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Developing a hydromorphology toolbox for the WFD using existing data and knowledge.
Dr Marc Naura KTS EPSRC Research Fellow University of Southampton ICER Engineering and the Environment October 2015
2
Decision
Support
Software
HYDROMORPHOLOGICAL
INDICES
DATA
+
EXPERTS
DIAGNOSTIC
TOOLS
ANALYTICAL
TOOLS
VISUALISATION
TOOLS
HYDROMORPHOLOGICAL
MODELS
Building a hydromorphological toolbox
• Applications:
– Developing morphological EQRs and assessing departure from ‘natural’ state
– Validating biological indicators
– Assessing the risk of fine sediment on biota and EQRs
– Combining bio-chemical and morphological survey data at a relevant (sub-waterbody) scale for environmental assessment, diagnostic and management under the WFD
• How the indices were derived and further applications
3
Application 1
Developing morphological EQRs and assessing departure from semi-natural state
Examples Large scale
6
Land Cover Map
0
Broad-leaved / mixed woodland
Coniferous woodland
Arable and horticulture
Improved grassland
Semi-natural grass
Mountain, heath, bog
Built up areas and gardens
Standing open water
Coastal
Oceanic seas
Examples Reach scale
10
11
Substrate difference
SemiNat pred - observed
-1.55000 - -1.50000
-1.49999 - -1.20000
-1.19999 - -0.90000
-0.89999 - -0.60000
-0.59999 - 0.40250
0.40251 - 0.78218
0.78219 - 1.09319
1.09320 - 1.51174
1.51175 - 2.10000
12
13
14
Application 2
Validating biological indicators and EQRs
16
PSI vs CSI
Blue: Fine
Red: Coarse
• PSI sites
Validation of PSI index
Validation of PSI index
17 Channel Substrate Index
10-1-2
100
80
60
40
20
0
10-1-2
PSI*SUBSTRATEI EXPECTED*SUBS_SN_GL
Comparison of PSI and CSI values for 3015 biological monitoring sites
PSIobs vs CSIobserved PSIexp vs CSIsemi-natural
R2 = 60% R2 = 58%
Silt Sand/silt/clay Gravel-pebble Cobble Boulder
bedrock Silt Sand/silt/clay Gravel-pebble Cobble Boulder
bedrock
Application 3
Assessing risk of fine sediment on biota
Map of channel fine sediment accumulation
19 Number of transects with silt, sand or clay
100
50
0
100
50
0
100
50
0
100
50
0
109876543210
100
50
0
Very High
High
Moderate
Low
Very Low
Pro
port
ion o
f RH
S s
ites
with 0
to 1
0 t
ranse
cts
with f
ine s
edim
ent
Proportion of RHS sites within 5 Fine Sediment
Accumulation categories with silt, sand or clay as
dominant channel substrates across 10 transects.
Map of agricultural fine sediment risk
20
54321
100
80
60
40
20
0
A gricultural Sediment Risk Classes
Sa
lmo
n o
ccu
rre
nce
(p
erce
nta
ge
)
100
80
60
40
20
0
54321
100
80
60
40
20
0
Agricultural Sediment Risk Classes
Me
an
FC
S p
red
icte
d l
ike
lih
oo
d w
ith
95
% C
I
100
80
60
40
20
0
A - Observed Salmon occurrence against agricultural sediment risk classes
at sites predicted to have high habitat suitability at reference condition
B - Likelihood of finding more Salmon at reference condition against agricultural sediment risk
at sites predicted to have high habitat suitability at reference condition
Application 4 Combining bio-chemical and morphological survey data at a relevant (sub-waterbody) scale for environmental assessment, diagnostic and management under the WFD
23
One reach =
.1 observable habitat type
•1 ‘historical’ natural type
Survey sites
|---------------| 5 km
24
Morphological
indices
450000440000430000420000410000400000
480000
475000
470000
465000
460000
455000
450000
X
Y
Substrate on the NIDD
450000440000430000420000410000400000
480000
475000
470000
465000
460000
455000
450000
X
Y
Activity on the NIDD
450000440000430000420000410000400000
480000
475000
470000
465000
460000
455000
450000
X
Y
Vegetation on the NIDD
450000440000430000420000410000400000
480000
475000
470000
465000
460000
455000
450000
X
Y
Flow on the NIDD
Predictions
every 500m
450000440000430000420000410000400000
480000
475000
470000
465000
460000
455000
450000
Eastings
No
rth
ing
s
Display of river segmentation for the river NiddEach dot represents a 500m section
Calinski Harabasz index = 235.7
Spatial
Clustering based on
similarity
Segmentation Method
Homogeneous
habitat units
25
0.5
0.0
-0.5
-1.0
200150100500
1.0
0.5
0.0
-0.5
200150100500
1.0
0.5
0.0
-0.5
-1.0
-0.2
-0.4
-0.6
-0.8
Substrate
Increasing distance to mouth (arbitrary scale)
Flow
Vegetation Erosion
Index value from source to mouth for the river NiddObserved values for 4 indices
