Water quality requirements for marine fish cage site selection in

Water quality requirements for marine fish cage

site selection in Tenerife (Canary Islands):

predictive modelling and analysis

using GIS

O.M. Perez *, L.G. Ross, T.C. Telfer, L.M. del Campo Barquin

Institute of Aquaculture, University of Stirling, Stirling FK9 4LA, Scotland, UK

Received 3 October 2001; received in revised form 4 April 2002; accepted 7 June 2002

Abstract

Site selection is a key factor in any aquaculture operation, affecting both success and

sustainability. The correct choice of site in any aquatic farming operation is vitally important since

it can greatly influence economic viability by determining capital outlay, and, by affecting running

costs, rates of productions and mortality factors. It is impractical to try control water quality

parameters in cage culture systems, therefore culture of any species must be established in

geographical regions having adequate water quality and exchange. This study used GIS and

related technology to build a spatial database using those water quality variables which were

considered to have an influence in developing marine fish-cage culture of seabass and seabream in

Tenerife (Canary Islands). The water quality variables identified were: temperature, turbidity

(runoff soil erosion and sewage), disease stress (sewage) and possibility of waste feedback from

fish-cages (bathymetry). Variables were grouped in a logical model and combined to generate

outputs showing the most suitable areas for siting cage culture. Most areas of the coastline of

Tenerife were identified as being suitable or very suitable, and none was identified as totally

unsuitable. Sensitivity to parameter changes was tested in order to evaluate the model, by

changing one parameter at a time. Values chosen were F 5%, 10% and 15% of the reference

0044-8486/02/$ - see front matter D 2002 Elsevier Science B.V. All rights reserved.

doi:10.1016/S0044-8486(02)00274-0

* Corresponding author. Investigaciones y Servicious Marinos (INSEMAR), San Clemente 14/5j, S/C de

Tenerife 38003, Spain. Tel.: +44-1786-473-171; fax: +44-1786-472-133.

E-mail address: [email protected] (O.M. Perez).

www.elsevier.com/locate/aqua-online

Aquaculture 224 (2003) 51–68

situation. This analysis shows that the model was especially sensitive to sea temperature and

suspended solids.

D 2002 Elsevier Science B.V. All rights reserved.

Keywords: Water quality; Cage; Aquaculture; Site selection; GIS; Tenerife

1. Introduction

Any material discharged in to the sea inevitably causes some change in the environ-

ment. Such change may be great or small, long-lasting or transient, wide spread or

extremely localised. If the change can be detected and is regarded as damaging, it

constitutes pollution (Clark, 1998). Pollution of various types is responsible for high fish

mortality in numerous cage farming operations (Beveridge, 1996). Cage culture, as with

any aquaculture venture, requires good water quality, thus water properties strongly affect

the choice of an aquaculture site. Hence, cages should be located in areas uncontaminated

by industrial, municipal and agricultural pollutants. Other water quality parameters, such

as temperature, pH, presence of nitrogenous compounds, dissolved oxygen, etc., should be

within the ranges that provide life support and growth for the cultured species. The correct

choice of sites is vitally important since it influences the economic viability of the facility

(Lawson, 1995). However, the availability of suitable areas for aquaculture is diminishing

because of water quality degradation. Therefore, the first prerequisite for sustainable

aquaculture is an adequate aquaculture resource allocation system. Such a system should

be implemented within the context of an integrated planning approach rather than simply

creating a series of regulations to avoid environmental deterioration. This system should

comprise a flexible adaptable integration of institutional arrangements using tools such as

Geographical Information Systems (GIS), allowing resource allocation.

It is not possible to describe, explain or predict ecosystem behaviour without knowing

how ecosystem components are distributed in time, space or with respect to each other and

understanding the relationships and processes that explain their distribution and behaviour.

