Journal of Hydrology 420–421 (2012) 37–45

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Journal of Hydrology

journal homepage: www.elsevier .com/ locate / jhydrol

A GIS based MCE model for identifying water colour generation potentialin UK upland drinking water supply catchments

Richard Grayson a,⇑, Paul Kay a, Miles Foulger b, Sarah Gledhill b

a School of Geography, University of Leeds, Leeds LS2 9JT, UKb Yorkshire Water Services Ltd., Western House, Western Way, Bradford BD6 2LZ, UK

a r t i c l e i n f o s u m m a r y

Article history:Received 8 July 2010Received in revised form 14 October 2011Accepted 9 November 2011Available online 18 November 2011This manuscript was handled by LaurentCharlet, Editor-in-Chief, with the assistanceof Ewen Silvester, Associate Editor

Keywords:Water colourDOCCatchment managementGISModelling

0022-1694/$ - see front matter � 2011 Elsevier B.V. Adoi:10.1016/j.jhydrol.2011.11.018

⇑ Corresponding author. Tel.: +44 (0)113343337; faE-mail address: r.grayson@leeds.ac.uk (R. Grayson

Water discolouration is one of the key water quality problems faced by UK water companies taking rawwater from peatland catchments. A water colour model has been developed using a combined Geograph-ical Information System and Multicriteria Evaluation approach. The model was used to predict water col-our production potential based on key land management practices controlling colour production in UKupland catchments. Calibration of the model with historic data collected at water treatment works treat-ing water from upland areas showed that the model was potentially capable of accurately predictingwater colour production potential at the catchment scale (c. 90%). Subsequent validation has shown thisto be the case. Rotational heather burning and vegetation type (particularly heather) were identified asthe two most statistically significant variables influencing water colour generation in the study catch-ments. It was predicted that colour is generated in particular hotspots and management to improve waterquality should, therefore, focus on such areas. Blending of water is also an important process in control-ling colour at the catchment scale and at water treatment works, with high colour often being diluted byrunoff from land elsewhere in the catchment with lower potential to generate colour.

� 2011 Elsevier B.V. All rights reserved.

1. Introduction

Peat bogs represent the single largest terrestrial carbon reservewithin the UK, currently acting as a net sink of carbon (Cannellet al., 1993). Over recent decades Dissolved Organic Carbon(DOC) losses from upland peat catchments have increased however(Evans et al., 2005, 2006; Worrall et al., 2003, 2004; Worrall andBurt, 2007a). Were these losses to continue then UK upland peatsmay switch to become a net source of carbon (Worrall et al.,2006). Losses of DOC from upland peat concern not only the scien-tific community but also the UK water industry as upland catch-ments contribute >70% of all drinking water (Watts et al., 2001).DOC and water colour have been found to be highly correlated asit is the presence of this DOC that results in water becoming col-oured (Worrall et al., 2003), although not all DOC results in the col-ouration of water. During treatment DOC can interact withchlorine to produce carcinogenic compounds, including Trihalome-thanes (THMs), Haloacetic Acids (HAAs) and other halogenatedcompounds (Fearing et al., 2004).

Within the UK, long-term DOC records from upland catchmentsare limited in number (Evans et al., 2005; Worrall et al., 2003,2008) and their spatial coverage tends to be small. Indeed, whilethe UK Acid Waters Monitoring Network (AWMN) has measured

ll rights reserved.

x: +44 (0)1133433308.).

DOC at 22 UK sites since 1988 the majority are located within Scot-land and Wales, with only 5 sites located in England, of which twoare lakes and only one riverine site is in northern England (Mon-teith and Evans, 2005).

The UK water industry routinely measures water colour atWater Treatment Works (WTWs) to ensure compliance with EUdrinking water regulation (EC, 1998). Due to the strong relation-ship between DOC and colour (Worrall et al., 2005) it is possibleto use the available long-term water colour records as proxies toestablish long-term trends in DOC. These records, therefore, pro-vide a valuable insight into long term trends in DOC/colour releasefrom UK upland peat catchments and offer an opportunity to com-pare catchments to identify key drivers, particularly the impacts ofland management and reasons for inter-catchment variability.

