This study was conducted to evaluate the wetland water quality (WWQ) over a period of 3 years and establish relationships between these qualities using Ramsar site Uchali Wetland, Pakistan (32 °33′N, 72 °01′E). WWQ data obtained were subjected to summary statistics, generalized linear model (glm), correlation, covariance and cluster analysis. The glm of the monthly mean water indices showed that the mean returned for pH (2.57) was not significant while means for others were significant (p < 0.05). The correlation analysis of the WWQ indices indicated that 56% of the pairing indices were inversely correlated while 44% were directly correlated and three piles of clusters of WWQ indices were distinct. The wetland water is not very safe for drinking but safe for other non-domestic uses since it contains fewer microbes and the water health model as arrived at in this study provides a management protocol for wetland water.

INTRODUCTION

A global quality standard for drinking water exists (WHO 1970, 1996) but wetland waters' uses transcend drinking. Wetland (WHO 1996) has been described as an area of Marsh, or fen, peat land or water which may be natural or artificial, permanent or temporary with static or flowing water and not more than 6 m depth. Active researches in Wetland quality assessment are on-going globally with the goal of developing a blue print on global wetland water quality standards (GWWQS) (Hatfield et al. 2004). Water remains the main component of the wetland ecosystem and it plays a major role in providing suitable habitat for aquatic life and water birds, retaining water for irrigation and domestic use by the farmers and the host community during dry times (Vincy et al. 2012). Despite the importance of the wetland water, comparisons of wetland water quality (WWQ) from site to site have been found impracticable (Credit Valley Conservation 2010) due to lack of known GWWQS. Research on WWQ abounds and includes, strategies for assessing the cumulative effects of wetland alteration on water quality (Brinson 1988), agricultural soil and water quality assessment, CO2 storage on wetland reserves (Servadio & Bergonzoli 2013), and water quality assessment of a tropical wetland Ecosystem (Vincy et al. 2012). Also, application of a model tree and evolutionary polynomial regression was adopted for sediment transport in pipes (Najafzadeh et al. 2016a). These works notwithstanding, there is need for the assessment of the water quality of the sites in order to establish the potentiality of the sites in supporting other expected systems aside from domestic uses. Indeed, optimal benefits as postulated by Arslan (2014) are not a function of the soil–water–plant balance alone; their interactions with the water birds are also essential. This work would thus provide a foundation for WWQ standards for the country as well as for GWWQS since the global standard is always from the conglomerates of the available individual national standards. It would also optimize the appropriation of the interdependence of wetland water qualities for sustainable management of any wetland. GWWQS would be useful in conserving the wetlands by providing indicators for and a guide against their over-exploitation and misuse. A few of the features of the supposed GWWQS should be that it is analysable, comparable with existing standards and statistically significant. The general linear model (glm) analysis was annexed to establish significance between means of different wetland water variables using n1 degrees of freedom. The correlation presents a half diagonal matrix of pairwise relationships between wetland water parameters while factor analysis was adopted to partition the wetland water into lower groups of unobserved variables called factors.

The objectives of this study were therefore to evaluate the quality of the wetland water over a period of 3 years, establish relationships between the water quality indices and determine factors controlling the water quality of the wetland.

METHODOLOGY

This study was conducted at Uchali Wetlands Complex (32 °29′N, 72 °14′E – Figure 1). It is located in a popular cup-shaped valley known as ‘Soon valley’ in the Salt Range of Punjab – Pakistan and has a mean elevation of 800 m above sea level. The annual rainfall of the Ramsar site wetland varies from 300 mm to 800 mm, and the relative humidity ranges from 22% to 85%. Temperatures of the study site fall between an average minimum of 0.5 °C in January to an average maximum of 36 °C in June. Marsh vegetation is confined to a few small patches along the lake's shore and there is a very rich growth of plankton in the lake. The catchment area of the lake comprises agricultural lands, forests and range lands and is also under regular degradation due to extensive agricultural practices, deforestation, forest fires and grazing.
Figure 1

A view (a) and map (b) of Ramsar site wetland (Uchali Wetland) (Adapted from PWP 2012).

Figure 1

A view (a) and map (b) of Ramsar site wetland (Uchali Wetland) (Adapted from PWP 2012).

From 2011 until 2013, data on bird species frequencies and bird frequencies were obtained using the point count method in January and February which represents the peak period of the migratory birds (Hostetler & Main 2001).

