case study on ganga river

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Ganga Pollution Case: A Case Study

This article is written by Abhinav Anand , a student pursuing B.A.LL.B(Hons.) from DSNLU, Visakhapatnam. The article deals with the Ganga pollution case and the peruses into reasons behind the pollution. It also discusses some of the schemes of the government to purify the river and critically analyses its impact. It further suggests changes that should be done to make the effective implementation.

Table of Contents

Introduction

Water Pollution has become a global crisis. The perennial threat of the water crisis is exacerbating because of uncontrolled and unbalanced development of the allied sectors such as industries and agriculture. According to the reports of NITI Aayog, 21 major Indian cities, including Delhi will completely run out of groundwater. This article deals with reasons behind the pollution of the river Ganga and it examines the effective measures taken by the government. It also suggests changes to expedite the cleaning process of the river.

Reasons behind the Pollution of Ganga

There are 4600 industries in Uttarakhand out of which 298 are seriously polluting industries. There are many industries which have not taken permission from the Uttarakhand pollution control board for their operations and they started their operation based on the advisory of the government in which the government exempted certain classes of industries from taking permission. The sewage treatment and advanced technology for the treatment of the wastes are not used despite government strict regulations.

Sewage is an important source of pollution and contributes 75% to the pollution caused by all sources of pollution. Urban development of different sizes contributes to sewage pollution in the river. The considerable efforts by the Ganga Action Plan are not able to improve the situation.

The report says that despite the failure of the Ganga Action Plan there is no disapproval on the part of the citizens as well as their representative living in urban areas on the banks of the river. The failure is on the part of the government agencies responsible for the effective implementation of the plan. 

The urban citizens residing near the river show a lack of interest in the cleanliness of the river. The representatives of the urban areas are not receiving enough complaints from the citizens and as a result, they refrain from raising this issue to the higher authorities. Based on the analysis done by the independent authorities, the political parties show reluctance to increase the taxes because they may lose the support of their voters. The taxes will help the authorities to have financial validity. The Kanpur Nagar Nigam has to pay operation and management taxes to the Uttar Pradesh Jal Nigam for the operation and maintenance of the services in the Ganga Action Plan. 

However, the Kanpur Nagar Nigam is unable to collect taxes from the users of the services of Ganga Action Plan to pay to the Uttar Pradesh Jal Nigam. So, the government directly transfers the money to the Uttar Pradesh Jal Nigam by cutting the share of the Kanpur Nagar Nigam. 

It has been contended that the decentralisation of funds and functionaries will help in improving the condition of the governance at Urban Level. But, it is evident that the urban local bodies are neither motivated nor passionate to do the assigned duty. 

Municipal Corporation

These are the following factors contributing to the waste in the river:

The use of plastic by people at large and its improper disposal ultimately reach in the river. Plastic pollution has been considered as one of the significant reasons for the pollution in the river. The government has failed in the implementation of Management and Sewage Waste Rules to curb the menace of plastic pollution.

The state should declare a complete ban on the use of plastic. The authorities pay no attention to the rampant use of plastics and the improper treatment of wastes before releasing them in the river. The pollution level of water has exponentially risen because of plastic wastes. The Tribunal while dealing with the matter of pollution on the ghats has banned the use of plastic in the vicinity of ghats.

However, the ban imposed by the tribunal has no effect on the ground level and the plastics are used rampantly. The plastic bags can be replaced by the jute bags which are nature friendly.

The Ghats are also one of the major sources of pollution in the river. Ganga is one of the important parts of our Indian culture due to which different kinds of pujas and other religious tasks are performed on the ghats, and the materials used are disposed of in the river. The materials are non-decomposable, highly toxic and hence pollute the river. 

Agriculture Waste

Agricultural water pollution includes the sediments, fertilizers and animal wastes. The unbalanced use of inorganic fertilizers and other fertilizers have immensely contributed to water pollution. The fertilizers rich in nitrates create toxic composition after reaching several other entities. Large quantities of fertilizers, when washed through the irrigation, rain or drainage to the river, and pollutes the river. The fertilizers rich in nitrate content are used to get more productivity from the land. This led to pollution in the entire food chain wherever the by-product of the produce is consumed. When these fertilisers wash away due to rain or other factors and pollute the river.

Effective Measures by Government to stop the Pollution

Ganga Action Plan

The Ganga Action Plan was started in 1986 for control of water pollution in the river Ganga. The main function of this plan was to make Ganga River free from the pollution from the disposal of waste from the cities settled on the banks of the river. The plan was to make Ganga pollution free from Rishikesh to Kolkata. The central pollution control board had prepared a plan of 5 years in 1984 to make Ganga pollution-free. The central Ganga authority was formed in 1985 and a Ganga action plan was launched in 1986 to make the Ganga pollution free. 

The first phase of the Ganga action plan was inaugurated by late Rajiv Gandhi at Rajendra prasad ghat of Banaras. The National Protection Agency was constituted for its implementation. During the first phase of Ganga Action Plan 256 schemes of 462 crores were undertaken in Uttar Pradesh, Bihar and West Bengal. Special stations have been created to check the quality of water.

The experts from Bharat Heavy Electricals Limited and National Environment Engineering Research Institute were appointed to check the quality of the water. Despite so much effort, the Ganga action plan failed miserably and crores of money were spent on the Ganga action plans. The failure of such a big plan has led to economic pollution.

The government launched the second phase of the Ganga Action Plan in 2001 wherein the central pollution board, central public works department and public works department are the bodies to carry out the plan. 

Namami Ganga Programme

A flagship Namami Ganga Programme was launched under separate union Water Ministry created under river rejuvenation programme. The project aims to integrate Ganga conservation mission and it is in effect to clean and protect the river and gain socio-economic benefits by job creation, improved livelihoods and health benefits to the population that is dependent on the river.

The key achievement of the Namami Ganga projects are:

• Creating sewage treatment capacity- 63 sewerage management project under implementation in the states of Uttarakhand, Uttar Pradesh, Bihar and West Bengal. 12 sewerage management projects launched in these projects.
• Creating riverfront development: 28 riverfront development projects and 33 entry-level projects for construction, management and renovation of 182 ghats and 118 crematoria has been initiated.
• River surface cleaning: The river surface cleaning is the collection of solid floating waste on the ghats and rivers.after collection, these wastes are pumped into the treatment stations.
• Public Awareness: Various activities such as seminars, workshops and conferences and numerous activities are organised to aware the public and increase the community transmission.
• Industrial Effluent Monitoring: The Grossly Polluting Industries monitored on a regular basis. Industries are following the set standard of the environmental compliances are checked. The reports are sent directly to the central pollution control board without any involvement of intermediaries.

Suggestions

These are the following suggestion for making the existing machinery robust to expedite cleanliness process of the Ganga:

Development of a comprehensive and basic plan

We need to develop a plan by which we can reach the problem in a holistic way. The already devised plans involve many intermediaries wherein the transparency factor is cornered and only paper works are shown to the people at large. 

The strategy should be formulated for different areas according to their demand. The people having apt knowledge of that area should be involved to know the actual problem of pollution in the river. A thorough check should be done and a customer-friendly platform should be formed wherein the views of every individual should be considered.

Measurement of the quality

The apt instruments are required to measure the quality of the water. We have many schemes for the cleanliness of the Ganga but the officials assigned the duty of measuring the quality of water either have authoritarian pressure or lack of knowledge to assess the quality of water. The quality of water should be measured by a recognised testing agency. Further, the research should be made to evolve better machinery for precision in quality measurement.

Getting the institutions right

The main task is to get the involved institution on the right path. The river cleaning task demands leadership, autonomy and proper management. The cities need to be amended. Ultimately they will be the custodians of the networks developed for the cleanliness process. Many cities have weak financial powers and their revenue generation is also weak so they should be given extra incentives. An awareness campaign should be launched in small cities where people have no idea about the pollution of the river and how it affects the environment. 

Engaging and mobilising all the stakeholders

The inhabitants of the river Ganga are people, elected representatives, and the religious leaders who consider the river as a pious and clean river. The mass awareness campaign can launch only when these people will be under sound financial conditions. So, if a portion is invested in these people, then it will help to develop their thinking on a large scale. 

A similar situation has arisen in Australia where the government has invested 20% of the funds in creating mass awareness among the people for the cleanliness of the Murray river basin. It has shown a great impact on the productivity of the programmes implemented in Australia. So, when we promote all the stakeholders in one or the other way we can see a holistic development in the situation.

Rejuvenation requires equal attention to quality and quantity

The rejuvenation of rivers requires quality and quantity at the same time. The old adage of “ solution to pollution is dilution” should be kept in mind while making any kind of plan. 

The improvement of water quality in Ganga during the Kumbh Mela is the result of the release of water barrage of the water upstream. The water in the upper stream is used in the agriculture process by the respective states. So, if the water is released on a regular basis it will also help to improve the quality of the water and reduce the pollution level in the water. 

Ganga is considered a pious river in the religious scriptures. The current situation demands holistic accountability from the authorities and people to make it clean. The global image is projected by the cleanliness of our rivers. The river Ganga is a part of our culture and it is our duty to maintain its sanctity. The government should formulate a more stringent policy to develop the quality of the water in the river. The environmental laws should be strictly followed and the violators should be punished. 

•   https://www.theigc.org/blog/ganga-pollution-cases-impact-on-infant-mortality/

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Story of the Ganga River: Its Pollution and Rejuvenation

• First Online: 08 February 2022

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• Monika Simon 2 &
• Himanshu Joshi 2  

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Water is indispensable for the basic subsistence of human beings. No wonder, most of the civilisations have come upon the banks of rivers or in the river valleys as elsewhere in the world (Chaturvedi, 2019). India is a blessed country in terms of having numerous rivers in this regard (Hudda, 2011). Unfortunately, in 2017, the Ganga River, the National Legacy, and the life support of millions of people was classified as the world’s highly polluted river (Mariya et al., 2019). Ganga, with over 2,525 km long main-stem along with her tributaries has constantly provided material, spiritual and cultural sustenance to millions of people living in and around its basin. The riverine water resources provide irrigation, drinking water, economical transportation, electricity, recreation and religious fulfilment, support to the aquatic ecosystem as well as livelihoods for many stakeholders. The myths and anecdotes about the river and its connection with the people and nature date back to ancient times (Kaushal et al., 2019).

