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Delineation of management regions for South African rivers based on water chemistry

J. A. Daya; H. F. Dallasa; A. Wackernagela a Freshwater Research Unit, Zoology Department, University of Cape Town, Rhodes Gift, Western Cape, South Africa

To cite this Article Day, J. A. , Dallas, H. F. and Wackernagel, A.(1998) 'Delineation of management regions for South

African rivers based on water chemistry', Aquatic Ecosystem Health & Management, 1: 2, 183 — 197 To link to this Article: DOI: 10.1080/14634989808656913 URL: http://dx.doi.org/10.1080/14634989808656913

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Aquatic Ecosystem Health & Management ELSEVIER

Aquatic Ecosystem Health and Management 1 (1998) 183 197

Delineation of management regions for South African rivers based on water chemistry

Downloaded By: [University of Cape Town Libraries] At: 08:16 27 May 2011

J. A. Day*, H. F. Dallas, A. Wackernagel Freshwater Research Unit, Zoology Department, University of Cape Town, 7701 Rhodes Gift, Western Cape, South Africa

Abstract The rivers of South Africa differ in water chemistry because of differences in geology and climate, and in the nature of the terrestrial vegetation. Thus the riverine biotas also differ in their water quality requirements. This paper describes the use of multivariate analytical techniques on a large inorganic chemical database as a means of dividing the country's rivers into regions of like water chemistry for the purposes of water quality management of aquatic ecosystems. Data were used from about 500 usable sites for the three earliest hydrological years (October 1980-September 1983 inclusive) for which information is available for most of the sites. In some cases separate winter (May-September) and summer (November-March) analyses were performed. Those observations in which orthophosphate-phosphorus (PO 3 -P) was greater than 0.1 mg 1-1 and/or nitrate- plus nitrite-nitrogen (NO2+3-N) was greater than 0.5 mg 1 i and/or conductivity > 500 mS m -~, were excluded. Some of the analyses were run on the subset of records for which the reading of weir height (as a surrogate for discharge) was within 10% of the mean. Chemical variables comprised conductivity and pH, and the concentrations of chloride, total alkalinity (TAL), sodium, calcium, potassium, magnesium, chloride, sulphate, fluoride, silicate, nitrate- plus nitrite-nitrogen, ammoniumnitrogen and orthophosphate-phosphorus, as well as the ratios C1 :(Cl- + TAL), CI-:(CI + SO42+)and Na+:(Na + + Ca2+). Multivariate techniques included principal components analysis, detrended correspondence analysis and cluster analysis. Eigenvalues are low ( < 0.05) for detrended correspondence analyses but high ( > 0.7) for principal components analyses. In all cases, [silicate] correlates most strongly with the first axis, and conductivity, [K ÷] and/or [SO 2 ] with the second. Alkalinity correlates inversely with [H+], while [Ca 2+] and [Mg 2+] are closely correlated with each other and with alkalinity. [Na +] and [CI-] are correlated with each other and weakly with conductivity. Cluster analysis of secondary drainage regions is used together with biogeographic and physiographic information to produce a map of South Africa divided into five major regions for the management of water quality for riverine ecosystems. © 1998 Elsevier Science Ltd and AEHMS. All rights reserved. Keywords." Water quality management regions; Multivariate analytical techniques; Cluster analysis; Physiographic; Water chemistry

1. Introduction The chemical constituents of river water differ naturally in concentration from region to region, river to river, and longitudinally from the headwaters * Corresponding author. Tel.: +27-21-650-3635; Fax: +27-21650-3301 ; e-mail: jday @botzoo.uct.ac.za,.

of a river to its lower reaches. Riverine organisms are adapted to the suite of conditions prevalent in that stretch of a fiver in which they live, and so the water quality requirements of riverine biotas also differ regionally. If aquatic ecosystems are to be appropriately managed, water quality guidelines for the natural aquatic e n v i r o n m e n t need to take account of these differential requirements.

1463-4988/98/$19.00 © 1998 Elsevier Science Ltd and AEHMS. All rights reserved. PII: S 1463-4988(98)00022-0

184

J.A. Day et at./Aquatic Ecm\7stem Health and Management 1 (1998) 183-197


A

R

B

Fig. 1. South Africa showing (A) the coding system used by DWAF for the principal drainage regions and (B) the provincial boundaries.

