Variability of the thermohaline structure of a coastal hypersaline lagoon and the implications for salinity gradient energy harvesting

Variability of the thermohaline structure of a coastal hypersaline lagoon and the implications for salinity gradient energy harvesting

Sustainable Energy Technologies and Assessments 38 (2020) 100645 Contents lists available at ScienceDirect Sustainable Energy Technologies and Asses...

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Sustainable Energy Technologies and Assessments 38 (2020) 100645

Contents lists available at ScienceDirect

Sustainable Energy Technologies and Assessments journal homepage: www.elsevier.com/locate/seta

Variability of the thermohaline structure of a coastal hypersaline lagoon and the implications for salinity gradient energy harvesting Oscar Reyes-Mendozaa, Oscar Alvarez-Silvab, Xavier Chiappa-Carrarac, Cecilia Enriquezc,

T



a Consejo Nacional de Ciencia y Tecnología –El Colegio de la Frontera Sur (CONACYT-ECOSUR), Departamento de Sistemática y Ecología Acuática, C.P. 77014 Chetumal, Q. Roo, Mexico b Universidad del Norte, Department of Physics and Geosciences, 081007 Puerto Colombia, Colombia c Universidad Nacional Autónoma de México (UNAM), Campus Yucatan, Mexico

A R T I C LE I N FO

A B S T R A C T

Keywords: Coastal lagoons Thermohaline structure Salinity gradient energy

Natural and artificial systems containing water resources that have different salinities can be used to generate salinity-gradient energy (SGE). In this paper, the feasibility of implementing SGE in hypersaline coastal lagoons is addressed, taking the coastal lagoon La Carbonera in Yucatan, Mexico, as an exemplary case. A realistic approach to the exploitation conditions and potential that could occur in a SGE plant in these ecosystems is presented. We first analyzed the variability of salinity and temperature in the three characteristic zones of the coastal lagoon and the correlation of these variables with atmospheric forcing. This was done using 1 year records of in situ measurements. Then the theoretical potential for SGE and the intra-annual variability of that potential were assessed considering the three possible harvesting configurations of mixing among fresh, sea, and hypersaline water. Results show that the thermohaline structure may vary significantly for the three locations at different time scales (diurnal to seasonal) depending on the tides, winds, air temperature, atmospheric pressure, and solar radiation. That suggests that the highest annual energy yield would be obtained from alternating throughout the year among the mixing configurations, depending on the specific seasonal thermohaline structure of the lagoon.

Introduction New renewable forms of energy are needed that do not contribute thermal pollution or emission of unwanted and environmentally harmful substances, including greenhouse gasses. Wind power, hydropower, biofuels, solar power, geothermal power, and ocean power are potential contributors to a more sustainable development using renewable energy [1]. Huge quantities of renewable energy can be extracted from the oceans, in the forms of tidal energy, current energy, wave energy, ocean thermal energy, marine biomass energy, and salinity gradient energy (SGE) [2,3]. SGE can be obtained by mixing two water masses with different salt concentrations. As they mix, a release of free energy occurs driven by the difference in chemical potential between the two water masses, increasing the entropy of the system; if this mixing is controlled, the potential can be used to generate electricity [4]. By comparison to other sources of marine energy, the global SGE potential is in the same order as wave energy or thermal gradients, and it is a hundred times higher than that of tidal energy [5]. The generation of SGE has been analyzed



in river mouths [5–7], where the freshwater from rivers meets with saltwater from the ocean, also using residual brines from desalination [8–10], and from hypersaline lakes and lagoons, where large salinity gradients exist and, therefore, large SGE potentials [10–15]. Previously analyzed hypersaline systems include the Urmia Lake in Iran, where the theoretical SGE potential has been calculated between 5.8 and 19 MJ/ m3 [13]; the Great Lake with theoretical potential of 37.5 MJ/m3 [16]; the Dead Sea with theoretical potential of 50.7 MJ/m3 [17]; and the Lake Torrens and Lake Eyre in Australia, with theoretical potential of 11.6 MJ/m3 [18]. Previously mentioned studies assessed the theoretical SGE potential considering average salinity gradients that are assumed constant over time. However, the thermohaline structure of natural water bodies is highly variable on time and space as a function of tides, changes in freshwater inflows, rain, solar irradiation, and wind regimes, among other meteorological and hydrological forcers. Therefore, a more precise estimation of the SGE resources at natural systems requires first the observation and understanding of the temporal and spatial variability of the salinity and temperature structure and gradients [19]. This real

Corresponding author. E-mail address: [email protected] (C. Enriquez).

https://doi.org/10.1016/j.seta.2020.100645 Received 31 August 2019; Received in revised form 20 January 2020; Accepted 22 January 2020 2213-1388/ © 2020 Elsevier Ltd. All rights reserved.