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Introduction

The pristine nature of river Ganges: its qualitative deterioration and suggestive restoration strategies

Abhilash, P.C., and Singh, N. (2009). Pesticide use and application: An Indian scenario. J. Hazard Mater, 165: 1-12. https://doi.org/10.1016/j.jhazmat.2008.10.061

Article   Google Scholar  

Acharya, S., Pandey, A., Mishra, S.K. and Chaube, U.C. (2016) GIS based graphical user interface for irrigation Management. Water Sci Technol Water Supply, 16: 1536-1551. https://doi.org/10.2166/ws.2016.081

Agrawal, A., Pandey, R.S., Sharma, B. (2010). Water Pollution with Special Reference to Pesticide Contamination in India. J Water Resour Prot, 02: 432-448. https://doi.org/10.4236/jwarp.2010.25050

Alam, P., and Ahmade, K. (2013). Impact of Solid Waste on Health and the Environment. Int J Sustain Dev …, 2: 165-168.

Google Scholar  

Bhattacharya, S., Bera, A., Dutta, A. and Ghosh, U.C. (2014). Effects of Idol Immersion on the Water Quality Parameters of Indian Water Bodies: Environmental Health Perspectives. Int Lett Chem Phys Astron, 39: 234-263. https://doi.org/10.18052/www.scipress.com/ilcpa.39.234 .

Bhutiani, R., Khanna, D.R., Kulkarni, D.B. and Ruhela, M. (2016). Assessment of Ganga river ecosystem at Haridwar, Uttarakhand, India with reference to water quality indices. Appl Water Sci, 6: 107-113. https://doi.org/10.1007/s13201-014-0206-6 .

Birol, E. and Das, S. (2010). Estimating the value of improved wastewater treatment: The case of River Ganga, India. J Environ Manage ., 91: 2163-2171. https://doi.org/10.1016/j.jenvman.2010.05.008

Boon, P.J. and Raven, P.J. (2012). River Conservation and Management. River Conserv Manag, 1-412. https://doi.org/10.1002/9781119961819 .

CAG (2017). Rejuvenation of River Ganga (Namami Gange).

CGWA (2020). Notification-Regulation and control of Ground Water management and development.

Chakraborty, P. (2020). Ganga Rejuvenation Enhancing Urban Renewal Conditions. BW Businessworld 1-3.

Chaturvedi, A.K. (2019). River Water Pollution—A New Threat to India: A Case Study of River Ganga.

Chaudhary, M. and Walker, T.R. (2019). River Ganga pollution: Causes and failed management plans (correspondence on Dwivedi et al., 2018. Ganga water pollution: A potential health threat to inhabitants of Ganga basin. Environment International, 117: 327-338). Environmental International 126: 202-206. https://doi.org/10.1016/j.envint.2019.02.033 .

Chauhan, P. and Bhardwaj, N. (2018). Assessment of Ganga water contamination at Haridwar: Studies on Some Physico-Chemical and Microbiological Characteristics. 12: 65-73. https://doi.org/10.9790/2402-1212016573

Ching, L. and Mukherjee, M. (2015). Managing the socio-ecology of very large rivers: Collective choice rules in IWRM narratives. Glob Environ Chang , 34: 172-184. https://doi.org/10.1016/j.gloenvcha.2015.06.012

Conley, D.J., Paerl, H.W., Howarth, R.W., et al. (2009). Ecology - Controlling eutrophication: Nitrogen and phosphorus. Science , (80- ) 323: 1014-1015. https://doi.org/10.1126/science.1167755 .

CPCB (2013a). Performance Evaluation of Sewage Treatment Plant Under NRCD

CPCB (2016a). Restoration/rejuvenation of River Ganga. 2016.

CPCB (2013b). Pollution Assessment: River Ganga.

CPCB (2016b). CPCB Bulletin Vol. I. CPCB Bull Vol. I, 1-26

CPCB (2020). In-situ Bioremediation Techniques for Wastewater Treatment.

CWMI (2018). Composite water management index India.

Das, K.K., Panigrahi, T. and Panda, R.B. (2012). Idol Immersion Activities Cause Heavy Metal Contamination in River. Int J Mod Eng Res , 2: 4540-4542.

Das, P. and Tamminga, K.R. (2012). The ganges and the GAP: An assessment of efforts to clean a sacred river. Sustainability , 4: 1647-1668. https://doi.org/10.3390/su4081647

Das, S. (2011). Cleaning of the Ganga. J Geol Soc India , 78: 124-130. https://doi.org/10.1007/s12594-011-0073-9

Del Bello, L. (2018). Indian scientists race to map Ganges river in 3D. Nature , 560: 149-150. https://doi.org/10.1038/d41586-018-05872-w

Diaz, R.J., Rosenberg, R. (2008). Spreading dead zones and consequences for marine ecosystems. Science , (80- ) 321: 926-929. https://doi.org/10.1126/science.1156401

Dourojeanni, A. (2001). Water management at the river basin level: Challenges in Latin America.

Downtoearth (2019). Aquifer mapping programme critical to raise groundwater levels. 1-25.

Dubey, R.S. and Dubey, A.R. (2016). Comparative Effects of Idols Immersion on the Quality of Flowing Holy Ganga Water and Stagnant Water of Ganga Sarovar: A Case Study at Varanasi. 1-9.

Dutta, V. (2019). 10 critical steps for Ganga revival. 1-5.

Dutta, V., Dubey, D. and Kumar, S. (2020). Cleaning the River Ganga: Impact of lockdown on water quality and future implications on river rejuvenation strategies. Sci Total Environ 743: 140756. https://doi.org/10.1016/j.scitotenv.2020.140756 .

Dwivedi, S., Chauhan, P.S., Mishra, S., et al. (2020). Self-cleansing properties of Ganga during Maha-Kumbh. Env Monit Assess 15.

Dwivedi, S., Mishra, S. and Tripathi, R.D. (2018). Ganga water pollution: A potential health threat to inhabitants of Ganga basin. Environ Int J , 117: 327-338. https://doi.org/10.1016/j.envint.2018.05.015

Ejaz, S., Akram, W., Lim, C.W. et al. (2004) Endocrine disrupting pesticides: A leading cause of cancer among rural people in Pakistan. Exp Oncol , 26: 98-105.

Environment Agency (2015). Water for life and livelihoods Part 1: Humber river basin district river basin management plan. 107.

Euler, J. and Heldt, S. (2018). From information to participation and self-organization: Visions for European river basin management. Sci Total Environ , 621: 905-914. https://doi.org/10.1016/j.scitotenv.2017.11.072

Fang, K. Sivakumar, B. and Woldemeskel, F.M. (2017) Complex networks, community structure, and catchment classification in a large-scale river basin. J Hydrol , 545: 478-493. https://doi.org/10.1016/j.jhydrol.2016.11.056

FAO (2020) AQUASTAT _ Land & Water _ Food and Agriculture Organization of the United Nations _ Land & Water _ Food and Agriculture Organization of the United Nations. In: FAO.

FAO (1992). Wastewater characteristics and effluent quality parameters.

FAO (2015). AQUASTAT Transboundary River Basin Overview – Amazon.

FAO (2016). Transboundary River Basin Overview - La Plata.

GAP (2020). Restoration of Flow. In: Ganga Action Parivar. https://www.gangaaction.org/restoration-of-flow/

Ghose, N.C., Saha, D. and Gupta, A. (2009). Synthetic Detergents (Surfactants) and Organochlorine Pesticide Signatures in Surface Water and Groundwater of Greater Kolkata, India. J Water Resour Prot , 01: 290-298. https://doi.org/10.4236/jwarp.2009.14036

Gopal, B. (2000). River conservation in the Indian subcontinent. Glob Perspect River Conserv Sci , 233-261.

Goswami, K., Gachhui, R. and Goswami, I. (2012). The Idol Immersion in Ganges Cause Heavy Metal Contamination. 84: 54-56.

Guzzella, L., Roscioli, C. and Viganò, L., et al. (2005). Evaluation of the concentration of HCH, DDT, HCB, PCB and PAH in the sediments along the lower stretch of Hugli estuary, West Bengal, northeast India. Environ Int , 31: 523-534. https://doi.org/10.1016/j.envint.2004.10.014

Hamner, S., Tripathi, A. and Mishra, R.K., et al. (2006). The role of water use patterns and sewage pollution in incidence of water-borne/enteric diseases along the Ganges River in Varanasi, India. Int J Environ Health Res, 16: 113-132. https://doi.org/10.1080/09603120500538226

Heinz, I., Pulido-Velazquez, M., Lund, J.R. and Andreu, J. (2007). Hydro-economic modeling in river basin management: Implications and applications for the European water framework directive. Water Resour Manag , 21: 1103-1125. https://doi.org/10.1007/s11269-006-9101-8

Hoag, H.J. and Mohamoda, D.Y. (2003). Nile Basin Cooperation: A Review of the Literature.

Hoffman, E., Lyons, J. and Boxall, J., et al. (2017). Spatiotemporal assessment (quarter century) of pulp mill metal(loid) contaminated sediment to inform remediation decisions. Environ Monit Assess , 189:. https://doi.org/10.1007/s10661-017-5952-0

Homa, D., Haile, E. and Washe, A.P. (2016). Determination of Spatial Chromium Contamination of the Environment around Industrial Zones. Int J Anal Chem , 2016:. https://doi.org/10.1155/2016/7214932

Hooper, B.P. (2012). Advancing integrated river basin management in the Mississippi basin.

Hudda, S. (2011). River Pollution: Causes and Actions “For a better tomorrow, act today”.

IITs (2013). Ganga River Basin environment management plan: interim report.

Kaur, B. (2018). Namami Gange: Five reasons why Ganga will not be clean by 2020. Down To Earth 1-16.

Kaur, B.J., George, M.P. and Mishra, S. (2013). Water quality assessment of river Yamuna in Delhi stretch during Idol immersion. Int J Environ Sci , 3: 2122-130. https://doi.org/10.6088/ijes.2013030600028

Kaushal, N., Babu, S. and Mishra, A., et al (2019). Improving River Flows- Towards a Healthy Ganga. Front Environ Sci , 7.

Khan, M.R., Voss, CI., Yu, W. and Michael, H.A. (2014). Water Resources Management in the Ganges Basin: A Comparison of Three Strategies for Conjunctive Use of Groundwater and Surface Water. Water Resour Manag , 28: 1235-1250. https://doi.org/10.1007/s11269-014-0537-y

Khanna, D.R., Bhutiani, R. and Tyagi, B., et al (2012). Assessment of water quality of River Ganges during Kumbh mela 2010. 2-7.