Estimates suggest that for England and Wales treatment for soilerosion and organic carbon losses are currently >£106 m per an-num (Pretty et al., 2000) catchment management offers the poten-tial to reduce these ‘end of pipe’ treatment costs by treating theproblem ‘at source’. DOC losses and colour production exhibit con-siderable spatial variability both between and within catchments,with small areas typically generating extreme discolouration(Butcher et al., 1992). Therefore, the identification of critical landmanagement practices and ‘hot spots’ would aid catchment man-agement strategies.

Bacterial breakdown of terrestrial organic matter is the majorsource of DOC observed in stream waters, with humic and fulvic

38 R. Grayson et al. / Journal of Hydrology 420–421 (2012) 37–45

acids accounting for the bulk of these (50–75%) (Thurman, 1985).Despite significant increases in DOC losses being observed over thepast few decades (Evans et al., 2005, 2006; Freeman et al., 2001a;Worrall and Burt, 2004; Worrall et al., 2003; Worrall and Burt,2007b) the mechanisms driving these are not fully understood.Worrall et al. (2007a) summarised the various drivers believed tohave an influence on increased DOC losses observed within the UK,these include: increased air temperature (Freeman et al., 2001a);land management (Worrall et al., 2003); changes in the amountand nature of flow (Tranvik and Jansson, 2002); eutrophication (Har-riman et al., 1998); severe summer drought (Freeman et al., 2001b;Worrall et al., 2004); and decreasing acid deposition (Clark et al.,2005; Evans et al., 2006; Monteith et al., 2007). Despite this thereis little consensus as to the major driver of these increases.

Land management practices have significantly impacted the UKuplands. Rotational burning is the most widely applied upland man-agement technique, with burning intensity having increased signif-icantly over the past few decades (Yallop et al., 2005, 2006). Evidenceindicates that burning influences the production of DOC and colour,however, debate exists as to whether burning increases (Miller,2008; Mitchell and McDonald, 1992; White et al., 2007) or decreases(Worrall et al., 2007a, b) colour production. Within the Yorkshire re-gion of the UK the proportion of new burn on deep peat (Winter Hillseries) has been suggested as the single largest predictor of waterdiscolouration (White et al., 2007; Yallop and Clutterbuck, 2009).

Furthermore, throughout the 1960s and 1970s large scale drain-age (gripping) of UK upland peats occurred (Holden et al., 2004). Thisprocess stimulates DOC production by increasing the volume of peatexposed to bacterial activity and oxidation (Mitchell and McDonald,1992; Worrall and Burt, 2005). Significantly increased DOC and col-our in soil water solutions have been observed in artificially drainedpeat compared with intact peat (Wallage et al., 2006). Although gullyand grip blocking are the main scientifically proven managementstrategies for reducing water colour (Kay et al., 2009) the impactsof grip blocking vary. Wallage et al. (2006) observed a 60–70% de-crease in water colour at blocked sites. In contrast, colour has alsobeen found to increase in blocked drains shortly after blocking oc-curred (Worrall et al., 2007b), although this may result from theflushing out of DOC already built up in the peat.

Vegetation may play an important control in the production ofwater colour. Within the UK upland land management has encour-aged the growth of Calluna (moorland heather) for various agricul-tural reasons. Evidence suggests that Calluna can suppress thewater table through evapotranspiration, therefore increasing col-our production (Clutterbuck and Yallop, 2010), with land undermature heather accumulating DOC through the summer (Whiteet al., 2007). The presence of Calluna can also promote the produc-tion of peat pipes (Holden, 2005) which will affect hydrologicalpathways in areas of peat dominated by Calluna. Miller (2008) ob-served elevated water colour loss from dried and re-wetted peatunder heather compared to grass. Indeed, heather dominatedgripped catchments produced the highest water colour followedby mixed vegetation and grass dominated catchments in a recentUK-wide study (Armstrong et al., 2007, 2010).

