Relationships between suspended particulate material, light attenuation and Secchi depth in UK marine waters

Estuarine, Coastal and Shelf Science 79 (2008) 429–439

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Relationships between suspended particulate material, light attenuation and Secchi depth in UK marine waters M.J. Devlin a, *, J. Barry b, D.K. Mills b, R.J. Gowen c,1, J. Foden b, D. Sivyer b, P. Tett d a

Catchment to Reef Research Group, ACTFR, James Cook University, Townsville, Queensland 4811, Australia Centre for Environment, Fisheries and Aquaculture Science, Pakefield Road, Lowestoft, NR33 0HT, UK c Aquatic Systems Group, AFESD, Department of Agriculture and Rural Development, Newforge Lane, Belfast, BT9 5PX, Northern Ireland, UK d School of Life and Health Sciences, University of Napier, 10 Colinton Road, Edinburgh, EH10 5DT, Scotland, UK b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 28 April 2008 Accepted 29 April 2008 Available online 16 May 2008

Measurements of sub-surface light attenuation (Kd), Secchi depth and suspended particulate material (SPM) were made at 382 locations in transitional, coastal and offshore waters around the United Kingdom (hereafter UK) between August 2004 and December 2005. Data were analysed statistically in relation to a marine water typology characterised by differences in tidal range, mixing and salinity. There was a strong statistically significant linear relationship between SPM and Kd for the full data set. We show that slightly better results are obtained by fitting separate models to data from transitional waters and coastal and offshore waters combined. These linear models were used to predict Kd from SPM. Using a statistic (D) to quantify the error of prediction of Kd from SPM, we found an overall prediction error rate of 23.1%. Statistically significant linear relationships were also evident between the log of Secchi depth and the log of Kd in waters around the UK. Again, statistically significant improvements were obtained by fitting separate models to estuarine and combined coastal/offshore data – however, the prediction error was improved only marginally, from 31.6% to 29.7%. Prediction was poor in transitional waters (D ¼ 39.5%) but relatively good in coastal/offshore waters (D ¼ 26.9%). SPM data were extracted from long term monitoring data sites held by the UK Environment Agency. The appropriate linear models (estuarine or combined coastal/offshore) were applied to the SPM data to obtain representative Kd values from estuarine, coastal and offshore sites. Estuarine waters typically had higher concentrations of SPM (8.2–73.8 mg l1) compared to coastal waters (3.0–24.1 mg l1) and offshore waters (9.3 mg l1). The higher SPM values in estuarine waters corresponded to higher values of Kd (0.8–5.6 m1). Water types that were identified by large tidal ranges and exposure typically had the highest Kd ranges in both estuarine and coastal waters. In terms of susceptibility to eutrophication, large macrotidal, well mixed estuarine waters, such as the Thames embayment and the Humber estuary were identified at least risk from eutrophic conditions due to light-limiting conditions of the water type. Crown Copyright Ó 2008 Published by Elsevier Ltd. All rights reserved.

Keywords: light attenuation suspended particulate matter Water Framework Directive Secchi depth

1. Introduction Many recent European and US directives aimed at the assessment of eutrophication in marine waters include some measurement of nutrients and phytoplankton and look to describe the ‘‘risk’’ of undesirable biological response to nutrient enrichment (Tett et al., 2007). Our understanding of the process of nutrient enrichment and its causative influence on eutrophication symptoms is an important component of any eutrophication assessment of marine waters. Recent changes in our conceptual understanding

* Corresponding author. E-mail address: [email protected] (M.J. Devlin). 1 Present address: Fisheries and Aquatic Ecosystems Branch, AFESD, Agri-Food and Biosciences Institute, Newforge Lane, Belfast, BT9 5PX, Northern Ireland, UK.

of eutrophication (Cloern, 1999, 2001; Costanza and Mageau, 2001; Tett et al., 2007), suggest that there are complex direct and indirect responses to anthropogenic nutrient inputs (Nixon, 1995). In addition ‘filters’ play a role in determining the sensitivity to enrichment and, in marine waters, these include the light climate and advective loss (Cloern, 1987; Bricker and Stevenson, 1996). Given this complexity, the process of linking anthropogenic nutrient enrichment to biological response is not a trivial task. A detailed assessment that would be required to achieve this and to cover all UK near-shore marine waters does not seem feasible or indeed scientifically justified. A more pragmatic approach is to first screen each water body to determine susceptibility to the impact of anthropogenic nutrient enrichment. Traditionally, identification of risk has relied on nutrient loading and/or observed winter nutrient concentration. While this approach is useful for initial screening, to identify those water bodies receiving high