Kulshrestha, H. and Sharma, S. (2006). Impact of mass bathing during Ardhkumbh on water quality status of river Ganga. J Environ Biol , 27: 437-440.

Kumar, B., Verma, V.K. and Naskar, A.K., et al (2013). Human health risk from hexachlorocyclohexane and dichlorodiphenyltrichloroethane pesticides, through consumption of vegetables: estimation of daily intake and hazard quotients. J Xenobiotics , 3: 6. https://doi.org/10.4081/xeno.2013.e6

Kumari, A., Sinha, R.K. and Gopal, K. (2001). Concentration of organochlorine pesticide residues in Ganga water in Bihar, India. Environ Ecol , 19: 351-356.

Lamond, J., Bhattacharya, N. and Bloch, R. (2012). The role of solid waste management as a response to urban flood risk in developing countries, a case study analysis. WIT Trans Ecol Environ , 159: 193-204. https://doi.org/10.2495/FRIAR120161

Mariya, A., Kumar, C., Masood, M. and Kumar, N. (2019). The pristine nature of river Ganges: its qualitative deterioration and suggestive restoration strategies. Environ Monit Assess , 191:. https://doi.org/10.1007/s10661-019-7625-7

Mathew, R.A. and Kanmani, S. (2020). A review on emerging contaminants in Indian waters and their treatment technologies. Nat Environ Pollut Technol , 19:549–562. https://doi.org/10.46488/NEPT.2020.V19I02.010

MDEQ (2020). Mississippi’s Basin Management Approach. https://www.mdeq.ms.gov/water/surface-water/watershed-management/basin-management-approach/

Misgan, S. (2013). The Nile Basin States: The need for genuine cooperation.

Mishra, N.K. and Mohapatra, S.C. (2009). Effect of Gangetic Pollution on Water Borne Diseases in Varanasi: A Case Study. Indian J Prev Soc Med , 40: 39-42.

Mishra, S., Kumar, A., Yadav, S. and Singhal, M.K. (2015). Assessment of heavy metal contamination in Kali river, Uttar Pradesh, India. J Appl Nat Sci , 7: 1016-1020. https://doi.org/10.31018/jans.v7i2.724

Misra, A.K. (2010). A River about to Die: Yamuna. J Water Resour Prot , 02: 489-500. https://doi.org/10.4236/jwarp.2010.25056

MoEF (2016). Swachh Bharat and Ganga Rejuvenation.

Mohan, V. (2017). Centre turns to ‘sewage- eating’ microbes to treat Ganga water at 54 new sites. Dev. News 1-3

Mohapatra, S.P., Gajbhiye, V.T., Agnihotri, N.P. and Raina, M. (1995). Insecticide pollution of Indian rivers. Environmentalist , 15: 41-44. https://doi.org/10.1007/BF01888888

MoHUA (2020). Assessment of 97 Ganga Towns.

Molle, F. (2009). River-basin planning and management: The social life of a concept. Geoforum 40:484–494. https://doi.org/10.1016/j.geoforum.2009.03.004

MoWRRD & GR (2018). Achievements of four years (2014-15 to 2017-18).

Mutiyar, P.K. and Mittal, A.K. (2013). Status of organochlorine pesticides in Ganga river basin: Anthropogenic or glacial? Drink Water Eng Sci , 6: 69–80. https://doi.org/10.5194/dwes-6-69-2013 .

Nandi, I., Tewari, A. and Shah, K. (2016). Evolving human dimensions and the need for continuous health assessment of Indian rivers. Curr Sci , 111: 263-271. https://doi.org/10.18520/cs/v111/i2/263-271

NEERI (2020). Sustainable Treatment Options for Sewage, In-situ Drain and Lake / River Rejuvenation in Indian context.

Newson, M. (1992). Land, water and development. River basin systems and their sustainable management. Land, water Dev River basin Syst their Sustain Manag 505378. https://doi.org/10.1016/0022-1694(93)90292-h

NFHS (2015). National Family Health Survey-4 (NFHS-4). New Delhi: Ministry of Health and Family Welfare, Government of India

NGM (2020). Namami Gange Programme: The key achievements under Namami Gange programme [Online]. Available: https://nmcg.nic.in/hi/NamamiGanga.aspx . (Accessed 5 Nov. 2020)

NMCG-NEERI (2017). Assessment of Water Quality and Sediment to understand the Special Properties of River Ganga.

NMCG (2017). Reference Note.

NMCG (2020a). Leading River Rejuvenation A case of Namami Gange.

NMCG (2020b). Treatment of sewage carrying drains joining Ribver ganga. https://nmcg.nic.in/csr/biodrains.aspx

NRCD, MoEF (2009). STATUS PAPER ON RIVER GANGA State of Environment and Water Quality.

O’Reilly, K., Dhanju, R. and Goel, A. (2017). Exploring “ The Remote” and “The Rural”: Open Defecation and Latrine Use in Uttarakhand, India. 93: 193-205. https://doi.org/10.1016/j.worlddev.2016.12.022

Pandey, J. and Singh, R. (2017). Heavy metals in sediments of Ganga River: up- and downstream urban influences. Appl Water Sci , 7: 1669-1678. https://doi.org/10.1007/s13201-015-0334-7

Pandey, K. (2019). Grossly polluting industries more than doubled in 8 years: SOE in Figures. Down to Earth.

Panigrahi, A.K. and Pattnaik, S. (2019). A Review on Pollution Status of River Bhagirathi-Hooghly in the Stretch of West Bengal, India. 9: 5

Pathak, D., Whitehead, P.G., Futter, M.N. and Sinha, R. (2018). Water quality assessment and catchment-scale nutrient flux modeling in the Ramganga River Basin in north India: An application of INCA model. Sci Total Environ 631-632: 201-215. https://doi.org/10.1016/j.scitotenv.2018.03.022

Paul, D. (2017). Research on heavy metal pollution of river Ganga: A review. Ann Agrar Sci , 15: 278-286. https://doi.org/10.1016/j.aasci.2017.04.001

PIB (2018). Achievements of ministry of water resources-river development and ganga rejuvenation during 2018. Press Inf. Bur. 1-15

PIB (2017). Ministry of Water Resources, River Development and Ganga Rejuvenation During the Last Three Years. 1-5

PIB (2019). Management of Ground Water. J. Am. Water Works Assoc ., 60: 640-644.

Rani, N., Vajpayee, P. and Bhatti, S., et al (2014). Quantification of Salmonella Typhi in water and sediments by molecular-beacon based qPCR. Ecotoxicol Environ Saf 108: 58-64. https://doi.org/10.1016/j.ecoenv.2014.06.033

Sah, R., Baroth, A. and Hussain, S.A. (2020). First account of spatio-temporal analysis, historical trends, source apportionment and ecological risk assessment of banned organochlorine pesticides along the Ganga River. Environ Pollut , 263: 114229. https://doi.org/10.1016/j.envpol.2020.114229

Sahoo, K.C., Hulland, K.R.S. and Caruso, B.A., et al (2015). Sanitation-related psychosocial stress: A grounded theory study of women across the life-course in Odisha, India. Soc Sci Med 139:80–89. https://doi.org/10.1016/j.socscimed.2015.06.031

SANDRP (2018). Is there hope from National Mission for Clean Ganga ? Listen to official agencies. 1-6.

Sanghi, R. and Kaushal, N. (2014). Introduction to Our National River Ganga via cmaps.

Book   Google Scholar  

Sarkar, R. (2013). Study on the Impact of Idol Immersion on Water Quality of River Ganga At Ranighat, Chandernagore (W.B.). 3: 24-29.

Schletterer, M., Shaporenko, S.I., and Kuzovlev, V.V., et al (2018). The Volga: Management issues in the largest river basin in Europe. River Res Appl , 35: 510-529.

Shah, T., Makin, I. and Sakthivadivel, R. (2001). Limits to leapfrogging: Issues in transposing successful river basin management institutions in the developing world. Irrig River Basin Manag Options Gov Institutions 31-49. https://doi.org/10.1079/9780851996721.0031

Shah, T. and Rajan, A. (2019). Cleaning the Ganga. Econ Polit Wkly 39: 57-66.

Sharmila, S. and Arockiarani, I. (2016). A pollution model of the river ganges through inter criteria analysis. Int J Ocean Oceanogr , 10: 81-91.

Singh, L., Choudhary, S. and Singh, P. (2012). Pesticide concentration in water and sediment of River Ganga at selected sites in middle Ganga plain. Int J Environ Sci , 3: 260-274. https://doi.org/10.6088/ijes.2012030131026

Sinha, S.N. and Paul, D. (2012). Detoxification of Heavy Metals by Biosurfactants. Bull Environ Sci Res , 1: 1-3. https://doi.org/10.6084/m9.figshare.1352038

Souza-Filho PWM, de Souza, E.B. and Silva Júnior, R.O., et al (2016). Four decades of land-cover, land-use and hydroclimatology changes in the Itacaiúnas River watershed, southeastern Amazon. J Environ Manage , 167: 175-184. https://doi.org/10.1016/j.jenvman.2015.11.039

Spears, D., Ghosh, A. and Cumming, O. (2013). Open Defecation and Childhood Stunting in India: An Ecological Analysis of New Data from 112 Districts. PLoS One 8:1–9. https://doi.org/10.1371/journal.pone.0073784

Srinivas, R., Singh, A.P. and Shankar, D. (2020). Understanding the threats and challenges concerning Ganges River basin for effective policy recommendations towards sustainable development. Springer Netherlands.

Srivastava, P., Burande, A. and Sharma, N. (2013). Fuzzy Environmental Model for Evaluating Water Quality of Sangam Zone during Maha Kumbh 2013. Appl Comput Intell Soft Comput 2013: 1-7. https://doi.org/10.1155/2013/265924

SwachhindiaNDTV (2019). All 97 Ganga Towns Will Achieve ODF Status By March 2019. SwachhindiaNDTV

Tare, V. (2010). River Ganga at a Glance: Identification of Issues and Priority Actions for Restoration.

