Marine Pollution Bulletin 80 (2014) 168–178

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Marine Pollution Bulletin

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The increasing impact of food production on nutrient export by riversto the Bay of Bengal 1970–2050

0025-326X/$ - see front matter � 2014 Elsevier Ltd. All rights reserved.http://dx.doi.org/10.1016/j.marpolbul.2014.01.017

⇑ Corresponding author. Present address: Faculty of Disaster Management,Patuakhali Science and Technology University, Dumki, Patuakhali 8602, Bangladesh.Tel.: +880 4427 56014x326 329; fax: +880 4427 56009.

E-mail address: abdus.sattar@pstu.ac.bd (M.A. Sattar).

Md. Abdus Sattar ⇑, Carolien Kroeze, Maryna StrokalEnvironmental Systems Analysis Group, Wageningen University, P.O. Box 47, 6700 AA Wageningen, The Netherlands

a r t i c l e i n f o a b s t r a c t

Keywords:Food productionRiver exportNutrientThe Bay of BengalEutrophicationScenario

The objective of this study is to assess the impact of food production on river export of nutrients to thecoastal waters of the Bay of Bengal in the past (1970 and 2000) and the future (2030 and 2050), and theassociated potential for coastal eutrophication. We model nutrient export from land to sea, using the Glo-bal NEWS (Nutrient Export from WaterSheds) approach. We calculate increases in river export of N and Pover time. Agricultural sources account for about 70–80% of the N and P in rivers. The coastal eutrophi-cation potential is high in the Bay. In 2000, nutrient discharge from about 85% of the basin area of the Baydrains into coastal seas contributes to the risk of coastal eutrophication. By 2050, this may be 96%. Wealso present an alternative scenario in which N and P inputs to the Bay are 20–35% lower than in thebaseline.

� 2014 Elsevier Ltd. All rights reserved.

1. Introduction

Nutrient export by rivers to the coastal waters of the Bay of Ben-gal have been increasing during the past decades (Das et al., 2004;De et al., 2011; Mahanta et al., 2005; Mukhopadhyay et al., 2006;Prasad, 2011; Subramanian, 2008). Food production is one of thereasons for increased river transport of nutrients such as nitrogen(N) and phosphorus (P) to many coastal waters (Bouwman et al.,2009; Han and Allan, 2011; Heisler et al., 2008; Rabalais et al.,2010; Srinivas et al., 2011; Yang et al., 2010). Synthetic fertilizeruse and the number of animal stocks have been increasing rapidlyin the drainage basin of the Bay of Bengal (Jain, 2002; Panigrahiet al., 2007; Singh and Ramesh, 2011; Srinivas et al., 2011;Subramanian, 2008). The same holds for the use of N and P feedin aquaculture (Biplob et al., 2004; Deb, 1998). In addition, Indiaand Bangladesh are major fertilizer consuming regions in SouthAsia (FAO, 2008; Sharma and Thaker, 2011) which cover a largepart of the drainage basin of the Bay of Bengal. These high nutrientinputs to agricultural land potentially lead to high nutrient concen-trations in surface waters as a result of runoff and leaching(Bouwman et al., 2009; Subramanian, 2008; van Breemen et al.,2002).

The economies and population in many Asian countries aregrowing fast (Bloom and Williamson, 1998; Freeman, 2002). In-creased food production is linked to population growth as well as

economic growth. Qu and Kroeze (2012) show that populationand economic growth are major drivers of increasing nutrientloads at river mouths in China. This is not only because with anincreasing population the demand for food increases, but also be-cause economic growth is associated with increased per capitameat consumption (Gerbens-Leenes et al., 2010). As a result, foodproduction has been increasing throughout Asia. Governments inthe Bay of Bengal area subsidize synthetic fertilizers to ensure suf-ficient food production (Fan et al., 2008). The contributing coun-tries are exporting part of their agricultural and aquacultureproducts. This all creates competition for land, especially in regionswith rapid population growth (FAO, 2008).

Economic growth and increased population have led to rapidurbanization in the drainage basin of the Bay of Bengal (Rana,2011). An increase in urban population is observed in the pastand may continue in the future in many Asian countries (Cohen,2004, 2006; UN, 2012). A likely consequence of urbanization isan increase in the number of people connected to sewerage sys-tems (Van Drecht et al., 2009). Uncontrolled wastewater disposal(containing human waste and detergents from laundry and dish-washers) is an important cause of increased nutrient exports tothe Bay of Bengal. Consequently, nutrient export to the coastalwaters from urban sewage has been increasing during the pastdecades (Grimm et al., 2008; Groffman et al., 2004; Wickhamet al., 2002).