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anthropogenic nutrient load, it ignores the question of susceptibility from a biological perspective, thus does not assess the limits of the system to sustain production. Nutrient enrichment alone does not diagnose eutrophication and consideration is needed of key physical characteristics of water bodies, which may modify the response of the dominant form of aquatic plant life (phytoplankton, macrophytes or angiosperms) and provide an additional level of screening. For aquatic plants, the sub-surface light climate has a major influence on growth (Boynton et al., 1982; Bricker et al., 1999; Gallegos, 2001; May et al., 2003) particularly in inshore and nearshore environments where a high level of suspended particulate material may severely restrict the availability of light (Bowers et al., 2000; Mills et al., 2002; Painting et al., 2007). The amount of photosynthetically active radiation (PAR) in natural waters is of fundamental importance in determining the growth of aquatic plants. Primary production by phytoplankton is a light dependent process that provides the energy to drive the plankton and microbial food web that typically takes place down to depths to which about 1% of surface light penetrates (i.e., the euphotic zone). Absorption and refraction by water, and dissolved and suspended matter determine the quantity and the spectral quality of light at a given depth (Jerlov, 1968, 1976; Prieur and Sathyendranath, 1981), which in turn affects the photosynthesis of aquatic plants. Characterisation of the sub-surface light climate could therefore provide a means of screening water bodies for biological susceptibility to changes in ecosystem structure and function. A high level of detail has been used previously to characterise UK marine waters by the availability of light, with detailed studies on the interaction between inherent optical properties (absorption and adsorption coefficients and backscattering ratios) and apparent optical properties (diffuse attenuation coefficient and radiance reflectance; Bowers et al., 2000; McKee et al., 2003; Bowers and Binding, 2006; McKee and Cunningham, 2006). There has been considerable success in relating the optical properties inherent in the waters to characterise ‘‘water type’’, including features such as ratios of particulate backscattering to non-water absorption (Bowers and Binding, 2006; Morel et al., 2006). Studies by McKee and Cunningham (2006) also characterise two optical water types differing in the ratio of particulate backscattering to non-water absorption at 676 nm, the ratio of non-water absorption coefficients and ratio of particulate scattering to non-water absorption. Further work on the difference between case 1 (light attenuation controlled primarily by phytoplankton) and case 2 waters (light attenuation controlled primarily by SPM and CDOM) showed variation in the empirical relationship between colour ratios and pigments and total suspended solids (Kratzer et al., 2000). Detailed studies such as these provide far more detail to the ability of the marine water type to attenuate light through the water column and the potential of the system to limit or encourage the production of phytoplankton. However, there has been little systematic assessment of the sub-surface light regime across UK marine waters and it is difficult to characterise water types dependent on any part of their optical properties over such large variable marine areas. It would be almost impossible to characterise each water type using a full suite of optical properties, so a simpler method was investigated using statistical examination of in situ measurements of apparent optical properties and an optically significant constituent. This one-dimensional model was tested to observe if predictions of light attenuation could be derived from only one optical component. Optically significant constituents which influence light attenuation include chromophoric dissolved organic matter (Kostoglidis et al., 2005; Foden et al., submitted for publication) suspended particulate matter (SPM) (Mills et al., 2002) and phytoplankton biomass (McMahon et al., 1992; Tett, 1992). The relative

contributions of these different components to total light attenuation in estuaries have been studied in many marine waters (Gallegos et al., 1990; Kirk, 1994). Understanding the contributions by different constituents responsible for attenuation of PAR is important in predicting the underwater light climate from the constituent concentrations (Bowers et al., 2000; Kostoglidis et al., 2005). The primary light-attenuating constituent in near-shore marine waters may vary from CDOM (Kirk, 1976; Bowling, 1988; Kostoglidis et al., 2005) to phytoplankton (Dubinsky and Berman, 1979), to inorganic suspended matter (Mills et al., 2002) or some combination of these constituents (Heinermann and Ali, 1988). The shallow depth and the large tidal movement in most near-shore marine types in UK waters make these systems fundamentally different from deeper clearer marine waters. Coastal lagoons and shallow estuaries have high sediment surface area to water volume ratios, frequent wave resuspension of sediments, and low pelagic and high benthic primary productivity because most of the sediment surface is in the photic zone (Sand-Jensen and Borum, 1991). These features suggest that sediment resuspension, not increased pelagic productivity, may be the dominant control on light availability in UK coastal waters (Bowers, 2005). Studies in the Indian River Lagoon (Gallegos and Kenworthy, 1996), the Lagoon of Venice (Zharova et al., 2001) and Hog Island Bay, Virginia (Lawson et al., 2007) have also shown suspended sediment to control light availability in these coastal systems. It is clear that the availability of good evidence regarding the light climate in the transitional and coastal waters of the UK is a limiting factor in the ability to undertake credible and effective risk assessments. This paper investigates the non-trivial relationship between suspended particulate matter and apparent optical properties as measured by diffuse attenuation coefficient. It presents the results of a spatially extensive survey of suspended particulate matter (SPM), light attenuation (Kd) and Secchi depth in UK and Irish waters (in the western Irish Sea). The focus is on a statistical examination of the empirical relationships between Kd and SPM and between Kd and Secchi depth. We demonstrate that the significant linear relationships between SPM and Kd for UK marine waters can be used to predict Kd from SPM. We also show that the significant relationship between Secchi depth and Kd in some types of marine waters can also be a useful predictor of light attenuation in UK waters. 2. Methods 2.1. Survey design The study was carried out between August 2004 and December 2005. A total of 382 locations were visited (Fig. 1). Sampling took place from bridges for some estuarine sites and small boats for spatial sampling in inshore areas. In July 2005, additional sampling was undertaken in the Clyde sea area and Irish Sea (including Irish coastal waters of the western Irish Sea) during a Department of Agriculture and Rural Development (DARD) RV Corystes cruise. 2.2. Marine water types Discrimination of marine water types have been based on the typology characteristics set out in the Water Framework Directive (CSTT, 1994, 1997). The WFD typology was put forward in Rogers et al. (2003) and uses the characterising properties of salinity, tidal range, mixing and exposure to define differences between types (Rogers et al., 2003). This typology separates marine waters into three broad types based on their salinity range, including an estuarine, coastal and offshore type. Estuarine waters are typically found in the salinity range between freshwater and 30 ppt. Estuarine waters are further characterised by tidal range, mixing, and substratum. Tidal range is

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431

Table 1 The physical characteristics of the marine water types sampled in this study. Types were characterised by the differences in salinity, exposure, tidal range and mixing ID No.