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Field measurements

variability is addressed in this research for the hypersaline coastal lagoon of La Carbonera, located in the northern extreme of the Yucatán Peninsula. Large salinity gradients are to be expected in coastal lagoons due to the presence of freshwater discharges, their connectivity with the sea, and their high evaporation rates. Therefore, coastal lagoons can be divided into three zones according to the salinity of the water, namely, the freshwater (FW), seawater (SW), and hypersaline water (HW) zones, and this may allow different configurations of SGE exploitation using waters from the different zones. The Yucatan Peninsula in Mexico has a low-lying coastal area, with 57% of it corresponding to systems of coastal hypersaline lagoons (with salinities ranging from 2 to 186 g NaCl/kg) of shallow depths, without rivers input [20,21]. The coastal lagoons are typically oriented parallel to the coast, separated from the ocean by a sandbar, closed or semienclosed, permanently or intermittently [22], and are regulated by atmospheric and hydrologic processes. Due to the hyper salinity, the lagoon system is used by artisanal and industrial salt harvesting among other ecosystem services, mostly on isolated villages [23]. The freshwater flow to the coastal lagoon systems of the Yucatan Peninsula is comming mainly through submarine groundwater discharges (SGD) from a karstic aquifer. The flow can be through point and/or diffusive sources, controlled by the hydraulic gradient [24–27], and can create large salinity gradients with the HW. Due to these geomorphological characteristics, it is reasonable to expect great power densities to be achievable from them, that might supply the energy demand of isolated small populations settled near the lagoon. This work analyzes the seasonal climatology and thermohaline structure of a tropical hypersaline coastal lagoon and the spectral correlations between them. The results are used to assess the theoretical SGE potential that could be harvested from this system, considering three possible exploitation configurations, namely, mixing FW and SW, FW and HW, and SW and HW over the course of an annual cycle, using in situ data. Finally, the implications of the results for the reliability of possible energy generation are given, and certain environmental considerations of the use of coastal lagoons for SGE purposes are presented.

Three CTD divers were fixed at the noted characteristic locations of the lagoon featuring FW, SW, and HW (Fig. 1), to measure salinity (g/ kg), temperature (°C) and depth of the water (cm Wc), every 10 min from August 2014 to August 2015. Data at the SW station were not recorded from the end of October to the beginning of February. In order to fill this data gap for the assessment of the energy potentials, a linear interpolation for salinity data was carried out, justified by the fact that the mean salinity at SW location is very similar before and after the data gap and the variance (before and after) is very low, as can be seen in the Section “Thermohaline variability of the lagoon”. On the other hand, water temperature at SW and HW locations have a similar mean, variance and variability before and after the data gap, therefore, temperature data on SW during the gap was assumed to be identical to HW temperature. For the same period, meteorological data, namely, air temperature (°C), atmospheric pressure (mb), wind velocity (m/s), precipitation (mm/hr), radiation (W/m2), and potential evapotranspiration (mm/hr), were recorded at the same temporal resolution, using a Davis Vantage Pro 2 Plus station, located at 21.163107° N, 90.047852° W, 16 km west of the study site. From mid-May to early June, the measurements were not reliable due to technical failures at the meteorological field station, and these data, therefore, were not included in the analysis. Data analysis Grubb’s test, also known as the maximum normed residual test [31], was used to detect anomalies in the meteorological time series and set under the assumption that the data could be expected to conform to a Gaussian distribution. × − × A = n− (1) S where [A] is the standardized anomaly, [×n ] is the doubtful value, [− × ] is the arithmetic average of all n values, and [S] is the estimate of the population standard deviation based on the sample data, calculated with n − 1 degrees of freedom. Frequency domain cross-correlation analysis was conducted between each of the six registered meteorological variables and the salinity time series from the three locations. This was done to identify coherent frequencies that evidence the interdependence between meteorological and salinity variability, and the associated phases representing the temporal lags of the interdependence. The analysis was conducted separately for each climatic season. The time series were divided into subseries of size 213 data (at a 10 min resolution), equivalent to 56.8 days. A Hanning window with 50% overlapping was used. The coherence analysis was conducted with 12.7 degrees of freedom and a 95% confidence interval [32]. These analyses allowed the identification of the main forcers of the thermohaline structure of the lagoon. It became possible to identify whether different environmental forcers dominate the physical characteristics of water in the different sectors of the lagoon and whether the main forcers changed over the course of the year. The results permitted the climatic conditions to be related to the SGE potential, identifying when in the year and where on the coastal lagoon the conditions are favorable and adverse for SGE generation.

