Keywords

12.1 Introduction

Pond ecosystems are universally accepted as a support system of significant biodiversity of the world (Deacon et al. 2019). Moreover, ponds provide diverse ecosystem services to human beings and other life forms (Fu et al. 2018). In terms of the basic ecology, there is no difference between the different types of pond ecosystems (De Macro et al. 2014). Moreover, several studies have described that ponds have substantial potential to mitigate climate change and at the same time tackle several water management issues (Quinn et al. 2007). Céréghino et al. (2014) revealed that a 500 m2 pond can seize and store as much carbon as emitted by a car throughout one annual cycle. Downing et al. (2008) emphasized that owing to the very high numbers of these lentic bodies, they occupy a substantial portion of the land surface and if properly managed they are capable of sequestering equal amounts of carbon as the oceans do at any time. Despite many exciting research findings, the ponds have received very little attention from policy managers and stakeholders throughout the world. In the present date, ponds continue to be one of the most neglected land-use classes.

Moreover, in the pond ecosystem dissolved organic matter (DOM) used as a proxy for water quality (Hudson et al. 2008), is composed of humic like materials, protein substance and carbohydrates and it especially affects the physical transport, chemical transformation and bio-availability of heavy metals in lake or pond ecosystems (Wufuer et al. 2014). Moreover, Guenther and Valentin (2008) described that how high conc. of DOM resulting from adjacent areas of the pond system can release an unpleasant odor and may contribute to eutrophication. Chromophoric DOM (CDOM) is the colored section of DOM that strongly absorbs light in the UV and blue region. CDOM is an optically energetic constituent in the aquatic bodies and affects the remote sensing assessments of the Chl-a conc and total suspended matter, attenuation of UV and photosynthetically active energy, global nutrient and carbon cycling, ecosystem productivity, heavy metal transportation, and adsorption, and drinking water treatment (Coble 2007; Gonsior et al. 2014). Therefore, the sources, cycle, transformation and composition of CDOM play an important character in lentic bodies. Moreover, numerous research works have been carried out to explore how hydrological environments in the upstream branches may influence the dynamics and sources of CDOM in downstream-connected ponds or aquaculture ponds in rainy or dry season (Stedmon and Markager 2005). Because CDOM is the most vital watercolor parameters and is also linked to nutrients variability (Liu et al. 2014), certain studies have defined the lake or pond trophic state by nutrient-color paradigm with CDOM absorption values (Webster et al. 2008). Therefore, speedy development in the CDOM research has been experienced, with an upsurge of nearly 160 SCI research articles each year in the past two decades (2000–2020). But the CDOM study on the pond ecosystem is very rarely documented especially in India.

Indian Sundarbans is located in the southernmost part of West Bengal, India. The Indian Sundarbans encompasses the largest mangrove cover (~4264 km2) in the world (Sánchez-Triana et al. 2014). The Indian Sundarbans comprise a group of 102 islands out of which 54 are populated (Mukherjee and Siddique 2019). The area is a transitional zone between freshwaters coming from the Hooghly River and the saline water from the Bay of Bengal (Akhter et al. 2018). Besides the mangrove forest and an array of diverse flora and fauna, the Indian Sundarbans hosts a highly dense population of 4.4 million people (Census of India 2011). Though agriculture is the principal means of earning a livelihood in this region, aquaculture fish farming practice is also very common among the people of Sundarbans (Dubey et al. 2016). Ponds are integral to the daily life activities of the people of Indian Sundarbans. Because the pond is the main source of water supply for agriculture in a large part of the Indian Sundarbans till data. For fish farming obviously, pond or aquaculture is needed. Natural ponds (freshwater) as well as brackish water, both types of ponds are there in the studied area. Natural ponds are nourished by rainwater only. Freshwater fishes are cultured in natural ponds. On the contrary, brackish aquaculture ponds are nourished by saline River water daily or weekly or bi-monthly or fixed time intervals according to the requirement. There are several pieces of research on the Indian Sundarbans but no research has been documented on the CDOM study of the pond water in Indian Sundarban. In the present era, it is important to know the CDOM variability of different pond waters to describe the lentic (aquatic) ecosystems. The present research aims to describe the optical properties of CDOM of two different kinds of pond ecosystems (freshwater pond and brackish water pond) situated in the Indian Sundarbans.