Salinity

Characteristics of waterbodies

WFD typea

Number of profiles measured in study

Macro Meso Macro

CW1 CW2 CW4

18 31 50

Meso

CW5

74

Meso n/a Meso Macro

CW8 CW10 CW11/12 TW1

63 13 24 15

TW2

31

TW3

28

TW4

4

Exposure

Tidal range

1 2 3

Coastal Coastal Coastal

4

Coastal

5 6 7 8

Coastal Coastal Coastal Estuarine

Exposed Exposed Moderately exposed Moderately exposed Sheltered Sheltered Sheltered Sheltered

9

Estuarine

Sheltered

Meso

10

Estuarine

Sheltered

Macro

11

Estuarine

Sheltered

Meso

12

Estuarine

Sheltered

n/a

13

Offshore

–

–

Mixing

Partly mixed Partly mixed Fully mixed Fully mixed Partly mixed –

TW6 48

a

WFD type refers to a UK typology as defined in the European Water Framework Directive (www.wfduk.org). Fig. 1. A map of the United Kingdom and Rebublic of Ireland showing the location of sampling sites where profiles of irradiance and optical backscatter were recorded and water samples collected for the estimation of suspended particulate matter. Shaded areas denote the transitional and coastal waters typology for UK and ROI waters.

differentiated into three types, including a microtidal (<1 m), meso-tidal (1–5 m), and macro-tidal (>5 m) ranges. The degree of mixing relates to the measure of the stratification, where typically, the larger, well-mixed estuaries have limited or no stratification. Coastal waters are defined as those waters within 1 nautical mile (nm) of the coast and are further characterised by tidal range and exposure, which is defined by potential wind strength (Vincent et al., 2002; Rogers et al., 2003). Details of the transitional and coastal water types sampled are given in Table 1. All sites sampled seaward of 1 nm coastal waters were grouped as offshore waters (Table 1). All sites sampled in this study were arbitrarily assigned type specific characteristics as illustrated in Table 1. Additionally, the sites were further identified against the typology classification for the Water Framework Directive (CSTT, 1994, 1997).

water were filtered through pre-weighed Whatman GF/C glass fibre filters in the laboratory. The filters were then dried and re-weighed according to standard laboratory practice. Suspended load concentration was calculated from differences in the weight. Additional data on SPM was also made available from the UK Environment Agencies ‘‘WIMS’’ database for the period January 2000–July 2006. This database includes approximately 32,000 data records from measurements across the full range of water types in the UK. SPM was measured gravimetrically in water samples collected in a similar manner to that described. This data were used to calculate Kd across UK marine waters from the SPM and Secchi algorithms. The calculated Kd was used to investigate similarity or dissimilarity within the water types by analysing the differences in SPM and Kd for each type. This additional data allow us to make some preliminary accounts on the types of marine waters in UK and separation into types was used to apportion a level of risk from the effects of nutrient enrichment. 2.4. Calibration of the optical backscatter sensor

2.3. Profiles and water samples At each sampling site profiles of optical backscatter and downwelling PAR (photosynthetically active radiation) were recorded using a Seapoint turbidity instrument and LI-COR (LI-192) underwater quantum sensor, respectively. Both instruments were protected in a stainless steel protective frame and interfaced with a solid state logger (CEFAS ESM2) sampling at 2 Hz. Care was taken to minimise the influence of shading on measurements with the irradiance sensors by profiling on the illuminated rather than shaded side of the sampling platform. Surface and near bottom water samples were collected using Niskin type water bottles for the estimation of SPM. Offshore water samples were collected in 5 l water bottles mounted on a rosette sampler from the near surface (2 m) and near bottom (2–3 m above the seabed). At all sites, Secchi disc depth was measured using a 30 cm diameter, black and white Secchi disc attached to a rope marked at 0.5 m intervals. Samples for SPM were preserved with mercuric chloride and returned to the laboratory for filtration. The concentration of SPM was measured by gravimetric analysis using the method of Strickland and Parsons (1972). Known volumes of

Concentrations of SPM were regressed (least squares linear regression) against corresponding values of optical backscatter (OBS) recorded as manufacturer calibrated FTU. Values of SPM concentration were predicted, using the slope and intercept from this regression, by

SPM ¼ ðFTU  interceptÞ=slope

(1)

and used to generate vertical profiles of SPM concentration. These profiles were then used to calculate a mean SPM concentration. For vertically mixed waters the whole SPM profile was used. In stratified, predominantly offshore waters, the SPM profile in the surface mixed layer was used to derive the mean concentration. These mean concentrations were used with corresponding values of Kd in the following analysis. 2.5. Calculation of Kd (PAR) The equations governing the propagation of light under water, called the radiative transfer equations, have no exact solution; but several computer programs have been written to solve the

M.J. Devlin et al. / Estuarine, Coastal and Shelf Science 79 (2008) 429–439

equations by various numerical methods. Despite the complexities of the radiative transfer equations, field measurements of underwater irradiance nearly always show a negative exponential attenuation of light with depth. In the absence of strong discontinuities in water quality, such as nepheloid layers, sub-surface chlorophyll a maxima or humic-stained surface layers, measurements of PAR (400–700 nm) are well described by a single exponential equation of the form called Lambert–Beer equation (Eq. (2)) (Dennison et al., 1993) The Lambert–Beer equation (Dennison et al., 1993) estimates the attenuation coefficient Kd (m1) from photosynthetically active radiation (PAR) from vertical profiles of down-welling irradiance. Kd is calculated from the slope of irradiance and depth.

(2)

where K represents the light attenuation coefficient, Iz ¼ light at depth, I0 ¼ light at surface and z ¼ depth. To be consistent with the method of calculating mean concentrations of SPM, Kd was derived from the whole light profile and surface mixed layer profile in vertically mixed and stratified waters, respectively.

