Rev. Biol. Trop. (Int. J. Trop. Biol.) • Vol. 69(1): 60-76, March 2021

Effect of climato-environmental parameters on chlorophyll a

concentration in the lower Ganga basin, India

Soma Das Sarkar

, Uttam Kumar Sarkar

*, Malay Naskar

, Koushik Roy

1,2

, Arun Kumar Bose

Subir Kumar Nag

, Gunjan Karnatak

& Basanta Kumar Das

1. ICAR-Central Inland Fisheries Research Institute, S.N. Banerjee Road, Barrackpore, Kolkata-700120, West Bengal,

India; soma.das@icar.gov.in, uksarkar1@gmail.com, malaynaskar@icar.org.in, koushik.roy.89@gmail.com, arun_

bose06@yahoo.co.in; nagsk_67@rediffmail.com, gunjankarnatak87@gmail.com, basantakumard@gmail.com

2. Faculty of Fisheries and Protection of Waters, University of South Bohemia, Ceske Budejovice 37005, Czech Republic.

* Correspondence

Received 03-VII-2020. Corrected 15-X-2020. Accepted 19-X-2020.

ABSTRACT. Introduction: Chlorophyll a concentration proxies the phytoplankton biomass which directly

involves in signifying the production functions of aquatic ecosystem. Thus, it is imperative to understand their

spatio-temporal kinetics in lotic environment with reference to regional climatic variabilities in the tropical

inland waters. Objective: In-situ studies were conducted to examine the changes in phytoplankton biomass in

lower Ganga basin as influenced by various environmental parameters under regional climatic variability during

2014-2016. Methods: Firstly, the most key influential environmental parameters on riverine Chl-a concentration

were determined. Then the direct cascading effect of changing climatic variables on key environmental param-

eters were derived through modeling and quantified probable changes in mean Chl-a concentration in the lower

stretch of river. Results: Only five environmental parameters namely water temperature, total dissolved solid,

salinity, total alkalinity and pH were key factors influencing Chl-a (Multiple R

: 0.638, P < 0.05). Present esti-

mates indicate that if the present rate of regional climatic variability over the last 3 decades (mean air tempera-

ture + 0.24 °C, total annual rainfall -196.3 mm) remain consistent over the next three decades (2015-2045), an

increase in mean Chl-a by + 170 µgL

-1

may likely be expected grossly reaching about 475.94 µg L

-1

by the year

2045 or more. Conclusions: The present study is first such comprehending a gross hint towards the probable

ecosystem response with an alternative model based methodology in data-deficient situations. Subsequently, the

output would also be of great benefit for increase water governance and developing strategy protocol for sustain-

able water management for greater ecosystem services.

Key words: chlorophyll a; climate change; environmental variable; predictive modeling; River Ganga.

Tropical riverine fisheries support millions

of people with food, nutrition, livelihood etc.

(Vass et al., 2011; Sarkar et al., 2019) and hence

contributing indirectly to a large extent to the

national economy of the associated countries,

especially India. The Ganga river basin has

already witnessed anthropogenic pressure due

to deforestation, water abstraction, irrational

fishing, mining, industrialization, economic

development, navigational movements (Das

Sarkar et al., 2019a). Changes in the climatic

regime such as increased water temperature,

uncertain monsoon variation and increased rate

of abnormal climatic events in the coastal area

have been the recent addition. Synergistically,

these affect the ecosystem biota, productivity

Das Sarkar, D., Sarkar, U.K., Naskar, M., Roy, K., Bose, A.K., Nag, S.K., … Das, B.K.

(2021). Effect of climato-environmental parameters on chlorophyll a concentration

in the lower Ganga basin, India. Revista de Biología Tropical, 69(1), 60-76. DOI

10.15517/rbt.v69i1.42731

ISSN Printed: 0034-7744 ISSN digital: 2215-2075

DOI 10.15517/rbt.v69i1.42731

Rev. Biol. Trop. (Int. J. Trop. Biol.) • Vol. 69(1): 60-76, March 2021

and ultimately livelihood security (Sharma,

Joshi, Naskar, & Das, 2015) of poor fishermen.

Primary production in an aquatic system

depends on phytoplankton biomass, the food

for planktivorous fishes. It is also a parameter

hinting overall system productivity. Chloro-

phyll is the green colouring matter which

enables photosynthesis to occur in the presence

of light. Out of nine different types of chlo-

rophyll, chlorophyll-a plays a major role in

photosynthesis (Ji, 2017) and hence indicates

phytoplankton biomass (Gregor & Marsalek,

2004). Although literature surveys indicate

several studies on ecology and biodiversity of

river Ganges (Manna et al., 2013; Roshith et

al., 2018); however, the role of climatic varia-

bility and their impact on aquatic ecosystem

using different biota are very scanty. Thus,

in this rising scenario of global warming it is

imperative to understand the impact of climatic

variability on primary producers. Microalgae or

phytoplankton form a valuable biological para-

meter in climate models (Ghosal, Rogers, &

Wray, 2000). Climate change itself represents

a complex combination of stressors, inclu-

ding alterations in temperature (Webb, Hannah,

Moore, Brown, & Nobilis, 2008), elevated

atmospheric CO

(Field, Barros, Mach, & Mas-

trandrea, 2014), and increased frequency and

intensity of droughts and extreme flow events

(Barnett, Adam, & Lettenmaier, 2005; Milly,

Dunne, & Vecchia, 2006). Changes in phyto-

plankton biomass vis-à-vis primary productivi-

ty in aquatic ecosystems resulting from climate

change are often attributable to increased water

temperatures and/or increased nutrient loading

(Wrona et al., 2006). In recent years, the focus

has been on predictive and/or trend identifica-

tion modeling with either isolated or combined

influence of an environmental driver or a set

of drivers on phytoplankton biomass in rivers.

