There are several principal driving forces behind the damaging coastal water resources depletion in many countries, including: high population growth, degrading water resources due to overexploitation and contamination, lack of awareness among local beneficiaries regarding sustainable management, and deficient government support and enforcement of conservation programs. To ensure a water resource system is productive in coastal areas, holistic and comprehensive management approaches are required. To address the aforementioned issues, a combined methodology which considers anthropogenic activities, together with environmental problems defined as the Overall Susceptibility Socio-Ecological System Environmental Management (OSSEM) has been investigated. The OSSEM model has been applied successfully in Spain based upon daily time series data. This research is ground breaking in that it integrates the OSSEM model in a geographic information system (GIS) environment to assess the groundwater contamination based on annual time series data and the assessment of system management by means of an overall susceptibility index (OSI). Centered on OSI indicators, the renewal, salinization and water deficit potentials in the Talar aquifer were estimated to be 4.89%, 4.61%, and 3.99%, respectively. This data demonstrates a high susceptibility in terms of environmental pollution, salinization, and water deficit.

INTRODUCTION

The Talar basin is situated between 35°54′0″–36°47′0″ latitude, and 52°35′10″–53°24′7″ longitude specifically in Mazandaran province to the south of the Caspian Sea, Northern Iran. The water resources in the aforementioned basin are influenced by diverse activities, but overwhelmingly via agriculture, which accounts for more than 60% of the area's total water use. Rice, grain, fruits, cotton, tea, tobacco, and sugarcane are cultivated in the Talar basin, principally in the lower reaches, bordering the Caspian shore. In addition, oil wealth has motivated industrial development across Mazandaran province, notably in food processing, cement manufacturing, textiles, and fishing (Soleimani et al. 2008). Furthermore, the area is a leading tourist destination due to its proximity to the capital of Iran, Tehran, situated to the south. As a direct result of the grand scale of activity in the basin, and the resulting mediocre quality and high sediment load in superficial water resources, important abstractions from the underlying aquifer are required to satisfy escalating demands, a consequence of which is water stress. This stress has resulted in the deterioration of the freshwater resources in terms of both (aquifer overexploitation, dry watercourses), and quality (eutrophication, organic matter pollution, saline intrusion).

The Talar basin is characterized by two linked subsystems including anthropogenic and the environmental pressures. To investigate the first subsystem it is necessary to obtain sufficient water of an acceptable quality. In addition, there is always a challenge raised from the human effects on natural environmental conditions (Gentile et al. 2001; EPA 2008). Water availability, and its quality in Talar basin, as with many other similar coastal lagoons, depends decidedly on anthropogenic activities and environmental stressors to nature (Romo et al. 2005). The pressures from agricultural activities can be introduced in five components: (i) increasing of nutrients and productivity (Bricker et al. 2003); (ii) high pesticide loading; (iii) water salinity variability (McLusky & Elliott 2007); (iv) water resources hydrodynamics; and (v) reduction of water resources availability (Oliveira et al. 2006; Perilla et al. 2012). Gohari et al. (2013) stated that for sustainable water resources management, minimizing agricultural water demand by changing crop patterns is the best solution in the Talar basin. Historically, there are several methods that are suitable for the assessment of water quality in terms of irrigation. As an example, the Wilcox et al. (1954) and Schoeller (1962) methods are two of those methods. Also, for the assessment of water quality, the morphological, biological, and hydrological parameters are employed. It is understood that the ecological status of the environment can be studied with three measures: an environmental monitoring program, pollutant emission monitoring networks, and multi-parameter methodologies (Bricker et al. 2003; McQuatters-Gollop et al. 2009; Perilla et al. 2012). To achieve a stable and well-managed water resources system, it is indispensable to use alternatives in which the main purpose is to reduce the amount of special nutrient load in the system. Pollution monitoring programs are focused on the description and identification of point and non-point sources. These systems are nevertheless expensive and time consuming in terms of design, procurement, installation, and maintenance.