3. Results

2.6. Statistics Least squares linear regression analysis was used to investigate the relationships between SPM and Kd. Linear models of the form

(3)

were used where the error is normally distributed with a mean of zero and constant variance. Predicted values of Kd were calculated from

10

(4) 6

(6)

s 2 are the least squares estimates of the parameters where b a, b b and b in Eq. (5). To test the predictive accuracy of the models, a statistic (referred to here as D), was used which is a form of cross validation measure. This statistic represents the percentage difference between predicted and actual values of Kd with lower values indicating greater accuracy. D is defined as

CW12

CW2

offshore

CW11

CW8

CW1

CW10

140 120 100 80 60 40 20

CW12

CW11

CW2

CW8

CW10

CW1

TW4

CW4

0

(7)

TW3

   j j  b n  K d  Kd  X   D ¼ 100  b j  K d  j¼1

Water Type

TW1

e K d

  b 2 =2 ¼ exp b aþb b lnðSecchiÞ þ s

TW3

0

CW4

where S represents Secchi depth and the error is normally distributed with a mean of zero and variance 2. To predict Kd on the untransformed scale, the standard back-transformation for a lognormal distribution was used (see Aitchison and Brown, 1957)

TW5

2

CW5

4

(5)

SPM (mg/L-1)

lnðKd Þ ¼ a þ b lnðSecchiÞ þ error

8

TW4

where b a and b b are the least squares estimates of a and b in Eq. (3). To predict Kd from Secchi depth we used the good empirical linear relationship between the natural logarithms of Kd and Secchi depth (denoted as S) (see Eq. (5)):

TW2

b ¼ b aþb bSPM K d

Over the time of this study 382 light and SPM profiles were taken in UK marine waters. The highest values of SPM are measured in transitional types. Calculated averages for each type for SPM and Kd are shown in Fig. 2.

CW5

Kd ¼ ða þ bÞðSPM þ errorÞ

3.1. SPM and Kd data for model development

TW1

lnðI0 =Iz Þ z

TW2

ðKd Þ ¼

using the model derived from the full data set and typology specific models were compared to see whether the type specific models gave better predictions. This ‘leave-one-out’ method of cross validation can sometimes underestimate the prediction variance (Baumann, 2003) but we use it here because our main purpose is to discriminate between competing models. We investigated whether separate models of the forms in Eqs. (3) and (5) were needed for each of the different water types – estuarine, coastal and offshore – by testing the statistical significance of the difference in residual sums of squares between a single model for all of the data and for separate models. We also examined the differences in the prediction ability as measured by D. The most appropriate linear models were applied to the SPM data held within the Environment Agency’s long term monitoring database. Each data point was characterised to either an estuarine, coastal or offshore type. In total, the linear SPM model was applied to 31,961 separate measurements of SPM from throughout UK marine waters to calculate a predicted Kd value. Further characterisation of the data according to tidal range, exposure and mixing (see Table 1) was done to investigate the spatial variation in SPM and predicted Kd. Fig. 1 shows the location of all the sites.

Kd (m-1)

432

Water Type

j

b is the value predicted for observation j using all of the where K d data except the jth observation, Kdj is the jth observation of Kd, and n is the number of cases in the prediction data set. For the Kd–SPM and Kd–Secchi models, the accuracy of Kd values was predicted

Fig. 2. Mean values of Kd (m1) and suspended particulate material concentration (mg l1) derived from measurements made in each water type and offshore waters between August 2004 and December 2005. The error bars represent 95% confidence limit.

M.J. Devlin et al. / Estuarine, Coastal and Shelf Science 79 (2008) 429–439

3.1.1. The relationship between SPM and Kd Using the full data set there was a strong linear relationship b ¼ 0:08596 þ 0:06729  SPMÞ between SPM and Kd (Fig. 3) ðK d which gave a D value of 26.4% and R2 ¼ 0.98. A 95% confidence interval for the intercept was (0.0505, 0.1217) and for the slope was (0.0662, 0.0683). We then investigated whether the fit and predictive abilities could be improved by fitting separate regression models for each of the three marine types (Fig. 3). Comparing this three component model to the single component model gave an Fvalue of 11.17 (p < 0.01), suggesting that there is a strong statistically significant improvement in fit. The practical implication of fitting separate water type components is that the D statistic was improved from D ¼ 26.4% (single model) to D ¼ 22.9% (separate models). We then further investigated using two different water body components rather than three. We did this, firstly, by combining coastal and offshore waters and, secondly, by combining coastal and transitional waters. When coastal and offshore water types were combined there was a statistically significant improvement from the single model (F ¼ 16.18, p < 0.01), with a D value of 23.1%. When coastal and transitional water types were combined there was no statistically significant improvement as compared to the single model (F ¼ 2.68, p ¼ 0.07, D ¼ 25.8%). For this last comparison, because the difference in fit is not statistically significant at the 5% level and because of the only marginal improvement in D from the full model, it was decided to fit a model with combined coastal and offshore components but with a separate transitional component. The two fitted components of this model are

b ¼ 0:325 þ 0:066  SPM Transitional K d

(8)

b ¼ 0:039 þ 0:067  SPM Coastal offshore K d

(9)