Materials and methods Study site The study was conducted in the coastal lagoon La Carbonera, located at the north shore of the Yucatan Peninsula, Mexico (Fig. 1a, b). The average depth of the lagoon is 0.5 m, and it covers an area of ~16.5 km2 [27]. This is a marine-biogenic littoral, conformed by sand, bedrock, and mud [28], bordered by mangroves towards the mainland and by a sand barrier against the ocean, with a permanent open channel (the mouth) [29] where SW is predominant (Fig. 1c, d). The lagoon is interconnected by wetland vegetation with other lagoons and its extension changes seasonally; therefore, lateral limits are not precisely established. On the southern side of the lagoon, a Petén ecosystem is present, formed by an SGD spring-seepage inside of a patch of mangroves (Fig. 1c, e), where FW predominates. On the northeastern side, there is a sector where HW is predominant (Fig. 1c, f), according to previous studies [30–32]. The tidal regime in this region is mixed, showing a diurnal dominance, and there is a tidal range of 0.1 m during neap tides and 0.8 m during spring tides [30]. The climate varies from a tropical wet to dry savanna climate (Aw in the Köppen climate classification), with a maximum monthly average temperature of 30 °C and a minimum monthly average temperature of 19 °C [20]. There are three characteristic climatic seasons in the region: the dry season (March–June), the rainy season (July–October), and the north-winds season (November–February). This last season is defined by strong northern winds and low air temperatures, associated with polar fronts crossing the Gulf of Mexico [30].

Assessment of SGE potential The theoretical SGE potential is the maximum energy that would be available if ideal efficiency could be achieved and it is considered to be independent of any harnessing technology [7]. When two waters with different salt concentrations come into contact, they spontaneously mix to form a homogeneous substance in a process driven by the difference in chemical potential between the solutions, where entropy, expressed 2

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Fig. 1. Study site. a) Yucatan Peninsula (YP); b) location of La Carbonera coastal lagoon (white square); c) details of La Carbonera coastal lagoon with locations of CTD measurements for seawater (SW), freshwater (FW); and hypersaline water (HW), satellite image from Esri, Digital Globe, UTM 16N, WGS 1984; d) intertidal exchange between marine water and lagoon water at SW location; e) Petén ecosystem at FW location; f) HW location.

estimated using in situ data by considering the three possible configurations of feed/draw solutions described previously (FW/SW, FW/ HW, and SW/HW). Therefore, a comparison of the theoretical potential available in each of the three configurations was carried out. A sustainable exploitation of SGE must guarantee a lower rate of usage of the resources than that of renovation of FW, SW, and HW. The most limited of these three resources is the FW, since the volume occupied by freshwater in the lagoon is about 16% of the volume occupied by HW, and SW may be considered unlimited. A long-term estimation of the FW discharge has been reported to be ~1 m3/s (104 m3/d) [26]. Therefore, in order to ensure a sustainable exploitation of SGE, and considering equal fluxes for all water sources, the energy assessments were carried out considering a unitary water flux from all sources.

by the Gibbs free energy, is released [33]. The theoretical energy released from mixing a concentrated and a diluted solution can be expressed as follows:

ΔGmix = Gb − (Gc + Gd )

(2)

where [G] is the Gibbs free energy (in Joules), the subscript [c] represents the concentrated solution, [d] represents the dilute solution, and [b] represents the brackish solution that results from the mixture. The Gibbs energy for each electrolyte solution, i = c, d, b, assuming that the pressure and the number of particles in the systems are constant, is described by:

Gi = −Ti ΔSi

(3)

where [T ] is the absolute temperature in K, [ΔS ] is the variation of entropy in J/K, that can be calculated as:

ΔSi = −Vi mR [x i ln(x i ) + yi ln(yi )]

Results

(4)

Regional meteorology and climatology

where [V] is the volume of each solution in m3, with Vb = Vc + Vd , [m] represents the total number of moles of water solution in mol/m3, [R] is the universal gas constant (8.314 J/mol K), and [x] and [y] are the molar fractions of the saline ions Na+ and Cl- and water respectively, which depend on the salinity of each zone of water. To estimate the theoretical potential, a unitary volume per unit time was assumed for both the dilute and concentrate solutions. The hourly series of temperature and salinity for each measurement point were used to assess the temporal variability of the potential for three configurations over the 1-year period. Calculations of the energy potential were corroborated comparing with theoretical assessments by Post et al. [4] for mixes of fresh, sea and hypersaline waters. The theoretical available energy potential in La Carbonera was

The atmospheric conditions on the study site were changing throughout the year, according to the climatic seasons (Fig. 2). Shortterm behavior was characteristic of tropical coastal ecosystems, with a strong diurnal variation, principally in atmospheric temperature and radiation. Table 1 describes the main statistical parameters of the atmospheric data. The maximum wind magnitudes (Fig. 2a) occurred between late October and late February, with strong gusts (Table 1), as expected during the north wind season in this region. The anomalies of wind magnitudes (Fig. 3b) show positives values (greater than the average) for these same months. Later, from early March to late July, the wind anomalies fell to near zero, indicating decreases in the wind magnitude due to the dominance of wind fields from the south during 3

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T air [°C]

Wind [m/s]

O. Reyes-Mendoza, et al.

30 20

30

Rad

2

[W/m ]

20 400

b)

c)

200

ET [mm/hr]

0 0.05

0 1030

Pr Atm [Bar]

a)

10 0

d)

e)