The models developed by Elliot, Thackeray,

Huntingford, & Jones (2005), Mooij, Janse,

Domis, Hulsmann, & Ibelings (2007), Friberg

et al. (2009) are some of the examples where

the implications of warming water temperature

on riverine phytoplankton biomass have been

modeled. Such models formed the basis of the

prediction made in Field et al. (2014) regar-

ding the increase of primary productivity and

tendency of eutrophication in majority of the

global aquatic ecosystems.

Review of literature has revealed that

studies on the climate driven changes on pri-

mary productivity and/or phytoplankton bio-

mass in rivers have been mostly concentrated

in temperate to sub-tropical climate (Klapper,

1991; Friberg et al., 2009; Sipkay, Kiss-Keve1,

Vadadi-Fulop, Homoródi, & Hufnagel, 2012;

Cloern, Foster, & Kleckner, 2014) and studies

on tropically situated Indian rivers is absent.

The only study in Indian context was in Bay

of Bengal and Arabian sea that too not on the

exact hypothesis per se but a related conclusion

was drawn from their expert knowledge and

extrapolating their results (Chaturvedi, Meghal,

& Jasraj, 2013). Authors have hypothesized

increased decadal changes of Chl concentration

due to high nutrient influx through freshwater

river drainage and increase in temperature due

to global warming. However, authors have not

developed quantitative relationship between

Chl concentration and climate-environmental

parameters. Although majority have repor-

ted an increase in primary production and/or

phytoplankton biomass in rivers under future

changed climate scenario, but few studies have

indicated the opposite i.e. decreased riveri-

ne phytoplankton population under changed

climatic scenario (Lewandowska & Sommer,

2010; Sommer & Lengfellner, 2008).

Modeling studies often face the problems of

lack of required data and access to them which

is considered one of the primary hindrances in

climate change related modeling research (Por-

ter et al., 2005; Sipkay, Kiss, Vadadi-Fulop,

& Hufnagel, 2009). Developing countries like

India have dearth of long-term data on riverine

chlorophyll concentration or any aquatic sys-

tem per se; although time series data of climatic

variables are well maintained and abundantly

available. The present approach explored an

innovative and alternative methodology which

could generate baseline information on trends

and gross quantification of changes in Chl-a

concentration in absence of long term data.

Rev. Biol. Trop. (Int. J. Trop. Biol.) • Vol. 69(1): 60-76, March 2021

Therefore, in the absence of long term and

continuous data on Chlorophyll-a concentra-

tion in River Ganga, it is worthwhile to adopt

a model-based framework to identify the key

climato-environmental parameters and their

isolated individual influences on phytoplank-

ton biomass (Chl-a concentration); thereby,

budgeted the Chl-a concentration considering

the multiple linear effect of all the key environ-

mental parameters identified. For budgeting,

the direct cascading effect of regional climate

change (increasing mean air temperature and

decreasing rainfall) on influential environmen-

tal parameters were first quantified individually

and then plugged together to obtain the estima-

tes of changes expected in Chl-a concentration

as per the present trend of changing climate

within the study area.

In the present study, the photosynthetic

pigment ‘Chlorophyll-a’ (henceforth abbrevia-

ted as Chl-a) was used as a potential marker

that reflects phytoplankton biomass vis-à-vis

primary production potential of the aquatic

system. With this background, the study was

conducted to examine the changes in phyto-

plankton biomass in lower Ganga basin as

influenced by various environmental parame-

ters under regional trend of climatic variability.

The objective of this study was to first identify

the most influential environmental parameters

on riverine chlorophyll-a concentration and to

quantify the direct cascading effect of chan-

ging climatic variables on key environmental

parameters through modeling for deriving the

expected changes in Chl-a concentration in the

lower Ganga basin.

MATERIALS AND METHODS

Study area: The lower stretch of river

Ganga (locally known as river Hooghly)

was selected in this study. Monthly water

samples were collected in duplicate during

October, 2014 to September, 2016 from two

different sites viz. Triveni (22°59’411’’ N &

88°24’310’’E) and Godakhali (22°23’629’’ N

& 88°07’948’ ’E) as shown in Fig. 1. Both

the stations are part of posterior lower stretch

of River Ganga. Each station was further sub-

divided into two sub-stations (Triveni-1, Trive-

ni-2 and Godakhali-1, Godakhali-2) to obtain

representative samples.

Fig. 1. Geographical location of the study area. Blue line indicates the longitudinal course of river Ganga.