Three sampling points were established and water samples were collected and labeled accordingly. These sampling processes were repeated four times during the period at intervals of 20 days and in the morning. The water quality indices (Pondus hydrogenii (pH), electrical conductivity (EC), dissolved oxygen (DO), total phosphorus-P, nitrogen-N, suspended solids (SS)) were evaluated using established methods (Table 1).

Table 1

Indices and their sampling/measurement instruments

s/nIndicesUnitInstrumentsMethods
pH – pH meter APHA (1989), USEPA (2002)  
Conductivity mS Conductivity meter WHO (1996)  
DO mg O2 l−1 Winkler method APHA (1989)  
Total SS ppm Gravimetric method WHO (1996)  
Total Nitrogen mg l−1 Phenol disulfonic acid method using spectrophotometer CWT (2004), APHA (1998)  
Total Phosphorus mg l−1 Spectrophotometer, APHA (1989)  
Population  In point method (using binocular) CWT (2004), USEPA (2002)  
No. of species  In point method (using binocular) APHA (1989), USEPA (2002)  
Water size ha Measuring tape and passing method – 
10 Rainfall mm Rain gauge – 
s/nIndicesUnitInstrumentsMethods
pH – pH meter APHA (1989), USEPA (2002)  
Conductivity mS Conductivity meter WHO (1996)  
DO mg O2 l−1 Winkler method APHA (1989)  
Total SS ppm Gravimetric method WHO (1996)  
Total Nitrogen mg l−1 Phenol disulfonic acid method using spectrophotometer CWT (2004), APHA (1998)  
Total Phosphorus mg l−1 Spectrophotometer, APHA (1989)  
Population  In point method (using binocular) CWT (2004), USEPA (2002)  
No. of species  In point method (using binocular) APHA (1989), USEPA (2002)  
Water size ha Measuring tape and passing method – 
10 Rainfall mm Rain gauge – 

Note: Some of these methods were adopted from USEPA (2002), CWT (2004) and others as listed in the table.

Data obtained were analyzed using summary statistics (mean and variance) and a general linear model (glm) with 2(n 1) degrees of freedom (df) for years and 1 df for months. The means of the significantly different parameters were partitioned into different mean classes using both Duncan multiple range test (DMRT) for the year and least significant difference (LSD) for the months. Also, variance–covariance matrices, correlation analysis, cluster analysis of the variables using Mahalanobis distances (dij) and factor analysis were carried out. Mahalanobis distance (Grower & Ross 1969) is defined as ‘if there are v quantitative variates and xij are the values of the jth variate at the ith unit’. The water health model was computed using a multiple linear regression model (MLRM). The water health model was expressed as a linear relationship between DO (Y) and other parameters (pH – x1, conductivity – x2, Total Suspended Solids – x3, Total Nitrogen – x4, and Total Phosphorus – x5). 
formula
1
where x1–x2 are as defined earlier and represent the slope and b–f are the regression coefficients. The model was also tested for co-linearity and the stepwise regression analysis was done to determine the contributions of each of the model components. Also, the variance inflation factor (VIF) was used to capture the extent of the colinearity of each of the components. VIF is the reciprocal of tolerance which is given as: 
formula
2
and therefore, 
formula
3

The statistical packages adopted in this study were SAS (V8) and PAST.

RESULTS

Summary statistics and WWQ

The summary statistics of the water quality indices showed that total phosphorus had the lowest mean of 1.783 mg l−1 while the total population had the highest mean of 25,360.167 (Table 2). The variance of the indices followed the same trend (0.01 – total phosphorus – 42,891,102.700 – the population) but mean-variance ratios were not uniform (Table 2). This implied that the variability of the water quality and bird population indices was dependent on the sizes of their data but their ratios were distinct. The glm of the monthly mean water indices showed that the mean returned for pH (2.570) was not statistically significant (p < 0.05) while those returned for all other indices were statistically significant (Table 2). Means returned for DO (9.940 mgO2/l), conductivity (11.740 mS), total suspended solids (5.450 ppm), total nitrogen (8.000 mg l−1) and total phosphorus (8.00 mg l−1) were all significant (p < 0.05 – Table 3). The LSD of the monthly means of the water quality indices showed that means returned for January for all the indices were significantly higher than those of February (Table 3). This implied fluctuation in the quality of the wetland water within the study period. The glm analysis of annual water quality showed that the annual mean returned for all the years in respect of all the indices were significantly different from one another. The F statistics, 308.680, 2,031.240, 1,900.360, 4,175.520, 1,701.380 and 857.370 returned respectively for pH, DO, conductivity, total suspended solids, total nitrogen and total phosphorus were significant (p < 0.05). Similar grouping patterns were obtained by DMRT for both DO and total suspended solids (Table 3). Means of both indices returned for 2011 were significantly higher than those returned for 2013 and the least means were obtained for 2012. Also, the grouping patterns of pH, total nitrogen and total phosphorus were similar (Table 3). Means of these indices returned for 2013 were significantly higher than the means of year 2011 and the least significantly different means were those obtained for 2012. This implied that WWQ for 2012 was the lowest.