Producing a specific set of guidelines for each stretch of every river is neither feasible nor desirable. The identification of rivers, or stretches of rivers, that are similar enough in water chemistry for their biotas to be protected by a single set of water quality guidelines, would assist in management of the resource. It would be even more convenient if similar rivers were geographically adjacent so that catchments or subcatchments could be managed as units. Fortunately, natural waters in particular geographical areas do tend to be chemically similar. The first attempt to categorise natural waters in South Africa was by Bond (1946), who divided ground-

waters according to their chemistry into five categories: 'pure waters' in the south-western Cape and most of the interior highlands; 'slightly saline chloride waters' along parts of the east coast; 'temporary hard carbonate waters' in the north-central region; 'alkaline soda carbonate waters' in the rest of the eastern parts of the country; and 'highly mineralised chloride sulphate waters' over much of the mid-west and the south-eastern coast (see Fig. 1 for maps of South Africa). This characterisation was a useful beginning but, since the chemical characteristics of groundwater may be very different from those of the overlying rivers, Bond's analysis cannot necessarily be used to

J.A. Day et al. / Aquatic Ecosystem Health and Management 1 (1998) 183-197


categorise rivers according to the chemistry of their waters. Examining only the major ions sodium (Na+), calcium (Ca2+), potassium (K+), magnesium (Mg2+), bicarbonate (HCO3), carbonate (CO32 ), chloride (C1-) and sulphate (SO42-), and using a single set of observations, Day and King (1995) have shown that South African rivers can be divided into four major categories. These are 1. rivers with waters dominated by Ca 2+, Mg 2+ and HCO3 ions (a common type of inland water found mostly in the east-central highlands); 2. rivers with waters in which Ca 2+, Mg 2+ and Na + are more or less co-dominant, but the major anion is HCO3 (found in a band encircling the eastcentral highlands); 3. rivers with waters in which the cations are more or less co-dominant, as are the anions HCO3 and C1(mostly found in the mid-west basin); and 4. rivers with waters dominated by Na + and C1- ions (similar to seawater and occurring mostly near the coast). These authors did not distinguish rivers differing in total dissolved solids (TDS), so some of the rivers dominated by Na + and C1- ions are dilute, and others fairly saline. In essence, this analysis showed that South African inland waters are distinctly different from coastal waters and that there are clear chemical gradients between the two. The two analyses discussed previously are based on very limited sets of data. In order to delineate South African rivers into management regions based on water chemistry, more detailed analyses are necessary. This paper describes an investigation into the feasibility of analysing existing data, collected by the Department of Water Affairs and Forestry (DWAF) over the last 25 years, as the basis for a regional classification of South African rivers based on water chemistry.

185

some of which have been sampled regularly since the late 1960s. Each sampling station is designated by a nine-digit code (e.g. G1H013-Q01). Only the first six digits are of significance here. The first character refers to the primary drainage region (in the example, 'G' refers to a number of adjacent catchments in the south-western Cape), of which there are 22 for the entire country (see Fig. 1A). The second character refers to the secondary drainage region (GI is the Berg River). The third character refers to the type of water body, 'R' for standing waters, including reservoirs, and ' H ' for running waters. The next three digits form the serial number given to a particular site (for further details see, for example, Swart et al., 1991). Of these 1000 stations, about 500 were suitable for use in the present analyses. The others were rejected because of problems associated with the data, such as the inaccuracy of certain values, and the low frequency of sampling or the location of some stations. For example, frequency of sampling varied from weekly or monthly to single observations, and sampling programmes were initiated at different times for different stations. Many sampling sites are in reservoirs, some are in estuaries (excluded from the present study) and some are very polluted. Despite these inadequacies, it seemed appropriate to investigate the use of this unique and massive set of data for dividing the country into regions for the management of water quality of aquatic ecosystems. Seventeen variables (constituents) for which adequate data are available were selected for analyses. These comprise conductivity, pH, the major ions, various nutrients, silicate and fluoride, together with a grid reference and weir height where the sampling station was near a gauging weir.

2.2. Selection of data The following section outlines the process followed in the selection of approximately 500 stations for which water chemistry data were suitable for use in multivariate analyses.

2. Methods

2.1. The database An extensive set of water-chemistry data has been collected by DWAF at approximately 1000 stations,

2.2.1. Type of aquatic ecosystem Only running-water sites (i.e. those in which the third digit of the station code is 'H') were included in the analyses.

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J.A. Day et al. / Aquatic Ecosystem Health and Management 1 (1998) 183 197

2.2.2. Choice of time period


The intention of this analysis was to find patterns based on as far as possible on natural water chemistry. While some sites have been sampled since the late 1960s, many have the earliest data from the 1980s. We have used data from the three earliest hydrological years (October 1980-September 1983 inclusive) for which information is available for most of the sites. In some cases, separate winter (May-September) and summer (November-March) analyses were performed to provide an indication of changes in water chemistry with season.