Rev. Biol. Trop. (Int. J. Trop. Biol.) • Vol. 69(1): 60-76, March 2021

Water sampling, analysis and estima-

tion of Chl-a: Water samples in duplicate

were collected from sub-surface region of the

water column in 1 Liter HDPE amber bottles.

A total of twelve physico-chemical parameters

viz. water temperature (°C), dissolved oxygen

(DO), free carbon-di-oxide (CO

), pH, elec-

trical conductivity (EC), total hardness, total

dissolved solid (TDS), total alkalinity (TA),

salinity, Secchi-disc transparency, nitrate and

phosphate were analyzed following Standard

Methods (APHA, 2012).

Samples for estimation of chlorophyll -a

(Chl-a) were collected separately and estima-

ted following APHA (2012). Samples from

each replicate were filtered in vacuum with

a membrane filters and Millipore membrane

filter of pore size 0.45 µm. The residues were

wrapped in aluminum foil and transported to

laboratory using refrigeration unit in a mobile

laboratory van and frozen (-20 °C) immediately

upon reaching laboratory. Chl-a pigment was

extracted with acetone using a digital labora-

tory tissue homogenizer (RQT-127AD), Remi

make, India. The absorbance of the extracts

at wave length 664 nm, 647 nm and 630 nm

were recorded in a UV-VIS spectrophotometer.

Concentration of Chl-a in water was calculated

using following equation: Chl-a = 11.85 (OD

664)-1.54 (OD 647)-0.08 (OD 630), where,

Chl-a = concentration of Chl-a in µgL

-1

, also

known as ppb (parts per billion).

Climatic data analysis: Standard “30-

years normal” data analysis procedure was

used. Daily mean air temperature and preci-

pitation data of 30 years from 1980 to 2015

was obtained from Indian Meteorological

Department (IMD). The data corresponding to

study location was extracted from 1° x 1° data

by using ‘Raster package’ in ‘R’ (R Core Team,

2015). Subsequently monthly means of air tem-

perature and total rainfall was computed from

the extracted daily time series data. Finally,

annual mean air temperature and total annual

rainfall were computed from monthly data. A

simple linear trend analysis was carried out to

find out the long-term trend. The total rainfall

during pre-monsoon, monsoon and post-mons-

oon) were used to quantify changes in seasonal

rainfall pattern (Karnatak et al., 2018).

Statistical analyses and modelling

Modeling climato-environmental

influence on Chl-a concentration: The data

was analyzed in multiple steps, employing

various statistical tools. In order to identify the

key climato-environmental parameters influen-

cing Chl-a concentration in river, a stepwise

regression with collinearity diagnostics was

applied, designating all environmental para-

meters as explanatory variables and Chl-a

concentration as the response variable. Further

the relative importance of each environmental

parameter (influencing Chl-a) in the optimized

linear model was computed in R software (R

Core Team, 2015) by using the computer inten-

sive and most recommended method (Linde-

man, Merenda, & Gold, 1980). Finally, looking

beyond linearity, non-linear Generalized Addi-

tive Model (GAM) (Wood, 2006) was applied

to unravel the pattern of individual influence

of the key environmental parameters on Chl-a.

Budgeting changes in Chl-a concentra-

tion under present rate of climate change:

The cascading effect of variations in climatic

factors (mean air temperature and rainfall) on

the key environmental parameters influencing

Chl-a concentration was quantified. To avoid

complexity in interpretation, only the direct

effect of climatic factors on the key environ-

mental parameters was quantified irrespective

of the interactions among other non-key para-

meters existing in the aquatic ecosystem. This

was accomplished through a two-step strate-

gy. Firstly, the relationship between the key

environmental parameter and climatic factors

(mean air temperature and rainfall) was esta-

blished by simple regression, taking climatic

parameters as independent variable and envi-

ronmental parameters as dependent variables

in order to quantify the nature and degree

of change in environmental parameters per

unit change of climatic factors, irrespective of

Rev. Biol. Trop. (Int. J. Trop. Biol.) • Vol. 69(1): 60-76, March 2021

spatial differences. Secondly, changes in mean

air temperature and rainfall over the sampling

stations during last three decades (1980-2015)

was assessed using IMD data and the present

rate of climate change was assumed to remain

true in the next three decades (2015-2045). The

present rate of change in mean air temperature

and rainfall which may cause direct changes in

the key environmental parameters ultimately

manifesting into change in Chl-a concentration

has been derived empirically by plugging the

estimates from regression models developed.

Finally, these predictive values of environ-

mental variables were utilized in the optimized

regression model for Chl-a to derive predictive

changes in Chl-a concentration.

RESULTS

Climate change trend: Analysis of the

IMD data on mean air temperature has revea-

led a unanimous warming trend among the

studied sites. During the period of 1980-2015

the mean air temperature has increased by 0.24

°C (Fig. 2). It has also been recorded that the

total annual rainfall during the same period has

decreased by196.3 mm (Fig. 3).

Modeling climato-environmental influence

on Chl-a concentration

Present observations: During the study

period, chlorophyll-a (Chl-a) concentration

were ranged between 18.92-1 565.45 µgL

-1

Fig. 2. Trend of increasing mean air temperature along lower stretch of river Ganga in West Bengal.