12.2 Materials and Methods

12.2.1 Study Region and Sampling Plan

We have selected one brackish aquaculture pond (B-Lat: 21° 34′ 37.23″ N, Long: 88° 14′ 53.50″ E) near to the Edward Creek of Frazergaunge (close to Frazergaunge Fishing Harbour), Namkhana Block, Indian Sundarbans (Fig. 12.1) and one freshwater pond (A-Lat: 21° 34′ 45.13″ N, Long: 88° 15′ 41.75″ E) in the same area. After conducting a survey, it is revealed that Pond B (brackish water) is nourished by nearby Edward creek water (saline) weekly and Pond A (freshwater pond) is nourished by rainwater only. To examine the temporal inconsistency of the optical properties of CDOM, the study was conducted for one annual cycle (February 2016-January 2017).

Fig. 12.1
figure 1

The study area map showing the sampling locations in Indian Sundarbans

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During the study phase, twelve times surveys (sampling) were carried out in each pond. One survey was piloted at each month. 01 sample (mean of triplicate) was collected from each point during surveys. Hence throughout the one annual cycle, in total 24 samples were collected from both ponds. The whole survey was performed during daytime only. Pond water were collected from the surface using amber color glass bottles. Pond water temperature (PWT) and Pond water salinity (PWS) were recorded immediately on site. Samples were stored into pre-rinsed amber colored plastic containers for chlorophyll-a (chl-a) and total suspended matter (TSM). CDOM sample was stored in amber color glass bottle (Sasaki et al. 2005).

12.2.2 Analytical Procedure

PWS and PWT were recorded using a Multi-kit (Company:Merck, Made:Germany). To quantify chl-a content, 2 L of water sample was filtered through GF/F glass-fiber filter paper, and the filtrate were kept in a cylinder (liquid nitrogen) before analysis. The samples were extracted (90% aceton) and measured using a spectrophotometer (Shimadzu UV-visible 1600 double-beam) according to Parsons (2013) for chl-a. To quantify TSM, a thoroughly mixed water sample was filtered (by weighed glass-fiber filter, pore size: 0.45 μM), and the residue retained filter paper was dried at 103–105 °C. Electronic Balance (precision of 0.0001 g) was used to weigh the dried filter paper. The following equation is used to calculate the TSM value: TSM [g m−3] = (A − B) × 1000/C (Strickland and Parsons 1972), where, A = (weight of filter + dried residue) [g], B = weight of filter [g], C = volume of water filtered [m3].

To measure CDOM absorption, samples were stored in glass bottles (amber-colored) for 04 h to equilibrate at room temperature. At first, Whatman GF/F (47 mm) was used to filter the samples for removal of coarse particles. Secondly, 47 mm Nuclepore membrane filter (pore size: 0.2 mm) paper was used to filter the filtered samples again for the removal of fine partilces. Scanning is performed to measure the CDOM absorption from 300 to 750 nm using a spectrophotometer (Shimadzu UV-Visible 1600 double-beam). During scanning procedure a 10 cm (path-length) cuvetter was used. As a reference, Milli-Q water was used. Accorinding to Zaneveld and Pegau (1993), the measured absorbance were normalized to 0 at 600 nm to nullify the temperature dependent artifacts. The CDOM absorbance was calculated according to Sasaki et al. (2005).

The spectral slope (S) has been determined using an exponential regression (Stedmon et al. 2000). According to Para et al. (2010) S was caclculated after relating a non-linear exponential regression to original CDOM absorbance data estimated (range 400–600 nm). The R2 values (determination coefficients) estimated from the exponential fits were always >0.97. S delivers information regarding CDOM origin (marine vs terrestrial), with commonly lower slopes in fresh and coastal waters compared to the open ocean (Blough and Del Vecchio 2002).

12.2.3 Statistical Examination

The regression models were verified and Pearson correlation coefficient was estimated of salinity, TSM, chl-a, and S with aCDOM (440). Moreover, one-way ANOVA was tested to differentiate the mean of each parametes in different season. SPSS version 13.0 was used for all statistical analysis.