As well as requiring knowledge of spatial distribution and relationships, the ability to

make reliable predictions demands knowledge about temporal trends. GIS are powerful

tools that can be used to organise and present spatial data in a way that allows effective

environmental management planning, and hence answers these questions. GIS technology

has been successfully applied in the analysis of the coastal zone to evaluate a number of

environmental problems (Meaille and Wald, 1990; Populus et al., 1995). GIS has several

advantages for aquaculture development programmes, not only providing a visual

inventory of the physical, biological and economical characteristics of the environment,

but also allowing generation of suitability maps for different uses or activities without

complex and time-consuming manipulations. The general usefulness of the methodology

of using GIS for aquaculture site selection has been explored and is now becoming

established (Aguilar-Manjarrez and Ross, 1995; Kapetsky and Nath, 1997; Nath et al.,

2000). The aim of this study was to select the most suitable sites for off-shore marine fish-

cage farming of seabream (Sparus aurata) and seabass (Dicentrarchus labrax) in Tenerife

(Canary Islands), based on water quality variables. A local sensitivity analysis was carried

O.M. Perez et al. / Aquaculture 224 (2003) 51–6852

out to identify the sensitivity of the model to each of the spatial data variables and to

determine their relative criticality.

2. Methodology

2.1. Study area

The Canary Archipelago, composed of seven main islands and several minor ones, is

located in the Northeast Atlantic Ocean between latitude 27.6j–29.5jN and longitude

18.2j–14.5jW, 100 km from the coast northwest edge of Africa (Fig. 1). The archipelago

Fig. 1. The study area, Tenerife, Canary Islands.

O.M. Perez et al. / Aquaculture 224 (2003) 51–68 53

emerged from the oceanic basin due to the successive overlay of volcanic material,

forming a set of independent islands with depths of 2000 m between them. The insular

shelf is reduced to only 200 m. Tenerife is the largest island of the Archipelago with an

area of 2036 km2, as well as having the longest coastline, 358 km. The island is a

triangular pyramid with a truncated apex at an altitude of 2000 m, from which the volcano

Teide rises to 3718 m. The volcanic origin of the island is responsible for the coast and

seabed topography, which generally can be described as abrupt and very uneven.

Both the particular oceanographic conditions, such as the Canary Current (a branch of

the Gulf Stream) and the seabed morphology, govern the marine environment in Tenerife.

The Canary Current is a cold surface current that flows in SSW direction, and is stronger

within the top 200 m (Fiekas et al., 1992). Outside of the island’s influence, its mean

velocity is about 15 cm s� 1, but when it passes between the islands it may reach mean

values of 25 cm s� 1 (Molina et al., 1996). The sea surface temperatures in the archipelago

annually range between 17 and 25 jC, and salinity is very stable, with values ranging from36–37x. Nutrient concentrations (phosphate, nitrate, nitrite, silicate and ammonium) are

very low in the euphotic zone (Braun et al., 1982), indicating the oligotrophic condition of

the waters.

At present there are only four cage farms, all located in the leeward part of the island,

although there are several projects for new sites planned for the near future. They are all

small-scale cage operations growing seabream (S. aurata), which is an introduced species

in Tenerife.

2.2. General methodology

A schematic diagram showing the logical steps involved is shown in Fig. 2. The first

stage of the study was to identify the most important water quality variables that were

considered to have any influence in developing marine fish-cage (seabass and seabream)

aquaculture in Tenerife. Once these variables were identified, satellite images, maps,

statistics and archive material were used to build up the spatial database. All data

integrated into the database needed some manipulation and reclassification to create the

final thematic maps in a common framework. Subsequent manipulations focused on

registering each thematic map to a common coordinate system, and on scoring the

thematic maps in terms of their suitability for aquaculture development in Tenerife. In this

study a scoring system of 1 to 8 was chosen, 8 being the most suitable and 1 the least.

Models were then built to identify the most suitable areas in Tenerife for cage farming. Fig.