The effects of increased nutrient loads to coastal waters are ob-served worldwide (Barile, 2004; Bouwman et al., 2009; Nixon,1995; Seitzinger et al., 2005, 2010). Increasing nutrient loadsmay alter the nutrient stoichiometry in the coastal waters, whichin turn may cause the growth of harmful algal blooms in the

M.A. Sattar et al. / Marine Pollution Bulletin 80 (2014) 168–178 169

marine ecosystem (Billen and Garnier, 2007; Glibert et al., 2006;Heil et al., 2005; Howarth, 2008; Yang et al., 2010). Coastal eutro-phication has also been observed in the coastal waters of Asia(Diaz, 2001; Hallegraeff, 1993; Selman et al., 2008). To our knowl-edge, the eutrophication potential of rivers draining into the Bay ofBengal has not been studied extensively. Some studies reporteutrophication problems in small ponds and in shallow lakes thatare located in our study area (Agrawal, 1999; Ambasht, 2008;Jahan et al., 2010). Poor water quality and algal blooms in riversin Bangladesh and India occur as a result of dumping urban sewageand effluents of shrimp farming (Pote et al., 2011). The nutrientpollution of the drainage basin of the Bay of Bengal is expectedto increase in the near future (Tripathy et al., 2005; Vass et al.,2010). Many local and national initiatives and laws (about onehundred), and about 40 international agreements have been dedi-cated for managing land-based pollution of the Bay (L.Kaly, 2004).So far, these initiatives have not been effective to reduce nutrientloads to the Bay of Bengal to a large extent. Besides, no studies ex-ist on a systematic assessment of the past and future trends innutrient export by the rivers to the Bay of Bengal and their mainsources that could support these initiatives. Furthermore, the asso-ciated potential for coastal eutrophication has not been studiedeither. Such integrated analysis would help in exploring manage-ment strategies to protect coastal ecosystems (Tare et al., 2003).Without effective policies, coastal eutrophication may threatenecosystems services in the future with negative societal conse-quences (Das et al., 2004; Vass et al., 2010).

The objective of this study is, therefore, to assess the impact offood production (agriculture and aquaculture) on river export ofnutrients to the coastal waters of the Bay of Bengal in the pastand future (1970–2050) and the associated potential for coastaleutrophication. In addition, the effects of selected environmentalpolicies to reduce nutrient export to the Bay of Bengal areanalyzed.

2. Methodology

2.1. Study area

The Bay of Bengal is the world largest bay and forms the north-eastern part of the Indian Ocean (Fig. 1). It is bordered by India andSri Lanka to the west, Bangladesh to the north and Myanmar andThailand to the east. It occupies almost 2.2 million km2. TheAndaman and Nicobar Islands separate the Bay of Bengal and theAndaman Sea. The study area includes selected sixty exoreic riverbasins, which occupy 2.9 million km2 (Mayorga et al., 2010) drain-ing into coastal waters of the Bay of Bengal (Table 1). These basinswere selected from the Global NEWS (Nutrient Export from Water-Sheds) model that is described below (Fig. 1). The Ganges river isthe largest of the sixty rivers and covers more than half of the totaldrainage basin of the Bay of Bengal (Fig. 1).

2.2. Global Nutrient Export from WaterSheds (Global NEWS) modeland Millennium Ecosystem Assessment (MEA) scenarios

We used the Global NEWS model to analyze the impact of foodproduction on nutrient export by rivers to the coastal waters of theBay of Bengal (Mayorga et al., 2010; Seitzinger et al., 2010). In addi-tion, data on nutrient export from aquaculture were derived fromBouwman et al. (2011). We used data for aquaculture to calculat-ing the relative contribution of this sector to the nutrient exportto the Bay of Bengal.

The Global NEWS model estimates nutrient export by rivers tocoastal waters (at the river mouth) for more than 6000 riversworldwide. The model was developed in 2002 by a working group

of UNESCO–IOC (Intergovernmental Oceanographic Commission)using consistent global databases (Seitzinger et al., 2005) andwas updated in 2009 (Seitzinger et al., 2010). The model estimatesriver export of N, P, carbon (C) and silica (Si) including dissolvedinorganic (DIN, DIP, and DSi), dissolved organic (DON, DOP, andDOC) and particulate (PN, PP, and POC) forms. The Global NEWSmodel outputs include river export of nutrients at the river mouthin loads (Mg yr�1) and yields (kg km�2 yr�1) by river, as well as theshares of different sources in these nutrient exports (Mayorgaet al., 2010; Seitzinger et al., 2010).

The Global NEWS model consists of two sub-models: one fordissolved nutrients and one for particulates.

In the dissolved sub-model, river export of dissolved inorganicand organic N and P is estimated. Nutrient fluxes are calculatedas a function of socio-economic drivers (e.g. gross domestic prod-uct, population density) and human activities on land (e.g. agricul-ture and sewage) while taking into account hydrologicalcharacteristics of river basins (e.g. river discharge, runoff, and pre-cipitation). The dissolved sub-model considers point and diffusesources of nutrients. Point sources include human waste (for Nand P forms) and detergents (for P forms) entering rivers from sew-age facilities (Van Drecht et al., 2009). Diffuse sources include fer-tilizer and manure use in agriculture, biological N2 fixation andatmospheric N deposition over agricultural and natural areas, soilweathering, and leaching from agricultural and natural areas(Bouwman et al., 2009). Nutrient export due to soil weatheringand leaching of organic matter is estimated based on an export-coefficient approach as a function of annual runoff from land tostreams (Mayorga et al., 2010). Dissolved silica export is calculatedon the basis of a regression analysis following after Beusen et al.(2009).

The particulate sub-model calculates river export of PN, PP andPC as a function of total suspended solids (TSS) in rivers, based onlinear regression and geophysical (e.g. lithology, land use class) andhydrological characteristics of river basins (e.g. precipitation)(Beusen et al., 2005; Mayorga et al., 2010).