2.2.3. Exclusion of obviously polluted sites In an attempt to exclude obviously polluted sites, all observations were scanned for high nutrient values. Those observations for which orthophosphate-phosphorus (po3--P) was greater than 0.1 mg 1-1 and/or nitrate- plus nitrite-nitrogen (NO2-+3-N) was greater than 0.5 mg 1 ~ were excluded from further analysis.

2.2.4. Dealing with high-conductiviO, sites The highest mean conductivity value for 19801983 for one station was 1820 mS m 1 (almost half the salinity of seawater). Because of mathematical difficulties in dealing with very skewed data, and because of both biological and chemical considerations, stations with a wide range of conductivity values were excluded. Thus analyses included only those stations for which the mean conductivity was < 500 mS m 1 for the 1980-1983 period. Of the 26 stations excluded on this basis, nine were in the upper reaches of estuaries and the rest were in rivers known to be salinising: the Berg (five sites), Breede (two sites), Gouritz (four sites), Gamtoos (two sites) and Sundays (three sites). Exclusion of these sites is justifiable on both biological and chemical grounds. Evidence (e.g. Williams, 1981; Hart et al., 1991) suggests that a truly freshwater biota becomes replaced by a brackish-water biota at about 3000-3500 mg 1-1 TDS (i.e. at about 500 mS m-l). Furthermore, complex changes occur in the chemical equilibria between major ions as total ionic concentration (i.e. TDS) increases. For instance, the solubility coefficients of Ca 2+ and HCO~ ions are much lower than those for Na + or C1- ions (see, for example, Day and Seely, 1988). Thus as TDS increases (as a result of evaporation, for instance),

so the proportions of Na + and C I - ions increase, while those of Ca 2+ and HCO3 ions decrease.

2.2.5. Dealing with the influence of discharge While the influence of factors such as geology on water chemistry is relatively constant over tens or hundreds of years, rainfall and discharge may have an effect over periods of hours or minutes. It is well known, for instance, that TDS, and even the proportions of the major constituents, change rapidly and predictably in any stream during a single spate (see, for instance, Britton et al., 1993). Where rainfall is seasonal, as it is over most of South Africa, there may also be predictable differences in water chemistry from wet season (winter: M a y - S e p t e m b e r in the south-west of the country) to dry season (also winter, in the north and east), although the magnitude of these effects is not known. Similar, but more exaggerated, effects occur as a result of prolonged drought, also a feature of many parts of South Africa. Again, the magnitude of these effects is unknown. Thus a major consideration was to exclude as far as possible the effects of varying discharge on TDS and on the proportions of the chemical constituents. In order to examine the complicating effects of varying discharge on water chemistry, principal components analysis (PCA) and detrended correspondence analysis (DCA) programs were run on that subset of records for which the reading of weir height (as a sun'ogate for discharge) was within 10% of the mean.

2.3. Selection of variables The chemical variables recorded in the D W A F database are: TDS; conductivity; pH; TAL; Na+; 9+ 2+ Ca- ; K+; Mg ; C1-; SO~-; F ; silicate; NOe+3-N; ammonium (NH4+); Kjeldahl nitrogen (KjN); POJ -P; and total phosphorus (TOT-P). Of these, KjN, TOT-P and TDS were discarded because too few data-points were available. All of the remaining variables are included in at least some analyses. An argument can be made for including all of these because each varies geographically to some extent and can, therefore, assist in grouping the sites. On the other hand, some variables (e.g. Na ÷ and C1-) are likely to be strongly intercorrelated and as such might swamp the information


J.A. Day et al. /Aquatic Ecosystem Health and Management 1 (1998) 183-197

provided by some of the lesser constituents that nonetheless have strongly geographical distribution patterns (e.g. F-: Lewis McCaffrey, University of Cape Town, South Africa, pets. comm.), varying at different spatial scales and in response to different processes. Thirdly, since clear regional differences, determined by lithology and precipitation, exist in the pattern of dominance of the major ions, an argument can be made for including a number of ionic ratios. Lastly, it can be argued that, since nutrient concentrations are often strongly dictated by the biota, these variables should not be included. Equally, though the biota is a natural component of the systems under examination, and therefore perhaps the distribution patterns of nutrients ought to be included. Preliminary analyses included nutrients, but these constituents were omitted from most further analyses because they tended further to complicate an already complex picture.

