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Water Research 39 (2005) 3967–3981
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Predicting faecal indicator fluxes using digital land use data inthe UK’s sentinel Water Framework Directive catchment:
The Ribble study
David Kaya,�, Mark Wyera, John Crowtherb, Carl Stapletona, Michael Bradfordb,Adrian McDonaldc, Jon Greavesd, Carol Francise, John Watkinse
aRiver Basin Dynamics and Hydrology Research Group, IGES, University of Wales, Aberystwyth SY23 3DB, UKbCREH, University of Wales, Lampeter, Ceredigion SA48 7ED, UK
cSchool of Geography, University of Leeds, Leeds LS2 9JT, UKdEnvironment Agency, North West Region, Richard Fairclough House, Knutsford Road, Warrington, Cheshire WA4 1HG, UK
eCREH Analytical Ltd., 50 Back Lane, Horsforth, Leeds, West Yorkshire LS18 4RS, UK
Received 28 August 2004; received in revised form 9 June 2005; accepted 7 July 2005
Available online 19 August 2005
Abstract
The Ribble drainage basin is the single UK sentinel study area chosen for examining the implementation of the EU
Water Framework Directive (WFD 20/60/EC). The study which has generated the data for this paper was initiated to
quantify ‘catchment-derived’ fluxes of faecal indicators originating from both point and diffuse sources to inform the
competent authorities on the potential for, and prioritization of, further options for reducing the faecal indicator
loadings to this crucial coastal environment. It represents the first UK drainage basin-scale ‘profile’ of faecal indicator
sources as recommended by WHO [1999. Health Based Monitoring of Recreational Waters: The Feasibility of a New
Approach; the ‘‘Annapolis Protocol’’. World Health Organisation Geneva, Switzerland; 2003. Guidelines for Safe
Recreational-Water Environments Volume 1: Coastal and Fresh-Waters. World Health Organisation Geneva,
Switzerland] and incorporated into current drafts of the revised Bathing Water Directive [Anon, 2004. Council of the
European Communities Amended proposal for a Directive of the European Parliament and of the Council concerning
the management of bathing water quality. Brussels 23rd June].
This paper focuses on the relationships between land use and faecal indicator organism concentrations in surface
waters within this very large drainage basin (1583 km2) containing some extensive urban areas. A geographical
information system comprising readily available digital elevation, remotely sensed land cover and digital map data was
used to generate the land use variables for subcatchments draining to 41 locations across the study area. Presumptive
concentrations of coliforms, Escherichia coli and enterococci (colony forming unit (cfu) 100ml�1) were measured at
each location on at least 20 occasions over a 44-day period within the 2002 bathing season. The sampling programme
targeted hydrograph events. Hydrometric records were used to allocate results as either base flow or high flow. At each
site, geometric mean faecal indicator organism concentrations were significantly elevated at high flow compared to base
flow. Stepwise regression modelling produced statistically significant models predicting geometric mean base and high-
flow faecal indicator organism concentrations from land use variables (r2: 49.5–68.1%). The dominant predictor
e front matter r 2005 Elsevier Ltd. All rights reserved.
atres.2005.07.006
ing author. Tel./fax: +44 1570 423 565.
esses: [email protected], [email protected] (D. Kay).
ARTICLE IN PRESSD. Kay et al. / Water Research 39 (2005) 3967–39813968
variable in each case was the proportion of built-up land in subcatchments, suggesting that this land use type, with
associated sewage-related inputs, is a critical source of faecal indicator organisms in this drainage basin.
r 2005 Elsevier Ltd. All rights reserved.
Keywords: Water framework directive; Drainage basin; Coliforms; Escherichia coli; Enterococci; Land use; GIS; Regression; Modelling
1. Introduction
Fluxes of faecal indicators from catchment systems
have received much less research attention world-wide
than other water quality parameters such as the
nutrients which are associated with eutrophication of
freshwater and marine ecosystems (Chorus and Bar-
tram, 1999). The main concern associated with catch-
ment microbial flux to receiving environments is the
impact on recreational and shellfish harvesting waters
and a number of studies have reported specific regional
problems associated with urban runoff in southern
California (Noble et al., 2003; Reeves et al., 2004),
Florida (Shibata et al., 2004), the United Kingdom
(Crowther et al., 2001; Lee et al., 2002) and in Italy
(Arienzo et al., 2001). In Europe, new catchment-based
regulatory tools, outlined in the Water Framework
Directive (WFD) (Anon, 2000), are raising the profile of
faecal indicators and defining the mechanism for
controlling their concentrations in catchment systems.
These new regulations are highlighting the need for
models able to predict faecal indicator flux from large
catchments (as is possible for the nutrients (DEFRA,
2002, 2003)) with a complex mix of land uses. This paper
reports a generic approach to faecal indicator modelling
using satellite-derived land use data within a GIS
framework which has been developed in studies commis-
sioned by the principal environmental regulator in
England and Wales with responsibility for WFD
implementation.
Article 6 of the WFD requires the 24 EU member
states to identify ‘protected areas’ which include
locations covered by the Bathing Water Directive
(BWD) (Anon, 1976). Article 11 of the WFD outlines
the ‘programme of measures’ required to implement the
Directive and defines ‘basic measures’ as those required
to achieve the criteria specified in a series of Directives
outlined in Annex VI, the first of which is the BWD.