1020 1010 1000 Sep14

Oct14

Nov14

Dec14

Jan15

Feb15

Mar15

Apr15

May15

Jun15

Jul15

Aug15

Sep15

Fig. 2. Atmospheric data: a) wind speed, b) air temperature, c) solar radiation, d) evapotranspiration, e) atmospheric pressure. A 24 h low-pass filter was applied.

direct solar irradiation, producing local stability of the water temperature. Meanwhile, at the SW site, temperature and salinity measurements were 27.6 ± 2.9 °C and 22.5 ± 6.5 g NaCl//kg, respectively (Fig. 2a, b). Salinity here was lower than offshore, reflecting the net outflow of FW from the SGDs through this inlet; this implies a negative impact on the energy potential in the configuration FW/SW and a positive impact on the energy potential in the configuration SW/HW. Temperature and salinity at the HW site were 26.8 ± 3.3 °C and 74.2 ± 17.0 g NaCl/kg, respectively, showing the highest variation rank for both variables, comparing with the other two measurement locations, in particular, in the salinity, which presented minimum and maximum values of 1.5 and 113 g NaCl/kg. This would have a negative impact on the reliability of the energy potential for the configurations FW/HW and SW/HW. The diurnal and seasonal irradiation cycles and the breezes had evident implications for the temporality and variation of the temperature at the locations SW and HW. Further, the shallowness of the coastal lagoon and its lack of vegetation contributed to either increasing the salinity at HW through evaporation or reducing it through the rain. The extreme reduction in the salinity that occurred at HW during heavy rain events can be seen in Fig. 5, which shows how the punctual precipitation peaks strongly reduced the salinity on October 28, 2014, from 87 to 16.6 g NaCl/kg (a reduction of 70.2 g NaCl/kg) associated with precipitation of 46.8 mm/hr. On July 16, the salinity dropped from 78.2 to 2.32 g NaCl/kg (a reduction of 76.0 g NaCl/kg) after a precipitation peak of 24.0 mm/hr. On August 24, the salinity dropped from 68.2 to 26.7 g NaCl/kg (a reduction of 41.5 g NaCl/kg), associated with a rain peak of 25.2 mm/hr (Fig. 5). At the SW and FW locations, the dilution effect was not clear for any season; however, the salinity gradients between HW and these sites decreased or disappeared,

the dry season. Finally, from July to September the anomalies were negative, showing that the rainy season had the lowest winds of the year. The atmospheric temperature reached minimum and maximum values around February (north winds season) and May (dry season) (Fig. 2b). Temperature (Fig. 3b) and evapotranspiration (Fig. 3d) anomalies showed positive values during the dry and rainy seasons and negative values during the north winds season. The evapotranspiration shows a collinearity with the radiation because of how it was calculated, using the meteorological station. The anomaly of the radiation shows positive values across a large part of the year. Only during the north season it was negative (Fig. 3c), and in a period beginning in April, the measurements were not reliable due to sensor failures. The greatest atmospheric pressure (Fig. 2e) and its positive anomaly (Fig. 3e) occurred between November and May, encompassing the north winds season and part of the dry season; the lowest atmospheric pressures occurred in September during the peak of the period of tropical storms over the Atlantic Ocean.

Thermohaline variability of the lagoon The salinity and temperature time series at the three characteristic locations of La Carbonera are shown in Fig. 4. Here, it can be seen that at FW gauge, both variables present stable conditions, showing quasipermanent FW characteristics. The salinity average and standard deviation were 1.4 ± 1.0 g/kg, respectively, with short-time events of salinity increases up to 20.7 g NaCl/kg during the north winds period, when the SGD was low and saline intrusion occurred due to the strong winds that transported saltwater from the north of the lagoon southward. The temperature was very stable, around 26.9 ± 0.2 °C because the water comes from a SGD surrounded by mangroves, so it avoids Table 1 Statistical summary of the meteorological data at the study site.

Mean Standard deviation Minimum Maximum

Air temperature [°C]

Solar Radiation [W/m2]

Evapotranspiration [mm/hr]

Atmospheric Pressure [mb]

Wind speed [m/s]

Rain [mm/hr]

25.6 3.0 14.0 39.0

168.50 270.90 0.00 1290.00

0.02 0.08 0.00 0.74

1015.06 3.47 1001.50 1026.00

6.90 7.40 0.00 48.30

0.032 0.797 0.000 60.0

4

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[m/s]

O. Reyes-Mendoza, et al.

2 0

[°C]

-2 2 0 -2

[W/m2]

-0.5

[mm/hr]

0.5

0.2 0 -0.2

[Bar]

0

2 0 -2 -4 Sep14

Oct14

Nov14

Dec14

Jan15

Feb15

Mar15

Apr15

May15

Jun15

Jul15

Aug15

Sep15

Fig. 3. Standardized anomaly of the atmospheric variables. a) wind speed, b) air temperature, c) solar radiation, d) evapotranspiration, e) atmospheric pressure A 24 h low-pass was applied.