Other variables found to influence colour production at the localscale include soil type (Hope et al., 1997; Tipping et al., 1999),slope angle and aspect (Mitchell and McDonald, 1995; Mitchell,1991), precipitation amount and intensity (Scott et al., 1998) andafforestation (Baker et al., 2008; Watts et al., 2001).

2. Modelling

While attention has focussed on the mechanisms driving in-creases in DOC and on measuring DOC release, modelling DOC/col-our release in upland catchments has received little attention.

Modelling offers considerable potential to not only predict DOCand water colour concentrations at a range of scales but also offersthe opportunity to identify problem areas and as a result of thisthose variables responsible for driving high DOC concentrationsand water colour.

As there is still considerable debate as to which processes drivethe production of water colour and DOC in upland catchments thedevelopment of a process driven numerical model to estimatewater colour concentrations is far from straightforward and wouldnot be possible with the data available at this time. Instead this re-search aimed to use current scientific knowledge outlined in theavailable literature along with long term records of water colour(and by proxy DOC) to:

1. Develop a model capable of predicting colour concentrations atthe catchment scale.

2. Use the model inversely to further investigate drivers of watercolour production and identify those key factors capable ofaccounting for differences between catchments.

3. Subsequently, validate the model in an additional upland watersupply catchment where historic water colour data are available.

The model output will then help inform future catchment man-agement decisions aimed at reducing water colour.

3. Methods

3.1. Site selection

The majority of the c. 120 reservoirs used in the supply of drink-ing water in Yorkshire are located along the eastern edge of thePennines (Southern Pennines and Peak District). Water colour isroutinely measured across the network of WTWs within Yorkshire,with concentrations given as mg l�1 Pt Co (Hazen, 1892; ISO2211,1973). Colour in drinking water must not exceed 20 mg l�1 (EC,1998; Hongve and Akesson, 1996; Hongve et al., 2004) and where60 mg l�1 is exceeded the capacity to remove colour is diminished(Pattinson et al., 1995). More typically, colour may also be mea-sured using light absorbance at specific wavelengths with 254and 400 nm being typical values (Armstrong et al., 2010; Clayet al., 2009). Within Yorkshire many of the WTWs taking waterfrom upland catchments have experienced increases in water col-our since the mid 1990s (Yorkshire Water, unpublished data). Anextensive search of water colour records from WTWs across York-shire identified 18 sites suitable for inclusion in this study to testand calibrate the water colour model; 12 with historic (post1995) mean colour >60 mg l�1 Pt Co and 6 < 60 mg l�1 Pt Co(Fig. 1). In order to be used in the study, the WTW colour datahad to relate to a known catchment and not receive raw waterfrom multiple sources (e.g. major river intakes piped to the WTWin addition to runoff from its natural catchment area) which chan-ged periodically. The data record also had to be of good temporalresolution (typically weekly data from 1995 to 2006, although inmany cases sampling was more frequent, i.e. daily). All of the mon-itoring data therefore either relates to a catchment with an individ-ual reservoir or several (<6) nested reservoirs.

3.2. Model development

Multi-criteria evaluation (MCE), including multiattribute deci-sion making (MADM), is a powerful tool for interrogating complexdatasets (Malczewski, 1999). Of the various spatial MADM ap-proaches simple additive weighting (SAW) is the most commonand when this is combined with GIS it is possible to use the tech-nique to compare datasets from spatially discrete areas. This

Skipton

Barnsley

Keighley

Bradford

Sheffield

Huddersfield

0 5 10 15 202.5Kilometers

Embsay

Keighley MoorWatersheddles

Ponden

Lower Laithe

Fixby

Blackmoorfoot

HolmbridgeIngbirchworth

Winscar

Thurlstone Moor

LangsettMidhope

Broomhead

Agden

Dale Dyke

Strines

Rivelin

27

36

42

48

55

67

68

72

91

94

95

123

140

147

Mean Colour (mg l )-1

Fig. 1. Mean colour concentrations (mg l�1 Pt Co.) since 1995 for all the sites included in the analysis. The inset map shows the regional setting of these sites within both theYorkshire region and the UK as a whole.