Tare, V., Bose, P. and Gupta, S.K. (2003). Suggestions for a modified approach towards implementation and assessment of Ganga action plan and other similar river action plans in India. Water Qual Res J Canada, 38: 607-626. https://doi.org/10.2166/wqrj.2003.039

TOI (2017). NGT asks NMCG to give detail of industrial clusters near Ganga. 18-19.

Tripathi, A., Tripathi, D.K., Chauhan, D.K. and Kumar, N. (2016). Chromium (VI)-induced phytotoxicity in river catchment agriculture: evidence from physiological, biochemical and anatomical alterations in Cucumis sativus (L.) used as model species. 7540:. https://doi.org/10.1080/02757540.2015.1115841

Tripathi, B.D. and Tripathi, S. (2014). Issues and Challenges of River Ganga. In: Our National River Ganga: Lifeline of Millions. pp 211-220

Trivedi, A. (2020). River Rejuvenation: An Innovative and Logistic Approach. In: Recent Trends in Agricultural Sciences & Technology. pp 195-207

Trivedi, R.C. (2010). Water quality of the Ganga River - An overview. Aquat Ecosyst Heal Manag , 13: 347–351. https://doi.org/10.1080/14634988.2010.528740

Tyagi, V.K., Bhatia, A. and Gaur, R.Z., et al (2013). Impairment in water quality of Ganges River and consequential health risks on account of mass ritualistic bathing. Desalin Water Treat , 51: 2121-2129. https://doi.org/10.1080/19443994.2013.734677

UrbanUpdate (2018). Solid waste management projects in 97 towns along Ganga. UrbanUpdate 3-5.

Vega, M., Pardo, R., Barrado, E. and Debán, L. (1998). Assessment of seasonal and polluting effects on the quality of river water by exploratory data analysis. Water Res , 32: 3581-3592. https://doi.org/10.1016/S0043-1354(98)00138-9

Vijgen, J., Abhilash, P.C. and Li, Y.F., et al (2011). Hexachlorocyclohexane (HCH) as new Stockholm Convention POPs-a global perspective on the management of Lindane and its waste isomers. Environ Sci Pollut Res , 18: 152-162. https://doi.org/10.1007/s11356-010-0417-9

Villar, P.C., Ribeiro, W.C. and Sant’Anna, F.M. (2018). Transboundary governance in the La Plata River basin: status and prospects. Water Int , 43: 978-995. https://doi.org/10.1080/02508060.2018.1490879

Walker, T.R., Willis, R. and Gray, T., et al (2015). Ecological Risk Assessment of Sediments in Sydney Harbour, Nova Scotia, Canada. Soil Sediment Contam , 24: 471-493. https://doi.org/10.1080/15320383.2015.982244

Welcomme, R.L. (1985). River Fisheries.

WHO (2003). Characteristics and quality assessment of surface water and groundwater resources of Akwa Town, Southeast, Nigeria. J Niger Assoc Hydrol Geol, 14:71-77.

WII-GACMC (2017). Aquatic fauna of the ganga river-Status and Conservation.

WWF, INTACH, Toxic link, SANDRP (2019). Rejuvenating Ganga—A Citizen’s Report.

Zhang, S.Y., Tsementzi, D. and Hatt, J.K., et al (2019). Intensive allochthonous inputs along the Ganges River and their effect on microbial community composition and dynamics.

Zhang, W., Jin, X., Liu, D., et al (2017). Temporal and spatial variation of nitrogen and phosphorus and eutrophication assessment for a typical arid river—Fuyang River in northern China. J Environ Sci (China), 55: 41-48. https://doi.org/10.1016/j.jes.2016.07.004

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Simon, M., Joshi, H. (2022). Story of the Ganga River: Its Pollution and Rejuvenation. In: Mukherjee, A. (eds) Riverine Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-87067-6_2

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Ganga River: A Paradox of Purity and Pollution in India due to Unethical Practice

D C Jhariya 1 and Anoop Kumar Tiwari 2

Published under licence by IOP Publishing Ltd IOP Conference Series: Earth and Environmental Science , Volume 597 , National Conference on Challenges in Groundwater Development and Management 6-7 March 2020, NIT Raipur, India Citation D C Jhariya and Anoop Kumar Tiwari 2020 IOP Conf. Ser.: Earth Environ. Sci. 597 012023 DOI 10.1088/1755-1315/597/1/012023

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In India, the river Ganga is believed as a goddess, and people worship it. Despite all the respect for the river, the river's condition is worsening, and we Indians are unable to maintain the purity of the river. The Ganga is a river of faith, devotion, and worship. Indians accept its water as "holy," which is known for its "curative" properties. The river is not limited to these beliefs but is also a significant water source, working as the life-supporting system for Indians since ancient times. The Ganga river and its tributaries come from cold, Himalayan-glacier-fed springs, which are pure and unpolluted. But when the river flows downgradient, it meets the highly populated cities before merging into the Bay of Bengal. From its origin to its fall, its water changes from crystal clear to trash-and sewage-infested sludge. Thousands of years passed since the river Ganga, and its tributaries provide substantial, divine, and cultural nourishment to millions of people living in the basin. Nowadays, with the increasing urbanization, the Ganges basin sustains more than 40 percent of the population. Due to the significant contribution of the growing population and rapid industrialization along its banks, river Ganga has reached an alarming pollution level.

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• Published: 30 September 2021

Modified hydrologic regime of upper Ganga basin induced by natural and anthropogenic stressors

• Somil Swarnkar 1 ,
• Pradeep Mujumdar 1 &
• Rajiv Sinha 2  

Scientific Reports volume  11 , Article number:  19491 ( 2021 ) Cite this article

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• Environmental impact

Climate change and anthropogenic activities pose serious threats to river basin hydrology worldwide. The Ganga basin is home to around half a billion people and has been significantly impacted by hydrological alterations in the last few decades. The increasing high-intensity rainfall events often create flash flooding events. Such events are frequently reported in mountainous and alluvial plains of the Ganga basin, putting the entire basin under severe flood risk. Further, increasing human interventions through hydraulic structures in the upstream reaches significantly alter the flows during the pre-and post-monsoon periods. Here, we explore the hydrological implications of increasing reservoir-induced and climate-related stressors in the Upper Ganga Basin (UGB), India. Flow/sediment duration curves and flood frequency analysis have been used to assess pre-and post-1995 hydrological behaviour. Our results indicate that low and moderate flows have been significantly altered, and the flood peaks have been attenuated by the operation of hydraulic structures in the Bhagirathi (western subbasin). The Alaknanda (eastern subbasin) has experienced an increase in extreme rainfall and flows post-1995. The downstream reaches experience reservoir-induced moderate flow alterations during pre-and post-monsoon and increasing extreme flood magnitudes during monsoon. Furthermore, substantial siltation upstream of the reservoirs has disrupted the upstream–downstream geomorphologic linkages.

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Introduction.

Since 1901 the global average surface temperature has risen by 0.89° due to direct and indirect impacts caused by human activities on earth system processes 1 , 2 . In turn, global warming has significantly impacted the local and regional hydrological cycle worldwide 3 , 4 , 5 . Significant variability in rainfall frequency and magnitude due to changing hydrometeorological conditions has been reported across the globe 6 , 7 , 8 , 9 . As a result, severe flooding to drought conditions have become more frequent and have significantly impacted socio-economic activities in different parts of the world 10 . In addition, direct human activities such as changes in land cover, surface & groundwater withdrawal, and operations of hydraulic structures have also significantly altered the river basin hydrology in several regions 11 , 12 .

In general, dams and reservoirs play a significant role in attenuating flood peaks, frequency, duration and magnitude globally, particularly low flows 13 , 14 . Further, dams and reservoirs have also disrupted the sediment delivery to the downstream reaches, causing alteration in river channel morphology and downstream sediment starvation 15 , 16 . Consequently, river deltas are sinking at unprecedented rates worldwide 17 . Apart from the hydrological, ecological, and societal stresses caused by these large dams and reservoirs, previous researchers have also questioned their economic viability 18 , 19 .

In India, rivers largely govern freshwater resources and are considered as the lifeline of the nation. More than 70% of the rural population depends upon freshwater resources for irrigation and agricultural demands fulfilled by several large and small rivers in India 20 . However, it has been observed that the changing climatic conditions in recent decades have significantly increased the extremity of severe droughts and devastating flooding events in several parts of the country 21 , 22 , 23 . The Himalayan regions are one of the worst affected regions in the recent decades 24 , 25 , 26 , 27 . Further, several major Himalayan Rivers, particularly the Ganga River basin, are regulated by more than 300 hydraulic structures (planned, commissioned and under construction) to harness the hydropower and cater for agricultural water demands 28 , 29 . As a result, the upstream–downstream linkages of hydrological, geomorphological and ecological processes in the Himalayan River basins are severely impacted 30 , 31 , 32 , 33 , 34 . Therefore, river practitioners and scientists need to understand the implications of hydrological modifications caused by changing climate and anthropogenic activities.

In the Himalayan regions of the Ganga basin, several studies have been done to assess the impacts of land use land cover change 35 , 36 , sediment dynamics 37 , 38 , 39 , climate change hazards 24 , 25 , 26 , 40 , heavy metal 41 , water quality 42 and glacier meltwater contribution 43 . Nevertheless, detailed studies focusing on hydrological alterations caused by these hydrological structures and changing climatic conditions are currently lacking. Therefore, in this work, we select the Upper Ganga Basin (UGB) to assess the role of changing climatic conditions and increasing human activities on stream flows. The available hydrological dataset at different gauging stations was used to perform the hydrological analysis for pre-and post-dam conditions in the UGB. We first estimate the changing magnitudes of flows at those locations where large hydraulic structures were built before 1995 in the UGB. We further investigate the hydrological changes owing to changing climatic conditions and operations of hydraulic structures. Finally, we have assessed how these upstream hydrological modifications altered the hydrological regimes of the downstream regions of the UGB. The inferences drawn from the present study would be immensely useful for sustainable river basin management.