Cross-spectral and coherence analysis

affecting the hypothetical reliability of a SGE plant under the configurations of either FW/HW or SW/HW, as shown in more detail in the Section "SGE potential". On average ( ± standard deviation), the salinity differences were 70.4 ± 23 g NaCl/kg between FW and HW, 52.6 ± 19.1 g NaCl/kg between SW and HW, and 18.9 ± 7.4 g NaCl/kg between SW and FW.

As noted, spectral cross-correlation analysis was conducted between each of the six meteorological variables and the salinity time series from the three locations and the three seasons. An example of the results is shown in Fig. 6 for the cross-correlation analysis of salinity at

Fig. 4. Time series of a) salinity (g NaCl/kg) and b) temperature at the three measurement sites at the lagoon La Carbonera. 5

Sustainable Energy Technologies and Assessments 38 (2020) 100645

60

120

50

100

40

80

30

60

20

40

10

20

0 Sep14

Oct14

Nov14

Dec14

Jan15

Feb15

Mar15

Apr15

May15

Jun15

Jul15

Aug15

Sep15

Salinity [g NaCl/kg]

Rain [mm/hr]

O. Reyes-Mendoza, et al.

0 Oct15

Fig. 5. Time series of salinity at the HW location and precipitation measurements.

salinity variability of the coastal lagoon, thus determining the transportation of salt within the system. Following this variable were atmospheric pressure and evapotranspiration, which in turn are closely related to wind speeds; winds determine the latent heat flux that strongly influences the salt concentration in the water. During the north wind season, wind speeds had a coherence peak with salinity around 68 h (2.8 days), which was associated with average duration of the north-wind events on the region, with implications for the hydrodynamics of the whole lagoon. SGE potential The assessment of the theoretical energy potential (TP) for the three analyzed configurations is presented in Fig. 7; it indicates that the TP had a wide range of variability from one exploitation configuration to the other, but it may also have significant temporal variability in a particular configuration. For the configuration FW/SW, the estimated TP was 0.88 ± 0.32 MJ/m3, with maximum values of 1.9 MJ/m3 in May during the dry season (Fig. 7). For the configuration FW/HW, the TP is 4.0 ± 1.0 MJ/m3; which is the maximum average TP for the three possible configurations (as expected, due to the highest salinity

50 0 a) -50 -100 -150 10-1 1

Coherence2 95 % IC

Co-spectrum

HW and wind speed during the north season. Four coherent peaks at 95% confidence can be observed (Fig. 6b) for periods (T) of 68.2 h (2.8 days), 24 h (1 days), 11.8 h (0.49 days) and 6 h (0.25 days), indicating lags (Δφ) of around 14.9, 11.6, 5.8, and −2.5 h. Negative values between signs were not interpreted (Fig. 6c). The first peak indicated a cycle of wind oscillation of about 3 days during the northernwind season, which produced changes in the salinity of the lagoon shown in the HW location, with a lag of 15 h between the wind and the salinity; this interpretation can be replicated for the other coherence peaks. Table 2 shows the results of all cross-correlation and coherence analyses. During the rainy season, 32 significant coherent peaks were present between salinity and atmospheric variables, on the north wind season was recorded 31 peaks of correlation, despite data SW station was not available (not interpolated). The lowest number of coherence peaks were found during the dry season, recording 17 coherence peaks on the analysis. That means a major atmospheric implication on the salinity structure of the lagoon during rainy and north seasons than during dry season. Wind speed is the individual atmospheric variable that showed the most significant correlation peaks with salinity. It is then the principal meteorological variable that is considered to determine the

b)

100 T= 68.2 hrs

T= 24 hrs

T= 11.8 hrs

T= 6 hrs

0.5

0 10-1 200 Phase degree

101

100

101

100

101

c) 0 -200 10-1

Fig. 6. a) Co-spectrum, b) coherence, and c) phase between salinity at HW and wind speed, with a window of 56.8 d and 12.7 degrees of freedom. 6

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Table 2 Results of coherence and phase analyses of salinity and atmospheric forcers for the three climatic seasons. The period (T) for each coherence peak (95% confidence) and the lags in hours (h) between signals are displayed. A window of 56.8 days and 12.7 degrees of freedom is used. Salinity g/kg