3 shows the four submodels proposed. Either simple overlays or Multi-Criteria Evaluation

(MCE) were used to combine the variables of each model. In MCE, an attempt is made to

combine a set of criteria (using a particular weight for each) to achieve a single composite

basis for a decision according to a specific objective. Finally, MCE was used to combine

the four submodels to generate a final output.

2.3. Identification of the most important water quality variables

From all possible aquaculture water quality parameters (Lawson, 1995), only the most

important influencing cage culture development in Tenerife were reviewed. Because of the


conservative nature of the marine environment and the oligotrophic nature of oceanic

water, water quality variables such as dissolved oxygen, total alkalinity, total hardness, pH,

nitrogenous compounds and hydrogen sulphide are considered of little importance here.

Salinity remains almost constant at 36–37xand was assumed, therefore, to be consistent

throughout. On the other hand, possible pollutants such as hydrocarbons, heavy metals and

pesticides have a strong potential influence and temperature and turbidity are also of great

importance.

Tenerife is located in an area of high oil tanker traffic, and thus has the potential for

hydrocarbon pollution. In addition, the Canary Current and Trade Winds could bring in

this pollutant even if originating far away from the island’s coasts. However, the sporadic

and localised nature of this poses little threat to cage culture (Pena-Mendez et al., 1996b,

1999). Despite the great concern about the effects of heavy metals on aquatic life and on

organisms higher in the food chain, there are very few studies on concentration of heavy

metals in the coastal areas around Tenerife. Dıaz et al. (1990) conducted a study in the

coastal waters around Santa Cruz de Tenerife city and concluded that, although the

presence of heavy metals in this area might be the highest in the island, they are not

considered as ‘‘polluted waters’’. Polychlorinated biphenyls (PCBs) compounds can be

taken up by fish via water or also accumulate via the food chain. Pena-Mendez et al.

Fig. 2. Schematic diagram of the steps involved in the development of this study.


(1996a) determined the content of seven individual PCB congeners in specimens of a

marine winkle (Ossilinus atratus) and limpet (Patella ullisiponensis aspera) in Tenerife,

which can indicate compliance with legislative food standards. The authors concluded that

in Tenerife coastal areas the seven PCB congers are present at low levels. Furthermore,

most of the samples showed either undetectable and/or non-quantifiable concentrations of

these congeners. No significant differences where found between sampling sites along the

coast. Therefore, it can be concluded that PCB contamination in Tenerife coastal

environments is not a problem for cage culture.

The water quality variables identified as greatly influencing cage siting in Tenerife were

temperature, turbidity (runoff and sewage), disease stress (sewage) and possibility of waste

feedback from the cage (bathymetry). Water temperature is the environmental parameter

that has the greatest effect on fish (Lawson, 1995), and can be thought of as a primary

factor affecting the economic feasibility of a commercial aquaculture venture. Temper-

atures on either side of the optimum can induce stress in the animal, affecting feeding,

growth, reproduction and disease inhibition. Turbidity produced by dissolved and

suspended substances, such as clay particles, humic substances, silt plankton, etc., can

be troublesome in fish (Lawson, 1995). Due to the oligotrophic nature of the waters in

Tenerife, the major source of turbidity comes from sporadic runoff episodes. Excessive

runoff coming from the watersheds can often cause clay and silt loads to exceed tolerable

limits for farmed fish. These particles can clog the gills of small fish and/or stress bigger

fish. Suspended solids at sufficiently high concentrations can cause gill damage and may

trigger diseases as a result of fish stress. Sewage discharges could also be hazardous for

cage culture development. Urban sewage is principally organic but also contains consid-

erable amounts of metals, oils and grease, detergents and industrial wastes. All human

Fig. 3. Flowchart showing relationships of submodels.