The Ganga River has two major tributaries in the upper mountainous region. The western tributary, the Bhagirathi, originates from the Gangotri glacier (30.92° N, 79.08° E) at an elevation of about 4023 m. The eastern tributary, the Alaknanda, originates from the Satopanth glacier (30.79 N, 79.37 E), an elevation of about 4600 m. At Devprayag, both tributaries join to form the Ganga. Here, we have selected the Upper Ganga Basin (UGB) up to Rishikesh (area 21,000 km 2 ) for this study (Fig.  1 a). In the Bhagirathi basin, there are four dams, namely Maneri Stage 1, Maneri Stage 2, Tehri and Koteshwar dam (Figs.  1 a, 2 a), and most of these became operational before 2010. There are only two dams in the Alaknanda basin, namely Tapovan and Srinagar, which became operational after 2015 (Fig.  1 a). Further, the Pashulok barrage is present downstream of Rishikesh. Furthermore, around 37 small and large dams are planned in the UGB (Fig.  2 b), 11 in the Bhagirathi and 26 in the Alaknanda basin 44 (Fig.  2 c,d). The mean annual rainfall in the UGB ranges from 840 to 1990 mm (Fig. S1 a). Almost 70% of the UGB receives 1000–1250 mm of rain annually, except for small patches in the western and eastern parts where more than 1500 mm of the mean annual rainfall occurs (Fig. S1 a). Overall, the Indian Summer Monsoon (ISM) contribution across the UGB varies from 51 to 86% (Fig. S1 b), leading to significant hydrological variabilities across the basin (Fig.  1 b).

(a) Digital Elevation Model (DEM) of the UGB. The major stream network is also shown in the map using magenta lines. The CWC stations and dams are illustrated using filled green circles, orange and yellow triangles, respectively. Parallel black lines show the Pashulok barrage, located below the Rishikesh. Further, the name of dams and corresponding opening year is also shown in the side table. The QGIS Version 3.2 ( https://qgis.org/en/site/forusers/download.html ) was used to generate this figure. (b) Flow duration curves (FDCs) for the period 1970–2010. The solid and dashed blue, red and green FDC lines are shown for Bhagirathi (Uttarkashi and Tehri), Alaknanda (Joshimath and Rudraprayag) and downstream stations (Devprayag and Rishikesh), respectively.

(a) Hydraulic structures in the UGB. The green and orange filled circles are showing the hydraulic structures commissioned pre-and post-2010 on the map. All these hydraulic structures’ circle sizes vary according to their hydropower generation capacity (MW). Further, A, B, C and D classes are defined based on each reservoir’s hydropower generation capacity. (b) The map shows planned hydraulic structures for the near future in the UGB. The QGIS Version 3.2 ( https://qgis.org/en/site/forusers/download.html ) was used to generate these figures. Furthermore, the bar plots show the current hydraulic structures and planned hydraulic structures for the near future in (c) the Bhagirathi and (d) the Alaknanda basin. The hill shade map of the UGB is used in the background.

Methodology

We used daily rainfall, discharge and suspended sediment load datasets for understanding the hydrological characteristics of the UGB. The details of the input dataset used in this study are listed in Table S1 (Supplementary). The exceedance probabilities were estimated in rainfall intensities, flows and sediment at each hydrological station. These estimates are depicted using the rainfall exceedance probability analysis, flow duration curve (FDC) and sediment duration curve (SDC). The daily rainfall, discharge, and suspended sediment datasets were divided into two periods—(1) 1971–1994 (pre-1995), and (2) 1995–2010 (post-1995). This temporal division was done based on the fact that the construction of a large hydraulic structure, i.e., Tehri dam (total capacity 4000 million cubic meters), started in 1995 in the Bhagirathi basin, and the frequency of flash flooding events increased in the UGB after 1995 (see Table S2 ). Hence, the anthropogenic and climate-induced alterations in the hydrology of the UGB could be captured by comparing the pre-1995 and post-1995 FDCs and SDCs. The FDCs and SDCs differences (in percentages) that showed for post-1995 were estimated with reference to pre-1995 FDCs and SDCs for all the gauge stations (see Figs. S3 and S4 in Supplementary). Further, the daily rainfall, discharge and sediments dataset for the 1971–2015 period at six stations are used for the hydrological analyses (Fig.  1 a and Table S1 ). The first five years, i.e., 1971–1975, were selected in the Bhagirathi (at Uttarkashi and Tehri) and Alaknanda basin (at Joshimath and Rudraprayag) for initial reference conditions. However, the initial reference conditions at Devprayag and Rishikesh were considered to be 1976–1980 due to the unavailability of discharge and sediments data for the 1971–1975 period. In addition, 5-yearly rainfall magnitudes, FDCs and SDCs were also estimated and compared with each station’s reference condition to assess the temporal hydrological variations. The differences (in percentage) in 5-yearly rainfall magnitudes, FDCs and SDCs were calculated by subtracting selected 5-year periods with the initial reference condition and plotted on the 2D-contour plot for each station.

We also used the frequency analysis of extreme flows (annual maximum discharge) with the Generalized Extreme Value Type-1 (Gumbel) distribution 45 at each station to estimate extreme discharge between 10- and 100-year return periods for pre-and post-1995. The Gumbel distribution for each station was selected based on Akaike Information Criterion (AIC) by comparing five widely used distributions, namely, (1) Lognormal, (2) Gamma, (3) Gumbel, (4) Weibull, and (5) Generalized Extreme Value (GEV; Table S3 ). The scale and location parameters of the Gumbel distribution were estimated using the Maximum Likelihood Estimation (MLE) method using the ‘FAmle’ package ( https://github.com/tpetzoldt/FAmle ) in R programming (see Fig. S5 in Supplementary). The differences between pre-and post-1995 extreme flows at different return periods were estimated and compared for all six gauging stations of the UGB (see Fig. S9 in Supplementary). It should be noted here that the credible extrapolation interval in flood frequency analysis is generally up to twice the record length. Hence, the 95% confidence bounds were also assessed and plotted in the return level graph for pre-1995, post-1995 and whole time series at each station of the UGB (see Figs. S6 , S7 and S8 in Supplementary).

Results and discussion

Pre-and post-1995 hydrological scenarios.

The UGB has experienced a widespread increase in high-intensity rainfall events after 1995 (Fig.  3 a,b). These are statistically increasing (p < 0.05) trends predominantly in the Alaknanda basin (Fig.  3 b). It is also noted that the Alaknanda basin has been experiencing a rising trend of high-intensity rainfall events compared to the Bhagirathi basin since 1995. The observed records also suggest an increase in extreme flooding events in the UGB (Fig.  3 c,d and Table S2 ). A total of 9 and 25 extreme flooding events are reported for the two basins together during the pre-and post-1995 period, respectively. The Bhagirathi basin has experienced 2 and 11 extreme flooding events during the pre-and post-1995 period (Fig.  3 c and Table S2 ). The Alaknanda basin has undergone 7 and 14 extreme flooding events during the pre-and post-1995 period (Fig.  3 d and Table S2 ). In terms of temperature, the Bhagirathi and the Alaknanda basins show statistically significant increasing trends. However, these increasing trends detected by the statistical tests are likely driven by the step-change that occurred between pre-and post-2000, possibly suggesting a shift in the instrumentation (Fig. S2 a,c). Further, there is no step-change or significant trend detected in the maximum temperature for both the basins (Figs. S2 b,d).

(a) Pre-and (b) post-1995 average 95th percentile rainfall magnitudes for 1970–2019. The different sizes of green filled circles represent the increasing Sen’s slope for the 95th percentile rainfall events at the 5% significance level. There is one orange-filled circle present in the Bhagirathi basin, which shows decreasing Sen’s slope for the 95th percentile rainfall events at a 5% significance level. (c,d) Shows bar plots 95th percentile rainfall of the Bhagirathi and Alaknanda basins for each year between 1970 and 2019. The blue and red bars show pre-and post-1995 annual cumulative rainfall magnitudes. The horizontal grey line shows the mean value of 95th percentile rainfall for the pre-and post-1995 period in both the bar plots. The mean (µ) and standard deviation (σ) of annual rainfall are also shown in both figures. Based on the available literature (see Table S2 in Supplementary Section), the extreme flooding events are also mentioned for the corresponding plots of the Bhagirathi and Alaknanda River basins.

In the Bhagirathi basin, the difference of post-and pre-1995 FDCs suggests a substantial reduction of up to 80% in very low flows (> 90% exceedance probability) at Uttarkashi (Fig.  4 and Table S4 ). The 5-yearly rainfall magnitude differences suggest around 50–100% reduction in low and moderate magnitude rainfall events from the reference period (Fig.  5 a). Further, the 5-yearly differences of FDCs reveal around 60–90% decline in low and moderate flows at Uttarkashi (Fig.  5 b). Coincidently, upstream of Uttarkashi, the Maneri Stage 1 dam construction was started in the 1960s, and this dam became operational in 1984 (Fig.  1 a). Therefore, a very sharp reduction in the low and moderate flows from the reference condition can be directly correlated to the operation of the Maneri Stage 1 dam. However, a decrease in the magnitude of low and moderate rainfall after 1991 (Fig.  5 a) further attenuated the low and moderate flows at Uttarkashi station (Fig.  5 b). Furthermore, the Maneri Stage 2 dam, located immediately downstream of the Uttarkashi, became operational in 2008 (Fig.  1 a), and this might have also started influencing the hydrology at Uttarkashi since then.

Difference between post-and pre-1995 flow duration curves (FDCs). These differences (%) are plotted for Uttarkashi (blue line), Tehri (dashed blue line), Joshimath (red line), Rudraprayag (dashed red line), Devprayag (green line) and Rishikesh (green dashed line) stations of the UGB. The division between high and moderate (at 20%) and moderate and low (at 70%) flows are shown by dashed black and dashed red vertical lines.

5-yearly differences in rainfall and flow duration curves (FDCs) are plotted using 2d contour plot for each station. The high (20% <), moderate (20–70%) and low (> 70%) flows are divided by black vertical lines. (a) The Uttarkashi station shows 90% reduction in low rainfall. The high rainfall slightly increased (up to 10% since 1986). (b) The Uttarkashi station shows up to 90% reduction (1981–1985) in low flows. The high flows also decreased up to 30%. (c) The Tehri station shows a reduction of 100% in low rainfall throughout the period. There is no anomalous behaviour observed in low and moderate rainfall magnitudes after 2005 at Tehri. (d) The Tehri station shows a reduction of up to 50% in low flows until 1990. After 2000, the reduction in low flows up to 85% is also appeared here. The high flows increased by 50% after 2005. The moderate flows have been increased up to 80% after 2005. (e) The Joshimath station shows increasing high, low and moderate rainfall magnitudes after 1996. The high and low magnitude rainfalls are increased up to 30% and 50%, respectively. (f) The Joshimath station shows a reduction from 20 to 70% in low and moderate flows. The high flows increased by 20% from the reference condition. (g) The Rudraprayag station shows increasing high magnitude rainfall by 30% from 1996. The moderate rainfall magnitudes have also slightly increased post-1995. (h) The Rudraprayag station shows a 10–20% reduction in all flows until 1995. The high flows have been increased 20–40% after 1995. The high rainfall magnitudes have increased up to 10% at (i) Devprayag and (k) Rishikesh stations. However, the high rainfall magnitudes have increased steeply (up to 30%) after 2005 at Devprayag than Rishikesh. The low and moderate rainfall magnitudes have decreased from the reference period at both stations. However, the percentage reductions in low and moderate rainfall magnitudes are slightly higher for the Rishikesh (up to 50%) than Devprayag (20% to 30%). The major changes in low and moderate flows up to 80% and 40% appeared at (j) Devprayag and (l) Rishikesh after 2005.

