Suir Catchment Flood Risk Assessment and Management Study Mark Adamson John Martin Judith Landheer

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SPATIAL DATA ANALYSIS FOR NATIONAL FLOOD
ESTIMATION PRELIMINARY FLOOD RISK
ASSESSMENT John Martin C.Eng, PhD., MIEI OPW
Flood Risk Assessment Management FSU Programme
Manager 25th June 2009
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HOW SPATIAL DATA AND GIS ANALYSIS TELL US HOW BIG
FLOODS IN IRELAND CAN BE, AND WHERE THE FLOOD
RISK AREAS ARE LIKELY TO BE
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Project Background

• Preparation of Digital Catchment Descriptors
• Compass Informatics
• Commissioned by OPW in June 2007
• Delineated catchments (gauged /ungauged
  locations)
• Digital metrices to describe the characteristics
  of catchments in Ireland (Spatial/physical and
  Hydrological)
• Indicator polygon of floodplain attenuation areas
  based on topographical / DTM analysis
• Completed in Spring 2009

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Project Structure

• Stage I
• To develop and map a flood attenuation indicator
  polygon along irish rivers, based on vectorised
  datasets of the river (blue line) network and a
  10m resolution digital elevation model.
• Stage II
• Based on existing catchment boundaries (from WFD
  work), to clip and develop Spatial and
  Hydrological Catchment Descriptors (Rivers
  characteristics) to gauged locations.

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Project Structure

• Stage III
• To delineate sub-catchment boundaries to
  un-gauged locations (nodes) down to a minimum
  catchment size of 1km2 at fixed intervals (500m)
  and at confluences along every watercourse, based
  on a hydrologically corrected digital elevation
  model.
• Stage IV
• Based on the un-gauged sub-catchment boundaries
  delineated in Stage III, to clip and develop
  Spatial and Hydrological Catchment Descriptors
  (River characteristics) at every un-gauged
  location (nodes).

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Base OPW Projects

• Flood Studies Update (FSU)
• A programme to develop new recalibrated flood
  estimation methods in Ireland to improve the
  quality and ease of flood estimation for flood
  risk management purposes, using
• (a) Data that is more up-to-date and more
  accurately reflects current climatic and
  catchment conditions than that used for the FSR
  (pre-1970) and
• (b) Technologies and techniques that are better
  developed for analysis, presentation and
  usability of flood estimation packages.
• Preliminary Flood Risk Assessment (PFRA)
• The first of 3 major deliverables required under
  the European Directive on the Assessment
  Management of Flood Risk (Directive 2007/60/EC)

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Part IFlood Studies Update (FSU)

• New flood estimation methods to improve the
  quality and ease of flood estimation for flood
  risk management purposes

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FSU Objectives (Gauged)

• Flood Estimation (FE) Model Development
• For each gauging station, to develop/clip
  Spatial and Hydrological Catchment
  Descriptors
• These are then used in the development
  calibration of Flood Estimation Models, through
  multiple regression analysis, to develop and
  calibrate methodologies for flood estimation
  parameters.

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FSU Objectives (Ungauged)

• Application of FE Models at Ungauged Sites
• For 140,000 sites where no gauged information
  (flows or water levels) is available, to
  develop/clip Spatial and Hydrological
  Catchment Descriptors (at 500m intervals for
  sub-catchment gt1km2)
• These are then used in the application of Flood
  Estimation Models, to generate flood estimation
  parameters and flood estimates at those sites

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Spatial Catchment Descriptors

• S1 Catchment Area Polygons (AREA)
• Hydrologically Corrected DTM (EPA)
• S2 Centroid (CENTE, CENTN)
• Hydrologically Corrected DTM (EPA)
• S3 Mean Elevation (ALTBAR)
• 10m Resolution DTM (OSi)

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Spatial Catchment Descriptors

• S4 Standard Period Average Annual Rainfall
  (SAAR)
• derived from a dataset provided by MetEireann of
  the long term average annual rainfall for the
  return period from 1961-1990.
• S5 BaseFlow Index (BFIsoils)
• a descriptor of flow regime representing the
  proportion of runoff that derives from stored
  sources such as groundwater and loughs, derived
  from soil, subsoils and aquifer types for Ireland
  (SOIL maps).

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Spatial Catchment Descriptors

• S6 Index of urban extent (URBEXT)
• Corine LC2000 (EPA, 2000)
• S7 Proportion of Peat Cover (PEAT)
• Corine LC2000 (EPA, 2000)
• S8 Proportion of Grassland/Pasture/ Agriculture
  (PASTURE)
• Corine LC2000 (EPA, 2000)

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Spatial Catchment Descriptors

• S9 Proportion of Forest Cover (FOREST)
• Coillte Teoranta forestry database
• Corine Landcover (EPA, 2000)
• FIPS Forest Inventory and Planning System
  (Forest Service, 1998)
• S10 Proportion of extent of floodplain alluvial
  deposit (ALLUV)
• based on the distribution of the single
  Alluvium class determined by reference to a
  national dataset of soil Parent Materials
  (Teagasc / EPA /Indicative Forestry Strategy
  project)

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Spatial Catchment Descriptors

• S11 Index of Arterial Drainage
• the percentage of the catchment area that
  Benefitings from existing OPW drainage schemes
  (Benefitting Lands).