sewage also contains enteric bacteria, viruses and the eggs of intestinal parasites. Formerly,

it was believed that pathogenic bacteria and viruses did not survive in seawater. However,

it has been shown that bacteria may enter a dormant phase so that they cannot be detected

by normal methods and viruses can be very persistent in seawater (Clark, 1998). Seafood

organisms that do not filter feed, such as most crustaceans and fish, do not accumulate

pathogens from sewage-contaminated water and are unlikely to represent a direct health

risk from this source (Clark, 1998). However, poor environmental conditions (from

sewage discharges) leading to fish stress may produce physiological or metabolic changes

in the fish which may increase the sensitivity of fish to other pollutants, increasing

moralities and reducing profits (Hedrick, 1998). Sewage-polluted sites should, therefore,

be avoided completely. Finally, a third possible source of water quality deterioration could

come from the cages itself. Of all the wastes released by marine fish farms into the

environment, particulate organic waste in the form of uneaten feed and faeces are usually

the most significant fraction (Beveridge, 1996). This material, which generally settles on

the seabed near to the cages, provides a net input of organic carbon and nitrogen to the

sediments, thus, the accumulation of waste can cause major changes in the benthic

community and may exceed the environment’s capacity to bioprocess this material

(Hargrave, 1994). Environmental deterioration due to high organic matter concentrations

in the sediments may affect the health of farmed fishes and hence profitability (Beveridge,

1996). Hence, floating cages should be located at sites where the water depth is sufficient

to maximise water exchange and to keep cage bottoms well clear of substrate at low tide,

and so, knowledge of the bathymetry is also an important factor.

2.4. Database generation and modelling

2.4.1. Sea temperature

Tenerife has very favourable sea temperatures (17–25 jC) for culture of seabass and

seabream, therefore, temperature is not a constraint. However, it is desirable to identify

those areas with the highest annual average temperature, which will enhance fish growth

and therefore reduce the growing cycle and production costs. It is impractical to take in

situ simultaneous and continuous sea temperature measurements around Tenerife, and so,

AVHRR sensor measurements on board NOAA-14 satellite were used for this study. A set

of approximately four NOAA-14 AVHRR images per month, with minimum cloud

coverage, was obtained from 1997 to 2000. A total of 135 satellite images, each composed

by five spectral bands, were provided already radiometrically corrected by the Center for

Reception, Processing, Archiving and Dissemination of Earth Observation Data and

Products (CREPAD; Canary Islands).

The accuracy of satellite observations of sea surface temperature retrievals is critically

dependent upon the ability of satellite radiometers to view the sea surface unobstructed by

cloud. Therefore, images were processed for cloud detection and elimination using

ERDAS 8.3.1 software. For this study, a modification of the Saunders and Kriebel

(1988) technique was used to optimise its functionality to Tenerife. The sequence of steps

to identify cloud free pixels was reduced to three tests from the usual five. Tests 2 and 3

were omitted because of their bad performance in detecting cloudy pixels over coastal

areas (Saunders, 1986).


The determination of SST from cloud-free satellite images was performed by means of

multi-channel algorithms using channels 4 and 5 of AVHRR. This study made use of the

latest state-of-the-art SST algorithm for the Canary region developed by Arbelo et al.

(2000). This algorithm has been validated with field data, and its standard deviation is 0.4

jK (Arbelo, personal communication). The split-window equation is:

SST ¼ 1:0186 T4 þ 1:2348ðT4 � T5Þ þ 1:3178ðT4 � T5Þðsech � 1Þ � 4:4616

where: SST = sea surface temperature; T4 = brightness temperature in channel 4;

T5 = brightness temperature in channel 5; h = satellite scan angle.

The SST algorithm was applied to each of the cloud-free images, creating a set of 135

SST images. The final processing step was the georeferencing of these images. Georefer-

encing involves precise transformation of the image from the sensor-based projection to an

earth surface-based projection by matching ground- and image-based control points, and

transforming and resampling the data to a map projection coordinate system. Images were

georeferenced to latitude–longitude. All sea surface temperature images were combined to

generate a composite map, which is used to generate average values of sea surface

temperature. This image was reclassified according to suitability scores. Fig. 4a shows the

final average SST suitability map.