R. Grayson et al. / Journal of Hydrology 420–421 (2012) 37–45 39

methodology involved combining a series of standardised spatialdatasets within a GIS and applying weightings to each based ontheir reported importance in generating colour. These were thenused to produce a colour score for each cell in the GIS, representingits colour production potential.

Following an extensive search of the literature to identify pro-cesses and land management practices controlling water colourproduction and DOC release, datasets for variables likely to influ-ence colour production were compiled. In total 12 individual data-sets were collated (Table 1). Slope, aspect and topographic index(Beven and Kirkby, 1979) were derived from a 10 � 10 m DigitalTerrain Model (DTM). Land cover was taken from the 25 x 25 m1990 ITE land cover map (ITE 1990). Soils data were provided bythe National Soils Resources Institute (NSRI) 100 � 100 m NationalSoil Map (NATMAP) (NSRI 1999) and the superficial geology data-set was derived from the British Geological Survey (BGS) 50 K data-set. Burning, gripping and extent of afforestation were digitisedfrom aerial photographs. Datasets not in raster format were raster-ised using a 10 � 10 m grid size.

3.3. Layers/dataset standardisation

The MCE approach requires that different datasets are convertedto a single scale. Where only two alternatives are possible a binaryapproach can be used to rescale values although where multiplealternatives are possible a linear scale transformation approach is re-quired. Where such an approach is used values are assigned across alinear scale with arbitrary minimum and maximum values beingbased on current understanding of processes controlling colour pro-duction (Table 1). Highest and lowest values were assigned to alter-natives likely to produce the highest and lowest colour, respectively.

3.3.1. SoilGiven the critical role of peat in colour production (Tipping

et al., 1999; Hope et al., 1997; Mitchell and McDonald, 1995) thehighest value for the soil dataset was assigned to Winter Hill (deeppeat) followed by Wilcocks and Belmont (shallower peat layers),whilst mineral soils were assigned zero values. For the superficial

Table 1Variables used to model water colour production along with rescaled values and weightings applied in the modelling process.

Variable Rescale values Used inmodel

Weighting

Soil Winter hill = 256; Wilcocks = 170; Belmont = 90; Other = 0 NoSuperficial

geologyPeat = 256; All other = 0 Yes 0.8

Rain Max = 256; Min = 0; All other = linear decrease based on max Yes 0.2Grip Grip = 256; no grip = 0 Yes 2Topographic

indexPlus or minus = 256; no change = 0 No

Naturalhydrology

0–2 = 256; 2–5 = 180; 5–8 = 150; 8–10 = 120; 10–15 = 90; 12–20 = 60; 20–25 = 30; 25–30 = 10; 30–50 = 5; 50–500 = 1; >500 = 0

Yes 1

Burn Burn = 256; no burn = 0 Yes 1Land cover 11, 17 And 24 = 256; 12 = 170; 10 and 13 = 120; 9 and 25 = 90; all other = 0 Yes 0.8Afforestation Coniferous = 256; Mixed = 170; Broadleaf = 90; none = 0 Yes 0.4Slope 3–5 = 256; 0 And 360 = 0; linear decrease to zero either side of 3 and 5 NoAspect 180 = 256; 0 And 360 = 0; linear decrease to zero either side of 180 NoTenancy No ownership = 256; ownership = 0 Yes 0.2

40 R. Grayson et al. / Journal of Hydrology 420–421 (2012) 37–45

geology dataset areas of peat were assigned the maximum valueand all other the minimum value.

3.3.2. RainfallAs the colour monitoring data is based on long term averages a

mean annual rainfall dataset was used (YW, unpublished rainfalldata). The maximum value was assigned to the catchment withthe highest average annual rainfall. An average annual rainfall ofzero was assigned, theoretically, the minimum value with a linearinterpolation approach being used to assign values for intermedi-ate rainfall values.