The principal compliance parameters within the BWD
are the faecal indicator bacteria, i.e. total coliforms,
Escherichia coli and intestinal enterococci. This places a
legally binding obligation on the European competent
authorities to implement plans, at the catchment scale,
to limit indicator bacterial concentrations in bathing
waters through a combination of point and diffuse
source control (WFD Article 10). In the UK, very little
attention has been given to faecal indicator monitoring
of rivers, thus producing a marked lack of empirical
data on which to base initial assessment of the potential
problem. Furthermore, study of the combined effect of
point source discharges from, for example, sewage
treatment works together with the quantitative assess-
ment of the diffuse sources of faecal indicator from
livestock is very limited (DEFRA, 2002, 2003). Thus,
catchment scale process-based models which would be
needed to inform and prioritize appropriate and cost-
effective remediation strategies as part of a ‘programme
of measures’ do not, at present, exist (Kay et al., 1999;
DEFRA, 2003). Despite this problem, newly drafted
daughter Directives such as the Commission Proposal of
October 2002 for a revised BWD (Anon, 2002b) clearly
state the expectation that the WFD will provide the
tools for implementation of new ‘health-evidence-based’
microbiological guidelines for recreational waters out-
lined in WHO (2003) and Kay et al. (1994, 2004) and
now forming the basis of the Commission proposals for
the EU member states. For example, Article 1, setting
out the objectives of Commission of the European
Communities (CEC) (Anon, 2002b) states that: ‘‘It shall
with particular emphasis on environment and health,
complement objectives and measures set out in Directive
2000/60/EC’’ (i.e. the WFD).
The Commission’s explanatory memorandum ex-
plaining the draft Directive (Anon, 2002b) it is stated
that: ‘‘This approach is made operational through
provisions established under the Water Framework
Directive, with a general objective of achieving ‘good
ecological status’ for all waters and specific objectives
for so-called ‘protected areas’ such as bathing waters’’.
The Ribble study described below is the first very
large scale (area some 1583 km2) catchment investigation
of faecal indicator fluxes and provides a case study
focused on the UK’s single sentinel drainage basin
chosen to test the implementation and application of the
new WFD principles. Previous investigations by the
Centre for Research into Environment and Health
(CREH) into the relationships between land use and
faecal indicator organism concentrations have focused
on smaller predominantly rural catchment areas
(o400 km2) (Crowther et al., 2002, 2003). At this scale,
it has been possible to map the land use data required
for predictive modelling directly from field-by-field
survey. In larger river catchments, of the scale envisaged
in the WFD, this approach is not feasible. This paper
explores the use of alternative sources of readily
available digital land cover data (i.e. the Centre for
ARTICLE IN PRESSD. Kay et al. / Water Research 39 (2005) 3967–3981 3969
Ecology and Hydrology (CEH) 1990 land cover and OS
1:50,000 colour raster maps) for modelling water quality
in larger catchments such as the River (R.) Ribble
drainage basin. In this paper data from five earlier
CREH study areas are used to ‘ground truth’ the CEH
data.
2. Materials and methods
2.1. Study area
This catchment discharges to the marine environment
close to the Fylde coast which includes some of the UK’s
premiere resorts and bathing beaches. This region
has received significant infrastructure investments
(4£600m) on marine point source remediation to
achieve the standards in Directive 76/160/EEC at
compliance locations and, hopefully, to avoid legal
action for non-compliance with EU legislation.
The R. Ribble basin comprises five main subcatch-
ments, which drain to the Ribble estuary: the R. Ribble,
the R. Douglas, the R. Darwen, the R. Lostock and the
R. Yarrow (Fig. 1). The largest subcatchment
(1130 km2) is drained by the R. Ribble itself, which
flows south from the flanks of the Pennine hills. Two
major subcatchments of the Ribble, the R. Hodder and
the R. Calder, drain from the west and east, respectively,
joining with the R. Ribble south of the town of
Clitheroe. The river then drains southwest through an
area of lower relief to the tidal limit near Preston. The
remaining four major rivers drain an area of compara-
tively low relief in the southern section of the river basin
(Fig. 1). The subcatchments of the Rivers Calder,
Darwen and Douglas contain extensive conurbations.
Land cover in the remaining area is largely pastoral with
some forestry in the upper reaches of the R. Hodder
subcatchment.
2.2. Water quality monitoring
A total of 41 sampling locations were selected across
the study area based on the existing Environment
Agency (EA) sampling point network (Fig. 1). Most
sampling sites were located at bridge crossings. The
sampling period covered 44 days during the England
and Wales bathing season (defined under Anon (1976) as
15th May–30th September), commencing at 09:00 GMT
on 29th July 2002. The sampling sites were selected to
include major catchment outlets, close to river discharge
gauging stations (Fig. 1), and to reflect the range of land
cover types in the study area. For instance, sampling
sites 1–7 follow the main channel of the R. Ribble from
the gauging station close to the tidal limit through to the
headwaters. Samples were taken at 2–3 day intervals,
supplemented by intensive, opportunistic sampling
under high-flow event conditions after rainfall. The
objective of this protocol was to characterize base- and
high-flow water quality within the Ribble basin during
the bathing season. The high-flow periods were defined
by standard base-flow separation of the hydrograph
record for the nearest hydrometric station. An alter-
native approach to water quality characterization could
have been to derive the flow weighted Event Mean
Concentration (EMC), as defined by Huber (Huber,
1993) for each site from a range of hydrograph events.
However, this would have required detailed information
on the ‘within-event’ microbial concentrations at each
site for a series of events which was not feasible given (i)
the scale of the catchment, (ii) the number of sites and
(iii) the requirement to maintain aseptic sampling as
outlined in Anon (2002a).
Aseptic sampling were achieved by lowering a clean
stainless-steel can into the flowing water. This was
repeated three times, on the first two occasions the water
discarded. The third collection of water was poured into
a 150ml sterile plastic container (Media DisposablesTM).
The can was then dried with absorbent paper towel and
wiped clean with single-use alcohol impregnated anti-
bacterial wipes (Azo-wipeTM, Vernon-Carus Ltd.). The
alcohol was allowed to evaporate on the journey to the
next sampling site. At sites where rivers and streams
could only be accessed from the bank side, samples were
obtained directly into a sterile plastic container using a
laboratory clamp and telescopic rod. A 250ml sample
was taken at one site on each sampling run to allow a
duplicate ‘quality control’ analysis. Samples were stored
in the dark inside a cool box during transport to the
laboratory and refrigerated on arrival.