2.4.2. Sediments (runoff)

This study only focused on runoff-sediments, which are the main contributing source of

sediments to the sea in Tenerife. Of the methods available, the Universal Soil Loss

Equation (USLE), in both its original and modified forms, is perhaps the most widely

applied of the empirical approaches in which predictive equations are developed from

analyses of source data (Kertesz, 1993). The USLE requires an estimated value for the

factor R, which depends upon each rainfall intensity period of a storm. Williams (1975)

and Williams and Berndt (1977) modified the USLE by replacing the R factor with a

runoff factor. The modification is based on the assumption that the total discharge and

peak discharge rate resulting from a storm on the watershed depend upon the duration,

amount and intensity of the storm. The equation was developed to estimate sediment yield

at the outlet of a watershed directly, rather than soil loss, on a storm-by-storm basis. The

modified equation (MUSLE) is:

Y ¼ 11:8ðVqpÞ0:56KCPðLSÞ

where Y is the mean soil loss (tons), V is the storm runoff (m3), qp is the peak flow rate (m3

s� 1), K is the soil erodibility factor for a specific soil horizon, C is a dimensionless

cropping management factor (expressed as a ratio of soil loss from the condition of interest

to soil loss from tilled continuous fallow), P is an erosion control practice factor

(expressed as a ratio of the soil loss with the practices to soil loss with farming up and

down the slope) and LS is the topographic factor, a combined dimensionless factor for

slope length and slope gradient. Full integration of this equation in GIS (each variable

from the MUSLE equation was created as a thematic map) made it possible to calculate the


soil loss for each individual grid cell (Fig. 3). The level of detail (10� 10-m pixel) made it

possible to take into account the high spatial variability of the landscape.

The data input for this equation came from the following sources and calculations. Values

for runoff (V) came from the NRCS Runoff Curve Number method, detailed in NEH-4

Fig. 4. Suitability maps for criteria used in modelling water quality for sitting cage culture in Tenerife where 8 is

most suitable and 1 least suitable. (a) Sea surface temperature suitability map. (b) Potential sediment yield

suitability map. (c) Sewage suitability map. (d) Bathymetry suitability map. (e) Overall water quality suitability

map. (For each score the suitable area is shown in km2). A zoom window is shown in some maps.


(USDA-SCS, 1986). The rain data used were the maximum P24 (the 24-h rainfall amount).

The method for approximating peak discharge ( pq) (Tabular Hydrograph method) is based

on that proposed by USDA-SCS (1973) and USDA-SCS (1986). The soil erodibility factor

(K) is a measure of the intrinsic ability of soil to erode, and thus varies as a function of soil

type. Data fromRodriguez et al. (1993) were used to determine theK factor corresponding to

each soil type. According to the authors, soils in Tenerife vary between considerably

Fig. 4 (continued).


resistant (Ultisols, Alfisols, Vertisols, Entisols, Sorribas and Inceptisols) (K = 0.10–0.25)

and fairly sensitive (Aridisols) (K = 0.25–0.35). C represents the ratio of soil loss under a

given vegetation canopy to that from bare soil. The C factor values used were those

presented by Zhou (1998). The conservation practice factor, P, was determined by the extent

of conservation practices such as strip cropping, contouring and terracing practices, which

tend to decrease the erosive capabilities of rainfall and runoff. In general, because the land

slopes are small and the infiltration rates are high for the farms (mostly banana) in Tenerife,

Dıaz-Dıaz et al. (1998) concluded that surface runoff could be set to zero in these areas, and

so P was set to a value of one. The LS factor was computed with the Wischmeier and Smith

(1978) equation. Based on elevations in the Digital Elevation Model (DEM), the IDRISI

command SLOPE was used to generate a raster map showing the spatial distribution of

slopes (S). The slope length coverage was produced by developing a flow direction grid

(using the module ASPECT) following the technique of Niedermeier (1998).