Fig. 3. Decreasing trend of total annual rainfall along lower stretch of river Ganga in West Bengal.

Rev. Biol. Trop. (Int. J. Trop. Biol.) • Vol. 69(1): 60-76, March 2021

with an annual mean of 305.94 ± 52.25 µgL

-1

The annual distribution pattern of Chl-a encou-

ntered is depicted in Digital appendix 1. The

inter-site variability (spatial) in Chl-a con-

centration was statistically insignificant (p =

0.679) as revealed by ANCOVA. Likewise,

Chl-a concentration did not vary significantly

between seasons pre-monsoon, monsoon and

post-monsoon (Pre-M vs. M P = 0.879; Post-

M vs. M P = 0.825). The overall descripti-

ve account of physico-chemical and climatic

parameters recorded during the study period is

presented in Table 1.

The chlorophyll budgeting framework

used in the present study therefore took into

the account merged dataset of two sampling

locations since no spatial variability exists. An

attempt was also made to chalk out the seasonal

thresholds (cut-offs) of Chl-a concentration (if

any) to demarcate signature minima or range of

Chl-a concentration characteristic in any sea-

son; be it pre-monsoon (Feb-May), monsoon

(Jun-Sept) or post-monsoon (Oct-Jan). These

thresholds were supposed to be super imposed

over thermal and precipitation windows to deri-

ve season specific climate optima (temperature

and rainfall) within which such thresholds are

attained in natural conditions. All this informa-

tion was assumed to serve better in regional

climate change impact assessment over lower

Gangetic basin on riverine ecological produc-

tivity. Unfortunately, no such season-specific

thresholds could be identified and therefore,

any further analysis involving seasonal influen-

ce on riverine chlorophyll was purposively

skipped. Following this, only annual data like

annual mean chlorophyll concentration were

forecasted through the budgeting framework;

not the seasonal changes in Chl-a over next

three decades.

Cumulative and overall influence of

habitat parameters on Chl-a concentration:

The study indicated that among 14 climato-

environmental parameters considered (mean air

temperature, water temperature, rainfall, secchi

disc transparency, pH, dissolved oxygen, free

carbon dioxide (CO

), total alkalinity, electrical

conductivity, total dissolved solids, phosphate,

nitrate and salinity), only five environmental

parameters namely water temperature, TDS,

salinity, TA and pH were found to be key

factors influencing Chl-a concentration in the

river as revealed through stepwise regression.

The generated optimized model was Chl-a =

-5.796 + (0.00521 * Total Alkalinity) + (0.347 *

pH) - (0.000389 * Salinity) + (0.00451 * TDS)

+ (0.0652 * Water temperature), which could

explain 63.8 % variability of the chlorophyll-a

concentration. Water temperature (P < 0.01),

TABLE 1

Descriptive account of habitat parameters recorded in River Ganga during the study period

Parameters Stretch

Water temperature (°C) 19.8-34.0 (27.64 ± 0.58)

Dissolved oxygen (ppm) 1.6-10 (4.51 ± 0.28)

Free carbon dioxide (ppm) 0-10 (3.95 ± 0.5)

pH (units) 7.5-8.6 (8.1 ± 0.04)

Total Dissolved Solids (ppm) 113-301 (228.76 ± 6.28)

Conductivity (µmhos) 160.1-432 (320.89 ± 8.87)

Alkalinity (ppm) 40-140 (100.41 ± 2.88)

Salinity (ppm) 80.3-700 (202.86 ± 18.96)

Nitrate (ppm) Trace-1.72 (0.84 ± 0.07)

Phosphate (ppm) Trace-0.56 (0.1 ± 0.07)

Rainfall (mm) 0-882.5 (150.47 ± 33.09)

Chlorophyll-a (µgL

-1

) 18.92-1565.45 (305.94 ± 52.25)

Data presented in range and mean ± standard error of mean (in parentheses).

Rev. Biol. Trop. (Int. J. Trop. Biol.) • Vol. 69(1): 60-76, March 2021

total dissolved solids (P < 0.01), total alkalinity

(P < 0.05) and pH (P < 0.05) had significant

positive influence on Chl-a concentration while

salinity (P < 0.05) had a significant negative

influence on the same.

In terms of the relative importance of

individual environmental parameters (Fig. 4) in

influencing Chl-a concentration, water tempe-

rature (33.13 % of R

) was the most important

while place effect was least important (3.4 % of

).The relative importance of environmental

parameters influencing Chl-a concentration of

the river can be summarized as: water tempera-

ture > alkalinity > TDS > pH > salinity > place-

effect; depicted in Fig 4. The ‘place effect’

diagnostics in the model reveal that Chl-a con-

centration in the river had no significant spatial

difference between two sites. More specifica-

lly, there is no significant spatial difference bet-

ween the two sites of River Ganga in terms of

environmental parameters-Chl-a relationship.

Individual influence pattern of habitat

parameters on Chl-a concentration: Indi-

vidual influence(s) of identified key envi-

ronmental parameters on Chl-a concentration

have been discussed in descending order of

their importance.

