ENVIRONMENTAL SUSTAINABILITY

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ENVIRONMENTAL SUSTAINABILITY INDICATORS
Department of Geography Kings College London
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RESEARCH TEAM
Principle Investigators Dr Angela Davenport
Kings College London Prof. Angela Gurnell
Kings College London Collaborating Scientists
Prof. Geoff Petts University of
Birmingham Dr Patrick Armitage CEH
Dorset Field Support May Lee Kings College
London
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AIMS OBJECTIVES

• Aims
• To develop and disseminate a set of Environmental
  Sustainable Urban River Indicators for
  application across the EU and Candidate
  Countries.
• To aid development and dissemination of a
  transferable method of land-use planning in
  urbanised river basins.
• Objectives
• To develop a transferable set of Environmental
  Sustainability Indicators for urbanised river
  basins.
• To contribute to a new urban river planning
  methodology (utilising latest G.I.S. techniques)
  that will support the delivery of the Water
  Framework Directive.

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DEVELOPMENT OF ENVIRONMENTAL SUSTAINABILITY RIVER
INDICATORS

• A hierarchical approach to managing urban river
  data
• A reach scale survey designed for urban rivers
• Primary Indicators - Stretch Scale Aggregate
  Indices
• Secondary Indicators - Stretch Scale
  Classifications
• A reach scale scoring system for scenario
  modelling by river managers
• Tertiary Indicators - Network Scale Water
  Quality, Water Quantity, Floodplain Constraints

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A HIERARCHY OF SPATIAL SCALESAT WHICH URBAN
RIVER DATA MAY BE COLLECTED
DRAINAGE BASIN
SECTOR Unbranched tributary or network
section between tributary junctions
Catchment Characteristics
STRETCH 500m length of a single
engineering type
HABITAT Physical habitat feature (riffle, bar)
PATCH Patch of vegetation or sediment
River corridor land use Water quality
data River flowdata
Channel morphology Geomorphic features Reinforce
ment materials Flow types Vegetation
Sediments Hydraulic data Biological data
Sediments Hydraulic data Biological data
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RESEARCH APPROACH
PHASE 1 Collection of information Transfer of
data to database
PHASE 2 Development of sustainable indicators.
PHASE 3 Validation
May/June 2004
PHASE 4 Scenario Modelling
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PRIMARY INDICATORS REACH SCALE AGGREGATE INDICES

• The URS provides a wealth of information on urban
  rivers that can be queried to assess urban river
  corridor character and change.
• Like the RHS, the survey includes a large number
  of measurements.
• The measurements are recorded on three different
  measurement scales frequencies, percentages, and
  other variable-specific scaled measurements.
• Synthetic indices were developed to integrate the
  measurements into PRIMARY INDICATORS expressed
  across similar measurement scales and ranges to
  describe MATERIALS, PHYSICAL HABITAT and
  VEGETATION properties of urban river stretches.

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SOME MATERIALS INDICES
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SOME PHYSICAL HABITAT INDICES
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SOME VEGETATION INDICES
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SECONDARY INDICATORS REACH SCALE CLASSIFICATIONS

• Cluster analysis was used to develop three
  classifications (Materials, Physical, Vegetation)
  or secondary indicators of urban river stretches
  from the primary indicators
• Because of the similar numerical range of the
  primary indicators, cluster analysis was applied
  to the untransformed data.
• Wards clustering algorithm was used.

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SEVEN CLASSES OF URBAN RIVER STRETCH DEFINEDBY
THEIR MATERIALS CHARACTERISTICS
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SIX CLASSES OF URBAN RIVER STRETCH DEFINED BY
THEIR PHYSICAL HABITAT CHARACTERISTICS
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EIGHT CLASSES OF URBAN RIVER STRETCH DEFINED BY
THEIR VEGETATION CHARACTERISTICS
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The classifications illustrate

• three broad secondary environmental indicators
  (Materials, Physical Habitat, Vegetation)
• all are associated with the type of engineering
  to some degree
• the strongest associations are with Materials and
  the weakest are with Vegetation.
• Thus, the indicators can be used to consider the
  consequences of changes in engineering and in
  vegetation and pollution management.