Table 2

Summary statistics of the water quality and bird population indices

 MeanVarianceN
pH 9.008 0.022 12 
Conductivity 52.173 10.375 12 
DO 5.794 0.053 12 
Total SS 381.250 1,965.295 12 
Total Nitrogen 2.320 0.021 12 
Total Phosphorus 1.783 0.010 12 
Population 25,360.167 42,891,102.700 12 
No. of species 38.833 22.333 12 
Water size 735.783 392.848 12 
Rainfall 15.450 300.777 12 
 MeanVarianceN
pH 9.008 0.022 12 
Conductivity 52.173 10.375 12 
DO 5.794 0.053 12 
Total SS 381.250 1,965.295 12 
Total Nitrogen 2.320 0.021 12 
Total Phosphorus 1.783 0.010 12 
Population 25,360.167 42,891,102.700 12 
No. of species 38.833 22.333 12 
Water size 735.783 392.848 12 
Rainfall 15.450 300.777 12 
Table 3

GLM and mean separation of the wetland quality indices.

SourcespHDOConductivityTotal SSTotal nitrogenTotal phosphorus
F statistics 308.680 2,031.240 1,900.360 4,175.520 1,701.380 857.370 
Year 1 9.033b 55.490a 5.580 415.500 2.393 1.830 
Year 2 8.823c 48.080c 6.095 321.500 2.128 1.648 
Year 3 9.168a 52.948b 5.708 406.750 2.440 1.873 
F statistics 2.570 9.940 11.740 5.450 8.000 8.000 
January 9.017 5.805 52.343 382.333 2.327 1.790 
February 8.998 5.783 52.002 380.167 2.313 1.776 
LSD 0.028 0.017 0.244 2.271 0.012 0.012 
SourcespHDOConductivityTotal SSTotal nitrogenTotal phosphorus
F statistics 308.680 2,031.240 1,900.360 4,175.520 1,701.380 857.370 
Year 1 9.033b 55.490a 5.580 415.500 2.393 1.830 
Year 2 8.823c 48.080c 6.095 321.500 2.128 1.648 
Year 3 9.168a 52.948b 5.708 406.750 2.440 1.873 
F statistics 2.570 9.940 11.740 5.450 8.000 8.000 
January 9.017 5.805 52.343 382.333 2.327 1.790 
February 8.998 5.783 52.002 380.167 2.313 1.776 
LSD 0.028 0.017 0.244 2.271 0.012 0.012 

LSD, least significant difference.

Correlation and factor analysis of the WWQ indices

The correlation analysis of the water quality indices indicated that 80% of the pairing indices were significant (p < 0.05). The correlation between rainfall and other water quality indices which were very low were not significant (Table 4). Generally, 56% of the pairing indices were inversely correlated while the remaining 44% were directly correlated. The highest but inverse correlations (0.990) were the correlation between conductivity and DO as well as that between conductivity and wetland water size. The least correlation (0.050) on the other hand was obtained between total phosphorus and rainfall (Table 4). Similarly, 45% of the variance covariance matrices were negative while the remaining (55%) were positive. None of the variance–covariance returned zero covariance, which indicates dependence on the variability of the indices. From these results it could be established that the majority of the water quality indices directly or indirectly affected the population of the bird species. Similarly, from the variance–covariance data, it was established that the indices are functionally independent. This connotes the sensitivity of the bird species to the WWQ and the randomness of the data, which makes it suitable for further analysis.