Temperature: GAM models showed that

Chl-a concentration in rivers can be expected

to initially increase with rise in water tempe-

rature (Fig. 5) but may reach a plateau beyond

30 °C (Deviance explained: 12.2 %). It is

Fig. 5. Influence of water temperature on chlorophyll a concentration.

Fig. 4. Relative importance of key environmental parameters for explaining variability in chlorophyll a concentration.

Rev. Biol. Trop. (Int. J. Trop. Biol.) • Vol. 69(1): 60-76, March 2021

emphasized that the present model might be

useful mostly for pattern identification and may

not be optimum for prediction purposes due to

short period of study. The prediction belt in the

model shows Chl-a concentration may either

rise or fall when water warms up beyond 34 °C

which requires further study to draw an infe-

rence. A gross statement can be made on the

fact that although global warming manifested

into warmer water temperatures round the year

may initially result in increased phytoplankton

population but in the long run this effect might

not prove beneficial. The study hints at possi-

bility of increased phytoplankton population

in the coming years due to enhanced thermal

regime mostly during winter. Water tempera-

ture was also found to be the most important

driver for influencing Chl-a concentration and

has been given special emphasis during model

interpretation and discussion in climate change

perspective; assumed as primary driver.

Total Alkalinity: A progressive increase

in Chl-a concentration with increasing alkali-

nity of river water has been predicted by GAM

model (Deviance explained: 39.6 %) (Fig. 6).

Furthermore, not much change in phytoplank-

ton population can be expected initially when

the total alkalinity values remain below 100

ppm while as the total alkalinity in river

increases beyond 100 ppm a steep response in

phytoplankton biomass is noticeable due to a

progressive increase in Chl-a concentration.

Alkalinity values > 100 ppm exists in the lower

stretch of River Ganga especially throughout

the winter followed by early (February to

March) and midsummer (March to April). An

increasing trend in mean air temperature and

decreasing rainfall in the area of study indicates

a likely increase in average alkalinity values of

river water in the coming years manifested into

increased phytoplankton biomass.

Total dissolved solids (TDS): No particu-

lar trend could be observed for change in Chl-a

concentration with increase in TDS owing to

the multiple peaks in the GAM model but pre-

diction belt in the model shows extreme TDS

> 300 ppm and < 175 ppm may be considered

as the critical limit beyond which steep fall in

Chl-a concentration can be expected (Deviance

explained: 72.9 %) (Fig. 7). Eliminating the

extremes of TDS, a moderate value of 210-

230 ppm TDS may be considered optimum for

healthy Chl-a concentration. This implies that

high TDS in river during intense summer and

diluted TDS due to high rainfall are both detri-

mental for phytoplankton population.

pH: Similarly, for pH-Chl-a relationship

indicated that water pH might have a linear

influence as the non linear influence seems to

be weak (Deviance explained: 5.29 %) (Fig. 8).

From the model it can be observed that respon-

se of phytoplankton biomass with changes in

pH of river water alone is very subtle and the

model does not permit any conclusion in this

Fig. 6. Influence of total alkalinity on chlorophyll a concentration.

Rev. Biol. Trop. (Int. J. Trop. Biol.) • Vol. 69(1): 60-76, March 2021

regard. The purpose of the model generated is

only for pattern identification.

Salinity: The influence of salinity on

Chl-a concentration showed that salinity may

have a linear influence owing to the near

straight curve symmetry and broad prediction

belt of the model at higher salinities implying

low confidence (Deviance explained: 3.43 %)

(Fig. 9). It is highlighted that the model might

be useful mostly for pattern identification

and may not be most favorable for prediction

purposes. From the model it is evident that

response of phytoplankton population in river

Fig. 7. Influence of TDS on chlorophyll a concentration.

Fig. 8. Influence of water pH on chlorophyll a concentration.

Fig. 9. Influence of salinity on chlorophyll a concentration.

Rev. Biol. Trop. (Int. J. Trop. Biol.) • Vol. 69(1): 60-76, March 2021

with changes in salinity alone is very subtle.

It was further observed that the phytoplankton

population may likely remain stable as the

salinity in the river increases up to around 600

ppm under increased temperature and reduced

rainfall scenario. Beyond 600 ppm salinity

a probable reduction in Chl-a concentration

vis-à-vis phytoplankton biomass might occur,

which can happen especially during summer.

Budgeting changes in Chl-a concentration

under present rate of climatic variability

Budgeting results: Change in mean

annual temperature over last three decades was

considered in the budgeting framework but not

the minimum or maximum air temperature data

(Winslow, Read, Hansen, Rose, & Robertson,

2017). Likewise, changes in total annual rain-

fall over last three decades were considered but

not the seasonal, minima or maxima. Accordin-

gly, the forecasting was done on mean annual

Chl-a basis (elaborated above).