2.3. Laboratory analyses
Enumerations of presumptive coliforms (PC), pre-
sumptive E. coli (PE) and presumptive enterococci
(PEnt) followed standard UK methods (Anon, 2002a).
PC were isolated by incubation on membrane lauryl
sulphate broth (Oxoid) for 4 h at 30 1C followed by 14 h
at 37 1C (71 1C). PE were isolated by incubation on
membrane lauryl sulphate broth (Oxoid) for 4 h at 30 1C
followed by 14 h at 44 1C (70.5 1C). PEnt were isolated
by membrane filtration on Slanetz and Bartley agar
(Oxoid), with incubation for 4 h at 37 1C followed by
44 h at 44 1C (70.5 1C). Appropriate sample dilutions
were determined from an initial trial sampling run and
pre-existing data. PC enumerations were performed at
two or three sample dilutions whilst PE and PEnt
enumerations were based on at least two sample
dilutions in triplicate to enhance measurement precision
(Fleisher and McFadden, 1980). Samples were analyzed
as soon as possible after collection (mean 7.1 h, standard
deviation 3.2 h). All samples were analyzed within
24 h of collection in accordance with official UK
ARTICLE IN PRESS
Fig. 1. Map of the R. Ribble drainage basin showing elevation, surface water sampling points and corresponding subcatchment
boundaries and hydrometric gauging stations. (This map is based upon CEH 1990 Landcover data, Ordnance Survey profile and
1:50,000 raster data. Environment Agency, 100026380, (2005).)
D. Kay et al. / Water Research 39 (2005) 3967–39813970
requirements (Anon, 2002a). All microbial concentra-
tion results were expressed as colony forming units (cfu)
100ml�1.
A total of 903 samples were analyzed during the study
period. PE and PEnt results were obtained from all
samples. For PC, results were obtained for 859 samples.
ARTICLE IN PRESSD. Kay et al. / Water Research 39 (2005) 3967–3981 3971
Here, the results from one complete sampling run were
discounted due to an incubator malfunction, and factors
such as overgrowth with non-lactose fermenting organ-
isms affected a further four sets of enumerations,
precluding calculation of valid results. No results
exceeded the upper limit of detection. Three PC results,
two PE results and one PEnt result were below the lower
limit of detection. For the purposes of statistical
analyses these samples were ascribed the lower detection
limit values: PC 909 cfu 100ml�1, PE 303 cfu 100ml�1
and PEnt 3 cfu 100ml�1.
2.4. Hydrometry
Hourly time series of either discharge (m3 s�1) or level
(m) were available from a network of gauging stations
(Fig. 1). Real-time access to river level information was
available for several stations via the EA telemetry
network. This was used to inform opportunistic
sampling during hydrograph events. In addition, tem-
porary staff gauges were installed at other sampling sites
where practical.
The discharge and level records and staff gauge
readings at the time of sampling were used in the
categorization of samples into two groups: (i) those
taken during base flow, dry weather, conditions and (ii)
those taken during hydrograph response to rainfall.
2.5. Digital map data
Digital map data were incorporated into a geogra-
phical information system (GIS) of the study area using
Environmental Systems Research Institute ARC/INFO
software. This software was also used for spatial
analysis.
A digital terrain model (DTM), also at a cell
resolution of 25m, was derived from Ordnance Survey
(OS) digital contour data (Fig. 1). This was further
modified to create a depressionless DTM using standard
ARC/INFO procedures to fill in anomalous sinks that
often occur along the valley floors in the DTM. This
‘filled’ DTM was used as a basis for flow path analysis
and derivation of a raster drainage network. Subcatch-
ment outlet points were positioned on this network as
close as possible to the actual sampling points. Standard
ARC/INFO routines were then used to derive the
topographic watershed boundaries for each subcatch-
ment (Fig. 1).
A digital map of land cover, at 25m resolution, was
available in the form of the CEH 1990 land cover map.
This derives from remotely sensed (Landsat) imagery
and categorizes land cover into 17 classes (Table 1).
Additional digital land cover data were available from
OS 1:50,000 colour raster maps. A common 25m
resolution cover was generated based on these maps.
2.6. Derivation of the Ribble land cover data
Accurate land cover data from previous detailed field
mapping were available for 100 subcatchments in five
study areas in England and Wales (Wyer et al., 1998a, b;
Crowther et al., 2002, 2003; Stapleton et al., 2005; Kay
et al., 2005). The 17 CEH land cover classes were
categorized according to the seven principal land use
classes attributed during field surveys (Table 1).
Comparison of the CEH land cover data with the field
survey data in the 100 subcatchments showed marked
discrepancies, particularly in relation to built-up land,
woodland and improved pasture, suggesting inaccura-
cies in the remotely sensed data. Comparison of built-up
areas and woodland shown by the OS 1:50,000 map data
and the CEH data for the Ribble basin also revealed
substantial misclassification.
The limitations of the CEH land cover data were
addressed as follows. First, maps of built-up land and
woodland were generated based on the unique colours
used to depict these land use types in the OS 1:50,000
map. The woodland area extracted from the OS 1:50,000
map data was found to correspond very closely with the
field data from the 100 subcatchments. Three problems
were identified in extracting the built-up land: (i) only
buildings are identified and not the roads, gardens, etc.
which would conventionally be classified as part of built-
up areas; (ii) some public buildings are excluded because
they are depicted using different colours; and (iii)
lettering is often superimposed on built-up areas, further
reducing the area of built-up land extracted. To quantify
this under-estimation 25 500� 500m squares which
would be conventionally mapped as 100% built-up were
selected from the OS 1:50,000 raster map set for England
and Wales. The area of built-up land was extracted as
outlined and on average was under represented by a
factor of 3.11.