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Index Immobile Substrate
Index Bank Protection (Inverse of Index No Bank
Protection)
BANKCAL
SEDCAL
Dominant Protection Type (Proportion OMP,
Proportion SOL)
MATERIALS CLASS
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Index Artificial Bank Profiles
Index Natural Bank Profiles
Number of Habitat Types
PHYSICAL CLASS
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Dominant Vegetation Type
Total Tree Score
Average BANKVEG (Face)
Average BANKVEG (TOP)
VEGETATION CLASS
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DEVELOPING A SCORING SYSTEM FOR MANAGING URBAN
RIVER STRETCHES

• Required to combine the different classifications
  to produce a single index of the overall
  quality of a stretch.
• The scores assigned to the materials classes
  reflect the change from semi-natural (score 1)
  to heavily engineered stretches (score 5).
• Scores assigned to the physical classes reflect
  the degree to which the channel has been modified
  and the degree to which the channel is recovering
  either some or all of its physical habitat
  features.
• Scores assigned to the vegetation classes reflect
  the level and type of in-channel vegetation, and
  the complexity of the riparian vegetation.
• A vegetated channel is more desirable than an
  unvegetated channel (except Algal channels).
• A complex riparian habitat is more desirable than
  a uniform one.
• A moderate to high tree cover is preferable to
  either no trees or a channel completely shaded by
  trees.
• With the exception of the ALG class, a mixture of
  these vegetation classes is required at the
  catchment or sector level, to provide variation
  along the river.

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SCORES MANAGEMENT RECOMMENDATIONS FOR URBAN
RIVER STRETCHES
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SCORES MANAGEMENT RECOMMENDATIONS cont..
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VERY GOOD
GOOD
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AVERAGE
BELOW AVERAGE
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POOR
VERY POOR
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A SCORING SYSTEM FOR SCENARIO MODELLING

• Direct human modification can only be applied
  to certain components of the decision trees,
  since others are not directly physically
  manipulable
• Materials All components of the Materials
  decision tree (exc. BANKCAL and SEDCAL) can be
  directly manipulated. The decision tree can be
  used to assess a new Materials score based on a
  scenario of changed engineering within 5 classes.
• Physical Habitat The proportion of artificial
  bank profiles can be manipulated but the
  proportion of natural bank profiles cannot.
  However, one important reason why there are more
  natural/active bank profiles in some classes than
  in others is the level of sinuosity of the
  channels. sinuous, Therefore a highly sinuous,
  intermediate or straight channel planforms have
  been introduced to discriminate between these
  three groups of classes for scenario modelling.
• Vegetation Algal channels reflect relatively
  poor water quality and so cannot be influenced by
  physical modification of reaches. For the other
  classes, presence or absence of in-channel
  vegetation cover cannot easily be manipulated,
  although shading of the channel will reduce the
  in-channel vegetation. Tree cover and the
  complexity of the riparian vegetation (BANKVEG)
  are manipulable factors that discriminate between
  the classes. Thus, the presence or absence of
  in-channel vegetation cover provides a context
  around which manipulation of vegetation on the
  banks can be undertaken.

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TERTIARY INDICATORS NETWORK SCALE WATER QUALITY,
WATER QUANTITY, FLOODPLAIN CONSTRAINTS

• Certain changes in the engineering of reaches (i)
  may be inherently unstable because of the energy
  of river flows, or (ii) may not meet
  flood-defence requirements.
• If water quality is low, changes in physical
  habitat are unlikely to yield any ecological
  benefit.
• Even if water quality and flow regimes do not
  present constraints on the outcomes of changes in
  the secondary environmental indicators, land use
  and land availability may restrict the space
  available for such changes.

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• FLOW-RELATED INDICATORS illustrate constraints
  in
• achieving flood defence targets (high flow
  magnitude),
• ensuring that any stretchscale modifications do
  not result in major channel instabilities (high
  flow energy),
• ensuring that there is sufficient aquatic habitat
  to support species during low flows (low flow
  magnitude and depth).
• WATER QUALITY INDICATORS illustrate constraints
  in
• Water quality that is too low for ecological
  benefits to accrue from physical habitat
  improvement
• Water quality that may be close to threshold
  conditions
• BIOTIC INDICATORS illustrate constraints in
• propagule availability
• the level of degradation at a site due to water
  quality problems
• FLOODPLAIN LANDUSE INDICATORS illustrate
  constraints in
• the spatial extent of land available for channel
  modification
• the quality of available land

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CONCLUSIONS

• The data generated by the URS, the synthetic
  indices and the classifications provide a range
  of important indicators for the assessment of the
  quality of urban rivers and their potential for
  enhancement or rehabilitation.
• The analyses indicate the over-riding impact of
  engineering on the physical character of urban
  rivers and thus the potential to investigate
  potential changes in physical character as a
  consequence of changes in engineering design
• The robustness of both the URS methodology and
  the classifications derived from it have been
  illustrated by the similarity in the
  classifications derived from more than one survey
  of the River Tame (West Midlands) and from a
  survey of the River Ravensbourne (London).
• The entire methodology will be tested on two
  other urban river systems in mainland Europe