State changes collectively with pressures and their effects are two elements which are linked through investment, qualitative, and quantitative links (McQuatters-Gollop et al. 2009). Integrated multidisciplinary systems attempt to identify pollution and its sources (Bricker et al. 2003; Llorens et al. 2009; Thieu et al. 2009; Nobre et al. 2010). The study of sustainability can be straightforward when environmental factors and water deficit are correlated. A number of the environmental factors, such as environmental pollution and salinization are influenced by natural, anthropogenic, and hydrodynamic nutrient load (Ferreira et al. 2005) which are in turn related to water renewal (Perilla et al. 2012). The prime management alternative consists of the reduction of the nutrient load related to human activities. Environmental purposes call for more comprehensive and cost-effective programs. For the abovementioned reasons, some hydrodynamic management systems have been applied and explained via numerical models (Monsen et al. 2002; Ferreira et al. 2005; Cucco & Umgiesser 2006; Oliveira et al. 2006; Mudge et al. 2008). According to Perilla et al. (2012), it is expected to have a supplementary and a higher degree of environmental pollution in coastal lagoons than in other areas which have experienced a shorter time period of environmental pollution. Conversely, it is essential to obtain enough data to achieve reliable results. Salt, in the form of fertilizers, increase crop yield and is included as one of the main elements for crop growth, as it can stimulate the development of the plant and also increase tolerance (Awad et al. 1996; Guohua Xu 1999). Then again, the existence of high concentrations of salt in water bodies such as lagoons can be a problem for agricultural usage and irrigation. The main problem caused by the existence of high salinity in a coastal area is the resulting toxic conditions. Poor quality of irrigation water can give rise to high concentrations of ions and trace elements, such as heavy metals in the soil and water (Nishanthiny et al. 2010). According to Wilcox et al. (1954), water quality in terms of suitability for irrigation, is categorized into 16 levels. These situations produce an effect on growth, crop tolerance, and the ability of the crops to restrict salt transport to the roots (Tarchitzky 2005). In this sense, taking into account the salinity susceptibility in coastal lagoons for establishing water restrictions in agricultural uses is important (Perilla et al. 2012; Avilés et al. 2013). In the majority of cases, poor water management and limitations in water availability can cause glitches in agriculture and irrigation systems. In addition, the ions such as Na, Ca, Mg, Cl and their movement and distribution in wells as a result of variable climate and hydrological modifications (Cucco & Umgiesser 2006) can transform the morphological and hydrological characteristics of an aquifer. Secondary salinization needs to be diminished by appropriate water management, thereby obtaining sustainable agriculture (Tarchitzky 2005; Kitamura et al. 2006; Finnegan et al. 2014). Environmental management requires updated data with minimum possible factors that can illustrate the whole characteristics of the basin (Abdelrhman 2005). According to De Lange et al. (2010), a good system can demonstrate the susceptibility of nature on a large scale. Perilla et al. (2012) is the first who applied the Overall Susceptibility Socio-Ecological System Environmental Management (OSSEM), to show the susceptibility of the groundwater system at large scales. The vulnerability of a system arises from its spatial possibility of a matter occurrence (Perilla et al. 2012). Nonetheless, in the OSSEM model, the reference values have some shortcomings, such as not being standard enough, and their quantification also gives rise to conflicts among decision makers about the criteria used (Bouleau et al. 2009). The salinity potential factor has been employed based on the domains suggested in different research, specifically by Tarchitzky (2005), Kitamura et al. (2006), Perilla et al.(2012), and Park et al. (2014).

Data availability at a daily scale is the main predicament associated with the OSSEM model in many regions. The aim of this study is to adapt the Perilla et al. (2012) model in the Talar aquifer, based on annual data and three criteria including the susceptibility of the system to pollution, salinity changes, and water shortage as a result of anthropogenic activities and natural pressures. The originality of this paper is to provide a methodology for groundwater quality assessment based on OSSEM model using annual time series data. The model integrates OSSEM and geographic information system (GIS) tools to assess the potential socio-ecological effects that relate to the groundwater used for agriculture.

Methodology

Methodological strategy (OSSEM)

The OSSEM was developed by Perilla et al. (2012) and first applied employing daily data in the Albufera lagoon, Valencia, Spain. The scope of this study is to adapt the OSSEM model integrated with GIS tools to annual time series data for the Talar aquifer in Iran. Here, the methodology is described in four steps.