The Global NEWS model calculates river export of nutrients forthe past (1970 and 2000) and future (2030 and 2050). Global dat-abases on past trends were used as inputs to model nutrient exportby rivers since 1970. For the future, the four Millennium EcosystemAssessment (MEA) scenarios (Alcamo et al., 2006) were used as abasis to generate input data sets for diffuse sources of nutrientsin rivers (Bouwman et al., 2009), point sources (Van Drecht et al.,2009) and hydrology (Fekete et al., 2010). Most input data areprovided with a resolution of 0.5� longitude by 0.5� latitude(Seitzinger et al., 2010).

The four MEA scenarios are named Adapting Mosaic (AM),Global Orchestration (GO), Order from Strength (OS) and Techno-Garden (TG). The MEA scenarios differ from each other with respectto assumptions on socio-economic developments (globalization orregionalization) and environmental management strategies(proactive or reactive). A globalized world is assumed in scenariosGO and TG, and a regional world in scenarios OS and AM. A reactiveapproach towards environmental management is assumed in sce-narios GO and OS, and a proactive management in scenarios AMand TG (Seitzinger et al., 2010). Bouwman et al. (2009) interpretedthe MEA scenarios with respect to agricultural trends. For instance,crop productivity is assumed to be higher in scenario GO than in OS,and intermediate in AM and TG. A rapid increase in N and P fertilizeruse in countries with soil nutrient deficits is assumed in theglobalized scenarios GO and TG. This is not the case in AM andOS. Scenario AM is characterized by local, pro-active solutions toenvironmental problems, and assumes improved integration of ani-mal manure, human sewage and households’ wastage in agricultureto reduce the use of synthetic fertilizer. The TG scenariofocuses more intensively on technological solutions which can be

Fig. 1. Overview of the sixty selected river basins draining into the Bay of Bengal. In the map, numbers represent the basin identities (see Table 1 for basin details) andnational borders are indicated by blue lines. Modified from Global NEWS (Mayorga et al., 2010; Seitzinger et al., 2010). (For interpretation of the references to color in thisfigure legend, the reader is referred to the web version of this article.)

170 M.A. Sattar et al. / Marine Pollution Bulletin 80 (2014) 168–178

implemented globally. It assumes, for instance, increased N useefficiency by technological improvements (Bouwman et al., 2009).Scenario GO assumes not only increased agricultural production,but also an increased productivity of aquaculture (Bouwmanet al., 2011). These, together with many other assumptions, formthe basis for the inputs to the Global NEWS model to calculate fu-ture nutrient export by the more than 6000 rivers. A detaileddescription of the MEA scenarios is provided by Alcamo et al.(2006), and details on the model inputs by Bouwman et al.(2009), Van Drecht et al. (2009) and Fekete et al. (2010).

The Global NEWS model does not appropriately account foraquaculture, while this may be an important source of nutrientsin Asian rivers. In order to estimate nutrient inputs from aquacul-ture, we used estimates by Bouwman et al. (2011) for four coun-tries (Bangladesh, India, Myanmar and Sri Lanka) which covermost of the total area of the Bay of Bengal region.

2.3. Model validation

The Global NEWS model has been validated and calibrated bothat global (world) scale (Mayorga et al., 2010) and at regional scales(Qu and Kroeze, 2010; Strokal and Kroeze, 2012; Struijk andKroeze, 2010; Yan et al., 2010; Yasin et al., 2010) indicating thatthe model in general performs reasonably well at the global to re-gional scale. To examine model performance for the Bay of Bengal,we compared measured nutrient export by rivers with modeledvalues. Measurements were available for DIN and DIP (Mayorgaet al., 2010) and for DON and TSS in GEMS–GLORI databases(Meybeck and Ragu, 1995).

The results show that the model estimates are in reasonableagreement with measurements of nutrients and TSS (Fig. 2).However, the model performs better for DIN than for TSS and othernutrient forms. We observe that the model underestimates TSSyields for all rivers except for Chao Phraya, Cauweri and Luan.The performance of the model varies from river to river. Bettermodel performance was found for DIN export by the Sakarya river,DIP export by Fuchun Jiang, DON export by the Huang He, and TSSexport by the Chao Phraya.

Deviations between modeled and measured yields can be causedby the model error and by the quality of the measured data. Thereare many model uncertainties, associated with assumptions aboutthe model structure and uncertainties in model parameters. Mainsources of uncertainties in measurements may be due to data collec-tion methods, errors in instruments used for sampling and seasonalvariations. Most of the measurements were collected during 1980swhile the modeled values were for the year 2000. We did not per-form statistical analysis because of scarcity of measurements.

Despite the uncertainties and the lack of data, we concludethat the Global NEWS model can be applied to the Bay of Bengalregion. This conclusion is based on our comparison of model re-sults with measurements and on the other validations performedby other researchers at the global and regional scales as men-tioned above.

2.4. Indicator for coastal eutrophication potential (ICEP) approach

The ICEP was developed to identify coastal areas at risk ofharmful algal blooms (Billen and Garnier, 2007; Garnier et al.,2010). The indicator shows the potential for developing

Table 1Sixty river basins draining into the Bay of Bengal. These basins were selected from theGlobal NEWS model (Mayorga et al., 2010; Seitzinger et al., 2010). Basins areidentified by river names for large basins, and by identification numbers (ID) forsmaller basins.