12.3 Results

12.3.1 Overall Hydrography of Both Ponds

The water temperature of the freshwater pond (Pond A) varied over a range of ~21.2 to ~31.8 °C during the course of sampling. The mean (seasonal) water temperature of Pond A was 25.1 ± 2.4, 27.3 ± 1.0, and 22.3 ± 2.2 °C during pre-monsoon, monsoon, and post-monsoon season respectively. The water temperature of the brackish water pond (Pond B) varied over a range of ~20.6 to ~30.6 °C during sampling phase. The mean (seasonal) pond (B) water temperatures were 24.6 ± 1.9 °C, 27.1 ± 1.3 °C, and 21.2 ± 2.0 °C during pre-monsoon, monsoon, and post-monsoon season respectively. The water salinity of Pond A ranged from 0.10 to 0.50 during the annual course of sampling (Table 12.1). A slight seasonal variability of salinity in Pond A was detected. The highest salinity values were observed in the pre-monsoon season (0.3 ± 0.1) followed by the post-monsoon (0.17 ± 0.1). The lowest salinity values was revealed during the monsoon period (0.1 ± 0.1). The water salinity of Pond B ranged from 11.1 to 30.1 during the sampling (Table 12.1). In Pond B, significant seasonal variability of water salinity was detected. The salinity were highest during the pre-monsoon period (28.2 ± 2.1) followed by the post-monsoon (20.2 ± 0.9). The lowest was observed during the monsoon months (13.7 ± 1.1). The water salinity was found much higher in Pond B than Pond A throughout the annual period of sampling (Fig. 12.2a). The TSM of Pond A ranged from 43.9 to 84.0 g m−3. TSM concentrations (Pond A) were higher in the monsoon months (75.0 ± 9.7 g m−3) followed by the post-monsoon (63.3 ± 13.7 g m−3) and pre-monsoon months (61.9 ± 18.7 g m−3). In Pond B, The TSM ranged from 70.1 to 135.7 g m−3. TSM concentrations (Pond B) were higher during the monsoon period (112.1 ± 17.7 g m−3) followed by the post-monsoon (87.5 ± 11.3 g m−3) and pre-monsoon period (73.9 ± 21.1 g m−3). Overall, TSM was higher in Pond B than Pond A in all the seasons (Fig. 12.2b). The chl-a of Pond A ranged from 13.2 to 32.4 mg m−3. Seasonal average chl-a values (Pond A) as low as 16.5 ± 2.2 mg m−3 were detected during monsoon. The highest mean (seasonal) value of chl-a (Pond A) was 24.0 ± 1.8 mg m−3 in pre-monsoon months (Fig. 12.2c). In Pond B, The chl-a ranged from 2.3 to 6.3 mg m−3. The chl-a concentrations (Pond B) were higher during the post-monsoon (5.1 ± 1.1 mg m−3) followed by the pre-monsoon (3.8 ± 1.1 mg m−3) and monsoon season (2.7 ± 1.1 mg m−3). However, the chl-a value was higher in Pond A than Pond B throughout the annual cycle (Fig. 12.2c).

Table 12.1 Mean ± standard deviation from mean along with minimum and maximum value (within parentheses) of the annual data set for the parameters salinity, TSM, chl-a, aCDOM (440) obtained from the respective ponds
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Fig. 12.2
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Spatio-temporal variability of a Salinity, b TSM, c Chl-a and d aCDOM(440) of two different kind of pond ecosystems (Fresh water and brackish water) in Indian Sundarbans

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12.3.2 Variability of Light Absorption Characteristics of CDOM of Fresh and Brackish Water Pond

In Pond A (freshwater), the aCDOM(440) ranged between 15.1133 and 27.5511 m−1 during the sampling period and the annual mean value was 20.4244 ± 3.7159 m−1. Whereas seasonal mean value was as lower as 17.1616 ± 2.1159 m−1 during pre-monsoon, which increased to 22.8029 ± 1.2112 m−1 in the monsoon period, and in the post-monsoon months, it was 21.3088 ± 3.0055 m−1 (Table 12.1, Fig. 12.2d). On the contrary, the aCDOM(440) varied between 2.2233 m−1 and 6.2238 m−1 during the annual cycle in Pond B (brackish water pond). The annual mean aCDOM(440) was nearly 5 times lower (i.e. 4.5866 ± 1.4975 m−1) in Pond B than Pond A. The seasonal mean value was as lower as 2.9186 ± 0.5136 m−1 during pre-monsoon, which increased to 5.9504 ± 1.0032 m−1 in the monsoon months and the post-monsoon season, it was 4.8908 ± 0.8061 m−1 (Table 12.1, Fig. 12.2d).