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Hydrological Descriptors

• H1 Network length (NETLEN)
• H2 Stream Frequency (STMFRQ)
• H3 Drainage Density (DRAIND)
• H4 Index of Arterial Drainage
• H5 Flood Attenuation by Reservoirs and Lakes
  (FARL)

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Hydrological Descriptors

• H6 Mainstream Length (MSL)
• H7 Mainstream slope (S1085)
• H8 Taylor Schwarz slope (TAYSLO)

All derived from EPA Blue Line Network
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How are these used?

• Ungauged estimation of Index Flood (Qmed)
• To determine similarity between different
  catchments
• To help estimate growth curves for design floods
• To determine parameters for synthetic hydrograph
  shapes

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Example Index Flood

• In order to estimate the magnitude of a flood of
  a given probability (say, 100-yr flood) at a site
  where we have no flow information, we need an
  Index Flood
• For each design flood (50-yr, 100yr), this Index
  Flood is multiplied by a known Growth Factor
• For the FSU, the Index Flood is the Median
  Annual Maximum Flood (i.e. 2-year flood)
• This is known for each gauging station

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Example Index Flood

• Station No. 36018
• Ashfield Bridge,
• Dromore River
• Annual Maximum
• Floods
• Qmed 16.25m3/s

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Example Index Flood

• Through Multiple Regression Analysis for 220
  Gauging Stations, for which
• We have the Qmed Values
• We have the Spatial Hydrological Catchment
  Descriptors
• We can build a model to estimate Qmed for any
  ungauged location

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Example Index Flood

• Qmed
• 1.237x10-5 x (AREA)0.937 x (BFIsoils)-0.922 x
  (SAAR)1.306 x (FARL)2.217 x (DRAIND)0.341 x
  (S1085)0.185 x (1ARTDRAIN2)0.408

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Example Index Flood

• Station No. 36018
• Ashfield Bridge,
• Dromore River
• No data.
• Apply ungauged Qmed model

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Example Index Flood
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Example Index Flood

• The same concept is applied to
• The Growth Curve (series of growth factors for a
  given location)

200yr
100yr
50yr
25yr
10yr
5yr
2yr
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Example Index Flood

• The same concept is applied to
• The parameters that define (and are used to
  construct) the typical shape of the flood
  hydrograph (plot of how quickly flow magnitude
  increases / decreases over time)

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Example Index Flood

• So for any river location, even if we have no
  gauged water level or flow information, we can
  estimate
• Index Flood
• Magnitude of the 100 year flood (in m3/s)
• Shape of the flow hydrograph (a plot of how
  quickly the flow magnitude increases and decrease
  over time)
• All we need are the Spatial Hydrological
  Catchment Descriptors at that location!

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Improvements since FSR (1975)

• FSU fully exploits a range of spatial data
  (raster, topographical, met and vector) to
  provide detailed description of the hydrology of
  the landscape.
• Complex GIS analyses allow instantaneous,
  automated and accurate metrices to hydrologically
  describe any (sub-)catchment.
• Practitioners can work with spatial layers and
  derived Catchment Descriptors to understand the
  hydrology perform detailed flood magnitude
  estimation.
• Methodology can be readily updated as new data
  and/or methods emerge

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Part IIPreliminary Flood Risk Assessment

• The first of 3 major deliverables required under
  the European Directive on the Assessment
  Management of Flood Risk (Directive 2007/60/EC)
  December 2011

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Indicative Flood Attenuation Polygon

• Create DEM (OSI data)
• 10 metre cell size
• 2-3m Z accuracy

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Indicative Flood Attenuation Polygon
Local DEM Errors
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Indicative Flood Attenuation Polygon

• Vector stream and DTM correspondence

Ideal
Local flat areas
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Indicative Flood Attenuation Polygon

• Placement of un-gauged nodes
• 1km2 drainage area threshold
• 140,000 nodes

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Indicative Flood Attenuation Polygon

• Node elevation
• Sampled from OSI 10m DEM
• Median elevation within window of 30x30 / 50x50
  / 70x70 m dependent on river size

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Indicative Flood Attenuation Polygon

• Back watering
• Anomalies in OSI elevation can indicate node
  elevations DECREASE in upstream direction
• Agreed protocol to increase node elevation by max
  1m to compensate

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Indicative Flood Attenuation Polygon

• Placement of Cross Section lines
• Orthogonal to direction of river segment
• Direction flexed dependent on size of river
• Maximum width limited to 5km per side

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Indicative Flood Attenuation Polygon

• Node and Inter-mediate Nodes and Cross-sections

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Indicative Flood Attenuation Polygon
Initial Polygon may have spikes and overlap
adjacent rivers
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Indicative Flood Attenuation Polygon

• Remove Spikes and Overlap

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Indicative Flood Attenuation Polygon

• Merge to single polygon

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Indicative Flood Attenuation Polygon
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Indicative Flood Attenuation Polygon

• Now being used to
• provide a preliminary
• indicator of Areas where
• there is a significant
• degree of flood risk
• (PFRA)
• Required by Floods
• Directive (Dec. 2011)

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Future Work

• Improved NDHM
• Test polygon for different floodplain depths
• Correlate polygons resulting from various
  floodplain depths (or functions thereof) with
  estimated probabilities of occurrence

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Thanks To

• Compass Informatics (Paul Mills)
• FSU Research Contractors (NUI Maynooth, NUI
  Galway)
• OPW Flood Studies Update Team
• OPW Preliminary Flood Risk Assessment Team