Table 4

Correlation and covariance matrices of Habitat and water quality

 PhConductivityDissolved O2Total SSTotal NTotal PPopulationSpeciesWater sizerainfall
pH 0.022 0.730** −0.790** 0.880** 0.950** 0.960** −0.680* −0.580* −0.670* −0.110 
Conductivity 0.350 10.375 −0.990** 0.970** 0.880** 0.860** −0.980** −0.950** −0.990** 0.160 
Dissolved O2 −0.030 −0.730 0.053 −0.980** −0.930** −0.910** 0.980** 0.940** 0.980** −0.220 
Total SS 5.830 137.840 −9.990 1,965.295 0.970** 0.960** −0.940** −0.880** −0.940** 0.090 
Total N 0.020 0.410 −0.030 6.190 0.021 1.000 −0.850** −0.780** −0.840** 0.070 
Total P 0.010 0.280 −0.020 4.360 0.010 0.010 −0.830** −0.760** −0.820** 0.050 
Population −663.750 −20,711.580 1,466.440 −27,3725 −804.930 −556.610 4,289,1102.700 0.960** 0.980** −0.230 
Species −0.410 −14.480 1.020 −185.140 −0.530 −0.370 29,633.300 22.333** 0.970** −0.350 
Water size −2.000 −63.450 4.460 −827.780 −2.400 −1.670 127,859.00 90.520 392.848 −0.240 
Rainfall −0.280 8.770 −0.860 67.180 0.180 0.090 −26,517.830 −28.550 −83.700 300.777 
 PhConductivityDissolved O2Total SSTotal NTotal PPopulationSpeciesWater sizerainfall
pH 0.022 0.730** −0.790** 0.880** 0.950** 0.960** −0.680* −0.580* −0.670* −0.110 
Conductivity 0.350 10.375 −0.990** 0.970** 0.880** 0.860** −0.980** −0.950** −0.990** 0.160 
Dissolved O2 −0.030 −0.730 0.053 −0.980** −0.930** −0.910** 0.980** 0.940** 0.980** −0.220 
Total SS 5.830 137.840 −9.990 1,965.295 0.970** 0.960** −0.940** −0.880** −0.940** 0.090 
Total N 0.020 0.410 −0.030 6.190 0.021 1.000 −0.850** −0.780** −0.840** 0.070 
Total P 0.010 0.280 −0.020 4.360 0.010 0.010 −0.830** −0.760** −0.820** 0.050 
Population −663.750 −20,711.580 1,466.440 −27,3725 −804.930 −556.610 4,289,1102.700 0.960** 0.980** −0.230 
Species −0.410 −14.480 1.020 −185.140 −0.530 −0.370 29,633.300 22.333** 0.970** −0.350 
Water size −2.000 −63.450 4.460 −827.780 −2.400 −1.670 127,859.00 90.520 392.848 −0.240 
Rainfall −0.280 8.770 −0.860 67.180 0.180 0.090 −26,517.830 −28.550 −83.700 300.777 

Note: Upper diagonal matrix is the correlation values while the lower diagonal matrix is the covariance matrix. * = Significant mean at 0.05; ** = significant mean at 0.01.

The essence of factor analysis in the present study was to reduce a number of correlating indices to a few manageable factors through a linear correlation of observed factor with a few other unobserved factors. Three main indices (water body size (ha), pH of the water and rainfall) have been isolated by the factor analysis (Table 5). The water body size returned the highest (0.898) factor loading for factor 1 with a proportion of 51% (Table 5). This was followed by the pH which returned the highest (0.932) factor loading for the second factor with the proportion of 38% and cumulative proportion of 89%. The third water quality index was rainfall with the factor loading of 0.982 and proportion of 11%. Other factor loadings are species, individual, total phosphorus, DO, total nitrogen, but their contributions were very insignificant because the cumulative proportion of the first three factors was 100%. It could therefore be established that wetland water body size, its pH and rainfall are the main determinant of the wetland quality.