In contrast, at Tehri, the flows between 30 and 85% exceedance probability (moderate to low flows) have increased by 80% in post-1995 (Figs.  4 , 5 d) with reference to pre-1995. The difference in the FDCs of pre-and post-1995 FDCs suggests that the moderate and low flows increased rapidly downstream of the Tehri dam after becoming operational (Fig.  4 ). Additionally, the very low flows (> 90% exceedance probability) have decreased by 90% at Tehri (Fig.  5 d). The 5-yearly differences in rainfall magnitudes suggest that the moderate and low rainfall magnitudes decreased significantly after 1991 (30% to 100%; Fig.  5 c). The magnitude of very high rainfall (< 10% exceedance probability) has increased up to 30% at Tehri. In comparison, the characteristics of high and moderate flows behaviour after 1995 do not match those of high and moderate rainfall magnitudes (Fig.  5 c,d). Such anomalous hydrological behaviour of the Bhagirathi River at Tehri strongly suggests alteration of flow regime caused by the Tehri dam operation. Hence, the existence of dams in the Bhagirathi basin has reduced the extreme flows and floods downstream. Further, the moderate and low flows have significantly increased up to 125% post-2005. These abrupt increments and decrements in the flows are not observed anywhere in the UGB (Figs.  4 , 5 ). Besides, the upstream (Uttarkashi) and downstream (Tehri) stations in the Bhagirathi behave differently. These distinct and abrupt hydrological behaviour indicate the significant impact of Maneri and Tehri dams in modifying the water outflux from the Bhagirathi basin.

In the Alaknanda basin, there was no dam before 2010 (Fig.  1 a). The Srinagar dam and Tapovan dam became operational in 2015 and 2020, respectively 46 . Thus, the possibility of anthropogenically altered river flow due to reservoir operation can be ruled out before 2010. The differences in the post-and pre-1995 FDCs suggest that the high and moderate flows increased at Joshimath and Rudraprayag (Fig.  4 and Table S4 ). These differences are more predominant at Rudraprayag (up to 40%). In particular, the 5-yearly differences of FDCs from their reference condition also reveal that the high flows increased significantly after 1995 at both locations (Fig.  5 f,h). High flows at Rudraprayag show an increasing trend until 2010. However, a sudden increase (up to 100% or doubled) in high flows is observed between 1995 and 2005 for Joshimath station. The 5-yearly rainfall differences suggest increasing high magnitude rainfall after 1995 at Joshimath (up to 150%) and Rudraprayag (up to 50%; Fig.  5 e,g). It is also evident that the increasing trends (p < 0.05) of high rainfall intensities (95th percentile) have doubled (0.6 mm/y in pre-1995 and 1.2 mm/y in post-1995) and are more widespread in the Alaknanda basin (Fig.  3 b). Therefore, we argue that the increase in the high flows is linked to increasing intensities of high-intensity rainfall events in the Alaknanda basin. Further, the reported extreme events strongly suggest an increase of extreme rainfall linked to flooding events in this basin (Fig.  3 d), which have doubled (7 events in pre-1995 and 14 events in post-1995). These observations indicate that the changing climatic conditions, remarkably increasing trends of high-intensity rainfall events primarily controlled the hydrology of the Alaknanda basin until 2010. However, after the opening of the Srinagar dam (in 2015) and the Tapovan dam (in 2020; Fig.  1 a), the current flows might have been anthropogenically modified in addition to the impact of changing climatic conditions.

In the downstream reaches, high and very low flows (20% < and > 90% exceedance probability) are governed by increasing and decreasing flows from the Alaknanda and Bhagirathi basins, respectively (Fig.  4 ). However, the moderate and low flows (20–90% exceedance probability) at Devprayag and Rishikesh are predominately influenced by the moderate flows coming out from the Tehri (Bhagirathi). The 5-yearly FDCs differences at Devprayag and Rishikesh further suggest a substantial increase in moderate and low flows (> 40%), particularly after 2005 (Fig.  5 j,l and Table S4 ). However, there are no such substantial increments observed in the moderate and low rainfall magnitudes at both downstream stations (Fig.  5 i,k). These patterns strongly correlate with Tehri’s post-2005 moderate and low flows fluctuations (Fig.  5 d). Therefore, these observations suggest that the Tehri dam's water flux increased the moderate and low flows at Devprayag and Rishikesh since 2005, although these fluctuations became more significant post-2010 (Fig.  5 j,l).

Further, sediment duration curves (SDCs) suggest that high sediment fluxes are nearly similar for downstream stations. However, moderate and low sediment fluxes are an order of magnitude higher for the Devprayag and Rishikesh stations (Fig.  6 a). These differences indicate that a significant amount of sediments has been deposited between Devprayag and Rishikesh, possibly due to reduced inflow. The post- and pre-1995 differences suggest that high sediment fluxes (50% < exceedance probability) have decreased up to 50% at both locations (Fig.  6 b). These differences indicate that a considerable part of high-magnitude sediment flux is deposited upstream of Devprayag (possibly in the Tehri and Maneri reservoirs; Fig.  1 a) and not reaching the main channel downstream. Moderate and low sediment fluxes (> 50% exceedance probability) have increased tremendously at Devprayag (up to 260%) and Rishikesh (up to 70%; Fig.  6 b). These incredibly increasing amounts can be linked to sediment reworking caused by abrupt behaviour of moderate and low flows at Devprayag and Rishikesh governed by the reservoir-induced increase of moderate and low flows (Figs.  4 , 5 d,j,l). Therefore, these observations strongly suggest that the Tehri dam in the Bhagirathi basin plays a crucial role in determining the hydrological variability of the downstream UGB region.

(a) Sediment duration curves (SDCs) of Devprayag (blue) and Rishikesh (red) for the period 1970–2015. The high sediment fluxes (20% <) are comparable for both stations. However, the peak sediment fluxes are slightly higher for the Devprayag station. Further, the moderate (20–70%) and low sediment fluxes (> 70%) are an order of magnitude higher for Devprayag than the Rishikesh station. (b) Difference (%) between pre-and post-1995 SDCs of Devprayag (blue) and Rishikesh (red) stations. The peak sediment flows have been increased (120%) at Devprayag. In contrast, the peak sediment fluxes have been decreased (− 25%) at Rishikesh. Furthermore, the moderate sediment fluxes have also been reduced (up to 50%) at both stations. However, moderate and low sediment fluxes greater than 50% and 55% exceedance probability are increased from their pre-1995 values at both stations.

Role of natural and anthropogenic stressors on changing extreme flows

Frequency analysis of extreme flooding events suggests that the UGB has experienced contrasting responses due to natural and anthropogenic forcing. For instance, at Uttarkashi and Tehri, the Bhagirathi basin exhibits a total reduction of extreme flows at different return periods. Around -14.5%, -17.9% and -21.3% reductions are observed in the magnitude of 10, 50 and 100-year return period floods at Uttarkashi (Fig.  7 a,b and Table S4 ). In comparison, around -7.3%, -2.5% and -1.1% reductions are observed in the magnitude of 10, 50 and 100-year return period floods at Tehri (Fig.  7 b). Such decreasing extreme flows in the Bhagirathi basin are primarily governed by two major factors: (1) presence of small and large hydraulic structures such as Maneri Stage 1, Maneri Stage 2, Tehri and Koteshwar dam (Fig.  1 a), and (2) no significantly increasing or decreasing trends in the high-intensity rainfall events (Fig.  3 a,b).

(a) Extreme flows at different return periods at the six stations of the UGB. The Rishikesh (downstream station) and Joshimath (upstream Alaknanda basin) stations show the highest and lowest extreme flows at different return periods. The standard errors of the scale and location parameters of the Gumbel distribution are used to predict the error bound and shown using shaded regions around each return level curve. The details of 95% confidence bounds around the prediction of return level for each station are given in Supplementary. (b) Post-and pre-1995 differences of extreme flows at different return periods for different stations.

In contrast, the Alaknanda river at Joshimath and Rudraprayag show an increase of extreme flows at different return periods. For instance, around 1.5%, -0.5% and -1.1% differences are observed in the magnitude of 10-, 50-and 100-year return period floods at Joshimath (Fig.  7 b and Table S4 ). In comparison, around 15%, 9.6% and 7.9% increments are observed in the magnitude of 10-, 50-and 100-year return period floods at Rudraprayag (Fig.  7 b and Table S4 ). Therefore, the extreme flows and flooding events in the Alaknanda basin (particularly at Rudraprayag) are primarily governed by two major factors: (1) no hydraulic structures present before 2010 (Fig.  1 a), and (2) widespread increasing high-intensity rainfall in this basin (Fig.  3 a,b,d). Further, the oldest dam, Maneri Stage 1, has been operational since 1984 in the Bhagirathi basin, whereas the Srinagar dam and Tapovan dam in the Alaknanda became operational in 2015 and 2020, respectively. Therefore, we argue that the increasing number of hydraulic structures after 2015 has also impacted the extreme flows of the Alaknanda basin.