Atmospheric forcings Atm T °C

Peaks of coherence at 95% IC, on-site

Wind m/s

Atm pressure mb

Radiation W/m2

ET mm/hr

Rain mm/hr

Season

Site

T

Δϕ

T

Δϕ

T

Δϕ

T

Δϕ

T

Δϕ

T

Δϕ

Rainy

Hyper

76.4 6.0 10.0 5.2 9.6

170.6 68.2 22.6

0.8 28.7 10.4

11.8

0.7

22.6 11.8

8.1 3.2

22.6 11.8

8.6 3.7

N/P

N/P

12

Sea

170.6 28.3 22.6 16.1 24.0

35.0 1.0 29.4 5.4

56.9 9.7 11.8

26.1 0.2 4.0

56.9 9.6 31.2 11.8 3.4

26.8 0.6 4.0 3.5 0.6

9

3.3

85.2 7.2 85.2 11.8

N/P

43.2

42.2 2.3 30.5 1.7 5.7

N/P

Fresh

85.2 17.1 113.8 19.9 17.8

0.7

3.2

11

5 24.0 14.6 12.0

9.1 2.0 3.3

7 24.0 11.8 6.0

9.8 3.9 2.9

1 12.0

3.1

32 19

No data 48.0 16.1 5.5

1.3 0.9 1.3

No data 56.9 5.9 28.3 13.2 5.5 1.1

No data N/P N/P

0 12

7 N/A

N/A

6 24.4 12.0

11.8 5.4

1 N/P

N/P

31 10

24.4

11.2

24.4

11.8

N/P

N/P

7

Summary North winds

Hyper

Sea Fresh

Summary Dry

6 17.0 11.8 14.2 7.0 11.8 4.7 0.5 3.2 No data N/P N/P

8 68.2 14.9 24.0 11.6 11.8 5.8 6.0 2.5 No data 56.9 14.0 15.4 7.2 11.0 5.2

5 24.0 17.0 12.0 5.8 No data 170.6 42.5 8.4

4 24.4 12.7 12.0 24.4

7 24.4 12.0 37.9 24.4 No data 4 16

Summary

No data 4

7 68.6 2.2 24.4 9.9 12.0 1.9 42.9 14.7 24.4 10.4 No data 5

Total Coherence peaks at 95% IC

14

20

Hyper

Sea Fresh

11.5 5.9 4.9 11.8

3.2 0.6 0.9 0.1 29.4 6.2 1.2 5.1 3.5 14.8 0.1

No data 1

No data 3

No data 0

13

16

2

No data 17

*T = Periods of spectral energy peaks; Δϕ = lag between signals, both in hours, N/P not significative coherence peaks.

gradients in this configuration), with a maximum of 6.8 MJ/m3 in February, when the north winds were most intense. The TP in this configuration only showed abrupt decreases during the period between October 21 and November 5, 2014, coinciding with the passage of tropical depression Hanna by the Yucatan Peninsula, and for most of

July 2015, coinciding with the rainy season. For the last configuration, SW/HW, the TP was 1.49 ± 0.70 MJ/m3, with a maximum of 3.3 MJ/ m3 in December during the north-wind season. A comparison of the theoretical energy potentials estimated in this study with the reference SGE potential from mixing FW (0 g NaCl/kg)

7 FW/HW FW/SW SW/HW

Theoretical potential (MJ)/m3

6 5 4 3 2 1 0

Oct14

Nov14 Dec14 3

Jan15

Feb15 Mar15

3

3

Apr15

May15

Jun15

Jul15

Aug15

Sep15

Fig. 7. Theoretical SGE potential (MJ/m ) from mixing 1 m diluted water and 1 m concentrated water for the three possible configurations: FW/HW (black), FW/ SW (blue), and SW/HW (red). Dotted lines are estimations to fill the data gap for salinity and temperature of SW. 7

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and SW (30 g NaCl/kg) at river mouths, which is 1.76 MJ/m3 [34–36] shows that for the configuration FW/SW, the TP had a mean value equivalent to 50% of the reference value. This was due to the relatively low salinity at SW site, compared with the salinity of open ocean waters, given that the measurement location served as a final outcome mouth for the FW. For the configuration FW/HW, the TP was 227% of the reference potential at river mouths. It was expected that the highest theoretical potential would be found at this configuration, given that the salinity gradient was highest there, but is also important to note that the variability of the potential was the lowest, increasing the reliability of the energy generation under this alternative. Finally, for the configuration SW/HW, the TP was equivalent to 84.6% of the average potential at river mouths. Comparing the SGE potentials found in this study with the estimations made for other hypersaline systems, it is observed that the mean SGE potential value for the configuration FW/HW is equivalent to 69% and 21% of the SGE potential from Urmia Lake [13], 10.6% of the potential from Great Lake [16], 7.9% of the potential from Dead Sea [17], 34.5% of the potential from Lake Torrens [18], and it is similar to the described values from brackish and brine systems from Western Australia [37]. As mentioned before, the variability of the salinity and temperature at the three locations implies a variability in the theoretical potential along the year for the three proposed exploitation configurations. Therefore, the theoretical potentials follow the empirical distributions shown in the histograms of Fig. 8a. The effect of this variability of the potential in the reliability of the energy generation is quantified by the capacity factor of the power plant, which depends on the desired installed capacity of a hypothetical power plant, as shown in Fig. 8b. The higher the installed capacity of the power plant, the lower will be the time of the year when the power plant can generate electricity at full capacity. A capacity factor of 90% -which implies high reliability of the power plant-, will be reached with installed capacities of 0.48 MJ for FW/SW configuration, 0.90 MJ for SW/HW configuration and 1.90 MJ for FW/HW configuration. Assessments shown in Fig. 8 were carried out considering a mixing flow of 1 m3/s for each solution.