In Pond A, the spectral slope values (S) varied between 0.011 and 0.029 nm−1 during the study period. The annual mean slope was 0.0179 ± 0.0059 nm−1. The mean slope (seasonal) was lower (0.0147 ± 0.0016 nm−1) during monsoon months and higher (0.024 ± 0.0033 nm−1) in the pre-monsoon period. The seasonal mean slope showed a substantial difference during the study. Slope and aCDOM(440) showed a statistically significant exponential relationship (negative) among each other during the study period (Fig. 12.3). The coefficient of determination was found high during the annual cycle (R2 = 0.99, p < 0.05). In Pond B, the spectral slope values (S) ranged between 0.04 and 0.049 nm−1 during the entire study period. The annual mean slope was 0.0436 ± 0.0032 nm−1. The seasonal mean slope was 0.0472 ± 0.0065 nm−1, 0.041 ± 0.0044 nm−1, and 0.0425 ± 0031 nm−1 during pre-monsoon, monsoon, and post-monsoon season respectively. However, the seasonal slope (mean) did not display any significant difference in the entire study (one-way ANOVA: F = 0.75, p = 0.48). aCDOM(440) and S also revealed a statistically significant exponential relationship (negative) among each in the entire study in Pond B (Fig. 12.3). The coefficient of determination was found high during the annual cycle (R2 = 0.96, p < 0.05). The distribution of slope showed that mean slope was considerably higher in the brackish water pond (Pond B) compared to the freshwater pond (Pond A) (one-way ANOVA: F = 1.78, p < 0.05).

Fig. 12.3
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Correlation between aCDOM(440) with slope of a fresh and b brackish water pond

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12.3.3 Relationship Between CDOM and Other Relevant Hydrographical Parameters of Fresh and Brackish Water Pond

The R2 values between aCDOM(440) and salinity, TSM, and chl-a (seeing the entire dataset) of the freshwater pond (Pond A) depicted no significant linear relation between them (Figs. 12.4a, 12.5a, and 12.6a). When analyzing the data set of the brackish water pond (Pond B), we found significant linear relation of aCDOM(440) with salinity and TSM (Figs. 12.4b and 12.5b). aCDOM(440) did not reveal any such substantial relationship with chl-a in Pond B (Fig. 12.6b). Since the annual data of Pond B, aCDOM(440) was correlated with salinity according to the equation [salinity = −{4.6626 × aCDOM(440)} + 42.136] (R2 = 0.94, p < 0.05) (Fig. 12.4b). aCDOM(440) was also associated with TSM according to the equation [TSM = {11.032 × aCDOM(440)} + 40.702] (R2 = 0.65, p < 0.05) (Fig. 12.5b). According to the R2 value, CDOM vs salinity relationship was more significant (strong) than CDOM vs TSM relationship in the brackish water pond (Pond B).

Fig. 12.4
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Correlation between aCDOM(440) with salinity of a fresh and b brackish water pond

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Fig. 12.5
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Correlation between aCDOM(440) with TSM of a fresh and b brackish water pond

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Fig. 12.6
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Correlation between aCDOM(440) with chl-a of a fresh and b brackish water pond

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12.4 Discussion and Final Remarks

The study region is very close to the northern Bay of Bengal (nBoB) i.e. a archetypal sea- fresh water mixing area of the world. The brackish water pond (Pond B) is nourished by nearby Edward creek water (saline) weekly and Edward creek is connected with nBoB. Therefore, it is expected that CDOM and other physicochemical parameters of Pond B would show the same trend as well nBoB. Das et al. (2017) while working in the inshore of nBoB has revealed substantial temporal variations in salinity and TSM values (particularly comapring the non-monsoon and monsoon months). Hooghly River acts as a perennial origin of freshwater input in this zone, this input is higher during the monsoon. This higher fresh-water input caused to decrease the salinity and an increase in TSM during the monsoon season compared to the non-monsoon. The brackish water pond data also showed the same trend, decreased salinity, and increased TSM values in the monsoon months. Chl-a values of the lentic ecosystem (in favorable conditions i.e. light, nutrients, etc.) are higher than the open water system (Bhattacharyya et al. 2020). Chl-a magnitudes also showed significant temporal changeability throughout the year in Pond B due to the availability of sunlight and nutrients. Though the annual mean Chl-a values were found five times lower in Pond B (brackish water) than Pond A (freshwater). Analyzing the annual data, it is observed that the TSM value is much higher in Pond B than Pond A. So that the light availability was higher in the freshwater pond (Pond A) and thus the chl-a values were higher in Pond A. But the water salinity was negligible in the freshwater pond (Pond A, annual mean salinity = 0.19 ± 0.15) because there was no source to contribute the salinity in Pond A whereas Pond B (brackish water pond) was nourished by saline Edward creek water every week. So that Pond B showed higher water salinity values (annual mean salinity = 20.75 ± 7.18). Guhathakurta and Kaviraj (2000) while working in the brackish water pond of the Indian Sundarban, revealed that the annual mean salinity was 13.01 ± 4.11 and 3.75 ± 5.07 in Sagar Island and Kakdwip respectively. Sagar Island is situated very near to the nBoB and thus showed a higher salinity value in the brackish aquaculture. The present study region is also very near to the nBoB thus showing a higher salinity value in the brackish water pond (Pond B).