The final steps were to run the module DISTANCE to measure distances from the

stream mouth, and then to reclassify the sediment yields according to their suitability. Fig.

4b shows the final suitability map for this variable.

2.4.3. Sewage

Unfortunately, there are no direct measurements of the quantity or quality of sewage

discharges from the island. Therefore, to quantify the suitability of cage culture in areas

close to sewage discharges, three factors were considered to characterise a sewage outfall

(domestic outfalls, with no industrial discharges); these were: number of people connected

to each sewage pipeline system, the presence or absence of any treatment before the

sewage is dumped, and the depth of the discharge. The number of people connected to each

Fig. 4 (continued).


pipeline network determined the quantity of sewage that each pipe discharged to the sea.

The presence or absence of any pre-discharge treatment determined its potential hazard.

The depth of the sewage dump provided information on the likelihood of the discharge

reaching the surface, and dilution time (dilution factor). When the mixed layer is

sufficiently shallow, plumes from the sewer outfalls are often trapped within the

thermocline. At other times, the mixed layer is sufficiently deep or does not exist, so no

trapping occurs. Under such conditions, at least a portion of the discharge undoubtedly

reaches the surface.

The number of people connected to each sewage (variable described as ‘‘population’’)

was determined by using a population map for each of the 743 Population District and the

sanitation network. The presence or absence of any treatment before sewage discharge was

determined using a map showing their location and connection to the sanitation network.

The depth of discharge of each pipe was determined by combination of the sewage-pipe

layer and the bathymetric map. Discharges that might occur above the seasonal thermocline

(100–150 m in mid summer and 15 m winter–spring; de-Armas, personal communication)

were considered potentially dangerous. MCE was used to combine the three variables

described above and, hence, determine which sewage pipelines present a higher threat to

fish farming development. Each variable was weighted as shown in Fig. 3. It was

considered that sewage treatment prior discharge was the most important variable, followed

by the number of people connected to the sewage (population), and finally the depth of the

discharge. The final step was to run the module DISTANCE to measure distances from the

pipes, and reclassify according to their suitability. Fig. 4c shows the final suitability map for

this variable.

2.4.4. Bathymetry

A set of four bathymetric charts (1:50,000) were digitised on a CalComp Drawing

Board III using Cartalinx 1.2 software. The digitised contours were exported to IDRISI

and linear interpolation between contours was used to produce a faceted model (complete

bathymetry surface).

Floating cages should be located at sites where the water depth is sufficient to keep

cage bottoms well clear or substrate, avoiding possible harmful feedback from wasted

material accumulated on the seabed. Bearing this in mind, the bathymetric map was

reclassified into to suitable zones (Fig. 4d).

2.4.5. Final output

The four submodels assembled to identify the most suitable areas for cage culture in

Tenerife were combined using another MCE (Fig. 3). However, to minimise the cost of

mooring, the extent of potential suitable area for cage culture was limited to depths bellow

50 m, assuming the cages were 20 m deep. The total available area was about 250 km2.

Fig. 4e shows the final model output generated for depths inferior to 50 m. For each class

the suitable area (km2) is shown.

2.4.6. Sensitivity analysis

Sensitivity analysis (SA) is the study of how the variation in the output of a model can

be apportioned, qualitatively or quantitatively, to different sources of variation (Malczew-


ski, 1999; Saltelli et al., 2000). SA aims to ascertain how the model depends upon the

information fed into it, upon its structure and upon the framing assumptions made to build

it. Local SAwas the method chosen here because it gives an idea of the factors that mostly

contribute to the output variability. Local SA computes partial derivatives of the output

functions with respect to the input variables (differential analysis). In order to compute the

derivative numerically, the input parameters are varied within a small interval around a

nominal value. The interval is not related to degree of knowledge of the variables and is

usually the same for all of the variables. In this study, interval values of F 5%, 10% and

15% of the reference values were chosen.