Comparison of the proportion of improved pasture
identified in the 100 subcatchments with that derived
from the CEH land cover data showed a strong linear
relationship. However, improved pasture is under-
represented where a high proportion is present and
over-represented where very little is present. A map of
improved pasture, rough grazing, arable and ‘other’
land use categories (Table 1) was generated based on the
CEH land cover data, the built-up and woodland
categories in this data source being reclassified as
‘unclassified’ at this stage. This map was then amalga-
mated with the built-up and woodland areas extracted
from the OS 1:50,000 map, with the latter map
categories being given precedence (i.e. a cell of improved
pasture on the CEH map classified as woodland on the
OS 1:50,000 map would be classified as woodland in the
final data set).
The resultant areas of each land use type in each
subcatchment were then adjusted. First, unclassified
ARTICLE IN PRESS
Table 1
Details of the Centre for Ecology and Hydrology (CEH) 1990 land cover classification (17 classes) and the corresponding land use type
to which they have been attributed
CEH
class
Description Land use type to which the CEH
class has been attributeda
0 Unclassified Unclassified
1 Sea, coastal waters and estuaries, inland to first bridging point or barrier Other
2 Inland fresh waters and estuarine waters above the first bridging point or barrier Other
3 Bare coastal mud, silt, sand shingle and rock, including coastal accretion and
erosion features above high water
Other
4 Intertidal seaweed beds and salt marshes up to normal levels of high water spring
tides
Other
5 Semi-natural, mostly acid, grasslands of dunes, heaths and lowland–upland
margins+Montane/hill grasslands, mostly unenclosed nardus/molinia moorland
Rough grazing
6 Pastures and amenity swards, mown or grazed, to form a turf throughout the
growing season+Meadows, verges, low-intensity amenity grasslands and semi-
natural cropped swards, not maintained as short turf
Improved pasture
7 Lowland marsh/rough grasslands, mostly uncropped and unmanaged, forming
grass and herbaceous communities, of mostly perennial species, with high winter
litter content+Ruderal weeds colonising natural and man-made bare
ground+Felled forest, with ruderal weeds and rough grass
Rough grazing
8 Upland, dwarf shrub/grass moorland+Lowland dwarf shrub/grass heathland Rough grazing
9 Upland evergreen dwarf shrub-dominated moorland+Lowland evergreen
shrub-dominated heathland
Rough grazing
10 Bracken-dominated herbaceous communities Rough grazing
11 Deciduous scrub and orchards+Deciduous broadleaved woodland and mixed
woodlands
Other
12 Conifer and broadleaved evergreen trees Woodland
13 Lowland herbaceous wetlands with permanent or temporary standing
water+Lowland herbaceous wetlands with permanent or temporary standing
water
Rough grazing
14 Arable and other seasonally or temporarily bare ground Arable
15 Suburban and rural developed land comprising buildings and/or roads but with
some cover of permanent vegetation
Built-up
16 Industrial, urban and any other developments lacking permanent vegetation Built-up
17 Ground bare of vegetation, surfaced with ‘natural’ materials Other
aBased on detailed notes that accompany the classification scheme.
D. Kay et al. / Water Research 39 (2005) 3967–39813972
land was re-allocated to the improved pasture, rough
grazing, arable and ‘other’ categories in proportion to
the area of these land use types identified within each
subcatchment (i.e. no adjustments are made to the areas
of built-up and woodland categories). Second, the area
of built-up land was increased by the factor of 3.11, the
area of land required for this being subtracted propor-
tionately from the areas of improved pasture, rough
grazing, arable and other categories.
In addition, land areas upstream of the outlets of all
identifiable lakes and reservoirs (from the OS 1:50,000
maps) were defined. Land use within these areas was
reclassified as ‘reservoir catchment’. This additional
classification attempts to account for low faecal
indicator organism concentrations that would be asso-
ciated with die-off and sedimentation processes within
such water bodies (Kay and McDonald, 1980) and the
resultant effect of water quality at the subcatchment
outlet not reflecting the land cover pattern within the
subcatchment. It should be noted that while these
procedures produce much more accurate data on the
overall proportions of different land use types within
each subcatchment, some adjustments made (e.g. built-
up land) are not location specific and cannot therefore
be represented on a map.
2.7. Statistical analysis
The distribution of microbial concentrations in
samples taken during base- and high-flow conditions
showed a closer approximation to normality when log10transformed. All microbial concentration data were
therefore log10 transformed prior to further statistical
analysis. The Minitab and SPSS software packages were
used for statistical analysis (Ryan and Joiner, 1994;
Minitab, 1995; SPSS, 1999). Descriptive statistics were
ARTICLE IN PRESSD. Kay et al. / Water Research 39 (2005) 3967–3981 3973
used to characterize the distribution of faecal indicator
concentrations in samples taken at base and high flow at
each location. These statistics include the geometric
mean (calculated as the antilog of the mean of log10transformed concentrations), the standard deviation of
log10 transformed concentrations, the 95% confidence
interval for the mean and the range. The significance of
differences between geometric mean concentrations at
base flow and high flow was examined using Student’s
t-tests to compare the mean log10 values.
The proportions (%) of land in each category were
calculated for each subcatchment unit to form a
predictor variable matrix (Xi). Multiple regression
(stepwise forward selection procedure) was used to
model relationships between mean log10 transformed
faecal indicator organism concentrations (dependent
variables, Y) and the predictor variables. Predictor
variables with X25% zero values were excluded and
log10 transformations were applied to those predictors
with skewness41.00. Regression models of the follow-
ing form were generated for each faecal indicator type
(i.e. PC, PE, and PEnt):
Y ¼ a1X 1 þ a2X 2 þ � � � þ a7X 7 þ b � u, (1)
where a is the slope, b the intercept, and u the random
error (stochastic disturbance) term.