Table 5

Factor pattern matrix (factor loadings)

 Factor 1Factor 2Factor 3Factor 4Factor 5Factor 6Factor 7Factor 8Factor 9Factor 10
pH −0.335 0.932 −0.111 0.006 0.031 −0.069 −0.015 0.000 0.000 −0.001 
Conductivity −0.879 0.471 0.047 0.037 0.039 −0.018 0.010 0.006 0.008 0.001 
Dissolved O2 0.808 −0.572 −0.124 −0.043 −0.005 −0.042 −0.019 0.012 0.001 0.000 
Total SS −0.733 0.680 0.011 0.019 −0.006 0.000 0.000 0.001 0.002 0.008 
Total N −0.552 0.830 0.033 0.007 −0.039 0.044 0.038 0.000 0.001 0.000 
Total P −0.523 0.847 0.015 −0.032 −0.022 0.074 −0.009 0.000 −0.001 −0.001 
Individual 0.886 −0.427 −0.120 −0.035 0.124 −0.011 −0.002 0.000 0.000 0.000 
Species 0.898 −0.332 −0.229 0.154 −0.019 −0.004 0.001 0.000 0.000 0.000 
Ha 0.898 −0.414 −0.129 −0.053 −0.043 −0.002 0.013 0.000 0.007 0.002 
Rainfall −0.156 −0.055 0.982 −0.001 −0.001 0.002 0.000 0.000 0.000 0.000 
Proportion 0.508 0.375 0.108 0.003 0.002 0.001 0.000 0.000 0.000 0.000 
Cumulative proportion 0.508 0.883 0.991 0.994 0.996 0.998 0.998 0.998 0.998 0.998 
 Factor 1Factor 2Factor 3Factor 4Factor 5Factor 6Factor 7Factor 8Factor 9Factor 10
pH −0.335 0.932 −0.111 0.006 0.031 −0.069 −0.015 0.000 0.000 −0.001 
Conductivity −0.879 0.471 0.047 0.037 0.039 −0.018 0.010 0.006 0.008 0.001 
Dissolved O2 0.808 −0.572 −0.124 −0.043 −0.005 −0.042 −0.019 0.012 0.001 0.000 
Total SS −0.733 0.680 0.011 0.019 −0.006 0.000 0.000 0.001 0.002 0.008 
Total N −0.552 0.830 0.033 0.007 −0.039 0.044 0.038 0.000 0.001 0.000 
Total P −0.523 0.847 0.015 −0.032 −0.022 0.074 −0.009 0.000 −0.001 −0.001 
Individual 0.886 −0.427 −0.120 −0.035 0.124 −0.011 −0.002 0.000 0.000 0.000 
Species 0.898 −0.332 −0.229 0.154 −0.019 −0.004 0.001 0.000 0.000 0.000 
Ha 0.898 −0.414 −0.129 −0.053 −0.043 −0.002 0.013 0.000 0.007 0.002 
Rainfall −0.156 −0.055 0.982 −0.001 −0.001 0.002 0.000 0.000 0.000 0.000 
Proportion 0.508 0.375 0.108 0.003 0.002 0.001 0.000 0.000 0.000 0.000 
Cumulative proportion 0.508 0.883 0.991 0.994 0.996 0.998 0.998 0.998 0.998 0.998 

Cluster analysis of the water quality and population indices

The goal of cluster analysis as used in this study was to technically organize arrays of information provided by the water quality indices into manageable and meaningful piles. The water quality and population indices of the wetland (Table 6) can be organized based on the cluster analysis into a 9 (n 1) Mahalanobis distances group. Similarly, the monthly water quality indices (Table 6) were organized into an 11(n − 1) Mahalanobis distance dij group. From the Mahalanobis distance, dij, of the water quality indices, it could be established that 80% of the indices were related while 20% were dissimilar. Also, three piles of clusters were distinct and are (i) total nitrogen, total phosphorus, DO, rainfall, conductivity, total SS, individuals (population), (ii) wetland size and (iii) number of species. These piles showed that water quality indices were significantly related to the bird population. Also, due to complex relationships, it was difficult to obtain a clear cut classification for the temporal variability of water quality. However, the Mahalanobis distances classification of the temporal variability of the water quality partitioned the monthly quality into two main piles. These were the water quality indices for both January and February, 2012 and those of other periods (Table 6) and the two groups eventually merged since it is a single tree clustering. This implied that the water quality in both 2011 and 2013 are similar to each other in terms of quality. From this cluster analysis, it could be established that water quality indices are related to one another in one way or the other.