3.3.3. GrippingAll visible grips were digitised from aerial photographs using

polylines which were then rasterised using a resolution of5 � 5 m as grips lower the water table within 2 m of their edge(Stewart and Lance, 1991). Cells with grips present were assignedthe maximum value and all others the minimum value. For sim-plicity, no account was taken of grip orientation or whether ornot they had been ‘blocked’. Grips also influence the effective topo-graphic index (Beven and Kirkby, 1979), altering natural hydrolog-ical pathways, increasing the upslope drainage area and reducingsaturation downslope (Holden et al., 2006). A DTM was createdwhere the presence of a grip lowered surface elevation by 1 m(grips were typically cut to a depth of 50 cm but can erode signif-icantly). Topographic index was calculated for both this DTM andthe original DTM, with differences between the two being assignedthe maximum value as any change would alter the water table. Allother cells were assigned the minimum value.

3.3.4. Natural hydrologyThe impact of natural drainage was taken to be similar to that of

artificial drainage, i.e. a lowering of the water table in the areaimmediately adjacent to the channel edge. As gullies and channelscan widen and migrate naturally over time maps of natural drain-age can be inaccurate. However, OS Mastermap data include infor-mation on much of the natural drainage present; therefore thedistance to the nearest water body was calculated using the Euclid-ian distance function within ArcGIS, with values decreasing withincreasing distance.

3.3.5. BurningAreas with evidence for rotational burning were digitised as

polygons and rasterised regardless of age as although rotationalburning can be classified by age (Yallop et al., 2006; White et al.,2007) the colour data are based on long term averages (c. 10 years)and the aerial photographs used for digitisation were taken in

2000. Burnt areas were assigned the maximum value and all otherareas the minimum value.

3.3.6. Vegetation and afforestationVegetation affects colour production unequally (Miller, 2008;

Armstrong et al., 2007; White et al., 2007). Therefore, values forland cover classes were based on their known ability to generatecolour. ITE land cover classes likely to be associated with colourgeneration include: 9 (moor land grass); 10 and 25 (open shrubmoor); 11 and 13 (dense shrub moor); 12 (bracken); 17 (uplandbog); and 24 (lowland bog). All other classes were assigned theminimum value. Afforestation can lower the water table and sub-sequently increase colour production (Watts et al., 2001). Areas offorest/woodland were, therefore, digitised and categorised asconiferous, broadleaf or mixed. Coniferous woodland was assignedthe maximum value and non woodland areas the minimum withmixed and broadleaf given intermediate values.

3.3.7. Slope and aspectSlopes of between 3� and 5� are believed optimal for colour pro-

duction (Mitchell and McDonald, 1995) and were, therefore, as-signed maximum values with linear decreases to the minimumvalue for slopes less than 3� and greater than 5�. Southern facingslopes are most likely to produce colour (Mitchell, 1991), thereforean aspect of 180� was assigned the maximum value with linear de-creases either side; 0 and 360� (i.e. north-facing) were assigned theminimum value.

3.3.8. Land ownershipThe effects of land ownership on influencing the production of

colour are highly complex and, difficult to quantify. However, adataset was available for the study region detailing land ownedby the company responsible for supplying drinking water. For thisdataset values were assigned using a simple assumption that landunder water company ownership may be better managed, due toprescriptions aimed at protecting water quality in tenancy agree-ments, and, therefore, less likely to produce colour. Areas of landowned by the water company were, therefore, assigned the mini-mum value and all other areas the maximum value.

3.4. Model construction and evaluation

Following assignment of the catchment attribute datasets to asingle scale, the modelling approach involved two key stages.The first was to calculate Pearson correlation r values using theSPSS statistical programme to determine the relationship betweenwater colour concentrations and the initial and standardised

Colour Map

Standardisation(0-256)

Weightings

Layer 1

Layer 2

Layer 3

Layer 4

Layer 5

Layer 6

x=Fig. 2. Simplified schematic of the model design showing the standardisation and weighting stages and how the final model is created through the addition of the differentstandardised and weighted layers.

Fig. 3. Colour hotspot map for the study catchments included in the model analysis. The inset map shows the regional setting of these sites within both the Yorkshire regionand the UK as a whole.