exploitation configurations present a unimodal annual cycle, where the FW/SW configuration behaved in an inverse manner to the other two configurations. While FW/SW configuration had the highest potentials from May to October, the other configurations presented their highest potentials from November to May. This complementary behavior was associated with the unimodal increase and decrease of salinity at the SW (Fig. 4a) location, which took place in mid-May and late October, respectively, associated with the annual cycles of the environmental forcers, mainly wind speed, atmospheric pressure, and air temperature. The energy potential in the configuration FW/SW increased as the HW pushed towards the mouth, increasing the salinity gradient with the FW, while the energy potential in the configuration SW/HW decreased. When this happened in reverse, FW was transported toward the mouth, and therefore, the potential of SW/HW increased while the potential of FW/SW decreased. The behavior of the anomalies in the configurations FW/HW and SW/HW presented similar patterns because both depended strongly on the salinity at the HW location. Results showed that the configuration FW/HW offers the highest energy yield but, the inverse behavior with the configuration FW/SW, makes this last configuration an appropriate backup for maximizing the annual yield of the power plant, improving its efficiency and economic feasibility. Comparing river mouths and coastal lagoons as possible natural environments for the generation of SGE, the main advantage of rivermouth systems is their greater availability of water resources, both fresh and salty; however, the salinity gradients are limited. On the other hand, the main advantage of coastal lagoons systems is the higher salinity gradients and the associated higher energy densities (energy per unit volume), along with the availability of three water sources with different salinities instead of two. This may allow the generation of electricity from salinity gradients using more than one configuration; furthermore, it would be possible, in cases like in La Carbonera, to alternate seasonally between exploitation configurations to maximize the annual yield of a hypothetical SGE plant. This because the environmental factors that produce seasonal reductions in the energy potential under one configuration could at the same time generate increases in the potential of another configuration (Fig. 8). Fig. 7 shows that the energy potential of the three configurations was strongly affected during the rainy season, especially in July and during the pass of tropical storms at the end of October and beginning of November. During these periods, a hypothetical SGE plant installed in this natural system would be required to halt energy generation due to the lack of potential; therefore, these periods of the year may be used

Discussion SGE potential at coastal lagoons The anomalies of energy potential shown in Fig. 9 indicates that all 50

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structure of the coastal lagoons of Yucatan, an energy conversion system based in reverse electrodialysis (RED) [4] might be more convenient to use in the exploitation of SGE in such lagoons. This because in RED technology, the concentrated and diluted waters do not entirely mix, so that, intake waters could be brought back into the natural system with similar salinity concentrations to the intake ones. The salinity of coastal lagoons is an important regulator of fish assemblage [27] and matters to other organisms and ecological and biogeochemical processes. Therefore, appropriate locations with the same salinity of the outcoming waters must be found for discharging the used water in order to ensure a neutral buoyancy and do not induce changes in the natural salinity of the coastal lagoon that may produce a detriment of the ecosystems or the SGE potential itself by reducing the natural salinity gradient. A mixed technology system like a salinity-gradient solar pond (SGSP) with a solar energy systems may also be considered since the lagoons are natural ponds, and solar radiation quickly creates a vertical salinity gradient (thermal zones) used on the system, that could be adapted to a small salinity gradient plant [41].

for preventive maintenance. It is also important to remember that the energy yield that could ultimately be obtained from any energy source might fall far below theoretical potential. For SGE, the main reasons for this would be the parasitic energy inputs required for water transport from the natural environment toward the power plant, pretreatment inputs, mass transfer limitations, changes in operating pressure, and non-ideal behavior of natural solutions, among other possible factors [13,10]. Limitations for exploiting SGE in coastal lagoons The main technical restriction for the exploitation of SGE in a coastal lagoon is the limitation of the water resources, which is, in turn a constraint on the total amount of harvested energy, unlike the case of river mouths, where the main technical limitation would be energy density. Additionally, the environmental importance and fragile equilibrium of such systems must be taken into account. Coastal lagoons provide ecosystem services such as acting as fisheries, providing freshwater storage, ensuring hydrological balance, performing climate regulation, protecting against floods, purifying water, producing oxygen, enabling biological fertility, and being sites for human recreation and ecotourism [36]. Thus, in coastal lagoons, a SGE project should consider only small power plants with the capacity to supply the energy requirements of small towns, and ensuring the conservation of proper environmental conditions. Also, a combination of renewable energy sources could increase energy yield. Solar radiation ponds [3], for instance, is another available power source in tropical HW lagoon environments. The theoretical potentials assessed here will reduce to a net potential when considering energy outputs that include i) water transportation from the intake points towards the power plant [19], ii) inefficiencies in energy conversion processes [13], and iii) required water pretreatment for removing suspended solids and reducing biofouling on the membranes [38]. Recent studies have estimated that the net potential after considering the previously mentioned energy outputs yields to 17 to 24% of the theoretical potential [39,40]. However, a more precise estimation of the net SGE potential depends on site-specific conditions such as the particular location of the power plant or the required intensity of water pretreatment.