The regression analysis was parameterized as follows:
(i) predictor variables with a variance inflation factor45
(i.e. tolerance ¼ 0.200) were excluded to minimize
multicollinearity (Rogerson, 2001) and (ii) the prob-
ability of F for a predictor to enter a model was set at
0.05. The strength of relationships was assessed by the
coefficient of determination (r2 expressed as %),
adjusted for degrees of freedom, whilst the normal
probability plot of standardized residuals was examined
to confirm the validity of each model. All statistical tests
were assessed at a ¼ 0:05 (i.e. 95% confidence).
3. Results and discussion
3.1. Faecal indicator concentrations
Between 20 and 27 samples were taken at each site
during the course of the study. Based on a detailed
analysis of corresponding hydrometric data, total
numbers of samples taken during hydrograph events at
each site (i.e. high-flow samples) ranged from 6 to 15. On
average 45% of the samples were taken under high-flow
conditions (range: 29–57%), with 410 high-flow sam-
ples being obtained at 21 of the 41 sites.
The mean, range and 95% confidence intervals for the
mean of log10 transformed PE concentrations from the
base- and high-flow categories at each site are summar-
ized in Fig. 2. Geometric mean base-flow PC concentra-
tions were on average three times higher than
corresponding PE concentrations (range: 1–15� ). At
high flow, this elevation was lower and less variable
(mean: 2� , range 1–4� ). Geometric mean base-flow
PEnt concentrations were on average 10 times lower
than corresponding PE concentrations (range: 2–15� ).
At high flow this reduction was also lower and less
variable (mean: 7� , range 3–13� ). Full details of the
actual faecal indicator concentrations are provided in
Wyer et al. (2003).
Geometric mean PE concentrations are further illu-
strated in Fig. 3. Figs. 2 and 3 show pronounced
variations in base- and high-flow PE concentrations
between sites, though base- and high-flow mean
concentrations show similar patterns. Sites with high
base-flow values tend to exhibit high values under high-
flow conditions. Relatively high PE concentrations are
associated with the catchment outlets of the Rivers
Calder (site 20), Darwen (site 33) and Douglas (site 39),
which drain subcatchments with relatively high propor-
tions of urban land use. Water quality at these sites is
impacted by associated inputs from combined sewer
overflows (CSOs) and sewage treatment works. The
lowest PE concentrations tend to be associated with the
headwater reaches of the Ribble, Hodder and Calder
catchments (e.g. sites 7, 12 and 32). On the R. Ribble
itself there is an attenuation of geometric mean faecal
indicator organism concentrations in the lower reaches
between site 2 and site 1 (at the catchment outlet). This is
evident for both base- and high-flow conditions and may
relate to the dominance of die-off and sedimentation
processes in the lower reaches of this river, which drain
through a largely agricultural landscape. PC and PEnt
concentrations showed similar patterns (Wyer et al.,
2003).
Mean log10 PE concentrations during high-flow events
were elevated at all sites. These elevations were
statistically significant based on Student’s t-tests. This
is indicated by the lack of overlap in the 95% confidence
intervals for the means of each group at each site (Fig.
2). High-flow geometric mean PE concentrations were
between 4 and 63 times higher than base-flow values
(mean: 15� ). Similar patterns were observed for PC
and PEnt concentrations (Wyer et al., 2003). For PC,
high-flow geometric mean concentrations were between
2 and 85 times higher than base-flow values (mean:
11� ). High-flow geometric mean PEnt concentrations
were elevated between 5 and 215 times (mean: 21� )
compared to base-flow values.
Similar elevations in faecal indicator and pathogen
concentrations following rainfall have been reported in
previous CREH investigations and in the international
literature (McDonald and Kay, 1981; Wyer et al., 1994,
1996, 1998a, b; Ashbolt et al., 2002; Crowther et al.,
2002, 2003; LeChevallier et al., 2002; Ashbolt and
Roser, 2003; Ferguson et al., 2003; Kay et al., 2005).
These are attributable to a combination of increased
ARTICLE IN PRESS
log10 concentration (cfu/100 ml)
log10 concentration (cfu/100 ml)
2 River Ribble at Ribchester
3 River Ribble at Great Mitton
4 River Ribble at Sawley
5 River Ribble at Cow Bridge
6 River Ribble at Settle Weir
7 River Ribble at Horton-in-Ribblesdale
8 Stydd Brook
9 River Hodder at Lower Hodder Bridge
10 River Hodder at Knowlemere manor
11 River Loud
12 Langden Brook
13 River Dunsop
14 Croasdale Brook
15 Bashall Brook
16 Smithies Brook
17 Holden Beck
18 Skirden Beck
19 Stock Beck
20 River Calder at Whalley Weir
21 River Calder at Altham Bridge
22 River Calder at Townley Park
23 Sabden Brook
24 Hyndburn Brook
25 River Brun
26 River Don
27 Pendle Water at Barden Weir
28 Pendle Water PTC Colne Water
29 Colne Water PTC Pendle Water
30 Colne Water at Laneshaw Bridge
31 Trawden Beck
32 Wycoller Beck
33 River Darwen at Blue Bridge
34 River Darwen at Pleasington Fields
35 River Darwen at Hoillins Paper Mill
36 River Lostock
37 River Yarrow at Fishery Bridge
38 River Yarrow at Pincock Bridge
39 River Douglas at Wanes Blades
40 River Douglas at Martland Bridge
41 River Tawd
1 River Ribble at Samlesbury 111210131112121012111210121012101310137138129129146148156157148138111414913911111210156138121013813914714714711101111111110131212111112151381310
n*
n*
high flow meanbase flow mean
base flow 95%confidence intervalhigh flow 95%confidence interval
* n = number of samples
1 2 3 4 5 6 7 8
1 2 3 4 5 6 7 8
Site
Fig. 2. Results of presumptive Escherichia coli monitoring at 41 surface water sampling sites in the R. Ribble drainage basin during a
44-day period in July–August 2002.