(Each dot represents a 500m section; index value on Y scale)
C obbles
Grav el-Pebbles
Grav el-Pebbles & Sand/SiltRiffle
Run-Riffle
Glide-Run
Glide-pool
Mosses
algae
Mosses/filamentous
Emergent/amphibious
Emergent/submerged Low activ ity lev els
Moderate activ ity lev els
26
The river Nidd
450000440000430000420000410000400000
480000
475000
470000
465000
460000
455000
450000
Eastings
No
rth
ing
s
Display of river segmentation for the river NiddEach dot represents a 500m section
Calinski Harabasz index = 235.7
27
Map of agricultural fine sediment risk
River segmentation
How the indices were derived … and potential applications
Ordination: Correspondance Analysis
29
Site BEdrock BOulder CObble GravelPebble SAnd SIlt CLay PEat
1 0 0 0 9 1 0 0 0
2 0 0 0 9 1 0 0 0
3 2 0 1 4 0 3 0 0
4 1 0 1 6 0 2 0 0
5 0 0 1 8 0 1 0 0
6 0 0 1 8 0 1 0 0
7 1 0 0 7 2 0 0 0
7 0 0 3 3 4 0 0 0
8 2 0 0 6 0 2 0 0
10 0 0 2 6 1 1 0 0
Substrate Coefficient
(Coeff)
Occurrence of substrate types at the
site across 10 spot-checks (Occ)
Coeff x Occ
Bedrock/Artificial 0.89 0 0
Boulder 0.95 0 0
Cobble 0.58 3 1.74
Gravel-pebble -0.60 6 -3.6
Sand -1.63 0 0
Silt -2.33 1 -2.33
Clay -2.28 0 0
Peat +0.08 0 0
Total for site
Average for site = Total for site / nb of spot-checks with no missing values
-4.19
-0.419
The overall substrate index score for the site is -0.419
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Peat
Clay
Silt
Sand
Gravel-pebble
Cobble
Boulder
Bedrock
30
Index derivation – Channel Substrate Index (CSI)
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
FRI
No flow
No perceptible flow
Smooth flow
Rippled flow
Upwellings
Unbroken standing waves
Broken standing waves
Chaotic flow
Chute flow
Free-fall
31
Index derivation – Flow Regime Index (FRI)
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
CVI
Submerged fine/linear-leaved
Submerged broad-leaved
Mosses/Liverworts/Lichens
Free floating
Floating rooted
Filamentous algae
Emergent reeds
Emergent broad-leaved
Amphibious
None present
32
Index derivation – Channel Vegetation Index (CVI)
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
GAI
None
Pool number
Riffle number
Exposed boulders
Unvegetated bars
Vegetated bars
Stable cliffs
Eroding cliffs
Index derivation – Geomorphic Activity Index (GAI)
34
The models
• First we predict CSI using GIS data
– CSI = aSlope + bAltitude + cGeology….+ error
• Then we correct the predictions error using CSI values from nearby RHS sites
– CSIcorr = aSlope + bAltitude + cGeology ….+ sum(wi * CSIi)
Altitude
Slope
Distance to source
Geology
Existing RHS sites
New prediction
35
Predicting hydromorphological index values
0%
10%
20%
30%
40%
50%
60%
70%
80%
CSI FRI CVI GAI
Model sample(n=10027)
Test sample(n=2660)
Amount of variability explained by models for the sample of sites used for modelling
and a separate test sample
36
0%
10%
20%
30%
40%
50%
60%
70%
80%
CSI FRI CVI GAI
Model sample(n=2100)
Test sample(n=400)
Predicting reference condition
Amount of variability explained by models for the sample of sites used for modelling
and a separate test sample
Further applications
37
Further applications
• More indices!
• Modelling coarse sediment budgets
• Assessing the impact of weirs and dams on GES
and prioritising weir removal/fish passage activity
38
Index derivation – Bank Face and Bank Top Vegetation structure (BFV and BTV)
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
BFV
Complex
Simple
Uniform
Bare
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
BTV
Complex
Simple
Uniform
Bare
Modelling coarse sediment budgets
40
CSI GAI
Sediment transport drivers
Map of Sediment Mobility
Applications:
• WFD hydromorphological assessment
• Sediment supply issues
• Deposition/erosion excess
• Gravel removal assessment and prioritisation
• Dredging impact assessment
Sediment supply Shear stress Stream power
balance
ST:REAM
41 |---------------| 5 km
Assessing the impact of weirs and dams on GES and
prioritising weir removal/fish passage activity
Barriers
Landscape ecology indices: • Network connectivity • Habitat fragmentation • Habitat isolation • …
Characterisation: habitat quality for
selected species or communities
Prioritisation of work: APASS
APASS is a GUI based program for optimising anadromous fish passage barrier mitigation decisions