2.4. Data analyses A number of different approaches were used in an attempt to find relatively robust relationships between the variables.

2.4.1. Indirect gradient analysis: PCA, DCA and cluster analysis Two different techniques were used on a number of datasets in an attempt to detect underlying patterns in water chemistry, and specifically to resolve which, if any, of the variables were the best indicators of differences between stations. Values were converted from mg 1 i to mmol 1-1 (meq 1 J for alkalinity) where appropriate and three ratios were calculated for inclusion in the analyses. These are:CI-:(CI-+TAL) (where 'TAL' = total alkalinity);C1 :(CI - +SO~~+);Na+:(Na++Ca2+).The choice between PCA and correspondence analysis (CA, including DCA) depends largely on the underlying assumptions about the shape of the response variable to the hypothetical environmental variable(s) (the ordination axes). If the response is likely to be linear rather than unimodal, then PCA should be used in favour of CA. CA does, however, have the advantage of relative robustness against violation of this assumption, relying as it does on weighted averaging rather than multiple linear regression techniques. When biolo-

187

gical species (presence-absence or abundance) are the response variables, the choice between PCA and CA depends on the length of the environmental gradient over which samples were taken. If this is short then the response is approximately linear and PCA should be chosen. The shape of the response of chemical variables to an underlying gradient is not clear a priori, however. For this reason both analyses were performed on summer (October-March) and winter (April-) data extracted for the hydrological years 1980-1983. In addition, the mean weir height was calculated for all stations, regardless of season. Those stations with more than five readings not more than 10% greater or less than the average were included as a third data set. In all three analyses, pH was converted to the concentration of hydrogen ions and all 14 chemical and physical variables transformed using CANOCO version 3.10 (ter Braak, 1990). The data were negatively skewed and the transformation resulted in an approximately normal distribution. PO 3-, NO2-/NO3 and NH~ were excluded from all indirect gradient analyses. Finally, in the CA analyses, the option of 'detrending by fourth order polynomials' was chosen as an alternative to the often criticised 'detrending by segments' option (Jongman et al., 1987). Because this step was included, the technique is known as DCA. Ordination analyses rely to a large extent on the assumed pattern of response variable(s) to the underlying gradient. The ordination of chemical and physical variables is partly confounded by: 1. the fact that we have no a priori reason for choosing one or the other; and 2. the fact that the response variables probably each respond differently, perhaps even with bimodal or more complex distributions.

2.4.2. Cluster analysis of secondary watershed regions Cluster analyses were performed to obtain a description of the differences in water chemistry at the spatial scale at which water quality guidelines are likely to be implemented. Records from the 1980-1983 data set were grouped by secondary watershed (all variables averaged over the entire drainage region) and subjected to cluster analysis (PRIMER, Version 4: Plymouth Marine Laboratory


188

A so,. I.Cl

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I +15.0

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~sf

B


+

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-5.0 i

*~NI~ K

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* Na : Na + Ca

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~ Mg *l

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e="

I

I

I

I

i

i

i

t

i

i +I 1.0

r

*K * CI : CI + TAL

so;

CI

°H

Fig. 2. Detrended correspondence analyses: (A) biplot of the first two axes generated for the winter dataset, 1980-1983; (B) biplot of the first two axes generated for the summer dataset, 1980-1983; and (C) biplot of the first two axes generated for records within 10% of mean weir height, 1980-1893. 'Species' (i.e. chemical variable) scores are weighted mean sample scores•

Table 1 Details of the results of the ordinations. 'h' is the Eigen value and "c%' the cumulative percentage variance

Summer Winter Discharge-related

h c% h c% X c%

Detrended correspondence analysis Axis I Axis 2

Axis 1

Axis 2

0.043 42.6 0.043 38.9 0.084 44.0

0.756 75.6 0.704 70.4 0.75 75.0

0.186 94.1 0.164 86.8 0.159 91.8

0.033 75.5 0.038 73.1 0.034 75.8

Principal components analysis


189

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Fig. 3. Correlation biplot (standard centred PCA) of chemical variables and sites for the first two axes generated fi'om the PCA for the winter dataset, 1980-1983.

software package) using the Bray-Curtis similarity matrix. Fourteen chemical variables were included in the analysis.