Step 1. System description

The model allows the linking of socio-ecological stressors and their potential impacts in coastal lagoons. In this stage, it is necessary to recognize the activities, processes, or human and natural conditions that can directly or indirectly affect the accomplishment of the model purpose. Perilla et al. (2012), calculated these end-points at a daily scale, but for the Talar aquifer there is no data available at this temporal resolution. To adapt the OSSEM model for the Talar aquifer, step 2 was performed.

Step 2. OSSEM customization

It can be proven that by having the average and the last recorded data for an annual time series, the concept of renewal potential (Equation (1)) can be achieved 
formula
1
in which is renewal potential of the system, is the initial value of the concentration introduced into the system, and is the value at the end of the day, 24 h.
Considering a time series for one variable (i.e. , , , ) recorded at a diurnal temporal resolution for a specific duration, the average value for the time series is obtained from Equation (2) 
formula
2
The 24 h period potential can be computed from Equation (3) 
formula
3
In Equation (3), instead of , the average A can be used. Then, substituting A in place of , Equation (3) can be written as Equation (4) 
formula
4
The difference between these two equations is obtained from Equation (5) 
formula
5
Substituting the right-hand side of Equation (2) instead of A, in Equation (5) and summarizing it, Equation (6) is obtained 
formula
6
Rearranging Equation (2) for , gives Equation (7) 
formula
7
By substituting in Equation (7), and referring to Perilla et al. (2012) definition for , Equation (8) is obtained 
formula
8
According to Perilla et al. (2012), salinity is defined by Equation (9) 
formula
9
Water deficit is defined by Equation (10) 
formula
10

In the same way, by substituting in Equation (8), it can be demonstrated that the average and the last record of investigated annual time series data can be used to obtain salinity ( and water deficit (.

The above analytical discussion indicates that two annual time series can be effectively employed to implement the OSSEM model; one is the annual average which is obtained using spatial interpolation of the investigated parameters, and the second time series is the last year of the investigated period. Then, the average data minus the last year of data would be employed instead of the 24 h data.

Step 3. Implementation of the model

Estimation of health indicators was conducted. The quality of the groundwater system from a health perspective, and its potential for agricultural use were estimated by means of three indicators, including renewal potential (), salinization potential (), and water deficit potential () (Perilla et al. 2012). For the assessment of the first criterion, the renewal potential equation (Equation (11)) shows how this system is capable of the reduction of the initial concentration in the aquifer 
formula
11
where A is the average value of pollution concentration in the system and is the last record of concentration of quality parameters in wells during the period of study. This parameter was taken from the quality data recorded during the period 1996–2011. The last recorded data are estimated by Equation (12) 
formula
12
where is the last recorded data for salinization which is extracted from the sodium absorption ratio (SAR) and B is the average value of the SAR in the aquifer calculated by Thiessen's method.
The final indicator was computed based on the same concept using Equation (13). The water deficit existing in the system during a 24 h period was obtained by average data value minus the last recorded value for the study 
formula
13
where c is the last recorded data value for the water level in wells, and is the reference value which is obtained by averaging the Thiessen result of water level recorded for the aquifer during the span 1996–2011.

Step 4. Determining overall susceptibility index

In this step, the output from the former step are added together to show the condition of the system in terms of its susceptibility. Table 1 was used to classify the overall susceptibility index (OSI) of each criterion in the Talar aquifer.

Table 1

Reference values for OSI after Perilla et al. (2012) 

Potential socio-ecological effectsIndicatorSusceptibility
LowHigh
Environmental pollution Renewal potential  ≥ 5% < 5% 
Salinization Salinization potential  <  
Water deficit Water deficit potential  ≥ 0% < 0% 
Potential socio-ecological effectsIndicatorSusceptibility
LowHigh
Environmental pollution Renewal potential  ≥ 5% < 5% 
Salinization Salinization potential  <  
Water deficit Water deficit potential  ≥ 0% < 0% 

Study area

The Talar basin covers a surface area of 3,385 km2 with an associated underlying aquifer of approximately 900 km2. According to Rahmani (2009), different sources of pollution have been identified, including urban areas, geological formations, and mining activities. Mining from rivers and the variability of precipitation in time are two factors that limit the water resources in the basin. Since the water in wells associated with the aquifer is of reasonably high quality and relatively accessible, the inhabitants of the region tend to use it for daily needs. Nevertheless, due to increasing population, and as a consequence the elevated indiscriminate abstraction of groundwater, saltwater has migrated inland and upward in the aquifer, resulting in saline contamination of the resource. The location of piezometric wells in the Talar aquifer is shown in Figure 1.