The downstream stations of the UGB behave differently when we compare the pre-and post-1995 extreme flows at different return periods. For instance, we document an increment of 10.3%, 17.5% and 19.7% in the magnitude of 10-, 50- and 100-year floods at Devprayag in the post-1995 period (Fig.  7 b and Table S4 ). However, the Rishikesh station records an -18.1% reduction in the magnitude of 10-, 50- and 100-year floods in the post-1995 period (Fig.  7 b and Table S4 ). A reduction in extreme flow magnitudes is possibly because of flow reduction caused by the Pashulok barrage downstream of the Rishikesh station. We have also observed a significant reduction in high magnitude stream flows at Rishikesh than Devprayag station (Fig.  5 j,l). The post-1995 extreme flows have decreased in the Bhagirathi basin but increased in the Alaknanda (Fig.  7 b). Therefore, a rise in extreme flooding events at Devprayag station is primarily governed by the changes in hydrometeorological conditions in the Alaknanda basin. The widespread increase in high-intensity rainfall in the Alaknanda basin and the reservoir-induced flow alterations are the primary drivers of these changes in observed extreme flow at Devprayag and Rishikesh (Fig.  3 a–c).

It is also observed that the downstream (Rudraprayag) region of the Alaknanda shows an incremental difference of up to 15% in the extreme flows (Fig.  7 b) which makes the entire downstream Alaknanda basin vulnerable to extreme flooding events in the near future. One such event was reported recently (in February 2021) near Joshimath, which destroyed the Tapovan dam 47 . Downstream of Rishikesh, the Ganga River debouches into the alluvial plains (Fig.  1 a), where several populous cities are situated. Therefore, these are the vulnerable regions where around 20% increase in extreme flooding events (at Devprayag) might enhance the flood risk manifold. Further, the Pashulok barrage downstream of Rishikesh was constructed in 1980 based on the past extreme flow information until then. However, the changing climatic conditions in the Alaknanda basin, and hence, an increase of 10–20% in extreme flows, might severely affect the operations of such structures.

Increasing anthropogenic activities and their future impacts

Overall, these hydrological analyses indicate that the flow in the Bhagirathi basin has been anthropogenically modified owing to the presence of several large and small dams (Figs.  1 a, 4 , 5 b,d). In particular, low and moderate flows, which occur primarily during pre- (Jan-May) and post-monsoon (Oct-Dec) periods, are significantly impacted (Figs.  4 , 5 b,d). The Alaknanda was a free-flowing river before the Srinagar dam was commissioned in 2015 (Figs.  1 a, 4 , 5 f,h), followed by the Tapovan dam in 2020 (Fig.  1 a,b). However, our data records could not capture these hydrological alterations at Joshimath and Rudraprayag in the Alaknanda (see Table S1 ). Recent hydrological records can be further used to verify these hydrological changes. We have demonstrated that the present low and moderate flows coming out from Devprayag and Rishikesh (downstream of the UGB) are entirely modified anthropogenically (Figs.  4 , 5 j,l). The interventions have severely affected the upstream and downstream hydrology and geomorphology of the Bhagirathi basin (Figs.  5 j,l, 6 a,b).

Around 11 and 26 additional dams of different power generation capacities in the Bhagirathi and Alaknanda basin, respectively, are planned 44 (Fig.  2 b–d). These planned hydraulic structures will be located on several small and large tributaries of the UGB (Fig.  2 b). These structures are likely to impact the low and moderate flows of the UGB further, as demonstrated in the case of the Bhagirathi basin. Additionally, the increasing number of dams will also influence the sediment transport processes across the UGB (Fig.  6 b). Further, a significant increase in the high magnitude flows is also observed in the Alaknanda River basin and Devprayag (Fig.  7 a,b). The impact of changing climatic conditions are more predominant in the Alaknanda basin (Fig.  3 a,b). Our extreme frequency analysis also suggests an increase in the magnitude of extreme flows for different return periods in the Alaknanda basin (Fig.  7 a,b). Further, the observed records indicate an increase in the frequency of extreme flood events in the UGB, especially in the Alaknanda basin (Fig.  3 a,b). During the flash flood event at Joshimath in February 2021 47 , high discharges were quickly managed because of the lean condition of the mainstream. However, if this event had occurred during the monsoon season, it could pose a severe flood risk in the downstream regions. In the past, the UGB region also witnessed the June 2013 Kedarnath disaster when rainfall magnitudes crossed a 111-year return period and produced a massive flood in the monsoon period 26 (Table S2 ). Thus, the changing extremity of streamflow in the UGB poses serious impacts on the hydraulic structures that need critical assessment and design modifications.

Conclusions

The Ganga River is the lifeline for close to half a billion people in the northern Indian region. During the twentieth century, the hydrology of the basin has been significantly modified owing to increasing anthropogenic interventions and changing climatic conditions. In particular, the upper Ganga basin (UGB) has witnessed modifications in the flow regime owing to several small and large hydraulic structures, particularly in the Bhagirathi basin (western tributary). In contrast, the Alaknanda basin (eastern tributary) has experienced increasing magnitudes of extreme rainfall events from 1970 to 2019. Therefore, the flow modifications in these basins have been influenced by different factors. Our results suggest that the reduction in rainfall magnitudes, Maneri dam in upstream and Tehri dam in downstream exert primary controls on the flows in the Bhagirathi. As a result, low and moderate flows increased at Tehri by 125%. In addition, the post-1995 extreme flows at different return periods have decreased by -21.3% in the Bhagirathi basin. Further, the Alaknanda basin was a free-flowing river until 2015. The extreme flows at different return periods have increased by 8–15% in the Alaknanda basin, primarily because of increasing high-intensity rainfall events post-1995. Therefore, the Alaknanda basin has witnessed some extreme flash flood events in recent years. Simultaneously, the downstream reaches experience anthropogenically modified low and moderate flows that are attributed to Tehri and other dams during pre-and post-monsoon months.

Our results further indicate that a significant amount of sediments transported during high flows are trapped in the Tehri and Maneri reservoirs in the Bhagirathi basin. Therefore, hydraulic structures have significantly disrupted the upstream–downstream geomorphologic linkages, thereby impacting the channel morphology in the downstream reaches as observed in several regions 48 , 49 , 50 , 51 . Furthermore, several hydraulic structures such as the Pashulok barrage were designed based on analysis of past extreme floods. However, the increasing magnitude of extreme flows (10-20%), particularly at Devprayag, might also affect the functioning of the Pashulok barrage during peak monsoon periods. The downstream regions also experience reservoir-induced flow increments during pre-and post-monsoonal months. Overall, the results obtained from this work should help in sustainable river basin management and encourage more serious work toward a better understanding of hydrology, ecology, and geomorphology in the UGB.

Raftery, A. E., Zimmer, A., Frierson, D. M., Startz, R. & Liu, P. Less than 2 C warming by 2100 unlikely. Nat. Clim. Change 7 (9), 637–641 (2017).

Article   ADS   CAS   Google Scholar  

Brutsaert, W. & Parlange, M. B. Hydrologic cycle explains the evaporation paradox. Nature 396 (6706), 30–30 (1998).

Scanlon, T. M., Caylor, K. K., Levin, S. A. & Rodriguez-Iturbe, I. Positive feedbacks promote power-law clustering of Kalahari vegetation. Nature 449 (7159), 209–212 (2007).

Article   ADS   CAS   PubMed   Google Scholar  

Trenberth, K. E. et al . Climate change 2007: The physical science basis. Clim. Change 235–336 (2007).

Steffen, W. et al. Stratigraphic and Earth System approaches to defining the Anthropocene. Earth’s Future 4 (8), 324–345 (2016).

Article   ADS   Google Scholar  

Mueller, E. N. & Pfister, A. Increasing occurrence of high-intensity rainstorm events relevant for the generation of soil erosion in a temperate lowland region in Central Europe. J. Hydrol. 411 (3–4), 266–278 (2011).

Dourte, D., Shukla, S., Singh, P. & Haman, D. Rainfall intensity-duration-frequency relationships for Andhra Pradesh, India: Changing rainfall patterns and implications for runoff and groundwater recharge. J. Hydrol. Eng. 18 (3), 324–330 (2013).

Article   Google Scholar  

Bürger, G., Heistermann, M. & Bronstert, A. Towards subdaily rainfall disaggregation via Clausius-Clapeyron. J. Hydrometeorol. 15 (3), 1303–1311 (2014).

Jena, P. P., Chatterjee, C., Pradhan, G. & Mishra, A. Are recent frequent high floods in Mahanadi basin in eastern India due to increase in extreme rainfalls?. J. Hydrol. 517 , 847–862 (2014).

Olsson, L., Opondo, M., Tschakert, P., Agrawal, A., & Eriksen, S. E. Livelihoods and poverty. In Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change 798–832 (Cambridge University Press, 2014).

Haddeland, I. et al. Global water resources affected by human interventions and climate change. Proc. Natl. Acad. Sci. USA 111 (9), 3251–3256 (2014).

Guyennon, N., Salerno, F., Portoghese, I. & Romano, E. Climate change adaptation in a Mediterranean semi-arid catchment: Testing managed aquifer recharge and increased surface reservoir capacity. Water 9 (9), 689 (2017).

Magilligan, F. J., Nislow, K. H. & Graber, B. E. Scale-independent assessment of discharge reduction and riparian disconnectivity following flow regulation by dams. Geology 31 (7), 569–572 (2003).

Magilligan, F. J. & Nislow, K. H. Changes in hydrologic regime by dams. Geomorphology 71 (1–2), 61–78 (2005).

Vörösmarty, C. J. et al. Anthropogenic sediment retention: Major global impact from registered river impoundments. Glob. Planet. Change 39 (1–2), 169–190 (2003).

Schmidt, J. C. & Wilcock, P. R. Metrics for assessing the downstream effects of dams. Water Resour. Res. https://doi.org/10.1029/2006WR005092 (2008).

Syvitski, J. P. et al. Sinking deltas due to human activities. Nat. Geosci. 2 (10), 681–686 (2009).

Ansar, A., Flyvbjerg, B., Budzier, A. & Lunn, D. Should we build more large dams? The actual costs of hydropower megaproject development. Energy Policy 69 , 43–56 (2014).

Best, J. Anthropogenic stresses on the world’s big rivers. Nat. Geosci. 12 (1), 7–21 (2019).

Jain, C. K. & Singh, S. Impact of climate change on the hydrological dynamics of River Ganga, India. J. Water Clim. Change 11 (1), 274–290 (2020).

Gosain, A. K., Rao, S. & Basuray, D. Climate change impact assessment on hydrology of Indian river basins. Curr. Sci. 90 , 346–353 (2006).

Google Scholar  

Mukherjee, S., Aadhar, S., Stone, D. & Mishra, V. Increase in extreme precipitation events under anthropogenic warming in India. Weather Clim. Extremes 20 , 45–53 (2018).