For the transitional water component in Eq. (8), the value of R2 is 0.98, a 95% confidence interval for the intercept is (0.204, 0.445) and a 95% confidence interval for the slope is (0.063, 0.068). The value of D for transitional waters is only 19.1%. In Eq. (9), R2 ¼ 0.98,

a 95% confidence interval for the intercept is (0.008, 0.071) and for the slope is (0.066, 0.0685). The combined value of D for offshore and coastal waters is 24.1%. In summary, fitting separate regression components for transitional waters and for coastal and offshore waters combined produce a prediction error of 23.1%. This compares to a prediction error of 26.4% for a single model. Prediction was better in the transitional waters (19.1%) than in the coastal/offshore waters (24.1%). 3.1.2. Relationship between Kd and Secchi depth (S) Plots of the data for the full data set (349 pairs) and for the three water types are shown in Fig. 4. Fitting the regression model in Eq. (5) b ¼ 0:112  0:938 lnðSÞ to all of the data gave the fitted model ln K d 2 2 b with s ¼ 0:162, R ¼ 0.87 and D ¼ 31.6%. A 95% confidence interval for the intercept was (0.025, 0.076) and for the slope was (0.976, 0.899). The comparison between the single model and that with the three separate water type components gave a statistically significant F-value of 8.33 (p < 0.01). However, the D ¼ 29.9% statistic for separate equations was only a slight improvement on the full model (D ¼ 31.6%). When coastal and offshore water types were combined there was a statistically significant improvement from the single model (F ¼ 16.18, p < 0.01). However, again, the D value of 29.7% was only marginally improved from that of the full model. When coastal and transitional water types were combined there was no statistically significant improvement as compared to the single model (F ¼ 0.35, p ¼ 0.70, D ¼ 31.7%). Although fitting a transitional and a combined offshore/coastal component to the model gave only a 1.9% improvement in prediction error we give the fitted equations for this model as this, albeit small, difference was statistically significant and did give the best possible model

b ¼ 0:253  1:029 lnðSÞ Transitional ln K d

(10)

b ¼ 0:010  0:861 lnðSÞ Coastal offshore ln K d

(11)

where S ¼ Secchi depth.

Full data

Coastal Waters 15

Kd (m−1)

Kd (m−1)

15 10 5 0

10 5 0

0

50 100

SPM

200

300

0

50 100

(mgl−1)

SPM

Transitional Waters

200

300

(mgl−1)

Offshore Waters 2.0

Kd (m−1)

15

Kd (m−1)

433

10

5

0

1.5 1.0 0.5 0.0

0

50

100

150

SPM (mgl−1)

200

250

5

10

15

20

25

30

SPM (mgl−1)

Fig. 3. The relationship between Kd and the concentration of suspended particulate matter (SPM) for the full data set and the coastal, transitional and offshore waters based on data collected between August 2004 and December 2005.

434

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Coastal Waters 2

2

1

ln Kd (m−1)

ln Kd (m−1)

Full Data 3

1 0 -1

0 -1 -2

-2 -3 -2

-1

0

1

2

3

-1

1

2

ln Secchi depth (m)

Transitional Waters

Offshore Waters

3

0.5

2

ln Kd (m−1)

ln Kd (m−1)

0

ln Secchi depth (m)

1 0 -1

-0.5 -1.5 -2.5

-2 -2

-1

0

1

2

-0.5

ln Secchi depth (m)

0.5

1.5

2.5

ln Secchi depth (m)

Fig. 4. The relationship between ln Kd and ln Secchi depth for the full data set and for coastal, transitional and offshore waters based on data collected between August 2004 and December 2005.

200

150

100

50

TW6

TW4

TW3

TW2

TW1

CW8

CW7

CW5

CW4

CW2

0 CW12

The UK Environment Agency SPM data set (Fig. 5) shows that mean concentrations of SPM in transitional water types ranged from 4.5 mg l1 (sheltered, mesotidal, fully mixed types) to 133 mg l1 (macrotidal and fully mixed). However, mean concentrations of 50 mg l1 were only exceeded in 13 water types. These were predominately macrotidal, fully mixed estuaries and included the Humber, Severn, Wash, Thames, Solway and the Dee. Concentrations of SPM for coastal types ranged from 3.9 mg l1 (sheltered and mesotidal) to 52.7 mg l1 (moderate exposure and macrotidal). Only one coastal water body, the inner Bristol Channel North had a mean SPM concentration greater than 50 mg l1. The linear models (Eqs. (8) and (9)) were applied to the Environment Agency SPM data set to predict Kd using the appropriate model (transitional, or coastal/offshore). The data were further characterised by the types identified in Table 1. Averaged mean values of Kd (Fig. 5) show that there was a gradient from high values of Kd in estuarine water types to coastal water types and the offshore typology. Initial characterisation into the three marine types was tested using the constituent concentration to identify the breaks between types. Measurements of SPM were higher than 100 mg l1 for 78% of all transitional sites and below 100 mg l1 for 76% of all coastal waterbodies. Transitional sites also had a higher frequency of elevated SPM concentrations, with 10% of sites being above 250 mg l1 compared to only 6% for coastal waterbodies, and no offshore waterbodies had SPM concentrations greater than 50 mg l1. It is important to note the variability of the data with

CW1

3.2. Comparison between measured and predicted SPM

high concentrations being measured in coastal sites and very low SPM concentrations measured in transitional types. Kd derived from direct light measurements and Kd estimated from the Environment Agency SPM data using Eqs. (8) and (9) compared favourably with an R2 of 0.89 (Fig. 6). There are persistent and significant differences between the water types characterised to the WFD typology. The low pelagic primary productivity demonstrated in most UK waters (Malcolm et al., 2002) suggests that light attenuation will be controlled primarily by the concentrations of SPM. Thus it is important to identify the characteristics which are the dominant controls on sediment suspension and consequently light attenuation, particularly in transitional waters.

Mean SPM (mg/L)

b 2 ¼ 0:221, 95% confidence For the transitional component: s intervals for the intercept and slope were (0.142, 0.363) and (0.908, 1.150), respectively, R2 ¼ 0.79, and D ¼ 39.5%. For the s 2 ¼ 0:128, a 95% confidence interval coastal/offshore component: b for the intercept was (0.0696, 0.0496) and for the slope was (0.980, 0.895), R2 ¼ 0.86, and the D statistic was 26.9%. Clearly, using Secchi depth, prediction is better in the coastal and offshore waters than in the estuarine waters.