R. Grayson et al. / Journal of Hydrology 420–421 (2012) 37–45 41

catchment variables. This allowed identification of any statisticallysignificant relationships existing between water colour and the ini-tial input variables and also ensured that the standardisation pro-cess did not affect any of these statistical relationships. The secondstage involved the construction of a SAW-based model; this

involved applying weightings to each input dataset based on theirperceived ability to influence water discolouration. These weigh-tings were applied to each dataset with total scores for each cellthen being calculated by adding all datasets together. Overallcatchment scores were then calculated by adding together the total

Table 2Correlations (r and p values) between the historic mean colour concentrations from the 18 catchments and the non-standardised (upper) and standardised variables (lower) usedin the modelling stages.

Initial datasetsBurn Grips Grip density Aspect Slope

r 0.8 �0.001 0.42 �0.5 �0.4p. <0.01 0.99 0.09 <0.05 0.1

TenancyAfforestation None Broadleaf Mix Coniferous

r �0.23 0.16 0.11 0.25 �0.1p. 0.37 0.54 0.67 0.32 0.68

Superficial geology Soil

Other Belmont Wilcocks Winter

r 0.34 �0.42 �0.09 �0.3 0.42p. 0.17 0.08 0.74 0.23 0.08

ITE land cover 0 2 5 5 7

r �0.46 �0.33 �0.39 �0.51 �0.71p. 0.06 0.18 0.11 <0.05 <0.01

8 9 10 11 12

r �0.43 �0.49 �0.11 0.68 �0.07p. 0.08 <0.05 0.66 <0.01 0.79

13 14 15 16 17

r 0.13 �0.62 0.07 0.14 �0.11p. 0.61 <0.01 0.8 0.58 0.67

18 20 21 22 25

r �0.32 �0.57 0.11 �0.1 �0.09p. 0.19 <0.01 0.68 0.7 0.74

Standardised datasetsBurnt Area Grips Topographic Index Aspect Slope

r 0.78 0.39 0.28 �0.07 �0.07p. <0.01 0.11 0.26 0.79 0.77

Afforestation Land cover Superficial geology Soil type Natural hydrology

r 0.24 0.76 0.54 0.45 0.19p. 0.35 <0.01 <0.05 0.06 0.46

Tenancy Rain

r 0.10 �0.06p. 0.68 0.81

y = 0.4329x - 76.293R2 = 0.886

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42 R. Grayson et al. / Journal of Hydrology 420–421 (2012) 37–45

scores for all the cells within the catchment. A simplified schematicof the model is shown in Fig. 2, this demonstrates how the rawdatasets/layers are first standardised (Table 1) before a weighting(Table 1) is applied. Once these two stages have been carried outthe end result is calculated by combining all the layers together.The model results for each catchment were compared against thehistoric water colour data to establish the relationship (r2) be-tween predicted and measured colour. Using a step-wise approachand current understanding of colour production in conjunctionwith the findings from stage 1, weightings were altered for thosevariables found to have statistically significant relationships withcolour concentrations. Some variables were excluded as they werefound to have little impact on the predictive capability of the mod-el; final weightings are shown in Table 1. The subsequent finalmodel was used to map water colour production potential acrossall sites included in the analysis (Fig. 3). To ensure the validity ofthe digitised burning, vegetation and artificial drainage data, fieldsurveys were also undertaken.

Colour Model Score

Fig. 4. Colour production scores from the colour model vs. post 1995 YorkshireWater mean colour monitoring data for each catchment studied (error bars = 1standard deviation).

4. Results and discussion

It was apparent that the standardisation of the various inputlayers/datasets had little impact on the statistical relationships be-tween these and water colour concentrations, with only small dif-ferences observed in the pre and post standardised r values(Table 2). Following production of the initial colour model and then

calibration against the monitoring data it was possible to explain90% (sig. < 0.01) of the variance in mean colour production atthe catchment scale (Fig. 4). By comparing catchment characteris-

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Mean Post 1995 Water Colour (mgl-1)Mean Post 1995 Water Colour (mgl-1)

Fig. 5. Extent of managed burn and moorland vegetation types per catchment and their relationship with mean raw water colour.