Table 6

Agglomeration schedule for the months, water quality and population variables

StepsTimes
Water quality and population indices
Object 1Object 2DistanceObject 1Object 2Distance
J013A J013B 0.372988 Total Nitrogen Total Phosphorus 0.000262 
J011A J011B 0.44818 pH DO 0.001588 
J012A F012B 0.490 pH Total Nitrogen 0.004 
F013A F013B 0.557 Conductivity Species 0.009 
F011A F011B 0.579 pH Rainfall 0.014 
J012A F012B 0.697 pH Conductivity 0.031 
J012A J012B 0.760 Total SS Water size (ha) 0.158 
J013A F013A 1.434 pH Total SS 0.455 
J011A J013A 3.704 pH Individuals 14.689 
10 J011A F011A 5.167    
11 J011A J012A 13.238    
StepsTimes
Water quality and population indices
Object 1Object 2DistanceObject 1Object 2Distance
J013A J013B 0.372988 Total Nitrogen Total Phosphorus 0.000262 
J011A J011B 0.44818 pH DO 0.001588 
J012A F012B 0.490 pH Total Nitrogen 0.004 
F013A F013B 0.557 Conductivity Species 0.009 
F011A F011B 0.579 pH Rainfall 0.014 
J012A F012B 0.697 pH Conductivity 0.031 
J012A J012B 0.760 Total SS Water size (ha) 0.158 
J013A F013A 1.434 pH Total SS 0.455 
J011A J013A 3.704 pH Individuals 14.689 
10 J011A F011A 5.167    
11 J011A J012A 13.238    

Water health model

The multiple linear model of the water health was given as: 
formula
4
where x1 = pH, x2 = conductivity, x3 = Total suspended solids, x4 = Total nitrogen and x5 = Total phosphorus.
The model statistics returned for this model include, the F-statistics of 464.97, the coefficient of determination, R2 of 0.995 and the standard error that ranged between 0.0035 (Total suspended solids) and 2.395 (intercept). The corrected Akaike information criteria (AICC) returned was 0.521 while the Durbin–Watson Statistics was 3.04. The multi-colinearity diagnosis indicated the presence of high colinearity among the model components. Further step-wise regression analysis showed that the cause of the colinearity was the conductivity and both total nitrogen and Total phosphorus were found to contribute nothing to the health model (Table 7). Based on the contributions of the model components, the water health model can be given as: 
formula
5
Table 7

Water health model and model statistics

s/nModelR2SEF statisticsRemarks
1 Y= 9.457 0.070x1 0.971 0.004 363.473** 0.029 34.483 Excluded variables are x2, x3, x4 and x5 
2 Y= 9.577 0.053x1 0.435x2 0.988 0.107 469.603** 0.929 1.076 Excluded variables are x3, x4 and x5 
3 Y= 5.949 0.042x1 1.169cx2+ 0.529x3 0.996 0.133 831.047** 0.992 1.008 Excluded variables are x4 and x5 
s/nModelR2SEF statisticsRemarks
1 Y= 9.457 0.070x1 0.971 0.004 363.473** 0.029 34.483 Excluded variables are x2, x3, x4 and x5 
2 Y= 9.577 0.053x1 0.435x2 0.988 0.107 469.603** 0.929 1.076 Excluded variables are x3, x4 and x5 
3 Y= 5.949 0.042x1 1.169cx2+ 0.529x3 0.996 0.133 831.047** 0.992 1.008 Excluded variables are x4 and x5 

** = Significant mean at 0.01.

where x1 = pH, x2 = conductivity, x3 = Total suspended solids.

The model statistics for this model include, coefficient of determination R2 of 0.996, F–statistics of 831.047 and standard error of 0.133. All these statistics were significant (p < 0.05) and the variance inflation factor (VIF) for this model was 1.008 (Table 7). This VIF falls within the acceptable level of <10 and it was less than the VIF returned for the relationship between DO and conductivity. A similar predictive model was adopted for estimating maximum scour depth around piers with debris accumulation (Najafzadeh et al. 2016b).

From the above, it is pertinent to note that the water health model could sufficiently be predicted from the basic water indices and that the use-based indices does not constitute an important component of the model. Also, the presence of colinearity was established for the model but the standard errors of the estimates do not tend to infinity, hence, the model is acceptable.