Fig. 5. Mean concentrations of SPM (mg l1) derived from the UK Environment Agency database. SPM concentrations are averaged over transitional and coastal type (Rogers et al., 2003). The error bars are 95% confidence limits.

R2 = 0.885

8

9.3 8.3 2.8 33.3 0.7 75

9

435

0.6 0.6 0.1 1.9 0.1

Offshore

5 4

3.2 3.2 3.1 3.3 0.1 2

Sheltered meso lochs

6

0.3 0.3 0.2 0.5 0.2

7

1 0

4.8 4.3 3.2 9.0 0.2 63 24.1 16.0 2.9 95.0 2.6 78 10.6 8.3 4.1 27.0 0.9 50 4.0 3.2 2.9 8.5 0.3 31 6.2 5.8 4.2 9.8 0.3 18 71.7 28.0 3.0 184.0 56.6 3 22.1 10.5 9.3 58.0 12.0 4 8.2 7.7 5.2 19.0 0.6 28 44.4 27.6 7.5 125.0 5.8 40 73.8 24.0 3.2 290.0 25.8 15 SPM (mg l1) Mean Median Minimum Maximum S.E. N

1.7 1.2 0.1 6.6 0.2 0.8 0.7 0.3 2.5 0.1 0.2 0.2 0.1 0.6 0.0 0.4 0.4 0.2 0.8 0.0 5.6 2.4 0.2 14.2 4.3 1.8 0.9 0.7 4.9 1.0

Sheltered mesotidal Sheltered macrotidal partly mixed

Sheltered lagoon partly mixed

Exposed macrotidal

Exposed mesotidal

Coastal marine waters

Sheltered macrotidal partly mixed

The aim of this paper was to present results from extensive measurements of the diffuse down-welling PAR attenuation coefficient (Kd), SPM and Secchi depth in UK transitional, coastal and offshore waters, and to analyse these results statistically in relation to marine water type (Rogers et al., 2003). Significant relationships were obtained between SPM and Kd, and between Secchi depth and Kd. The Kd–SPM relationship was used to augment the database of 382 Kd calculated from submarine irradiance measurements made during this study, by calculation of Kd from the 12,000 values of SPM concentration taken from the UK Environment Agency database. We propose that these Kd–SPM and Kd– Secchi relationships can be used to estimate Kd from SPM or Secchi depth data when submarine irradiance has not been, or cannot be, measured. However, consequent issues, which require discussion here, are the theoretical basis of the Kd–SPM relationships, the reliability with which they can be used to predict diffuse attenuation in UK waters and an explanation for the values of Kd in UK waters. Typically the vertical light attenuation can be decomposed as a set of partial attenuation coefficients, each characterising absorption and scattering by a different waterborne material (Xu et al., 2005). Kd has often been modelled as a linear function of water quality concentrations (Smith, 1983; Stefan et al., 1983; Xu et al., 2005) where the light attenuation is calculated as a series of specific attenuation coefficients for chlorophyll, total suspended

Estuarine marine waters

4. Discussion

Type characteristics

By analysing the data looking at the specific characteristics, it is evident that tidal range is the most significant factor influencing both the SPM concentrations and light attenuation (Table 2). Both coastal (Fig. 7) and transitional waterbodies (Fig. 8) had 80% of all concentrations greater than 100 mg l1 in types CW1 and CW4, TW1 and TW3, all characterised by large tidal ranges. However, identifying tidal ranges as one of the components driving the higher SPM concentrations is too simplistic and there are other forces, such as mixing in estuarine waters and exposure in coastal waters. Many UK estuarine and coastal waters are large, well mixed, exposed waters and as such, have typically very high and variable suspended loads throughout the year. Fig. 9 illustrates the factors that are most likely to be influencing the light dynamics of each water type. It should be noted that this is a simplistic ranking, and does not account for diurnal, daily, long term changes that are also influencing the availability of light. The physical properties of the water types are identified, however, outside forcing such as wind direction and strength are not accounted for. These outcomes support other recent work (Lawson et al., 2007) showing that wind forcing is the dominant control on sediment suspension and consequently light attenuation, particularly in shallow areas of a lagoon.

Table 2 Summary statistics for Kd and SPM concentration for individual water types within the transitional and coastal water typologies and in the offshore water typology

Fig. 6. Water body comparison between Kd obtained from linear regression model (Eqs. (8) and (9)) and from direct light measurements.

0.4 0.3 0.1 1.1 0.0

7

0.8 0.7 0.4 1.9 0.1

6

3.4 2.3 0.8 9.0 0.4

5

5.6 2.6 0.2 18.0 1.6

4

Kd (m1) Mean Median Minimum Maximum S.E.

3

Measured Kd(m-1)

Moderately exposed mesotidal

2

Moderately exposed macrotidal

1

Sheltered macrotidal partly mixed

0

6.1 4.1 3.2 12.2 0.9 13

Sheltered lagoons

2

0.7 0.5 0.1 1.3 0.1

3

Sheltered macrotidal partly mixed

Predicted Kd (m-1) from SPM

M.J. Devlin et al. / Estuarine, Coastal and Shelf Science 79 (2008) 429–439

5

Exposed, macrotidal (CW1)

4 3 2 1 0 1999

2001

2002

2004

2005

Predicted Kd (m-1)

M.J. Devlin et al. / Estuarine, Coastal and Shelf Science 79 (2008) 429–439

Predicted Kd (m-1)

436

2006

40

Mod exposure, macrotidal (CW4)

30 20 10 0 1999

2001

10

Exposed, mesotidal (CW2)

8 6 4 2 0 1999

2001

2002

2004

2005

2006

15

3 2 1 2002

2004

2005

2006

Date

Predicted Kd (m-1)

Predicted Kd (m-1)

sheltered, mesotidal (CW8)