3. Results

Fig. 2 plots the first two axes for DCA of all usable sites in winter (Fig. 2A), in summer (Fig. 2B) and at average weir height (Fig. 2C), each for the 1980-1983 hydrological years. The distance between chemical variables is a measure of their similarity (Euclidian distance) and the distance between stations approximates their X2 distance• In both cases a smaller distance indicates a greater similarity. Statistics for these analyses are given in Table 1. For PCA (Figs 3-7), the scaling methods used here (cf. ter Braak, 1990) require that the angles between the lines (chemical variables in Figs 4 - 6 ) be interpreted as a measure of their correlation (0 < 0 < 45 = correlation high (+); 45 < 0 < 90 =

Fig. 4. Correlation biplot (standard centred PCA) of chemical variables and sites for the first two axes generated from the PCA for the summer dataset, 1980-1983.

correlation low (+); 90 < 0 < 120 = correlation low (-); and 120 < 0 < 180 = correlation high (-). The distance between stations is a measure of their similarity (standardised Euclidian distance). The Eigen values (Table 1) for the analyses shown in Figs 3 - 5 are much higher than they are for Fig. 2, although they are based on identical data. The relative positions of the variables are virtually identical in Fig. 3 (winter), Fig. 4 (summer) and Fig. 5 (standardised for weir height). For convenience, subcatchments represented by the points in Fig. 5 are replotted in Fig. 6 so that the relationships between the subcatchments can be visualised. The directions of the major response variables are summarised in Fig. 7. A dendrogram of percentage similarity between sites is presented in Fig. 8. The first feature of note is that the sites all show a similarity of > 75%. Subcatchments represented in Group 1 are mostly in the southern interior; catchments in Group 2 are all in the south-west; catchments in Group 3 are almost all

190


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°

o~

°

• :" "it' )..;-.: • J°:a


H +1.0

.I • •

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Na : Na + Cac~: CI + TA L

"7

Fig. 5. Correlationbiplot (standard centred PCA)of chemical variables and sites for the first two axes generated from the PCA for records within 10% of mean weir height, 1980-1983. on the east coast or in the north; catchments in Group 4 are mostly in the eastern half of the country but also include the few data for the lower Orange River; catchments in Group 5 are mostly in the south. (Note that these five groups are not the same as the four shown in Fig. 7 or the five regions discussed later.)

4. D i s c u s s i o n

The chemistry of natural river waters is determined mostly by the chemical composition of the underlying rock and soil formations; by the length of time the water may be trapped underground before appearing as stream flow; by the opposing effects of evaporative concentration in dry conditions and dilution by rain; and by the uptake and release of chemical substances by the biota (for details see, for example, Stumm and

Morgan, 1981; Drever, 1982; Palmer and Cherry, 1984; Day and King, 1995).

4.1. Conductivity and the major ions Gibbs (1970) proposed that three factors, namely precipitation, concentration as a result of evaporation, and the chemistry of the substratum, can explain the proportions of the major ions in river waters. Fig. 9 shows Day's (1993) modification of Gibb's illustration of the relationships between ionic ratios [Na + : (Na + + Ca 2+) and C1- : (C1- + HCO3)], TDS concentration and the mechanisms controlling the major ion composition of natural river waters. Briefly, Gibb's explanation for this pattern is as follows (see Day and King, 1995 for details). Where the alkali earths (particularly Ca 2+ and Mg 2+) are present in the rocks over which water flows, they are dissolved by HCOf + H30 + ions


191

SUBCATCHMENTS A,B,C,D E,F,G,H,J,K L,M,N,P,Q,R,S T,U,V,W,X

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Fig. 6. Euclidean distance biplot for data represented in Fig. 5 (standard centred PCA, first two axes, records within 10% of mean weir height) but indicating the positions of the drainage region represented by each point.

HIGH Ca : Na HIGH pH HIGH ALKALINITY

• 3 ,.",':

CONDUCTIVITY /

J

HIGH CONDUCTIVITY

":.i-:~-..-.

LOW pH LOW ALKAUNITY HIGH Na : Ca

Fig. 7. Summary plot showing the scatter of points representing the stations on the plane of the first two axes of the standard centred PCA in Fig. 5. Arrows represent the directions in which the most important variables determine the positions of the sites (dots).

192

J.A. Day et al. / Aquatic Ecosystem Health and Management l (1998) 183-197 80

GROUP 1

g

I

90

I

I

GROUP ,

I G OUP


--

GROUP4

I

GROUP5

,

GROUP 3a

100

Fig. 8. Bray-Curtis similarity analysis for secondary drainage regions, all data 1980-1983.