R. Grayson et al. / Journal of Hydrology 420–421 (2012) 37–45 43

tics and the water quality data it became clear that certainvariables were most important in determining water colour. Byfar the most significant in the catchments studied were heatherburning and vegetation type, although the extent of peat and grip-ping also had some influence. In contrast, some catchment attri-butes appeared to be unimportant in determining the level ofwater discolouration (i.e. afforestation, precipitation, slope, aspectand water company tenancy).

Rotational burning was a practice that was evident in all of thestudy catchments and was strongly correlated with water colour(r2 = 0.64, sig. < 0.01) (Fig. 5). In those catchments that experiencedthe highest colour concentrations burnt areas covered more than40% of the land surface area whereas in the Fixby catchment,where colour is relatively low, the total burnt area accounted forless than 6% of land cover. Water colour was also related to vege-tation type. A statistically significant positive relationship existedbetween water colour and dense shrub moor (r2 = 0.46sig. < 0.01) and a significant negative relationship with moorlandgrass (r2 = 0.24 sig. < 0.05) (Fig. 5). The relationship between watercolour and dense shrub moor improved significantly when the sin-gle outlier located towards the top of the plot in Fig. 5 was re-moved (r2 = 0.641 sig. < 0.001). This supports the findings of anumber of recent studies (Armstrong et al., 2007, 2010; Clutter-buck and Yallop, 2010; Miller, 2008; White et al., 2007) which havefound that vegetation type has a strong influence on water colour.It has been hypothesised that the greater evapotranspiration in

areas of heather causes a fall in the water table and ingress ofoxygen into the peat which facilitates bacterial breakdown of thesoil and hence DOC production (Clutterbuck and Yallop, 2010). Dis-aggregation of the impacts of burning and vegetation is particu-larly difficult as the two are closely linked, with burning beingused to promote heather growth. This is an important area for fu-ture research.

Artificial drainage (gripping) densities were typically low (lessthan 0.5 km grip/km2) in the study catchments, although excep-tions were Thurlstone Moor in the Ingbirchworth catchment(35 km grip/km2) and Watersheddles (5.38 km grip/km2).Although gripping has been suggested as having a significant im-pact on water colour production (Holden et al., 2004; Worralland Burt, 2005) no significant relationship was found in the currentstudy (r2 = 0.18). Catchment observations showed that where grip-ping densities are low extensive natural drainage networks oftenexist, frequently in the form of highly eroded gullies. This couldhave limited the need for any artificial drainage but had a similarhydrological impact, however. Despite this finding, in those studycatchments where gripping was more extensive the fit of the mod-el with the measured colour data was better with gripping in-cluded in the analysis. This suggests that gripping is actually animportant factor in colour production in those catchments whereit exists but that in others different factors are more significant.Nevertheless, despite the generally accepted relationship betweengripping and water colour, some workers (Yallop and Clutterbuck,

44 R. Grayson et al. / Journal of Hydrology 420–421 (2012) 37–45

2009) have also recently reported finding few links between DOCand either grip length or density. Of significance to future use ofthe model, the datasets describing gripping and topographic indexwere found to have similar impacts on the model output and so theformer should be used alone to avoid the more time consumingcalculation of topographic index.

Other catchment variables were found to be much less impor-tant in explaining differences in water colour. Whilst high levelsof water colour only occur in catchments comprising a significantpeat component this did not account for the variations observedin the current study, a finding also reported by Yallop and Clutter-buck (2009). For instance, although colour levels for the Fixbycatchment (65% deep peat) are low compared to others(36 mg l�1 Pt Co) only 6 catchments have proportionally moredeep peat.

Although it has been suggested that aspect and slope are impor-tant factors in colour production (Mitchell and McDonald, 1995;Mitchell, 1991) there is little evidence of this from the currentstudy with neither of these factors having a significant impact onthe extent to which the model could predict the observed levelsof colour. This, in part, may simply be due to the fact that mean as-pect is similar in most of the study catchments (39–57�). Alterna-tively, other variables may be more important in influencing colourproduction and as a result any impacts of aspect and slope areovershadowed. Similarly, rainfall had limited impact on the fit ofthe modelled data.

