developing a hydromorphology toolbox for the wfd indices and... · developing a hydromorphology...

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