I

~o4 i~

/

g

~ .~/

/ j '/

03 ,.J

0o') Q ILl

>, 0 o')

/ /

.

evaporation

V .... / of rainfall[ d:mr~n:nce" (~ 102.1 X I

d°wmalneasted

.J

10

0

HIGH

0.5

[Ca 2+ ] [HCO~ ]

1:0

HIGH

[Na+ ] [Ci" l

Fig. 9. The relationship between ionic ratios, TDS and the mechanisms controlling the major ion composition of natural river waters (adapted from Day, 1993 and from Gibbs, 1970).

resulting from the dissolution of C O 2 in the water. Thus waters with high proportions of Ca 2+, Mg 2+ and HCO~ ions (i.e. low ratios of Na + : (Na + + Ca 2+) and C1- : (C1 + HCOy)) are 'rock dominated' (central portion of the 'boomerang' in Fig. 9), their ionic proportions being largely dictated by the lithology of the rocks over which they flow, and their TDS values are in the intermediate range between 50 and 1000mgl-1 (about 10 and 150mSm-~). Because most European and North American waters fall in this category, it was inappropriately labelled 'world average river water'. In contrast, rocks containing mostly the alkali metals (particularly Na + and K +) are less soluble, even in the presence of the H C O [ buffering system, and, therefore, few ions leach from these rocks. Rain, mist, snow and dry fallout provide the greatest proportion of ions in these cases, which Gibbs calls 'precipitation dominated'. The major ions are Na + and C1-. (Close to the coast, the concentration of NaC1 is greatly increased by salt-laden onshore winds.) The TDS of the resulting solution is low. Thus the lower part of the 'boomerang' in Fig. 9 represents precipitation (rainfall) dominance. As water becomes more concentrated, for instance, as a result of evaporation, salts precipitate out, first CaCO3 and CaSO4. Ultimately only Na + and C1- ions



are left in solution. Thus the upper part of the 'boomerang' in Fig. 9 represents the effects of evaporation and crystallization (confusingly also called 'precipitation' by Gibbs) at higher TDS values. All of Gibb's data fell within the dashed lines of the 'boomerang' in Fig. 9. Day and King (1995) have shown that, for South African rivers, drainage regions A, C, T and V (see Fig. 1A), the major regions of igneous rock in South Africa, fall within the 'rock dominance' area of the 'boomerang'. A few stations in drainage regions L, M and N, which cover the south-eastern 'drought corridor', fall within the 'evaporation dominance' area; a few from drainage regions E, G and H, in the mesic south-west, fall within the 'rainfall dominance' area. All of the others fall to the right of the 'boomerang', indicating direct evaporation of rainfalldominated waters. Because calcium carbonate is the first salt to precipitate or crystallise out, the water contains fewer and fewer Ca > and HCOy ions as concentrations increase. As water becomes concentrated, the pattern of its major ions mimic more and more closely the pattern found in very dilute rainfall-dominated Na+/ C1 waters. Thus Na+/C1--dominated waters may range from very dilute to very concentrated, whereas (except in unusual circumstances) Ca2+/Mg2+/HCO3 waters will always be dilute. Ratios of major ions were thus included in the analyses because the proportions of the major ions are important indicators of both lithology and concentration. A further complication arises when organic acids such as the 'humates' reduce the pH of dilute (and ,therefore, poorly buffered) waters. The pH of most natural waters varies around neutrality because of the HCO~/CO~- buffering system (CO2 + H20 ~ HCO3 + H+). When little bicarbonate is present, the pH can be lowered to 4 o1" less by the weak organic acids, pH can also be elevated by photosynthesis but this usually occurs only in more strongly buffered and meso- or eutrophic waters where sufficient CO2 is available. Alkalinity, as a measure of HCOy/CO~- (and thus of the buffering capacity), and pH are, therefore, expected to vary together, being highest in rock-dominated waters and lowest in waters affected by organic acids. It is possible, then, to predict something about the proportions of major ions from first principles, and

193

also to explain patterns shown in the covariance biplots. Although we know that nutrient levels are strongly determined by biotic activities, we have little in the way of first principles, and too few biological data, to be able to predict or explain anything much about the concentrations of nutrients. The same is true for variations in fluoride and silicate ions, although fluoride at least is unlikely to be affected by biotic activity.