Basin name (ID) Area (km2)

Ganges (14) 16,26,470Godavari (67) 3,11,206Krishna (78) 2,51,385Mahanadi (127) 1,41,040Cauweri (203) 78,587Damodar (267) 59,591Brahmani (285) 57,289Penner (298) 53,845Basin (434) 31,150Ponnaiyar (555) 24,126Subamarekha (574) 22,810Palar(620) 21,062Kaladan (642) 20,033Lemro (858) 14,426Basin (865) 14,247Basin (1012) 11,642Basin (1199) 9178Vellar (1213) 9085Basin (1277) 8771Vamsadhara (1282) 8745Basin (1642) 6126Basin (1701) 6035Basin (1723) 6011Basin (1735) 5992Gundalakamma (1752) 5958Basin (1761) 5943Basin (1770) 5929Basin (1777) 5921Basin (1793) 5889Basin (1800) 5873Basin (1801) 5873Basin (1828) 5803Basin (1853) 5746Basin (1854) 5736Basin (1863) 5705Basin (2847) 3059Basin (2862) 3056Basin (2893) 3052Basin (2942) 3048Basin (3099) 3012Basin (3100) 3012Basin (3108) 3005Basin (3123) 2999Basin (3196) 2957Basin (3205) 2957Basin (3219) 2949Basin (3234) 2941Nagavali (3240) 2932Basin (3266) 2924Basin (3267) 2924Basin (3284) 2915Basin (3285) 2915Basin (3300) 2906Basin (3310) 2906Basin (3354) 2878Basin (3385) 2868Basin (3386) 2868Basin (3395) 2858Basin (3403) 2858Basin (3423) 2847

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Fig. 2. Modeled versus measured (log–log) river export of nutrients and totalsuspended solids (TSS) for selected South Asian Rivers. Nutrients include dissolvedinorganic N and P (DIN and DIP), dissolved organic N (DON). Measured yields areconsistent with datasets used for the Global NEWS model validation (Mayorga et al.,2010; Meybeck and Ragu, 1995). The dashed line indicates 1:1 line.

M.A. Sattar et al. / Marine Pollution Bulletin 80 (2014) 168–178 171

non-diatoms phytoplankton biomass (non-siliceous algae) as a re-sult of changes in nutrients stoichiometry (C:N:P:Si) caused by in-creased nitrogen and phosphorus inputs to coastal waters (Billenand Garnier, 2007; Garnier et al., 2010).

The ICEP indicator is based on the Redfield molar ratio(C:N:P:Si = 106:16:1:20). It was implemented in the Global NEWSmodel (Garnier et al., 2010). ICEP values are expressed in kg C km�2

day�1. Either N-ICEP or P-ICEP is calculated by using the followingformulas:

N-ICEP ¼ ½fNFlx� ð14� 16Þg � fSiFlx� ð28� 20Þg� � 106

� 12 ð1Þ

If N:P < 16 (N is limiting nutrient)

P-ICEP ¼ ½ðPFlx� 31Þ � fSiFlx� ð28� 20Þg� � 106� 12 ð2Þ

If N:P > 16 (P is limiting nutrient)where PFlx, NFlx and SiFlx are thefluxes of total N (TN), total P (TP) and silica (Si) respectively,delivered at the mouth of river in kg km�2 day�1. TN fluxes are cal-culated as the sum of DIN, DON and PN loads, and TP fluxes as thesum of DIP, DOP and PP load, taken from the Global NEWS model(Run 5). Silica fluxes are derived from Beusen et al. (2009). TheN:P ratio is calculated:

N : Pratio ¼ ðTNyld� 14Þ � ðTPyld� 31Þ ð3Þ

Where TNyld and TPyld are the total N and P fluxes in kg km�2 yr�1.ICEP values were calculated for each river basin. Negative

values of ICEP (ICEP < 0) indicate low eutrophication potentials asSi loads are in excess over N and P loads. A negative ICEP indicatesthat on average the risk for harmful algal blooms is low, but it is noguarantee that locally harmful algal blooms never develop. Positivevalues of ICEP (ICEP > 0) indicate a potential risk of eutrophicationas Si loads are limited over N and P which may lead to growth ofnon-siliceous harmful algae. It should be noted that ICEP valuescalculated based on Global NEWS outputs are basin averagevalues.

2.5. Alternative scenario for managing N and P fluxes

We developed an integrative alternative scenario for managingN and P fluxes. The Global Orchestration (GO) scenario for 2050 isour baseline (Table 2). The alternative scenario assumes improvedfertilizer management in agriculture (Scenario ASF) with increasednutrient use efficiency (Table 2). To this end, we reviewed the lit-erature and estimated the potential effect of management optionson inputs of N and P in agriculture (Table 2).

We identified several options to reduce nutrient inputs fromagriculture to water bodies (Table 2). One option is that syntheticfertilizer use can be optimized by considering crop demands andimproving application techniques. In Scenario ASF we tentativelyassume that synthetic fertilizer use and manure inputs to soilsare 20% and 40% lower than in the baseline, respectively. Thisassumption implies that many of the options listed in Table 2 areeffectively implemented. The ASF scenario also assumes that thepercentage of the population that is connected to sewage systemis at least 50% in all river basins, which is more than in the baseline

Table 2Environmental management options to reduce nutrient export to the coastal waters of the Bay of Bengal for the alternative scenarios (ASF) and their assumed effects on modelinputs relative to the Global Orchestration (GO) scenario for 2050 according to Qu and Kroeze (2012).