2001

2005

2006

10 5 0 1999

2001

2002

2004

2005

2006

Date

4

0 1999

2004

Moderate exposed, mesotidal (CW5)

Date 5

2002

Date Predicted Kd (m-1)

Predicted Kd (m-1)

Date

15

sheltered, mesotidal (CW11/12)

10 5 0 1999

2001

2002

2004

2005

2006

Date

Fig. 7. Mean values of Kd (m1) for distinct coastal waterbody types predicted from Eqs. (8) and (9) using the UK Environment Agency SPM data set. Data are measured from 1999 to 2006. Note variation in scale on the y axis.

solids and chromophoric dissolved organic matter (CDOM). Water quality data collected in this study have previously been used to calculate an empirical linear light model relating Kd and water quality concentrations, chl a, SPM and CDOM. By far the strongest and clearest relationship was between Kd and SPM, with logtransformed SPM explaining 91% of the variance in log-transformed Kd. Many other studies also illustrate the importance of SPM as an explanatory variable for light attenuation, most particularly in turbid waters. Xu et al. (2005) identifies SPM as the most important factor in controlling light attenuation in a study in Cheasapeake Bay, explaining 58% of the total variability in Kd. This supports other work by Cloern (1987), May et al. (2003), Lawson et al. (2007), Painting et al. (2007) that identify strong relationships between Kd and SPM, particularly in estuaries, as primarily a function of suspended sediment concentrations. Our approach is also exemplified by the study of Lund-Hansen (2004) in Århus Bay (Denmark), which lies at the transition between North Sea saline waters and low-salinity, high-CDOM waters of the Baltic outflow and has a weak tidal regime. During the course of a year, Kd varied from 0.15 to 0.56 m1 (mean 0.26 m1) and under these average conditions, 9% of light attenuation was due to water, 17% due to CDOM, 32% due to phytoplankton, and 42% due to mineral suspended solids (MSSs). The latter averaged 4.5 mg l1 and was approximately 81% of total SPM, which ranged from 1.3 mg l1 to 9.1 mg l1. Compared to these Danish waters, many UK near shore waters typically contain higher levels of SPM and as the data in Table 2 show, four out of five UK estuarine water types surveyed in the present study had median SPM concentrations that exceeded the maximum values reported from Århus Bay. Gallegos (2001) used an optical model to determine that in water bodies with chlorophyll concentration less than 10 mg l1, only a reduction in TSS can improve light availability. On the other hand, in systems with very low turbidity (<1 NTU) only a reduction in chlorophyll

can improve light availability. Christian and Sheng (2003) found that colour accounted for 5–25%, chlorophyll accounted for 10–26%, and non-algal particulate matter accounted for 59–78% of light attenuation in the Indian River Lagoon, with few measured values of chlorophyll over 10 mg l1. In highly eutrophic lagoons, such as the Maryland coastal bays, chlorophyll a concentrations can be as high as 45 mg l1 and may be a more important predictor of light availability (Boynton et al., 1982). This optical dominance of SPM in UK waters arises not just from its contribution to light absorption, but also because of its strong effect on scattering (Bowers and Binding, 2006). Finally, phytoplankton and its detrital products also contribute to, or correlate with, SPM as measured gravimetrically, and hence the Kd–SPM relationship may well include most of the variance in Kd (Xu et al., 2005). Thus, SPM might be expected to play a more important part in light attenuation in most UK waters than in other European waters. As such, this paper presented a relatively simple optical model for estimating the diffuse light attenuation coefficient from variations in concentrations of only one of the optical active constituents. The Kd–SPM relationship was strong, explaining 98% of the variance in attenuation in each of the cases of the full data set, the coastal water data set, and the estuarine water data set. Only in the case of the offshore water data set was the relationship less explanatory, predicting 87% of the variance. In these waters the range of attenuation (and SPM) values was less than the ranges in coastal and transitional waters, and furthermore, SPM might be expected to play a small part in determining water column optical properties in waters that tended to be less strongly tidally stirred and further from sources of inorganic particulates such as river discharges or coastline erosion. The D-statistic, giving the mean percentage difference between observed values of Kd and those predicted by an appropriate

50

Partly mixed, macrotidal (TW1)

40 30 20 10 0 1999

2001

2002

2004

2005

Predicted Kd (m-1)

Predicted Kd (m-1)

M.J. Devlin et al. / Estuarine, Coastal and Shelf Science 79 (2008) 429–439

2006

80

Fuly mixed, macrotidal (TW3)

60 40 20 0 1999

2001

Partly mixed, mesotidal (TW2)

40 20 0 1999

2001

2002

2004

2005

2006

20

Predicted Kd (m-1)

2004

2005

2006

Fully mixed, mesotidal (TW4)

15 10 5 0 1999

2001

Date 20

2002

Date Predicted Kd (m-1)

Predicted Kd (m-1)

Date 60

437

2002

2004

2005

2006

Date

Partly mixed, mesotidal (TW6)

15 10 5 0 1999

2001

2002

2004

2005

2006

Date Fig. 8. Mean values of Kd (m1) for distinct estuarine waterbody types predicted from Eqs. (8) and (9) using the UK Environment Agency SPM data set. Data are measured from 1999 to 2006. Note the variation in scale on the y axis.

regression, confirms the practical utility of using SPM concentrations to predict Kd in UK waters. For a single regression based on the entire Kd–SPM data set collected during this study, D was 26%. A similar error was obtained for offshore waters alone, but improved values were obtained by using separate regressions for coastal waters (D ¼ 24%) and transitional waters (D ¼ 19%). The Secchi depth is a common standard parameter in marine ecosystem studies. It measures transparency of the water Poole and Atkins (1929) found empirically that the Secchi depth can be used to estimate Kd (PAR), since it is approximately inversely proportional to Kd (PAR). Kirk (1994) discusses the relationship