The model variables considered in the SA were sewage, sea surface temperature (SST)

and sediments. These variables were varied and the resulting changes in the number of

square kilometers under each suitability class were examined to evaluate the sensitivity of

the results to variations in the input parameters (Table 1). Fig. 5 shows the differences

between the area in square kilometers, under each suitability class, from the baseline

model for each of the changed variables. Absolute Sensitivity (S), a mathematical

Table 1

Sensitivity analysis for temperature, sediment and sewage

Suitability scores 1 2 3 4 5 6 7 8

Baseline model 0 2.27 4.49 15.88 12.02 51.75 32.76 156.52

Temperature

+ 15% 0 3.05 5.75 16.35 11.12 55.23 31.06 153.12

+ 10% 0 1.30 4.76 15.25 10.08 48.42 31.71 164.17

+ 5% 0 0.33 5.48 14.73 9.22 48.22 30.64 167.06

� 5% 0 2.79 4.49 16.85 11.03 56.66 31.13 152.75

� 10% 0 3.40 6.24 17.79 9.45 62.79 27.18 148.84

� 15% 0 3.92 6.37 18.03 11.51 65.31 151.44 19.11

Sediments

+ 15% 0 2.50 4.81 15.97 12.18 62.31 35.04 142.89

+ 10% 0 2.28 4.71 15.65 12.04 56.29 34.39 150.34

+ 5% 0 2.22 4.71 15.56 12.02 54.92 33.73 152.52

� 5% 0 2.08 4.36 15.21 12.23 48.96 31.28 161.57

� 10% 0 1.83 4.29 14.93 12.59 48.55 32.25 161.24

� 15% 0 1.46 3.81 14.77 13.37 44.24 30.14 167.88

Sewage

(population)

+ 15% 0 2.49 4.68 16.37 12.28 51.17 32.87 155.82

+ 10% 0 2.49 4.68 16.37 12.28 51.17 32.87 155.82

+ 5% 0 2.27 4.51 16.18 12.23 51.8 32.87 155.82

� 5% 0 2.27 4.5 15.9 12.03 51.66 32.32 157.01

� 10% 0 2.27 4.5 15.9 12.03 51.66 32.32 157.01

� 15% 0 2.27 4.5 15.9 12.03 50.99 32.06 157.94

Each variable was altered by F 5%, 10% and 15% and the resulting changes in the number of square kilometers

for each suitability score calculated. The number of square kilometers for each suitability score for the baseline

model is shown for reference.


Fig. 5. Differences between the area under each suitability class and the baseline model for each of the changed

variables; (a) sea temperature, (b) suspended solids, and (c) sewage discharges.


expression of sensitivity which provides a consistent measure for comparing the selected

model parameters (Shukla, 1998), was also calculated for all the variables as

S ¼ ðRa � RnÞ=ðPa � PnÞ

where Ra and Rn were the model response for altered and nominal parameters, and Pa and Pn

were the altered and nominal parameters. The S values for different parameters were

compared to identify sensitive parameters. Absolute sensitivity provided a consistent

measure for comparing various model parameters. Based on the calculated S values,

variables can be arranged in the following order for their importance in affecting water

quality suitability area: SST>sediments>sewage. Overall, the water quality model was

shown to be highly sensitive to SST and sediments. The model was more sensitive to lower

values of SST than higher values of this variable. On the other hand, the model was more

sensitive to higher values of sediments and sewage than lower values of these two variables.

3. Discussion and conclusions

Although a larger number of variables could be used for cage siting (Beveridge, 1996),

this study only focused on those controlling water quality. It is impractical to try to control

water quality parameters in cage culture systems, therefore culture of any species must be

conducted in areas that have adequate water quality prior to the establishment of the farm.

Four water quality variables were identified as greatly influencing cage culture in Tenerife;

temperature, turbidity (runoff and sewage), risk of diseases (sewage) and possible waste

feedback from the cages (bathymetry).