Alternative scenario (AS) Management options in agriculturea Assumed changes in model inputs relative to the baseline (GO2050)

Fertilizer management inagriculture (ASF)

Optimize fertilizer use:– Adapt fertilizer dose to crop needs based on soil testing and region-

and crop-specific fertilization recommendations– Rotate crops to minimize N and P losses from soils– Use plant residue to improve farmland– Use of slow release fertilizers– Use fertilizers in liquid form or granular form depending on soil

type and irrigation facilities– Use N and P from human waste as fertilizer– Place fertilizer in bands or at the beneath of the crop

– Synthetic fertilizer (N and P) and manure inputs to water-sheds are reduced by 40% and 20%, respectively

– The number of people connected to sewage is at least 50% inall basins

– 50% of the N and P in human waste is used in agriculture as asubstitute for synthetic fertilizers

Improvements in animal production:– Improved integration of animal and crop production systems– Improved manure collection and storage methods– More efficient use of manure and human sewage as a fertilizer in

crop production– Improved management of raising and grazing of animals

Reduce animal manure excretion:– Low-N diets for animals– Reduced meat consumption

a Based on literature review (Chen et al., 2006; Chikowo et al., 2004; Dinnes et al., 2002; Ferreiro-Domínguez et al., 2012; Lekasi et al., 2003; Metcalfe, 2000; Sharpley et al.,2000; Shaviv and Mikkelsen, 1993; Thavarajah et al., 2003; Wilkins, 2008).

172 M.A. Sattar et al. / Marine Pollution Bulletin 80 (2014) 168–178

scenario for a number of basins. This measure can lead to an in-crease in point source emissions of nutrients to rivers. However,we also assume that 50% of the N and P in human waste collectedin sewage systems will be used in agriculture as alternative forsynthetic fertilizers, as proposed in recent studies (Bragugliaet al., 2011; Ferreiro-Domínguez et al., 2012; Mihelcic et al.,2011; Poulsen et al., 2012; Vaca et al., 2011). This implies thatthe nutrients collected in the sewage systems are brought backto the land, as a result of which the point source inputs to riversare reduced considerably. Finally, we ran the model for the alterna-tive scenario and analyzed the potential reduction of N and P fluxesloads compared to the base line scenario for 2050.

3. Nutrient export by rivers and the potential for coastaleutrophication

3.1. Drivers of nutrient export

We analyzed major anthropogenic drivers of N and P export byrivers draining into the Bay of Bengal in the past (1970 and 2000)and future (2030 and 2050) for the four MEA scenarios (Fig. 3). Wefocused on economic growth (GDP at purchasing power parity:GDPppp on average in 1995 US$ person�1 yr�1), population den-sity, and population connected to sewage systems (number of per-son km�2), TN and TP inputs to watersheds from manure andfertilizer applications in agriculture (kg km�2 yr�1) and TN andTP feed used in aquaculture (kton yr�1).

Agriculture and aquaculture production increased with popula-tion and economic growth in countries of the bay in the past. Theaverage population density and GDPppp in the drainage basin ofthe Bay of Bengal increased by a factor 2 and 2.4, respectively, be-tween 1970 and 2000 (Fig. 3a). Access to improved sewage systemsincreased by more than a factor of 4, but still it is low compared toindustrialized countries (Fig. 3b). Synthetic fertilizer use and ani-mal manure excretion increased by a factor of about 8 and 1.5 be-tween 1970 and 2000, respectively (Fig. 3c and d). Furthermore,the expanding aquaculture sector has rapidly increased the useof N and P feed for shellfish production. TN and TP in feed in-creased from 0.2 and 0.1 kton yr�1, respectively, in 1970 to 22and 9 kton yr�1 in 2000 (Fig. 3e).

Many drivers of nutrient inputs to coastal seas tend to increasein the future. The average population densities and economies pro-jected to increase in all four MEA scenarios. Between 2000 and2050, the population predicted to double, while GDPppp predictedto increase ten folds. GDPppp is projected to increase even fasterthan in the past. In addition, the number of people connected tosewage systems is predicted to increase rapidly in the scenarios;by 2050, the number of people connected to sewage predicted tobe 2–5 times higher than in 2000, depending on the scenario. Anincreasing trend in TN and TP inputs both from synthetic fertilizerand manure to the drainage basin of the Bay of Bengal projected forthe future in the all scenarios except for the AM scenario where theuse of synthetic fertilizer is assumed to decrease (Fig. 3). Manureinputs predicted to double in the GO scenario between 2000 and2050. Feed use in aquaculture in 2050 predicted to be 3–6 timeshigher than in 2000.