Figure 1

Layout of the study area.

Figure 1

Layout of the study area.

Data collection

Water quality data were facilitated from the Mazandaran Regional Water authority. Habitually, uncertainties are embedded in the observed data as well as development processes which are propagated through synthesizing, developing thresholds based on unimpaired flow and assessing resource gaps (Liang et al. 2012). According to Table 2, currently, there are 27,991 registered wells in the Talar aquifer dedicated to diverse demands as illustrated in Table 3.

Table 2

Cumulative and individual numbers of wells during the period 1951–2011 in the Talar aquifer

Year19511961197119811991200120062011
Number of wells 64 187 795 3,984 14,168 8,045 718 30 
Cumulative 64 251 1,046 5,030 19,198 27,243 27,961 27,991 
Year19511961197119811991200120062011
Number of wells 64 187 795 3,984 14,168 8,045 718 30 
Cumulative 64 251 1,046 5,030 19,198 27,243 27,961 27,991 
Table 3

Wells characteristics based on different usages in the Talar aquifer

ParametersCategory
DrinkingIndustrialAgricultureFishingTotal
Number avg. depth (meter) 4,090 53 23,785 63 27,991 
11.8 15.3 3.2 26.9 – 
Min. depth (meter) – 
Max. depth (meter) 150 130 170 130 – 
Mean discharge (lit/s) 4.2 5.4 8.2 5.7 – 
ParametersCategory
DrinkingIndustrialAgricultureFishingTotal
Number avg. depth (meter) 4,090 53 23,785 63 27,991 
11.8 15.3 3.2 26.9 – 
Min. depth (meter) – 
Max. depth (meter) 150 130 170 130 – 
Mean discharge (lit/s) 4.2 5.4 8.2 5.7 – 

RESULTS

To demonstrate the groundwater fluctuations, a unit hydrograph for the aquifer was devised. The hydrograph was obtained from monthly data available for 42 piezometric wells over 16 years, starting from 1996 to 2011. The mean areal precipitation was determined based on mean annual rainfall data available for the rain gauges located in the study area and employing the Thiessen polygon method available in GIS software. Figure 2 shows the annual unit hydrograph, stressing the fluctuations of the groundwater level in the Talar aquifer up to 2011.

Figure 2

Annual unit hydrograph of the Talar aquifer.

Figure 2

Annual unit hydrograph of the Talar aquifer.

As shown in Figure 2, the annual unit hydrograph has three principal groundwater ascending periods, which are 1996–1997, 2002–2004, and 2009–2012 quantified at 0.13, 1.73, and 0.76 m, respectively. According to Figure 2, the greatest groundwater level recuperation occurred in 2004. This can be related to the increasing mean annual rainfall over the Talar basin.

Groundwater level maps

The groundwater level was mapped based on water level data from piezometric wells. By overlying a derived map in diverse years, spatial variation, and the trend of groundwater levels in the Talar aquifer were identified. Maximum water levels correspond to the upper parts of the aquifer at 34 m above mean sea level (see Figure (3a) and (3b)).

This trend is stable until 1991. According to Figure 2, from 1991 to 1992 the water level in parts of the aquifer increased about 2.45 m, changing from −25 to −22.65 m. The annual average of the groundwater level for the investigated period and 2011 were used to create annual averages and contrasted with the 2011 groundwater level map of the aquifer. To illustrate the spatial pattern of the groundwater fluctuations, a water level map was established from the mean annual groundwater level and the groundwater level for 2011. To determine the groundwater level fluctuations, the simple Kriging interpolation technique was employed to the observed piezometric elevation data for 42 wells. The interpolated values were modeled by a Gaussian process governed by prior covariance's, and embedded in ILWIS GIS software. Gaussian semi-variogram model provides optimal interpolation and generates best linear unbiased estimate at each location (Akbari et al. 2009). Considering Figure 3, it is evident that the groundwater level in the northern part of aquifer is higher than other zones. Also, the groundwater level during the period 1996–2011 has remained relatively unchanged in the southern part of the aquifer. This trend continues up until 2011 for the majority of the aquifer. Other water quality maps required for the OSSEM model, including SAR, Ca, Mg, K, and Na, were generated via the same techniques for the same periods of study.