4.2. Indirect gradient analysis The most striking difference between the results of the DCA and the PCA lies in the low calculated Eigen values for all DCA analyses. Eigen values range between 0 and 1 and are a measure of the importance of that axis. In PCA the Eigen value refers to the fraction of the total sum of squares in the response variable data extracted by the axis, while in weighted averaging methods the Eigen value is a measure of the separation of response variable distributions along the ordination axis. Reliable Eigen values for ecological data are expected to be > 0.3. The Eigen values for the DCA are very low ( 0.7 for axis 1: Table 1), suggesting that a linear-response model is able to extract more structure from the data set than is the unimodal-response model applied in DCA. That the


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J.A. Day et al. /Aquatic" Ecosystem Health and Management 1 (1998) 183-197

plots in Figs. 3 - 5 are so similar provides confidence in the data and in the analyses. In particular, it suggests that standardising for weir height (as a surrogate for discharge) is an appropriate way to treat the data. In all cases, [silicate] correlates most strongly with the first axis (y-axis in this case), while conductivity, [K +] and/or [SO42-] correlate most strongly with the second (x-) axis. Alkalinity is inversely correlated with [H +] (i.e. pH converted to molar values), while [Ca 2+] and [Mg 2+] are closely correlated with each other and with alkalinity, [Ca 2+] slightly more so. [Na +] and ]C1-], again predictably, are correlated with each other, are weakly correlated with conductivity and are not correlated with [Ca 2+] or [Mg2+]. The following points are of interest with regards to the PCA: • [Silicate], which is normally low in fresh waters, is the most important response variable. This is a fascinating result because, although [silicate] is insignificantly low in most fresh waters, an examination of the DWAF database shows a clear north-south gradient in South Africa (pers. obs.), the highest [silicate] values being associated with the igneous rocks of the northernmost region. • Since all three ratios [C1-:(C1-+SO42+); Na+:(Na++Ca2+); C1 :(CI-+TAL)] correlate fairly closely with each other, and with the first axis, and the anion and cation ratios vary together, any one of them can provide a good deal of information about major-ion chemistry. • [SO 2-] is a major determinant of the patterns shown here. It would be worthwhile ascertaining if any of the subcatchments included in the present analyses are subject to acid mine drainage or to pollution from mine effluents. If this is not the case, [SO42-] is a useful discriminator. • Conductivity varies particularly with the major ions but little with pH, which is negatively correlated with alkalinity and [Ca2+]. Fig. 6 shows the subcatchment numbers of the points represented in Fig. 5. All of the south-western Cape catchments for which data are available (G, H, J4, K, L7-9, M, N4, P) fall in a distinct group well below the x-axis, suggesting that inorganic variables are sufficient to distinguish the rivers in this region. By examining Fig. 6 in conjunction with Fig. 7, it can be seen

that, although the south-western rivers cover the entire spectrum of conductivity values, they are all characterised by low pH, low alkalinity and a high ratio of Na : Ca or high [Na+]:([Na+]+[Ca2+]). Few clear patterns emerge for the other subcatchments, although catchments T, U, V and X tend to cluster in the top left-hand quadrant (i.e. low conductivities, major ions co-dominant or dominated by Ca 2+) and most of the Q and S sites are in the top right-hand quadrant (higher conductivities, high alkalinity, high pH).

4.3. Difficulties related to the analyses Because rivers are longitudinal systems, their water chemistry often reflects conditions pertaining upstream. The extent to which upstream conditions influence those further downstream depends largely on discharge, which varies with stream order as well as from season to season and year to year. Consider, for instance, two adjacent rivers, a large one rising in an easily eroded mountainous area and a smaller one (or one suffering from severe water abstraction) strongly influenced by local geology. The one with the greater discharge will probably show a chemical profile characteristic of its headwaters a great distance away (as is the case of the Orange River near the sea in the far north-west), while the smaller one may reflect local geology and climate along its length. For this reason, it may not be possible to divide the country's rivers into a few geographically defined regions that are useful for management purposes, at least not without taking into account altitude and stream order. A further complication may be related to the algorithms built into the software packages that are normally used for analyses of this sort. Most software of the type used here was designed for the analysis of biological data, particularly for grouping sites based on similarity of species assemblages, so presence or absence of taxa is an important consideration. In the present case, all of the variables are always present, albeit in different concentrations. This may explain the high level of similarity between all subcatchments in Fig. 6, but it is a disadvantage when trying to ascertain dissimilarity. The concentrations and proportions of many chemical variables are discharge-related, and discharge varies seasonally and inter-annually. Thus


~/

REGION5

195

measure of humates or polyphenolics, since many rivers of the southern Cape are acid and humicstained. These variables alone would probably provide an adequate delineation of regions that could be used for water quality guidelines. Inorganic variables are useful but the absence of biotic and physical variables is a significant gap.