aCDOM (440) exhibited a substantial temporal variation in both ponds in uniformity with the dynamics of salinity and TSM, i.e. considerably higher values were detected in the monsoon months and lower values were estimated in pre-monsoon and post-monsoon. But in the freshwater pond, the aCDOM (440) value of post-monsoon season (i.e. 21.3088 ± 3.0055 m−1) is very close to the monsoon season (22.8029 ± 1.2112 m−1). During the monsoon, terrestrial organic matter runoffs enter the pond ecosystem through rainwater. This might be the reason to find higher aCDOM (440) values in Pond A during monsoon. But the higher value during the post-monsoon season indicated some other issues. Seeing the temporal variations of aCDOM(440) it can be showed that the aCDOM(440) levels increased in the freshwater pond in post-monsoon when a alike increase in chl-a was also detected. This might be an sign of autochthonous CDOM production in the freshwater pond (Pond A) since chl-a magnitude designates higher phytoplankton which when degrades could act as a source of CDOM (Hong et al. 2012; Guo et al. 2011). In the brackish water pond, we have found a similar trend like Das et al. (2017) revealed while working in the nBoB. Only the magnitude of aCDOM(440) was higher because of the lesser dilution effect in the Edward creek water.

Spectral slopes indicate the rate at which CDOM absorption declines with increasing wavelength over various ranges. These changes have been attributed to photochemical and biological processing and differences in molecular weight (Osburn et al. 2011). Higher spectral slopes have been attributed to autochthonous material of recent production (Zhang et al. 2011). Wallace (2020) described that spectral slope (S) data decreased from dry season (low rainfall) to wet season (high rainfall) while working on the Hog’s Pond, USA. In Hog’s pond, in the dry season (low rainfall), spectral slopes S had an mean value was 0.018 nm−1, (standard deviation of S 0.0028 nm−1). In the wet season (high rainfall), spectral slopes S had an mean value was of 0.012 nm−1 (standard deviation of S 0.00088 nm−1). In the present study also, we have found a lower value of S in the monsoon season (higher rainfall) in both ponds indicating a very rare chance of autochthonous character of CDOM. Miao et al (2019) while working in the freshwater lake (i.e. Lake Taihu and Chaohu in China) also found the same trend of S (range 0.0154–0.0291 nm−1). During monsoon, terrestrial organic matter runoff to the pond ecosystem through rainwater increases the CDOM values. Hence, the CDOM of fresh water and brackish water pond is allochthonous in the Indian Sundarbans regarding the present study except for post-monsoon season in the freshwater pond.

Several studies carried out in the estuarine water of the world detected that CDOM acted conservatively and presented significant correlations (negative) with salinity (e.g., Bowers et al. 2004; Para et al. 2010; Zhang et al. 2013). The present study also observed a strong negative relationship between CDOM and salinity following the conservative behavior in the brackish water pond. In the freshwater pond, CDOM did not show any significant relation with salinity thus might indicate non-conservative behavior.

In the future, fluorometric estimations of CDOM along with the depiction of the CDOM sources and chemical configuration should be done in the pond ecosystem of Indian Sundarbans for a better understanding of CDOM dynamics. Moreover, we have cover only one block (namely Namkhana) of the Indian Sundarbans in the present study, sample number should be increased to portray the overall CDOM variability of the pond ecosystem in Indian Sundarban. Although, the present study could be a good case study for future researchers regarding CDOM dynamics of pond ecosystems in Indian Sundarban.