Figure 3

Groundwater level maps in the Talar aquifer. (a) Mean annual water level, (b) water level for the year 2011.

Figure 3

Groundwater level maps in the Talar aquifer. (a) Mean annual water level, (b) water level for the year 2011.

Application of the methodological strategy

The OSSEM model was adapted for the sustainable management assessment of quality and quantity using GIS tools in the Talar basin, considering a scenario for system operation initiated in 1996 and continuing to 2011. The information required to apply the methodological strategy is piezometric level, and quality parameters including SAR, magnesium (Mg), sodium (Na), and potassium (K). To prime these data for model input, their spatial averages were calculated using the GIS for each year individually, then subsequently the average for the complete period was engaged as a reference value (see Figure 4). Finally, the last recorded data for the investigated period were used, as opposed to the 24 h data, as used in the OSSEM model in the Albufera case.

Figure 4

Spatial variation of groundwater quality in Talar aquifer. (a) Spatial distribution of mean annual, (b) spatial distribution of for the year 2011, (c) spatial distribution of mean annual SAR, (d) spatial distribution of SAR for the year 2011, (e) spatial distribution of mean annual Na, (f) spatial variation of Na for the year 2011, (g) spatial distribution of mean annual Mg, (h) spatial variation of Mg for the year 2011.

Figure 4

Spatial variation of groundwater quality in Talar aquifer. (a) Spatial distribution of mean annual, (b) spatial distribution of for the year 2011, (c) spatial distribution of mean annual SAR, (d) spatial distribution of SAR for the year 2011, (e) spatial distribution of mean annual Na, (f) spatial variation of Na for the year 2011, (g) spatial distribution of mean annual Mg, (h) spatial variation of Mg for the year 2011.

OSI application

The OSI for sustainable management of the Talar basin is presented in Table 4. From this data, it becomes evident that the Talar aquifer is highly affected by all OSI measures. Bearing this in mind, decision makers should pay more attention to the system and its current situation. From a renewal potential perspective, it is illustrated that is less than 5%, salinization potential is less than the renewal potential at 4.61%, and the water deficit potential is 3.99%.

Table 4

Results of the application of the OSSEM model in the Talar aquifer

Socio-ecological effectsCriterionsOSI (%)OSI status
Environmental pollution Renewal potential 4.89 < 5% High 
Salinization Salinization potential 4.61  High 
Water deficit Water deficit potential 3.99  > 0% High 
Socio-ecological effectsCriterionsOSI (%)OSI status
Environmental pollution Renewal potential 4.89 < 5% High 
Salinization Salinization potential 4.61  High 
Water deficit Water deficit potential 3.99  > 0% High 

According to the OSSEM model, environmental and economic factors can play an important role in decision-making for optimum management of water resources. Perilla et al. (2012) used the OSSEM model for a 24 h period and then simulated the amount and condition of three parameters used as endpoints in their study, for longer time duration.

SUMMARY AND CONCLUSIONS

The OSSEM model was successfully adapted and developed for the Talar aquifer clearly illustrating the problems present with groundwater contamination. The model employs the socio-ecological and health indicators including,, and. Subsequently, these indicators are integrated into the OSI to assess goals accomplishment, and defining possible actions. The study achieved a methodology to obtain health indicators based on both average and final record of annual data. As a final point, using the OSI indicators with two series of data (average and final year), the management sustainability was evaluated. In addition, GIS tools and techniques were integrated with the modified OSSEM model for environmental management. It was shown that environmental pollution is the main effect of socio-ecological pressures in the aquifer. Water deficit is observed, and high level salinization identified. The renewal potential, salinization potential, and water deficit potential in the Talar aquifer was estimated at about 4.89%, 4.61%, and 3.99%, respectively. This demonstrates a high degree of susceptibility in terms of environmental pollution, salinization and water deficit in the aquifer. In this study, groundwater characterization was made based on OSI indicators. This methodology could be further developed by a reliability assessment if daily data could be obtained.

ACKNOWLEDGEMENT

The authors would like express their gratitude to the Mazandaran Regional Water Authority for facilitating data for this research.

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