D. Kay et al. / Water Research 39 (2005) 3967–39813974
surface runoff, the extension of the stream network
and corresponding expansion of the contributing areas
of the catchment, collectively producing enhanced
connectivity to faecal indicator source areas, plus the
entrainment of organisms from stream bed stores
(Kay et al., 1999). During hydrograph events increased
ARTICLE IN PRESS
Fig. 3. Spatial patterns of geometric mean presumptive Escherichia coli monitoring in the R. Ribble drainage basin at base and high
flow during a 44-day period in July–August 2002. (This map is based upon CEH 1990 Landcover data, Ordnance Survey profile and
1:50,000 raster data. Environment Agency, 100026380, (2005).)
D. Kay et al. / Water Research 39 (2005) 3967–3981 3975
stream flow velocities and turbidity will also act to
reduce opportunities for die-off and sedimentation
processes whilst enhancing the transportation of
microorganisms.
3.2. Land cover
The results of the land use classification are summar-
ized in Table 2 and Fig. 4. It should be noted that
mapped areas of built-up land portrayed in Fig. 4
underestimate actual areas by a factor of 3.11� . The
extensive conurbations in the Ribble drainage basin are
reflected in the high proportions of built-up land in
several subcatchments. Indeed, built-up land in 10
subcatchments exceeds 10%. For subcatchments 34 (R.
Darwen), 36 (R. Lostock), and 41 (R. Tawd), the
proportion of built-up land exceeds 25% (Table 2). The
urban areas have associated continuous discharges of
treated sewage effluent and, during high-flow events,
inputs of untreated effluent from storm tank overflows
(STOs) and combined sewage overflows (CSOs).
The principal agricultural land use in the Ribble
drainage basin is improved pasture. In several subcatch-
ments (e.g. subcatchments to sites 8, 15, 17 and 18)
improved pasture comprises between 70% and 80% of
the catchment land cover (Table 2). Arable land use is
largely confined to the lowlands of the drainage basin,
particularly in the southern area (e.g. subcatchments to
sites 36, 37 and 39: Table 2). Extensive areas of rough
grazing are mostly confined to the higher ground in the
far west and north of the drainage basin. Rough grazing
exceeds 60% in three subcatchments draining these
areas (subcatchments 7, 12 and 13).
3.3. Regression models
Subcatchment 25 was excluded from the modelling
because it comprised 100% reservoir catchment (Table 2).
ARTICLE IN PRESS
Table 2
Results of the land use classification in 41 subcatchment units in the R. Ribble drainage basin
Site no. Site description Area (ha) Improved
pasture (%)
Rough
grazing (%)
Arable
(%)
Woodland
(%)
Built-up
land (%)
Other
(%)
Resr catch
(%)
1 Samlesbury 113017.37 49.87 23.02 7.93 3.89 4.67 0.26 10.35
2 Ribchester 106909.62 49.13 23.89 7.57 3.71 4.64 0.22 10.84
3 Great Mitton 44444.75 59.72 24.21 7.77 3.61 2.28 0.16 2.27
4 Sawley 33397.37 58.96 26.92 6.54 3.69 1.64 0.14 2.11
5 Cow Bridge 19465.87 46.44 42.56 5.70 3.11 1.31 0.23 0.65
5 Settle Weir 12432.12 34.40 56.24 4.76 3.33 0.94 0.33 0.00
7 Horton 6145.81 27.32 62.80 3.01 6.09 0.72 0.07 0.00
8 Stydd Brook 166.19 79.22 2.94 10.31 4.03 3.52 0.00 0.00
9 Lower Hodder
Br.
25792.19 40.47 31.79 4.83 4.44 1.05 0.15 17.27
10 Knowlemere 10215.38 34.93 22.69 2.71 2.71 0.52 0.13 36.32
11 R. Loud 4606.88 62.62 7.07 9.45 1.77 2.92 0.01 16.16
12 Langden Brook 2726.63 8.38 88.06 1.84 1.14 0.30 0.29 0.00
13 R. Dunsop 2857.06 10.15 81.59 1.53 5.92 0.28 0.52 0.00
14 Croasdale
Brook
2070.94 39.45 55.88 2.39 1.41 0.59 0.27 0.00
15 Bashall Brook 1656.31 71.27 15.50 5.58 5.45 2.19 0.01 0.00
16 Smithies Brook 2671.00 69.72 13.95 13.39 1.52 1.30 0.12 0.00
17 Holden Beck 1081.94 73.17 15.45 5.25 4.89 1.22 0.02 0.00
18 Skirden Beck 2699.25 76.92 7.21 5.77 9.08 1.02 0.00 0.00
19 Stock Beck 3449.81 64.99 2.25 9.86 1.64 4.41 0.04 16.81
20 Whalley Weir 31158.37 38.79 20.55 8.09 2.40 10.58 0.38 19.21
21 Altham Bridge 21423.25 35.92 21.01 6.77 1.93 9.92 0.26 24.20
22 Townley Park 1645.31 38.54 50.16 3.00 4.08 3.51 0.72 0.00
23 Sabden Brook 1771.81 57.67 20.63 7.08 3.76 2.40 0.03 8.42
24 Hyndburn
Brook
5496.13 34.30 26.48 10.66 1.70 16.19 1.01 9.65
25 R. Brun 1504.94 0.00 0.00 0.00 0.00 0.00 0.00 100.00
26 R. Don 952.87 36.34 54.73 4.33 3.25 1.35 0.00 0.00
27 Barden Weir 10821.87 39.68 22.90 6.28 1.26 8.44 0.22 21.22
28 Pendle Water 2678.56 44.11 11.59 9.18 2.19 2.98 0.14 29.80
29 Colne Water 5482.63 39.82 35.55 3.37 0.84 5.96 0.31 14.14
30 Laneshaw
Bridge
1098.50 49.42 21.75 0.28 0.50 1.12 0.05 26.88
31 Trawden Beck 1164.00 47.44 47.20 0.93 0.95 3.12 0.36 0.00
32 Wycoller Beck 1077.69 25.70 72.12 0.64 1.11 0.38 0.06 0.00
33 Blue Bridge 13492.62 21.75 30.57 5.95 2.90 15.76 0.49 22.57
34 Pleasington
Fields
6710.81 20.64 25.83 9.69 1.47 25.86 0.89 15.61
35 Hollins Paper
Mill
1655.31 11.73 37.38 6.42 2.29 18.92 0.66 22.60
36 R. Lostock 5633.50 41.16 19.11 11.90 2.53 25.03 0.28 0.00
37 Fishery Bridge 9556.44 28.61 10.08 10.63 2.17 10.36 0.29 37.86