4.4. Proposed regions for management of water chemistry


REGION3

REGION1 Fig. 10. Proposed regions for management of water quality based on DWAF secondary catchments (see also Table 2). A question mark indicates unclassified subcatchments.

any single analysis, even one that allows for these variations, cannot provide a complete picture of the range of variation in the concentration of any variable. In a semi-arid country such as South Africa, the biota is adapted not only to a certain range for each variable but also to a degree of variability, which is itself sometimes unpredictable. Although this issue has not been explored, and no attempt has yet been made to quantify it, variability itself may be an important feature of rivers, particularly in arid regions. It is not always possible to ascertain from the data themselves, which sites reflect some measure of pollution. It is known, for instance, that [SO~-] increases and pH decreases in areas affected by acid mine drainage but the extent to which these features in the present data base reflect pollution from mines is not known. In addition, it is not possible, without considerable analysis, to tell the time-course of pollution in these and other cases. Finally, the variables for which data are available in the DWAF database are all inorganic chemical variables. But some of the variables that are probably most important for delineating the kinds of regions that would be useful in the present case are physical or biotic. The most important of these would be some measure of turbidity and/or TSS (total suspended solids); diurnal temperature range and/or degreedays; dissolved organic carbon (DOC); and some

Demarcated regions must be suitably large for management purposes, including the provision of water quality guidelines for aquatic ecosystems. In this paper the assumption is made that, given the longitudinal nature of rivers, and the natural variations in water quality through the seasons and over periods of drought and flood, boundaries between regions are indistinct. More definite demarcation of boundaries (e.g. using tertiary or quaternary catchments) will require more detailed regional analysis of the data. In isolation, none of the analyses described above provides an adequate division of the country's rivers into geographical regions. Combining all of the analyses produced over the years for a variety of purposes does, however, suggest a small number of chemically and biologically distinct regions. For convenience, their boundaries are defined to coincide with the boundaries of DWAF subcatchments. These regions are delineated in Fig. 10 and the major features on which they are based are listed in Table 2. Some subcatchments are excluded from the proposed regions because they have too few rivers or too few data-points are available to allow even preliminary allocation to a particular region. It should be noted that this is a preliminary exercise and the boundaries of the regions will have to be examined before they can be used for management purposes. For example, Regions 1 and 3 (which more or less coincide with political boundaries of the Western and Eastern Cape, respectively) seem to have rivers that are distinct chemical and biological entities, but the precise geographical extent along the coast, as well as inland, still needs careful defining. Region 2 (the arid interior and the 'drought corridor' of the eastern Cape coast) may consist of two subregions. Data are so scarce for these regions, however, that it is not yet possible to address this

G, H, J4, K, L7-L9, M, N4, P

1, 2, 3a

4 Pure waters

Cape supergroup and late proterozoic groups

A, B 4

Catchments ~

Commonest Bray-Curtis groups b (Fig. 8)

Day and King's categories~ Groundwatera

Dominant geological formations~

Hydrobiological region ~ Limnological Region g

a D W A F s u b c a t c h m e n t n u m b e r i n g system. b B r a y - C u r t i s ordination. c D a y and K i n g (1995). d B o n d (1946). e T a n k a r d et al. (1982). t Harrison (1959). Allanson et al. (1990).

Southern and western coast (1)

Region

C 5

Karoo sediments

3 and 4 Highly mineralized chloride/sulphate waters

4

D3-D81,2,3a

Arid interior (2a)

D 5

Karoo sediment

3 Highly mineralized chloride/sulphate waters

1, 5

NI-N3, Q, R, S

E Cape drought corridor (2b)

E 2, 3

Karoo sediments; mixed basement complex and Cape supergroup on Natal coast

2 inland, 3 and 4 on coast Slightly saline chloride waters

T (excluding T3), U, V2, V4-V5, Wl-W3 3b, 4

East coast (3)

F, G (some C, L) 2

Karoo basalts 8 sediments; some Kaapvaal cover rocks in the north

1, 2 (few 3) Alkaline soda carbonate/ temporary hard carbonate waters

4

C, DI-D2

Upper Orange/Vaal (4)

l, 2, 3 Pure, temporary hard carbonate and alkaline soda carbonate waters Kaapvaal and Namaqualand basement complex; Kaapvaal cover rocks H, J, K, L 1,2

3b, 4

A, B, X

North-east (5)

Characteristics of the five p r o p o s e d regions (the region numbers are given in parentheses) for m a n a g e m e n t o f w a t e r chemistry b a s e d on D W A F s u b c a t c h m e n t boundaries (see Fig. 10). S u b c a t c h m e n t s E, F, J1-3, L I - 6 , T3, V1, V3, V6, V7, W 4 - 7 are excluded

Table 2


I

ga

,~

~"

"