Conclusions In this study, a comprehensive analysis of the climatology and possible applications to energy generation, was carried out for a hypersaline coastal lagoon in Yucatan, Mexico, based on one year of in situ data. We evaluated the magnitude, distribution, and variability of the thermohaline structure of this coastal system, relating its behavior to the main atmospheric environmental forcers and the seasonality of the study region. The feasibility of harnessing SGE was evaluated in relation to the variability of the salinity in three zones of the coastal lagoon with dominant FW, SW, and HW conditions. Three possible exploitation configurations of the different water sources were analyzed. A high seasonal variability of the energy potential for the three analyzed exploitation configurations was identified, as a function of the seasonal variability of the atmospheric forcers of the thermohaline structure of the lagoon, mainly the wind speed, air temperature, and atmospheric pressure. This high variability of the potentials highlights the importance of implementing long-term measurement schemes of the thermohaline structure of natural systems for the assessment of realistic estimations of the SGE resources and the reliability of the energy exploitation at those systems. Additionally, it was identified that the availability of three water masses with different salinities, instead of two like at river mouths, may offer the advantage of alternating the feed and draw solution along the year in order to maximize the annual yield

Technological considerations Given the described environmental fragility and thermohaline 9

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of the power plant, improving its efficiency and economic feasibility. The quantification of the natural resources and the spatio-temporal variability addressed in this study are the first step required to evaluate the suitability of the implementation of an SGE plant in tropical coastal lagoons. Nevertheless, the evaluation of the economic feasibility, review of environmental considerations, and assessment of the technical challenges associated with the upscaling of SGE-generation technologies are equally fundamental and depend on site-specific conditions like the particular location of the power plant or the required water pretreatment, these issues should be addressed in further studies. Finally, it is important to highlight that tropical coastal hypersaline lagoons are fragile and valuable ecosystems that offer important services to the natural and anthropogenic communities. Their exploitation for renewable energy generation must not endanger their sustainability, therefore, only small scale exploitation for supplying the energy needs of isolated communities is recommended.

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CRediT authorship contribution statement Oscar Reyes-Mendoza: Conceptualization, Methodology, Formal analysis, Investigation, Writing - original draft, Writing - review & editing. Oscar Alvarez-Silva: Conceptualization, Methodology, Formal analysis, Investigation, Writing - original draft, Writing - review & editing. Xavier Chiappa-Carrara: Conceptualization, Investigation, Resources, Project administration, Funding acquisition. Cecilia Enriquez: Conceptualization, Methodology, Investigation, Resources, Data curation, Writing - original draft, Writing - review & editing, Project administration, Funding acquisition. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments Part of this research was performed with grant from CONACYTSENER-SUSTENTABILIDAD ENERGÉTICA project: FSE-2014-06249795 “Centro Mexicano de Innovación en Energía del Océano (CEMIE Océano)”. The first author received a posdoctoral grant from CONACYT-SENER-SUSTENTABILIDAD ENERGÉTICA (CTAFSE-7-X-1810). We acknowledge Patrick Findler by the English revision. We would like to thank Maribel Badillo-Aleman and Daniel Arceo for the photographs used in Fig. 1. References [1] Veerman J, Saakes M, Metz SJ, Harmsen GJ. Reverse electrodialysis: a validated process model for design and optimization. Chem Eng J 2011;166:256–68. https:// doi.org/10.1016/j.cej.2010.10.071. [2] Zhou Z, Benbouzid M, Frédéric Charpentier J, Scuiller F, Tang T. A review of energy storage technologies for marine current energy systems. Renew Sustain Energy Rev 2013;18:390–400. https://doi.org/10.1016/j.rser.2012.10.006. [3] Tufa RA, Pawlowski S, Veerman J, Bouzek K, Fontananova E, di Profio G, et al. Progress and prospects in reverse electrodialysis for salinity gradient energy conversion and storage. Appl Energy 2018;225:290–331. https://doi.org/10.1016/j. apenergy.2018.04.111. [4] Post JW, Veerman J, Hamelers HVM, Euverink GJW, Metz SJ, Nymeijer K, et al. Salinity-gradient power: evaluation of pressure-retarded osmosis and reverse electrodialysis. J Membr Sci 2007;288:218–30. https://doi.org/10.1016/j.memsci. 2006.11.018. [5] Emami Y, Mehrangiz S, Etemadi A, Mostafazadeh A, Darvishi S. A brief review about salinity gradient energy. Int J Smart Grid Clean Energy 2013;2:295–300. https://doi.org/10.12720/sgce.2.2.295-300. [6] Labrecque R. Exergy as a useful variable for quickly assessing the theoretical maximum power of salinity gradient energy systems. Entropy 2009;11:798–806. https://doi.org/10.3390/e11040798. [7] Alvarez-Silva O, Osorio AF. Salinity gradient energy potential in Colombia considering site specific constraints. Renew Energy 2015;74:737–48. https://doi.org/ 10.1016/j.renene.2014.08.074.

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