It is logical that burning and heather coverage could be themain reasons for the increases in water colour experienced over re-cent decades as the extent of burning is known to have risen(Yallop et al., 2006). This could subsequently have increased theextent of heather coverage of peatland catchments. In contrast,many of the factors that have been included in the analysis pre-sented here have not changed during the period over which colourconcentrations have risen. These include gripping, soil type, slopeand aspect.

By mapping the colour risk scores produced using the model(Fig. 5) it is evident that areas with high potential to generate col-our typically occur as ‘hot-spots’ within catchments and that mostcatchments actually contain large areas which are likely to gener-ate little water discolouration. This scenario has previously beenproposed by Butcher et al. (1992). This will logically result in adilution effect where water colour is reduced at the catchmentscale and indicates that management efforts to reduce water col-our should focus on those relatively small areas generating rela-tively high water colour. Even within some of the studycatchments not experiencing high colour (e.g. Fixby and Black-moorfoot) at the catchment scale the model indicated that local-ised hot spots do actually exist. Indeed blending of water fromcatchments with high and low colour has previously been recom-mended as an alternative approach to catchment management forreducing water discolouration in raw drinking waters (McDonaldand Naden, 1987). Such an approach is illustrated at IngbirchworthWTW where water from the Winscar catchment, which has the po-tential to generate high colour concentrations, is mixed with waterfrom the direct catchment which has little potential to generatecolour and contains very little peat.

5. Model validation

To validate the model it was subsequently run on the upper partof the Nidd catchment, North Yorkshire, which includes both theScar House and Angram catchments as well as a series of catchwa-ters and intake catchments in the surrounding area. The modelpredicts water colour for this catchment is likely to average96.6 mg l�1 Pt Co, which compares with a historic mean water col-

our in the combined Nidd intake at Chellow Heights WTW of96.4 mg l�1 (722 measurements taken between 1997 and 2007).This goes some way to indicating that the model can successfullybe used to predict colour concentrations at the catchment scaleand also implies that the variables identified as producing waterdiscolouration and their weighting in the model are accurate. Fur-ther validation would be useful where water colour data areavailable.

6. Conclusions

Water discolouration is a key concern for those watercompanies taking raw water from peatland catchments, particu-larly given increases measured during recent decades. Althoughend-of-pipe treatment is possible catchment management ap-proaches are now favoured by water industry regulators for sus-tainability reasons. For the first time, a GIS-based risk mappingapproach has been developed which is capable of highlightingcatchment areas likely to produce high levels of colour and so al-lows the targeting of catchment management actions. Moreover,the model also allows prediction of instream colour concentrationsbased on catchment attributes. The model was calibrated using 18study catchments in the Yorkshire region and has since been accu-rately validated in the Nidd catchment. Further use of the modeland in catchments where water colour monitoring data are avail-able will allow additional validation.

In developing the model it was found that the catchment attri-butes most influential in determining levels of water colour wereheather burning and vegetation cover and that further investiga-tions are needed to understand if burning is a key concern aloneor whether this activity simply encourages the vegetation typefound to increase colour the most; heather. A caveat is applied herethough in that some other factors found not to have been so impor-tant in the catchments studied in this investigation may actually beso elsewhere due to their greater prevalence (e.g. gripping). Never-theless, burning (and subsequent presumed heather growth) is theonly parameter known to have increased over the period that col-our levels have risen and so these two factors would seem to havegreat importance.

The production of water colour hazard maps using the modeldeveloped here will facilitate targeting of catchment managementefforts to reduce water discolouration. These might include in-creased control over burning in terms of rotation time and inten-sity or the complete cessation of burning. Whilst a reduction inburning is unlikely to be popular amongst many land owners andmanagers due to potential changes to grouse habitat and increasedchances of wildfire, alternative management options available in-clude heather cutting and mowing to promote the generation ofnew shoots and the removal of potential litter. Furthermore, it isunknown whether a heather monoculture really is needed to sup-port large numbers of grouse.

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