3.2. Trends in nutrient export to the Bay of Bengal

3.2.1. River export of nitrogenTotal N (TN = DIN + DON + PN) export by rivers to the Bay of

Bengal increased in the past (Fig. 4a, c and e). Increases are calcu-lated for all nutrient forms of N, except for DON. We estimated a44% increase in total N export between 1970 and 2000, almost adoubling for DIN and a 30% increase for PN. DON export did notchange a lot over time. In 1970, the TN export from aquaculturewas about 100 times lower than in 2000 reflecting the rapid in-crease in aquaculture activities (Fig. 4g).

Future trends vary among scenarios. River export of dissolved N(DIN and DON) predicted to increase in all scenarios between 2000and 2050, except for the AM scenario. About half of the DIN in riv-ers is from agriculture. The 2050 level of PN export predicted to10–35% lower than in 2000 (Fig. 4e). This can be explained by fu-ture river regulation activities like construction of dams and/orby erosion control that decrease the exports of suspended solidsand particulate forms of nutrients. TN losses from aquaculture pre-dicted to be about four times higher in 2050 than in 2000. About60–90% of nutrient losses from aquaculture are from brackishwater aquaculture. Overall, for 2050 we projected levels of TN

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Fig. 3. Major anthropogenic drivers of total N (TN) and P (TP) export of rivers draining into the Bay of Bengal in the past (1970 and 2000) and for the future (2030 and 2050)for the four Millennium Ecosystem Assessment scenarios: Adapting Mosaic (AM), Global Orchestration (GO), Order from Strength (OS) and Technogarden (TG). Source: GlobalNEWS model (Mayorga et al., 2010; Seitzinger et al., 2010). In Fig. a. P indicates population density with and without connected to sewage system and S indicates populationdensity with connected to sewage system.

M.A. Sattar et al. / Marine Pollution Bulletin 80 (2014) 168–178 173

export that are 10–50% higher than in 2000, depending on thescenario.

We analyzed the share of agriculture and aquaculture in totaldissolved N (TDN) export from the drainage basin of the Bay ofBengal by considering fertilizer and manure, and N depositionand leaching over agricultural areas as agricultural sources(Fig. 5a). We found that agriculture is the major source of TDN inrivers than aquaculture. About 70–80% of TDN in rivers draininginto the Bay of Bengal is from agriculture while aquaculture con-tributes less than 2%.

3.2.2. River export of phosphorusTotal P export (TP = DIP + DOP + PP) by rivers to the Bay of Ben-

gal increased by about 40% between 1970 and 2000 (Fig. 4b, d andf). All forms of P increased. River export of DIP more than doubledand this increase is closely linked to urbanization. P export fromaquaculture increased almost 100 times between 1970 and 2000,but accounts for about 1% of total P export (Fig. 4h).

Future trends in river export of P depend on scenario assump-tions. In GO and TG, DIP export predicted to increase by 45% be-tween 2000 and 2050, while we predict a 22% reduction in the

AM scenario and DIP export remains the same in the OS sce-nario. These differences are largely associated with the assumedincreased number of people connected to sewage systems in GOand TG compared to AM and OS (Qu and Kroeze, 2010; VanDrecht et al., 2009). Future DOP export rates remain at their2000 levels. And, P export from aquaculture predicted to increaseby about 35% between 2000 and 2050 for the all MEA scenarios.River export of particulate forms of P predicted to decrease inthe future, as a result of increased damming of rivers. As a resultof all these trends, the total input of P to the Bay of Bengal in2050 is predicted to decrease by 10–25% between 2000 and2050.

More than 50% of the total dissolved P (TDP) export is fromagriculture (Fig. 5b). It indicates a large impact of the agriculturalsector on the coastal waters of the Bay of Bengal.

3.2.3. Indicator for coastal eutrophication potential (ICEP)We analyzed past and future trends in coastal eutrophication

potentials for the selected sixty river basins by using the ICEP indi-cator (Fig. 6). We exclude aquaculture from calculating the ICEPvalues as for this sector basin-specific estimates were not available.

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Fig. 4. Past (1970 and 2000) and future (2030 and 2050) nutrient export by rivers to the Bay of Bengal. Future trends are shown for the four Millennium EcosystemAssessment scenarios (see Fig. 3). Results are shown for dissolved inorganic and organic, and particulate forms of N and P (DIN, DIP, DON, DOP, PN and PP). For dissolved formsthe river export is shown by source, where, AA and NA are agricultural and natural areas, respectively. Source: Global NEWS (Mayorga et al., 2010; Seitzinger et al., 2010),except for TN and TP export from aquaculture (Bouwman et al., 2011).

174 M.A. Sattar et al. / Marine Pollution Bulletin 80 (2014) 168–178

We calculate an increasing potential for coastal eutrophicationover time. This potential varies greatly among rivers. Most of thebasin area of the Bay of Bengal, including the Ganges River (largestriver), drains into coastal seas that are at risk for coastal eutrophi-cation in the past. For 1970 we calculate that about 80% of thedrainage basin of the Bay of Bengal had positive ICEP values(ICEP > 0) (Fig. 6a), and for 2000 85% (Fig. 6b). The ICEP value ofthe Ganges River doubled between 1970 and 2000.

In the future, the potentials for coastal eutrophication in the Bayof Bengal predicted to increase, in particular in the Western part ofthe Bay. By 2050, nutrient discharge from about 96% of the totalbasin of the Bay of Bengal is draining into coastal seas may

contribute to the risk of coastal eutrophication in all scenarios(ICEP > 0) (Fig. 6c and d). We predict large differences in ICEP val-ues among rivers (Fig. 6), ranging from relatively low eutrophica-tion potentials values for many small river basins, to relativelyhigh potentials for the larger basins, including the Ganges.