A trend of 0.24 °C rise in mean air tempe-

rature in West Bengal may reflect in increased

mean water temperature by + 0.20 °C (R

= 0.843, Fig. 10) in the coming years. This

alone may lead to an increase in mean Chl-a

concentration by + 10µgL

-1

in the estimates of

multiple linear model (Multiple R

= 0.684)

as deduced from the linear models. Likewise,

a 0.20 °C increase in mean water temperature

= 0.377) and 196.3 mm decrease in rainfall

= 0.577) may result in a net gain of average

TDS by + 26.21 ppm which may alone lead to

increased mean Chl-a concentration by +110

µgL

-1

(Multiple R

= 0.684) in the coming

years. There is a possibility that, 196.3 mm

decrease in rainfall over the area may probably

bring an increase in average salinity by + 68.51

ppm (R

= 0.261, low confidence) and average

total alkalinity by + 9.03 ppm (R

= 0.292,

low confidence) in the coming years that may

individually manifest changes in mean Chl-a

concentration by -20 µgL

-1

(Multiple R

0.684) and +50 µgL

-1

(Multiple R

= 0.684),

Fig. 10. Air-water temperature regression in the river.

Rev. Biol. Trop. (Int. J. Trop. Biol.) • Vol. 69(1): 60-76, March 2021

respectively. Lastly, a 0.20 °C increase in mean

water temperature (R

= 0.421) and 196.3 mm

decrease in rainfall (R

= 0.335) may be reflec-

ted through a net gain in average pH by + 0.09

units that may alone increase mean Chl-a con-

centration by + 30 µgL

-1

(Multiple R

= 0.684)

in the coming years.

Inference of Budgeting: Keeping in view

the rise in mean air temperature (+ 0.24 °C) and

decrease in rainfall (196.3 mm) during the last

3 decades (1980-2015) over the lower stretch

of river Ganga passing through West Bengal,

India (Fig. 1, Fig. 2), we assume that this pre-

sent rate of climate change might remain true in

the next three decades (2015-2045) . Based on

this assumption and quantified cascading effect

of such climatic change on the key environ-

mental parameters influencing Chl-a concen-

tration, our model summarizes that a gradual

increase in the existing mean Chl-a concentra-

tion (305.94 ± 52.25 µg L

-1

) by approximately

+ 170 µgL

-1

(ppb) (Multiple R

= 0.6839) in the

lower Ganga basin may probably occur over

the next three decades (2015-2045).

DISCUSSION

Importance of Chl-a mapping and envi-

ronmental influence: Chl-a indicates health

of water in aquatic ecosystem (Boyer, Christo-

pher, Peter, & David, 2009). It also provides

trophic status based on primary productivity

of the ecosystem (Das Sarkar et al., 2020) and

measures trophy index (Bbalali, Hoseini, Ghor-

bani, & Kordi, 2013). Ecohydrological interac-

tion studies in natural river system showed that

physical variables such as, water discharge,

suspended solids and turbidity regulate the

development of phytoplankton biomass criti-

cally in comparison with nutrients (Salmaso &

Braioni, 2008).

In the present study, only five environ-

mental parameters namely water temperature,

TDS, salinity, TA and pH were found to be

key factors influencing Chl-a concentration

in the river. The nature of their individual

influence was found to be in conformity with

published literatures. Rising temperature has

aided to increase growth and productivity of

algae which may be boosted by the higher

enzymatic activity (Nweze & Ude, 2013). The

changing temperature and precipitation pattern

over lower Gangetic stretch are in the line with

Rathore, Attri, & Jaswal (2013), Paul & Birthal

(2015), Naskar, Roy, Karnatak, Nandy, & Roy

(2017). High nutrient influx through freshwater

river drainage in lower Ganga has supported

rising trend of chlorophyll concentration. Simi-

lar positive impact of elevated water tempera-

ture for short term on phytoplankton biomass

productivity and photosynthetic activity is also

suggested tropical rice fields (Roger & Kulas-

ooriya, 1980) and in subtropical waters (Willis,

Chuang, Orr, Beardall, & Burford, 2019). A

positive correlation was found between alka-

linity and TDS with Chl-a concentration (P <

0.01) in the present study. The study further

denotes a positive correlation (P < 0.05) bet-

ween Chl-a and pH which may be due to higher

phytoplankton photosynthesis and biologi-

cal productivity. Simultaneously, time series

models also showed influence of nutrients and

pH on phytoplankton biomass for shorter time

span (Jeong, Kim, Whigham, & Joo, 2003).

Salinity (P < 0.05) ingression shows a signifi-

cant negative influence on the Chl-a concentra-

tion in the lower Ganga which may be due to

inability in generating turgor pressure to produ-

ce intracellular osmolarity needed with incre-

asing salinity (Mitra, Zaman, & Raha, 2014).

However, present study of identifying the rela-

tive influence of environmental parameters on

Chl-a concentration shows water temperature

(33.13 % of R

) as the most significant variable

followed by TA (29.5 % of R

) and TDS (23.1

% of R

). Rainfall plays a vital role in mixing

of environmental variables and also responsible

for physical dispersal of phytoplankton in wet-

lands of tropical semiarid region (Brasil et al.,

2020), hence resulting into considerable reduc-

tion in chlorophyll concentration. At the same

time, rainfall did not contribute as influential

climatological parameter in the present study.