38 Pincock Bridge 6076.06 19.04 8.55 5.22 1.99 8.96 0.35 55.89
39 Wanes Blades 16617.19 24.05 6.43 18.09 3.25 19.65 0.92 27.62
40 Martland Bridge 8940.94 13.34 3.73 9.84 1.73 19.02 1.00 51.33
41 R. Tawd 1615.13 30.67 9.92 22.82 2.52 32.71 1.36 0.00
D. Kay et al. / Water Research 39 (2005) 3967–39813976
The models were, thus, based on 40 subcatchments. The
resulting models predicting mean log10 faecal indicator
concentrations are summarized in Table 3. All models
showed high statistical significance (po0:0001) and an
acceptable fit of standardized residuals in normal
probability plots. In each model the log10 transformed
proportion of built-up land was entered at the first step
with a positive slope (a) value. This variable was the
ARTICLE IN PRESS
Fig. 4. Land use map of the R. Ribble drainage basin and predicted high-flow geometric mean presumptive Escherichia coli
concentrations at 5 km intervals along the digital elevation derived drainage network. (This map is based upon CEH 1990 Landcover
data, Ordnance Survey profile and 1:50,000 raster data. Environment Agency, 100026380, (2005).)
D. Kay et al. / Water Research 39 (2005) 3967–3981 3977
dominant predictor variable accounting for the largest
proportion of explained variance (r2, Table 3). In the
base-flow models for PC and PE the log10% built-up
variable was the sole variable entered.
In each high-flow model, the variable entered at the
second step was the log10 transformed proportion of
rough grazing (Table 3) with a negative slope value. The
final proportion of explained variance was higher,
ARTICLE IN PRESS
Table 3
Results of stepwise regression analysis for relationships between mean log10 faecal indicator organism concentrations and land use
proportions in 40 subcatchment units in the R. Ribble drainage basina
Dependent
variable (Y)bVariables (X i) entered
in regression equation
Coefficientc Adjusted r2 (%)d Fit of normal probability
plot of standardised
residualse
Significance p
BASE FLOW:
MLgPC log10% built-up +1.0970 51.0 * o0.0001
Constantc +3.5572
MLgPE log10% built-up +1.0388 59.0 * o0.0001
Constantc +3.1819
MLgPEnt log10% built-up +0.9785 44.2
log10% arable �0.5626 49.5 * o0.0001
Constantc +2.7058
HIGH FLOW:
MLgPC log10% built-up +0.8795 62.3
log10% rough grazing �0.4277 68.1 * o0.0001
Constantc +5.3209
MLgPE log10% built-up +0.6249 48.2
log10% rough grazing �0.5224 59.7 * o0.0001
+5.3485
MLgPEnt log10% built-up +0.8636 52.5
log10% rough grazing �0.6187 58.7
log10% arable �0.7224 66.3 * o0.0001
Constantc +5.0779
aSite 25 was excluded since its catchment comprised 100% reservoir catchment.bMLg ¼ Mean log10 concentration. PC ¼ Presumptive coliforms, PE ¼ Presumptive Escherichia coli, PEnt ¼ Presumptive
enterococci.cValues adjacent to predictor variables are slope coefficients (a) and the constant is the intercept value (b): Y ¼ a1X 1 þ a2X 2 þ
a3X 3 þ b:d% of variance in Y explained by the predictor variable(s) in the equation. The values given are cumulative, i.e. the highest value for
each equation relates to all variables in the model.e** ¼ Good fit; * ¼ acceptable fit; ? ¼ poor fit.
D. Kay et al. / Water Research 39 (2005) 3967–39813978
exceeding 59%, for the three models predicting high-
flow mean log10 faecal indicator organism concentra-
tions than the corresponding base-flow models. In the
models predicting mean log10 PEnt, the log10 trans-
formed % arable land use was entered at the final step
with a negative slope value (Table 3).
These models suggest that built-up land is the key
source of faecal indicator organisms in the Ribble
drainage basin. This contrasts with results of similar
modelling exercises in pastoral agricultural catchments
where the proportion of improved pasture was found to
be the dominant predictor variable particularly at high
flow (Kay et al., 1999; Crowther et al., 2002, 2003). The
fact that improved pasture does not feature in any of the
models in Table 3 underlines the dominance of built-up
land as the key source of faecal indicator organisms
within this heavily urbanized drainage basin. It should be
noted, however, that improved pasture and rough grazing
are inversely related, and therefore the negative relation-
ship with rough grazing in the models may be used to
infer a positive relationship with improved pasture.
Fig. 5 shows plots of observed vs. predicted mean
log10 PE concentrations based on the base- and high-
flow models in Table 3. These plots allow an analysis of
‘outliers’, which deviate from the model prediction
(indicated by the 451 solid line on the plots). For
example, several sites showed much higher observed
values (in excess of 0.5 log10) than the models would
predict and could be considered as PE pollution ‘hot-
spots’ for further investigation. For example, site 39
shows the largest deviation in both models (41.0 log10at base flow) and is the outlet of the R. Douglas. This
site is close to a major input of faecal indicators from a
sewage treatment works (Wyer et al., 2003).