3.2.4. Managing N and P fluxesFrom the above it is clear that eutrophication is potentially a

large problem in the Bay of Bengal. This is in line with recent stud-ies, arguing that nutrient loads need to be reduced (Islam, 2003;Panigrahi et al., 2007; Prasad, 2012). Our results, furthermore, indi-cate that food production has a large impact on the water quality in

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Fig. 5. Contribution of agriculture, aquaculture and other sources to inputs of total dissolved N (TDN) and total dissolved P (TDP) to the Bay of Bengal in the past (1970 and2000) and the future (2030 and 2050) for the four Millennium Ecosystem Assessment scenarios (see Fig. 3). Sources: (Bouwman et al., 2011; Mayorga et al., 2010; Seitzingeret al., 2010).

a b

c dFig. 6. ICEP (indicator for coastal eutrophication potential) values indicating low and potential risk for coastal eutrophication for the selected sixty river basins draining intothe Bay of Bengal in the past (1970 and 2000) and for the future (2050) for two Millennium Ecosystem Assessment (AM and GO; see Fig. 3). ICEP < 0 indicates a low risk forcoastal eutrophication whereas ICEP > 0 indicates a potential for harmful algal blooms. ICEP values are calculated as in Global NEWS (Garnier et al., 2010; Seitzinger et al.,2010). Legends of Fig. a are applicable for Fig. a–d.

M.A. Sattar et al. / Marine Pollution Bulletin 80 (2014) 168–178 175

the Bay of Bengal, since most N and P in rivers is associated withagriculture.

The MEA scenarios that we analyzed so far do not include pol-icies to improve coastal water quality. We, therefore, explore anintegrative alternative scenario (Scenario ASF) assuming differentmanagement strategies to reduce nutrient inputs to the riversdraining into the Bay of Bengal (for a scenario description seeTable 2). We calculated potential reductions in nutrient inputs tothe Bay of Bengal of about 30% for DIN, 35% for DIP, and about

20% for DON and DOP relative to the baseline (Fig. 7). These resultsindicate that management strategies in agriculture may reduce therisk of coastal eutrophication.

4. Discussion

We analyzed nutrient export by rivers to the Bay of Bengal andtheir associated potential for coastal eutrophication. Earlier studies

176 M.A. Sattar et al. / Marine Pollution Bulletin 80 (2014) 168–178

(Jain, 2002; Panigrahi et al., 2007; Rahman and Bakri, 2010; Srinivaset al., 2011; Subramanian, 2008) focused on nutrient export by riv-ers for specific river basins and for specific periods. Our integratedanalysis is unique in that it covers past (1970 and 2000) and future(2030 and 2050) trends in nutrient export by 60 rivers using anintegrated spatially explicit model (Global NEWS) and four MEAscenarios. In our analysis, we identified the main sources of differ-ent nutrients (N, P, Si) in different forms (dissolved inorganic, dis-solved organic and particulate) (Fig. 4). Therefore, this study isthe first integrated analysis of the nutrient pollution in the Bay ofBengal. Our results reveal a considerable increase in DIN, DIP,DOP, PN, PP exports by rivers to the coastal waters of the Bay of Ben-gal between 1970 and 2000. Anthropogenic activities such as agri-culture are the main reason for these increases. As a consequence ofthese increases, coastal eutrophication has become an environmen-tal problem in the Bay of Bengal (ICEP larger zero). These results arein the line with the existing studies. For example, some studiesfound a dramatic increase in N and P loads to the rivers betweenpre-industrial period and 1990s due to anthropogenic activities(Green et al., 2004; Islam, 2003; Panigrahi et al., 2007; Prasad,2012).

It is not easy to describe how the world may develop in the fu-ture in terms of socio-economic aspects and environmental man-agement. We, therefore, used the four MEA scenarios (Alcamoet al., 2006; Seitzinger et al., 2010) as a basis for our analyses. How-ever, the MEA scenarios have been downscaled to the basin scale inthe Global NEWS approach. For the Ganges basin, this implies thatit includes Bangladesh and part of India (Fig. 1). This makes it dif-ficult to calculate how much nutrients are from Bangladesh. Never-theless, we belief the model can be used as a good tool for scenarioanalysis.

The MEA scenarios do not explicitly account for environmentalpolicies in the Bay of Bengal. This is because these are global sce-narios, not focused on the problem of coastal eutrophication. We,therefore, developed an alternative scenario to account for possibleenvironmental management in the study area. Our alternative sce-nario integrates management options to reduce nutrient inputs torivers from agriculture and sewage. These management options arebased on assumptions that are supported by literature sources(Braguglia et al., 2011; Ferreiro-Domínguez et al., 2012; Mihelcicet al., 2011; Poulsen et al., 2012; Vaca et al., 2011) and by our ex-pert knowledge on the studied area. Using this integrative alterna-tive scenario, we were able to show that it seems possible toreduce nutrient export by rivers by 20–35% relative to the GO base-line by efficient use of fertilizers and manure, and by recyclingsewage waste in agriculture (Fig. 7). This also illustrates the useful-ness of scenario analysis to assess the effects of environmental

Perc

enta

ge re

duct

ion

rela

tive

toG

O 2

050

Fig. 7. Percentage reduction in river export of dissolved inorganic N and P (DIN,DIP) and dissolved organic N and P (DON and DOP) (Mg yr�1) in the alternativescenario for fertilizer management (ASF) relative to the GO baseline for the year2050. Results are the total for the selected sixty rivers draining into the Bay ofBengal. See Table 2 for scenario assumptions.

policies on nutrient pollution in coastal waters such as the Bay ofBengal.