SPM Kd

-1 >100mgl-1 >50mgl -1

>10m

Salinity Tidal range Exposure

Mixing

High risk

Susceptibility to nutrient enrichment

Low risk

Partly

-1

>8m

10 -50mgl-1 -1

5 -8m

-1

<5m

Estuarine

Coastal

Meso

Macro

Micro

Sheltered

Exposed

<2m-1

Offshore*

Semi exposed

Fully West

Timing

<10mgl-1

Summer

Spring

East

Location

Winter

Fig. 9. Diagramatic representation of the different factors that influence susceptibility to nutrient enrichment. SPM and Kd ranges are approximate for the different level of factors.

between Secchi and Kd and states that Kd can be estimated from Secchi as per Eq. (12).

Kd ¼ 1:4=S

(12)

By taking natural logs of both sides, Kirk’s equation can be written as

lnðKd Þ ¼ 0:34  lnðSÞ

(13)

This equation is similar to our Eq. (10) for the relationship between Kd and Secchi depth in transitional waters. However, Kirk’s relationship is less similar to our relationship in coastal/offshore waters defined in Eq. (11). Overall the regression of (logtransformed) Kd and Secchi depth was less accurate in its predictive ability. For the whole data set, D ¼ 32%. Better prediction was obtained for the offshore water set (D ¼ 22%), compared to the full data set and prediction of Kd from SPM for the offshore water set (D ¼ 26%), but worse for the transitional water data set (D ¼ 40%). It is recommended that regressions of Kd on Secchi depth should not be used for transitional waters. The poor performance of the transitional water regression may have been the result of the difficulty of measuring Secchi depths in highly turbid waters. Related studies in turbid waters discussed the difficulty of using Secchi depth for empirical light models in turbid waters (Gallegos et al., 1990; Xu et al., 2005). The relationship between Secchi depth and light attenuation is not fixed and can vary by up to as much as sevenfold in waters with large variations in CDOM and turbidity (Koenings and Edmundson, 1991). Thus, it may be feasible to use Secchi depth in clearer coastal waters, with relatively small concentrations of SPM and CDOM, but highly impractical in turbid estuarine waters (Foden et al., submitted for publication). The water body typology employed for this paper uses characteristics of tidal range, mixing and exposure to differentiate between the water body types within UK marine waters. Additionally,

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the types have been linked to the Water Framework Directive typology and follow the WFD in distinguishing transitional waters (of varying salinity) from coastal waters. The typology (Rogers et al., 2003) follows WFD Annex VII system B by using wind/wave exposure, tidal range and, in the case of transitional waters only, the strength of vertical mixing, to distinguish several types of coastal and transitional waters (Table 1). It might be expected that these factors would influence SPM concentration (and hence Kd), with greatest amounts of suspended particulates being found in highenergy environments, i.e. those with large tidal range and high exposure to wind and waves. The effect of tidal stirring on SPM has been well established on the scales of the Irish Sea (Bowers, 2005) and southern North Sea (Jago et al., 1993). However, proximity to sources of SPM in river discharges and eroding coasts is also important, and this may be the reason why most of the transitional waters in this survey had higher SPM loads (and consequently greater PAR attenuation) than coastal waters. Furthermore, there was also a gradient in increasing SPM concentrations (and associated attenuations) from the west coast to the east coast of Britain. Coastal water types 4 and 5 (moderately exposed) had high SPM loads and Kd values, and are typical of eastern British coastal waters. The CW1 and CW2 types (Lands End, North Cornwall, South Pembrokeshire, Dorset and Plymouth Coast), which are also exposed waters have significantly lower SPM and Kd values associated with them, and are all located on the West Coast off England and Wales. Similarly, high concentrations of SPM are associated with TW3 types, which are more representative of the large exposed east coast estuaries (Humber, Wash, Thames). Although many of the water body types for which data are given in Table 2, are distinguished by their SPM concentrations, a high degree of variability exists within each of the water types. Some of this variability is geographic, relating to proximity to a particulate source or sink but it is likely that there is a seasonal component to the variability. Whereas Lund-Hansen (2004) found that Kd values were consistent throughout most of the year in Århus Bay, becoming substantially higher only during the period of the spring phytoplankton bloom, Kratzer et al. (2003) pooled data from 1963, 1964 and 1996 to demonstrate an annual cycle in the SPM load in the Menai Strait in North Wales, with higher concentrations in late Autumn and Winter. The Strait is a sheltered, mesotidal water, but its contents exchange vigorously with the open Irish Sea where SPM levels are lower (Weeks et al., 1993). These issues need further examination, and suggest that our models for Kd can be further improved by taking more account of water body typology and temporal variability. Nevertheless, the data and regression models, reported in this paper, extend the range of Kd values for UK estuarine and coastal waters and improve the reliability with which the sensitivity of water bodies to nutrient enrichment can be estimated. This information will extend our understanding of risk and susceptibility in UK marine waters. It is expected that water types that exhibit consistently high measurements of SPM, and thus limiting the light attenuation available in these waters would be at far less risk of eutrophication problems than in the better-illuminated water types. This is represented in the simple diagram of factors influencing the role of light attenuation in UK waters. Transitional waters, most particularly those with large macrotidal areas have Kd values consistently above 5 m1, demonstrating light-limiting conditions for phytoplankton growth. Data were collected throughout the year, indicating that that these water types are light limited both in and out of the growing season. In contrast, offshore waters and coastal waters with small tidal ranges have a much lower range of SPM values, with associated lower attenuating light values. This would suggest that these type of waters are more at risk from the effects of nutrient enrichment and more susceptible to eutrophication problems.

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