Fig. 4 illustrates a further application of the regression
models, based on land cover in the entire Ribble
drainage basin including the area around the Ribble
estuary. Here, a network of points on the DTM drainage
network was generated at 5 km intervals along the
network. Land cover in the subcatchment to each point
was then calculated. Subcatchments with areas o100 ha
were excluded along with any subcatchments containing
ARTICLE IN PRESS
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0
Pre
dict
ed m
ean
log 1
0 co
ncen
trat
ion
(cfu
/100
ml)
Observed mean log10 concentration (cfu/100 ml)
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0
Pre
dict
ed m
ean
log 1
0 co
ncen
trat
ion
(cfu
/100
ml)
Observed mean log10 concentration (cfu/100 ml)
Base flow
High flow
3941
39
33
32
+1.0
+0.5
-0.5-1.0
14
+1.0
+0.5
-0.5-1.0
153
14
13
30
24
32
Fig. 5. Comparison of observed and predicted base and high-
flow mean log10 presumptive Escherichia coli concentrations for
40 subcatchment monitoring sites in the R. Ribble drainage
basin. (This map is based upon CEH 1990 Landcover data,
Ordnance Survey profile and 1:50,000 raster data. Environment
Agency, 100026380, (2005).)
D. Kay et al. / Water Research 39 (2005) 3967–3981 3979
450% reservoir catchment. The models in Table 3 were
then applied for each point allowing generation of
corresponding predicted water quality maps. The
example in Fig. 4 summarizes the results for PE at high
flow. Further analyses, for base-flow, PC and PEnt are
presented in Wyer et al. (2003). This analysis provides
insight into likely spatial variations in water quality
across the entire drainage basin, reflecting closely the
pattern of built-up land, and highlighting potential
sources of faecal indicators along with corresponding
poor water quality.
4. Conclusions
The combination of readily available remote-sensed
and digital map data provides a basis for generating a
reasonably precise digital map of land use in relatively
large drainage basins. Such basins form the fundamental
spatial units in the WFD. The accuracy of such land use
data could be improved by the application of more
modern satellite-derived data sets (e.g. CEH land cover
2000 data) and high-resolution satellite imagery (e.g.
SPOT 5). Such data would also allow development of
land cover data that are contemporaneous with water
quality data.
The water quality monitoring programme successfully
generated faecal indicator organism concentration data
covering base- and high-flow event conditions. Gather-
ing such data requires intensive effort to target highly
episodic conditions, which are often missed in routine
water quality monitoring programmes. Given the
importance of high-flow conditions in faecal indicator
flux estimation, this is a very significant problem for the
use of data derived from routine monitoring pro-
grammes which are systematically biased to base-flow
conditions, leading to erroneous appraisal of the
importance of catchment-derived peak concentrations
from both diffuse and point (i.e. CSOs and STOs)
discharges.
Faecal indicator organism concentrations were sig-
nificantly elevated at all monitoring points under high-
flow event conditions. The movement of these organisms
from catchment sources is thus associated with relatively
short duration hydrograph events which may account
for delivery of 490% of organisms from the drainage
basin to the estuary and coastal zone. This reinforces the
need for sampling programmes to target such events
when examining faecal indicator organism concentra-
tions and delivery.
The regression modelling exercise, combining the
results of the land cover and water quality analyses,
was successful in producing statistically significant
models predicting geometric mean faecal indicator
concentrations from readily available land cover data.
Levels of explained variance were reasonably high (i.e.
r2: 49.5–68.1%), and were particularly strong for the
most important predictions of high-flow water quality.
The models suggest that built-up areas are a significant
source of faecal indicator organisms in the Ribble
drainage basin, which contains extensive urban areas.
This contrasts with results from rural catchments where
improved pasture is the dominant faecal indicator
organism source. Indeed, the sewage works derived flux
ARTICLE IN PRESSD. Kay et al. / Water Research 39 (2005) 3967–39813980
of faecal indicators accounted for over 67% of the total
riverine flux to the receiving estuarine environment for
the bathing season study period. This has direct policy
implications through the development of strategies for
WFD ‘protected area’ compliance with microbiological
standards defined in Directives covering recreational
and shellfish harvesting waters. The previous studies of
this team have highlighted the need for diffuse pollution
control by the agricultural community which has been
prioritized even in moderately urbanised catchments. In
contrast, the Ribble catchment data, presented here,
suggest that the highest priority should be given to the
sewerage infrastructure and sewage treatment systems
within the catchments discharging to the Ribble estuary
and Fylde coast recreational waters.
The regression models provide a useful tool for
examination of potential pollution ‘hotspots’ through:
(i) comparison of observed and predicted values; and (ii)
prediction of spatial water quality patterns across the
drainage basin. The technique outlined could be
extended to provide similar analyses of other water
quality parameters of importance in the WFD. The
study has demonstrated that readily available digital
map data can provide a basis for modelling surface
water quality at the large drainage basin scale, providing
a starting point and focus for further detailed investiga-
tions related to water quality objectives.
Acknowledgements
The study was funded by the Environment Agency,
North West Region. The views expressed in this paper
are those of the authors and do not necessarily reflect
those of the Environment Agency. The authors wish to
thank the following people who contributed to the
study: Sarah Stapleton, Matthew Hopkins, Nicola
Poulter, Andrew Davies, Niki Kay, Lee Whitehead,
Colin Last and Gary Beasley (CREH)—sample collec-
tion and transport; Paula Hopkins (CREH)—field
logistics organization; Michael Shankster, Robb Turner,
Gareth Owen, Wally Ball, Phil Heath, Terry Bucknall,
Carol Holt, Andy Brown and Helen Rice (Environment
Agency)—GIS, hydrometry and field logistical support;
Hanna Boynton, Diane Corscadden and Lenka Rushby
(CREH Analytical Ltd.)—laboratory analytical assis-
tance.
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