We quantify river export of nutrients, their main sources andassociated coastal eutrophication potentials (ICEP) using the Glo-bal NEWS model. The main strength of this model is that it usesan integrated approach towards estimating nutrient export by riv-ers, their sources and ICEP values for more than 6 thousands riversin the world taking into account the potential drivers (e.g. popula-tion and economy), human activities on land (e.g. agriculture andsewage) and basin characteristics (e.g. land use and runoff)(Mayorga et al., 2010). The model has been applied on the regionalscale such as the Black Sea (Strokal and Kroeze, 2012; Strokal et al.,2014), South America (van der Struijk and Kroeze, 2010), coastalwaters of Africa (Yasin et al., 2010), watersheds of the Seine,Somme and Scheldt rivers (Thieu et al., 2010) to nutrient analyseson a global scale (Seitzinger et al., 2010; Van Drecht et al., 2009).Global NEWS also has limitations that contribute to model uncer-tainties. One of these limitations is the steady-state approach toquantify nutrient export. This means that the dynamics of N andP accumulation in soils over time is not explicitly accounted andthis may affect N and P release from soil to water bodies. Thismight have a considerable effect on P export from soils to rivers be-cause P has stronger ability for accumulation in soils than N(Schoumans and Groenendijk, 2000). However, Strokal and deVries (2012) showed that incorporating time-dependency for Paccumulation in soils does not affect considerably P inputs thatare exported by rivers to the coastal waters. Another limitationof the model is that the model does not take into account N andP emissions to rivers from industries and from households thatare not connected sewerage systems. Thus, the model may under-estimate sewage-related nutrient inputs to the coastal waters. De-spite of these limitations we believe that the model is a useful toolbecause it contributes to a better understanding of the causes andeffects of nutrient pollution in the Bay of Bengal.

5. Conclusion

Nutrient inputs to the coastal waters of the Bay of Bengal havebeen increasing and may continue to increase in the future. Theseincreases are largely caused by trends in agriculture, increasingnutrient loads in rivers draining into the bay. These increasednutrient levels affect coastal ecosystems through eutrophication.

For the period 1970–2000, we calculate increasing river exportN and P, associated with human activities on the land. Around70–80% of this N and P is from agricultural sources. Aquacultureis a minor source of N and P in rivers. In 2000, river export ofTN (DIN + DON + PN) was about 50% higher than in 1970.TP (DIP + DOP + PP) export increased by about 35% between 1970and 2000.

We assess future trends up until the year 2050 for four Millen-nium Ecosystem Assessment scenarios. These scenarios assume in-creases in population and economic activities. As a result, foodproduction increases in the region, resulting in higher inputs ofnutrients to the rivers. This is the case for most scenarios and mostdissolved forms of N and P. The increased in river export ofdissolved N and P between 2000 and 2050 amounts to be about15–35%. Particulate N and P loads, however, are decreasing overtime by 10–35%, mainly because of assumed damming of rivers.By 2050, agriculture accounts for about 80% for TDN in rivers,and 60% of TDP. Thus, agriculture is the major contributing sectorto nutrient export to the Bay of Bengal.

We analyze the potential for coastal eutrophication using theICEP indicator. Our analysis indicates that the chemical composi-tion of most river draining into the Bay of Bengal is such that algalblooms may develop (ICEP > 0). Before 2000, this was the case for

M.A. Sattar et al. / Marine Pollution Bulletin 80 (2014) 168–178 177

80–85% of the total drainage basin of the Bay of Bengal. There arelarge regional differences in future trends in nutrient inputs to riv-ers and the associated risks for coastal eutrophication in the Bay ofBengal. For most rivers we calculate increasing nutrient exportrates and ICEP values. In the future up to 96% of total basin areamay have positive ICEP values. This implies that almost the totaldrainage basin contributes to the increased risk for harmful algalblooms in the bay.

Finally, we explore some environmental management strategiesto reduce nitrogen and phosphorus loads to this bay. Our resultsindicate how environmental measures for the agricultural sectormay reduce nutrient loads in the future. In an illustrative scenario,the nutrient inputs to the Bay of Bengal are 20–35% lower than thebaseline. Since food production has a large impact on nutrient ex-port by rivers to the coastal waters of the Bay of Bengal, we arguethat future management needs to focus on increased N and P effi-ciency in agriculture first. Our analysis serves as a first step to-wards the development of environmental policies to managecoastal eutrophication in the Bay of Bengal. It could form the basisfor future studies to identify basin-specific management strategies,taking into account the relative shares of different human activitiesin nutrient loads of individual rivers.

Acknowledgements

We would like to thank the Anne van den Ban Fund (ABF) andWageningen University Fund for their funding of this research.

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