Taken together, it could be interesting to note

that this study has also sketched out the relative

Rev. Biol. Trop. (Int. J. Trop. Biol.) • Vol. 69(1): 60-76, March 2021

importance of climato-environmental parame-

ters influencing Chl-a concentration in inland

open waters which is unavailable in most of

the studies consulted. Therefore, the present

information on relative importance may also

be regarded as one of the recent additions

towards better understanding of phytoplankton

response to environmental dynamics in riverine

ecosystems.

Relatively high dependence on water

temperature and relevance for climate

research: The prime importance of water

temperature besides other environmental para-

meters on dictating phytoplankton production

or phytoplankton biomass in lotic systems

such as rivers is well documented (Schabhuttl

et al., 2013; Wrona et al., 2006). Climate

change is altering the physical, chemical and

biological characteristics of freshwater habitats

(Hartmann et al., 2013) including inland fish

and fisheries (Carlson & Lederman, 2016).

The importance of temperature over other

water quality parameters in influencing Chl-a

concentration observed during the present

study has both biological and ecological basis.

According to Cloern et al. (2014), phytoplank-

ton growth rate is regulated by water tempe-

rature, nutrient concentrations and forms, and

the amount and quality of photosynthetically

available radiation (PAR).

From the ecological perspective, tempera-

ture regulates ecosystem functioning directly

by influencing primary production (Dallas,

2008). Studies have shown that increase tem-

perature is accompanied by a shift in the

dominance of phytoplankton groups from dia-

toms (< 20 °C) to green algae (15 to 30 °C) to

blue-green algae (> 30°C) (DeNicola, 1996).

According to DeNicola (1996), phytoplank-

ton biomass increases with temperature from

approximately 0 to 30 °C and decreases at 30

to 34 °C; while species diversity increases from

0 to 25 °C and decreases at temperatures > 30

°C. This was further confirmed in a lab-based

experiment by Schabhuttl et al. (2013) where

it was observed that temperature is among the

major determinants to influence phytoplankton

growth rates positively but higher temperatures

resulted in higher fractions of blue green algae

over diatoms and green algae.

In the present study, although a gradual

increase in Chl-a concentration was obser-

ved between 20-30 °C but a plateau was

reached between 30-34 °C instead of directly

decreasing. However, the prediction belt in

our temperature-chlorophyll predictive model

also hinted the probability of either decrease or

increase in Chl-a concentration beyond 30 °C

which might be attributed to the role of other

key environmental parameters that significantly

influence phytoplankton biomass as well as

in the presence of increasing temperature, as

concluded in Sipkay et al. (2012). Several

modeling methodologies have also forecasted

increase of phytoplankton biomass in rivers

under increasing temperatures (Klapper, 1991;

Elliot et al., 2005; Komatsu, Fukushima, &

Shiraishi, 2006; Mooij et al., 2007; Sipkay et

al., 2012). Similarly, predictive models have

also forecasted the broad-spectrum increase

in picophytoplankton with rise in temperature

in subtropical gyres (Agusti, Lubián, Moreno-

Ostos, Estrada, & Duarte 2019). However, few

studies also report otherwise i.e. phytoplank-

ton biomass in rivers to decrease with rising

temperature (Sommer & Lengfellner, 2008;

Lewandowska & Sommer, 2010).

Implications of regional climatic varia-

bility on riverine Chl-a: collating the present

observations: The changing climate has impli-

cations on thermal regime and nutrient availa-

bility in aquatic ecosystems through changes

in temperature and precipitation (flow) pattern,

that is manifested into altered phytoplankton

biomass or primary productivity (Webb &

Nobilis, 2007; Schabhuttl et al., 2013; Das

Sarkar et al., 2019b). According to Whitehead

et al. (2012), in many rivers the phytoplankton

blooms in summers are a feature of river ecolo-

gy and the frequency and intensity of these may

increase under future climatic scenario. On the

other hand, study suggests less pronounced for

tropical phytoplankton communities already

acclimatized to warmer temperatures round

Rev. Biol. Trop. (Int. J. Trop. Biol.) • Vol. 69(1): 60-76, March 2021

the year (Schabhuttl et al., 2013). Situations

of higher water temperatures and lower flow

have been attributed to enhance phytoplankton

growth in rivers (Verdonschot et al., 2010;

Whitehead et al., 2012).

The present study has also hinted eviden-

ces of increased phytoplankton biomass viz.

Chl-a concentration under increasing tempe-

rature and decreasing rainfall over the region.

Additionally, estimates indicate that if the

present rate of climatic variability over the last

3 decades (mean air temperature + 0.24 °C

and total annual rainfall -196.3 mm) remain

consistent over the next three decades (2015-

2045) as well, an increase in mean Chl-a

concentration by +170 µgL

-1

may likely be

expected. The resulting mean Chl-a concentra-

tion may grossly arrive at around 475.94 µgL

-1

over the next three decades. Further repetitive

time-series investigations are required to vali-

date this estimate. It remains to be a researcha-

ble issue whether the dynamics of this change

will follow a direct or alternating waveform

pattern. The present study also could not con-

clude whether the degree of quantified change

of phytoplankton biomass in river system by

2045 or more could be tagged as progressively

eutrophic or lie within the mesotrophic range;

difficult to probe without flow data. Nonethe-

less, our observations with short-term data are

in line with several others who have forecasted

increased phytoplankton biomass and/or pri-

mary production in rivers due to climate chan-

ge using long-term data (Wrona et al., 2006;

Friberg et al., 2009; Verdonschot et al., 2010;

Sipkay et al., 2012; Whitehead et al., 2012).