DISCUSSION AND CONCLUSION

Our study established a higher pH status than the one reported in Vincy et al. (2012) and Jan et al. (2014). This pH which is a measure of acid–base equilibrium is outside the WHO threshold of 6.5–8.0 for drinking water (WHO 1996). Meanwhile, Jan et al. (2014) had established a strong but inverse relationship between pH and some selected microbes in Hokersar wetland, Himalayas. This implied that the wetland water is not very safe for drinking but safe for other non-domestic uses since it contains fewer microbes. Also, the EC total phosphate and DO as established in this study were higher than Vincy et al. (2012) and Jan et al. (2014). The high DO as established in our study might be accounted for by high abundance of microbes in the water body. This is because a significant relationship between microbes and DO has been established in urban California streams (Hall & Anderson 2013). Two microbes, Escherichia coli and Clostridium perfringens have been identified to be present in the wetland water (Imran et al. 2012). The total suspended solids and total nitrogen were however less than those established in these two other studies (Vincy et al. 2012; Jan et al. 2014). Some of the elements were higher than those recorded elsewhere but they still fall within the safety range for the use of water for drinking and other domestic uses. Conductivity which is said to be an indication of the total dissolved ions of a water body has a safety range of 0–70 μS/cm (Sverdrap et al. 1942). The inverse relationship between conductivity and DO as established in this study is in concord with Vincy et al. (2012). The DO as established in this study meets the European standard for drinking water which is 5 mg/l (WHO 1970). The high values of some of the water quality indices recorded might have been due to the high level of the compounds' accumulation from the surrounding agricultural activities. This is in agreement with Manikannan et al. (2012) and Bird & Day (2014) and the safe level of phosphorus as obtained in this study is an indication of non-susceptibility of the waterbody to eutrophication, which is good for the water birds (Mayumba et al. 2009).

Three main WWQ indices (pH, water body size and rainfall) have been isolated in our study. Rainfall has similarly been established as a major determinant of wetland elsewhere (Acreman 2003). Although the correlations of rainfall with other variables were low and insignificant due to lack of rainfall data during the peak of the wetland activities, it remains the major source of wetland water. Indeed, a slight change in rainfall and accompanying hydrological processes may be said to result in alteration of the wetland process and associated services (CWT 2004). This might be linked to the fact that wetland water is provided both directly and indirectly through rainfall. Directly is when rain falls directly onto the water body, while indirectly is through the run-off from the rainfall that fell elsewhere and found its way to the wetland. Similarly, pH was found to be one of the factors governing the occurrence and distribution of water borne conidial fungi (Jan et al. 2014), hence our study is in agreement with the finding. The pH had also been recognized as a primary indicator of some of the other water qualities such as conductivity and DO (Mayumba et al. 2009). A Ramsar site wetland has been described as an important bird area (Manikannan et al. 2012). Wetland size was established in our study as one of the determinants of WWQ because large water body size have been described as compact and has extensive surface for water bird accommodation (Moreno-Mateos et al. 2009).

The temporal variability of the WWQ as established by the cluster analysis of the monthly water quality could be linked to the temporal variation and stratification in the activities and rainfall pattern over the study periods (Boeckman & Bidwell 2007). These results however contrast with Detenbeck et al. (1996) who established stability in the water quality except for nitrogen. This difference could be because of a difference in the study sites and activities around the water body in each of the sites. The parsimonious status of the water health model indicated that basic water quality indices are the essential ingredients in the determination of water health rather than use-based quality indices.

CONCLUSIONS

  1. WWQ is a major index of the three indices of wetland health assessment. Correlation analysis, as established in this study, presents a sustainable approach to the WWQ assessment through reduced sampling. Reduced sampling is required for reduced parameters since relationships between the indices have been established. Also, the water health model as arrived at in this study provides a management protocol for wetland water.

  2. The identification of three important WWQ indices for sustainable sampling is noteworthy. The implication of this for conservation is that adequate attention needs to be given to annexing the interdependence of these three WWQ indices for sustainable management of any wetland. If the water quality is beyond bearable threshold level for the water birds, they would be harmed and so the wetland water size equally affects the water quality.

  3. It could also be established that GWWQS is unavailable or unknown, thus several quality standards abound. This is inimical to wetland water conservation given the extent of damage that wrong/misleading information can cause.

  4. It is therefore recommended that the WWQ indices established in this study can be annexed to improve the quality of the wetland water.

Acknowledgments

The authors appreciate the support provided by the WWF – Pakistan to Hafiz M. Baksh and the TWAS-USM Postdoctoral Fellowship provided to Taofik O. Dauda.

