brunswick Flood Frequency Analyses Water Resource Planning Branch Government Records (UNB Libraries) Water Resource Planning Branch. (1987). New Brusnwick Flood Frequency Analyses. Environment Canada, New Brunswick Department of Municipal Affairs and the Environment , Fredericton.
    The Flood of 1979 Saint John River Basin New Brunswick Inland Waters Directorate & Water Resources Branch Government Records (UNB Libraries) Inland Waters Directorate & Water Resources Branch. (1981). The Flood of 1979 Saint John River Basin New Brunswick. Environment Canada, New Brunswick Department of the Environment, Fredericton.

    Grand Lake Meadows

    Much of the province of New Brunswick has had the elevation mapped by LiDAR. A complete catalog of data is available from Service New Brunswick, GeoNB web portal.

    For those areas that do not have LiDAR coverage, there are other elevation datasets available, including data provided through Esri, Bing and Google, via REST API Services. This data is available with ~30m resolution.

    As the area of GLM is low lying, with an elevation range of just 14m above sea level, it was of interest to see how the accuracy of the LiDAR collected in this region compared to these open elevation datasets, to get an understanding of the potential accuracy of these datasets.

    Click Here to access the report written up by Michel Leger, an undergraduate student at UNB in the GGE department.  data flow

    Figure 1, Elevation of Study area, as derived from LiDAR point data  data flow

    Figure 2 Absolute elevation difference rasters created by subtracting REST elevation API data (a) Bing, (b) Esri and (c) Google from LiDAR data in Grand Lake Meadows

    This web-based map-mashup of historical maps and educational content was put together for H. McGrath Masters Thesis

    This site is designed to promote awareness of the Grand Lake Meadows wetland region in New Brunswick through a review of historical maps of the region. While aimed towards education of middle school children, this site is available for all to learn more about the importance of Grand Lake Meadows to New Brunswick and its peoples.

  • Historical Maps of Grand Lake Meadows.