This increase in phytoplankton biomass projec-

ted for lower Ganga basin under future climatic

scenario can be partly explained through the

conceptual mechanism discussed in Whitehead

et al. (2012) and inferences drawn by Sipkay

et al. (2012). The limitation of DO in lotic

systems is not a serious issue due to its flowing

nature, the observed trend of increased Chl-a

concentration under future climatic scenario

may only be beneficial from fisheries point

of view (increased abundance and biomass of

fish food organisms) if, (a) the phytoplankton

succession does not get significantly dominated

by blue green algae (tropical waters at low risk

as explained (Schabhuttl et al., 2013) and, (b)

water stress does not become evident resulting

into eutrophication.

The present study pioneers the ecological

research in Indian rivers which conceptualized

a model based framework and identified key

climato-hydrochemical parameters for water

managers dealing with complex water ecosys-

tems. The study signifies the model based

assessment of the impact of climatogical varia-

bles on phytoplankton biomass which stands as

an emerging topic for environmental biologist

and ecologists. Developing countries like India

have dearth of long-term data on riverine chlo-

rophyll concentration or any aquatic system

per se; although time series data of climatic

variables are well maintained and abundantly

available. The present methodology explored

an alternative approach to generate baseline

information on trends and gross quantification

of changes in aquatic Chl-a concentration

under future climatic scenario –in absence of

long term data. In the present study, the pho-

tosynthetic pigment Chl-a was used as a poten-

tial marker that reflects phytoplankton biomass

vis-à-vis primary production potential of the

aquatic system. Looking at the multitude of

environmental influence on dictating riverine

chlorophyll concentration, it is quite difficult to

pinpoint any climate change related hypotheses

in open waters. The present study may not be a

key estimate of the future climate change sce-

nario but certainly a gross hint of the probable

ecosystem response and would fit for develo-

ping managerial strategies for water managers

which augment the sustainable management

practices for stakeholders.

Ethical statement: authors declare that

they all agree with this publication and made

significant contributions; that there is no con-

flict of interest of any kind; and that we followed

all pertinent ethical and legal procedures and

requirements. All financial sources are fully

and clearly stated in the acknowledgements

Rev. Biol. Trop. (Int. J. Trop. Biol.) • Vol. 69(1): 60-76, March 2021

section. A signed document has been filed in

the journal archives.

ACKNOWLEDGMENTS

Authors are thankful to the Indian Council

of Agricultural Research, New Delhi for finan-

cial help under project National Innovations

on Climate Resilient Agriculture (NICRA).

We duly acknowledge the support and coo-

peration of M. Prabhakar, PI NICRA, ICAR-

CRIDA, Hyderabad for all technical facilities.

We acknowledge the efforts rendered by San-

jeev Kumar Sahu, ICAR-CIFRI, Barrackpore

for schematic representation of the study area

using GIS tools.

RESUMEN

Efecto de los parámetros climáticos ambientales

sobre la concentración de clorofila a en la cuenca baja

del Ganges, India. Introducción: La concentración de

clorofila a representa la biomasa de fitoplancton la cual

influye directamente en las funciones de producción de

los ecosistemas acuáticos. Por lo tanto, es imperativo com-

prender su cinética espacio-temporal en el ambiente lótico

con respecto a las variabilidades climáticas regionales en

las aguas continentales tropicales. Objetivo: Se realiza-

ron estudios in situ para examinar la influencia de varios

parámetros ambientales en la biomasa del fitoplancton en

la cuenca baja del Ganges durante 2014-2016. Métodos:

En primer lugar, se determinaron los parámetros ambien-

tales más influyentes en la concentración de Chl-a fluvial.

Luego, el efecto directo en cascada de las variables cli-

máticas sobre los parámetros ambientales clave, mediante

el modelado y los cambios en la concentración media de

Chl-a en el tramo inferior del río. Resultados: Solo cinco

parámetros ambientales, entre ellos, temperatura del agua,

sólidos disueltos totales, salinidad, alcalinidad total y pH,

fueron factores clave que influyeron en la Chl-a (R2 múl-

tiple: 0.638, P < 0.05). Las estimaciones actuales indican

que si la tasa actual de variabilidad climática regional

durante las últimas 3 décadas (temperatura media del aire

+ 0.24 °C, precipitación total anual -196.3 mm) permanece

constante durante las próximas tres décadas (2015-2045),

se presente un aumento en el promedio de la Chl-a en

+170 µgL

-1

y alcance aproximadamente 475.94 µgL

-1

para

el 2045 o más. Conclusiones: Este estudio presenta una

metodología basada en modelos alternativos en situacio-

nes de escasez de datos, la información generada también

podría contribuir a mejorar la gobernanza del agua y a

desarrollar un protocolo para la gestión sostenible del agua

y de esta manera mejorar los servicios ecosistémicos.

Palabras clave: clorofila a; cambio climático; variable

ambiental; modelado predictivo; río Ganges.

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