REFERENCES

REFERENCES
Acreman
M. C.
2003
Wetlands and Hydrology
.
MedWet Publication 9
.
Tour du Valat
,
France
.
American Public Health Association – APHA
1989
Standard Methods for the Examination of Water and Wastewater
, 17th edn,
APHA
,
Washington DC
, p.
52
.
American Public Health Association – APHA
1998
Standard Methods for the Examination of Water and Wastewater
, 20th edn,
APHA
,
Washington DC
, p.
102
.
Boeckman
C. J.
Bidwell
J. R.
2007
Spatial and seasonal variability in the water quality characteristics of an ephemeral Wetland
.
Proceedings of Oklahoma Academy of Science
87
,
45
54
.
Clean Water Team-CWT
2004
Conductivity/Salinity Measurement Principles and Methods, DQM IP-3.1.3
.
in The Clean Water Team Guidance Compendium for watershed Monitoring and Assessment
.
Division of water Quality, California State Water Resources Control Board (SWRCB)
,
Sacramento, CA
.
DQM-IP-3.1.3 (EC)V2g-p, 9 pp
.
Credit Valley Conservation
2010
Monitoring Wetland Integrity within the Credit River Watershed: Wetland Hydrology and Water Quality 2006–2008
.
Credit Valley Conservation
,
vii + 79 pp
.
Grower
J. C.
Ross
G. J. S.
1969
Minimum spanning trees and simple linkage cluster analysis
.
Journal of the Royal Statistical Society
18
(
1
),
54
64
.
Jan
D.
Mir
T. A.
Kamilli
A. N.
Pandit
A. K.
Aijaz
S.
2014
Relationship between fungal community and Physico-chemical characteristics in the Hokersar Wetland, Kashmir Himalayas
.
African Journal of Microbiology Research
8
(
4
),
368
374
.
Hatfield
K.
Annable
M.
Cho
J.
Rao
P. S. C.
Klammer
H.
2004
A direct passive method for measuring water and contaminant fluxes in porous media
.
Journal of Contaminant Hydrology
75
(
3
),
155
181
.
Doi:10.1016/j.jconhyd.2004.06.005.
Hostetler
M. E.
Main
M. B.
2001
Florida Monitoring Program: Point Count Method to Survey Birds
.
University of Florida Cooperative Extension Service, Institute of Food and Agriculture Sciences, EDIS
,
Gainesville
, p.
45
.
Imran
U.
Ahmad
K.
Zafar
A.
2012
Coliforms and halophiles pollution in surface and sub-surface water of Salt Range Wetlands, Punjab, Pakistan
.
Rec. Zool. Surv. Pakistan
21
,
42
46
.
Najafzadeh
M.
Laucelli
D. B.
Zahiri
A.
2016a
Application of model tree and Evolutionary Polynomial Regression for evaluation of sediment transport in pipes
.
Water Engineering
21
(
5
),
1956
1963
.
Doi:10.1007/s12205-016-1784-7.
Najafzadeh
M.
Balf
M. R.
Rashedi
E.
2016b
Prediction of maximum scour depth around piers with debris accumulation using EPR, MT, and GEP models
.
Journal of Hydroinformatics
18
(
5
),
867
884
.
DOI: 10.2166/hydro.2016.212.
Pakistan National Wetlands Policy (PWP)
2012
The Ministry of Climate Change's Pakistan Wetlands Programme. Islamabad, Pakistan
.
Servadio
P.
Bergonzoli
S.
2013
Agricultural soil and water quality assessment and CO2 storage on wetland reserve
.
Journal of Environmental Protection
4
,
20
26
.
Sverdrap
H. H.
Johnson
M. W.
Fleming
R. H.
1942
The Oceans: Their Physics, Chemistry and General Biology
.
Prentice Hall
,
New York
, p.
247
.
USEPA
2002
Methods for Evaluating Wetland Condition I. Introduction to Wetland Biological Assessment
.
Office of water, US Environmental Protection Agency
.
Washington, DC
.
EPA-822-R-02-014
, p.
37
.
Vincy
M. V.
Rajann
B.
Pradeep
K. A. P.
2012
Water quality assessment of a tropical wetland ecosystem with special reference to Backwater Tourism, Kerala, South India
.
International Research Journal of Environment Sciences
1
(
5
),
62
68
.
WHO
1970
European Standards for Drinking – Water
, 2nd edn.
World Health Organization
,
Geneva
, p.
58
.
WHO
1996
pH in Drinking-water in WHO
.
Background documents for development of WHO Guidelines for Drinking – Water Quality
.
World Health Organization
,
Geneva
.
WHO/SDE/WHO/03.04/12. 2nd edn, 2, pp. 35–42
.