From the 250 km2 of available coastal area (using only that area above the 50 m depth

isobaths), most was identified as being suitable (score 7) and very-suitable (score 8),

respectively. There were very few areas with low scores (scores 2, 3 and 4), and none was

identified as totally unsuitable (score 1). These areas are located where sewage or high

suspended solid loadings may have occurred.

A mathematical model is defined by a series of equations, input factors, parameters and

variables aimed to characterize the process being investigated. Input is subject to many

sources of uncertainty including errors of measurement, absence of information and poor

or partial understanding of the driving forces and mechanisms. This imposes a limit on our

confidence in the response or output of the model. Further, models may have to cope with

the natural intrinsic variability of the system such as the occurrence of stochastic events.

Modelling requires an evaluation of the confidence of the proposed model, possibly

assessing the uncertainties associated with the modelling process and with the outcome of

the model itself. Possible sources of inaccuracy in the present model were the use of

satellite images to derive sea temperature and the use of the USLE for estimation of

potential sediment yield.

The use of NOAA–AVHRR satellite images provided the opportunity of creating an

average sea surface temperature (SST) map, which could not have been achieved by other

means. However, despite all possible advantages using AVHRR images, there are some

points of concern when working with AVHRR-derived SSTs. The SST measurement is of

the surface temperature, and not the bulk temperature (Schuluesso et al., 1990). Most


successful uses of SST data concentrate on identifying spatial temperature gradients rather

than absolute temperature values. The modelling of the topographic potential for erosion

was done by applying the very common USLE equation. However, some authors have

argued its limitation when applied to complex topographic conditions (Mitasova et al.,

1997), and exclusion of depositional areas.

Sensitivity to parameter changes was tested in order to evaluate the model. The analysis

was done by changing SST, sewage and sediments variables at a time. Values chosen were

F 5%, 10% and 15% as in the reference situation. A considerable level of variation was

found in the results when the model parameters were varied. The sensitivity analysis shows

that the model was especially sensitive to sea temperature and sediments, respectively.

There is a growing need for complex analyses of the coastal zone because of the

mounting barrage of threats created by intensive and diverse human activities. One of the

potentially useful tools for this kind of analysis is GIS. GIS are powerful tools that can be

used to organise and present spatial data in a way that allows effective environmental

management planning. GIS technology in aquaculture has been used now for about 15

years and there are several advantages for aquaculture development programs. However,

GIS does not provide a definitive answer to a given problem, rather, it generates outputs to

a range of input data. What it does provide is an aid to support decisions of managers built

up with the outputs from the GIS, and perhaps other related material. This study selected

the most suitable areas for aquaculture in terms of their water quality, and has suggested

limits to where aquaculture can be placed. However, those sites that are identified as more

suitable should be further investigated if any aquaculture operation were to be developed.

The model presented here could be modified by expert panels in the near future, so as to

fine-tune the model.

Overall, the methodology adopted in this study was able to identify the most suitable

areas for cage farming in Tenerife based on water quality. Sensitivity analysis revealed

that considerable changes in the model could result from the uncertainties associated

with the input parameter estimation. The reliability of the results predicted by the

methodology used in this study would, therefore, mainly be a function of the extent to

which the estimated input parameters represent the field situation. As expected, no

totally unsuitable sites for cage farming were identified. This is because Tenerife has

very favourable environmental conditions for culture of marine fish because of its clean

and well-oxygenated waters, favourable temperatures for growth (17–25% jC) and

stable oceanic salinity (36–37x). Most of the areas were identified as being suitable

and very suitable.

Acknowledgements

The authors thank the assistance of CREPAD for providing the AVHRR satellite images

and their support during the image processing. Also, we acknowledge Dr. Manuel Arbelo

for his valuable suggestions and guidelines in the AVHRR processing. We extend our

gratitude to Tenerife Council for the valuable use of their CD-MAP information. This work

was carried out as part of a training project financed by the EC under the FAIR program (GT

973516).


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