Rickards, N. et al. Understanding future water challenges in a highly regulated Indian river basin—modelling the impact of climate change on the hydrology of the Upper Narmada. Water 12 (6), 1762 (2020).

Chawla, I., Osuri, K. K., Mujumdar, P. P. & Niyogi, D. Assessment of the Weather Research and Forecasting (WRF) model for simulation of extreme rainfall events in the upper Ganga Basin. Hydrol. Earth Syst. Sci. 22 (2), 1095–1117 (2018).

Chawla, I. & Mujumdar, P. P. Isolating the impacts of land use and climate change on streamflow. Hydrol. Earth Syst. Sci. 19 (8), 3633–3651 (2015).

Singh, D. et al. Severe precipitation in Northern India in June 2013: Causes, historical context, and changes in probability. Bull. Am. Meteorol. Soc. 95 (9), S58–S61 (2014).

Nie, Y. et al. Glacial change and hydrological implications in the Himalaya and Karakoram. Nat. Rev. Earth Environ. 2 (2), 91–106 (2021).

Pandit, M. K. & Grumbine, R. E. Potential effects of ongoing and proposed hydropower development on terrestrial biological diversity in the Indian Himalaya. Conserv. Biol. 26 (6), 1061–1071 (2012).

Article   PubMed   Google Scholar  

Bhatt, J. P., Tiwari, S. & Pandit, M. K. Environmental impact assessment of river valley projects in upper Teesta basin of Eastern Himalaya with special reference to fish conservation: A review. Impact Assess. Project Apprais. 35 (4), 340–350 (2017).

Larinier, M. Environmental issues, dams and fish migration. FAO Fish. Tech. Pap. 419 , 45–89 (2001).

Grumbine, R. E. & Pandit, M. K. Threats from India’s Himalaya dams. Science 339 (6115), 36–37 (2013).

Article   ADS   PubMed   Google Scholar  

GRBMP. Management Plan-2015. Consortium of 7 “Indian Institute of Technology” (IITs) (2015).

Nepal, S., Flügel, W. A. & Shrestha, A. B. Upstream-downstream linkages of hydrological processes in the Himalayan region. Ecol. Process. 3 (1), 1–16 (2014).

Swarnkar, S., Sinha, R. & Tripathi, S. Morphometric diversity of supply limited and transport-limited river systems in the Himalayan foreland. Geomorphology 348 , 106882 (2020).

Tsarouchi, G. & Buytaert, W. Land-use change may exacerbate climate change impacts on water resources in the Ganges basin. Hydrol. Earth Syst. Sci. 22 (2), 1411–1435 (2018).

Shukla, A. K. et al. Population growth, land use and land cover transformations, and water quality nexus in the Upper Ganga River basin. Hydrol. Earth Syst. Sci. 22 (9), 4745–4770 (2018).

Swarnkar, S., Malini, A., Tripathi, S. & Sinha, R. Assessment of uncertainties in soil erosion and sediment yield estimates at ungauged basins: An application to the Garra River basin, India. Hydrol. Earth Syst. Sci. 22 (4), 2471–2485 (2018).

Sinha, R. et al. Basin-scale hydrology and sediment dynamics of the Kosi river in the Himalayan foreland. J. Hydrol. 570 , 156–166 (2019).

Swarnkar, S., Tripathi, S. & Sinha, R. Understanding hydrogeomorphic and climatic controls on soil erosion and sediment dynamics in large Himalayan basins. Sci. Total Environ. 795 , 148972 (2021).

Dubey, S. & Goyal, M. K. Glacial lake outburst flood hazard, downstream impact, and risk over the Indian Himalayas. Water Resour. Res. 56 (4), e2019WR026533 (2020).

Boral, S., Sen, I. S., Tripathi, A., Sharma, B. & Dhar, S. Tracking dissolved trace and heavy metals in the Ganga River from source to sink: A baseline to judge future changes. Geochem. Geophys. Geosyst. 21 (10), e2020GC009203 (2020).

Santy, S., Mujumdar, P. & Bala, G. Potential impacts of climate and land use change on the water quality of Ganga River around the industrialized Kanpur region. Sci. Rep. 10 (1), 1–13 (2020).

Article   CAS   Google Scholar  

Boral, S., Sen, I. S., Ghosal, D., Peucker-Ehrenbrink, B. & Hemingway, J. D. Stable water isotope modeling reveals spatio-temporal variability of glacier meltwater contributions to Ganges River headwaters. J. Hydrol. 577 , 123983 (2019).

Zarfl, C., Lumsdon, A. E., Berlekamp, J., Tydecks, L. & Tockner, K. A global boom in hydropower dam construction. Aquat. Sci. 77 (1), 161–170 (2015).

Gumbel, E. J. The return period of flood flows. Ann. Math. Stat. 12 (2), 163–190 (1941).

Article   MathSciNet   MATH   Google Scholar  

CWC. National Register of Large Dams (Central Water Commission, 2018).

Shugar, D. H. et al. A massive rock and ice avalanche caused the 2021 disaster at Chamoli, Indian Himalaya. Science 373 , 300–306 (2021).

Smith, N. D., Morozova, G. S., Pérez-Arlucea, M. & Gibling, M. R. Dam-induced and natural channel changes in the Saskatchewan River below the EB Campbell Dam, Canada. Geomorphology 269 , 186–202 (2016).

Warrick, J. A. et al. World’s largest dam removal reverses coastal erosion. Sci. Rep. 9 (1), 1–12 (2019).

Ritchie, A. C. et al. Morphodynamic evolution following sediment release from the world’s largest dam removal. Sci. Rep. 8 (1), 1–13 (2018).

Zhou, Y. et al. Impacts of Three Gorges Reservoir on the sedimentation regimes in the downstream-linked two largest Chinese freshwater lakes. Sci. Rep. 6 (1), 1–10 (2016).

CAS   Google Scholar  

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Acknowledgements

The first author acknowledges the National Postdoctoral Fellowship (NPDF) grant (PDF/2020/000496) received from the Science and Engineering Research Board (SERB), Department of Science and Technology (DST), Government of India. The second author acknowledges the support received through the JC Bose Fellowship (Number, JCB/2018/000031). The funding received from the Ministry of Earth Sciences (MoES), Government of India, through the project, “Advanced Research in Hydrology and Knowledge Dissemination”, Project No.: MOES/PAMC/H&C/41/2013-PC-II, is gratefully acknowledged. We also acknowledge the India Meteorological Department (IMD) for the high-resolution daily gridded rainfall & temperature datasets and Central Water Commission (CWC) for the daily discharge & sediment datasets provided for this work.

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S.S. analysed the hydrological records, applied the hydrological analysis and prepared the first draft of the manuscript, including figures. P.M. conceptualised the hydrological analysis, reviewed and edited the manuscript. R.S. helped to develop the hydrological analysis, reviewed and edited the manuscript.

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Swarnkar, S., Mujumdar, P. & Sinha, R. Modified hydrologic regime of upper Ganga basin induced by natural and anthropogenic stressors. Sci Rep 11 , 19491 (2021). https://doi.org/10.1038/s41598-021-98827-7

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Received : 01 June 2021

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DOI : https://doi.org/10.1038/s41598-021-98827-7

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Proceedings of the Indian National Science Academy (2024)

Glacier–glacial lake changes and modeling glacial lake outburst flood in Upper Ganga Basin, India

• Manish Rawat
• Rayees Ahmed
• Kailash Chandra Tiwari

Modeling Earth Systems and Environment (2023)

Contrasting changes of snowfall in response to temperature and rainfall over two distinct geographic ranges of the Himalayas: A study using the High Asia Refined analysis data

• Kandula V Subrahmanyam
• J Srinivasulu
• M V R Sesha Sai

Journal of Earth System Science (2023)

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Case Study – Ganges/Brahmaputra River Basin

Flooding is a significant problem in the Ganges and Brahmaputra river basin. They cause large scale problems in the low lying country of Bangladesh. There are both human and natural causes of flooding in this area.

Human Causes

Deforestation – Population increase in Nepal means there is a greater demand for food, fuel and building materials. As a result, deforestation has increased significantly. This reduces interception and increases run-off. This leads to soil erosion . River channels fill with soil, the capacity of the River Ganges and Brahmaputra is reduced and flooding occurs.

Natural Causes

• Monsoon Rain
• Melting Snow
• Tectonic Activity – The Indian Plate is moving towards the Eurasian Plate. The land where they meet (Himalayas) is getting higher and steeper every year ( fold mountains ). As a result, the soil becomes loose and is susceptible to erosion. This causes more soil and silt in rivers. This leads to flooding in Bangladesh.

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INTRODUCTION

River ganga: origin, expanse and cultural significance, factors responsible for changes in the dynamics of river ganga, impacts of climate change on river ganga, management aspects and research priorities, conclusions, acknowledgements, impact of climate change on the hydrological dynamics of river ganga, india.

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C. K. Jain , Surya Singh; Impact of climate change on the hydrological dynamics of River Ganga, India. Journal of Water and Climate Change 1 March 2020; 11 (1): 274–290. doi: https://doi.org/10.2166/wcc.2018.029

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Rivers provide innumerable ecosystem services to mankind. However, anthropogenic activities have inflicted a host of pressures to the riverine ecosystems. Climate change is also one of the human induced consequences which is of serious concern. A number of studies have predicted devastating effects of climate change. In the Indian context, where a river such as the Ganga is already suffering from industrial and municipal waste disposal, unhygienic rituals, and other activities, effects of climate change may further aggravate the situation. Climate change will not only result in disasters, but effects on water quality, biodiversity, and other ecological processes also cannot be denied. In this paper, an attempt has been made to evaluate the effects of climatic change on the dynamics of River Ganga. The study focuses on the impacts on fundamental ecological processes, river water quality, effect on species composition, and hydropower potential etc. The paper also discusses management aspects and research needs for rejuvenation of the River Ganga.

Climatic variability and climate change have received significant attention from the scientific community across the globe. The basic reason underlying the fact that climate change is the most discussed topic in every forum is that climate affects all the spheres which all living organisms are exposed to. The ill-effects of adverse climate change can destabilize all the facets of the environment and thereby destabilize the bonds between the abiotic and biotic components of any ecosystem. The effects of global climate change are already visible in various components of the environment – glaciers are shrinking ( Dyurgerov & Meier 2000 ) and ice is melting faster ( Gregory et al. 2004 ; Overpeck et al. 2006 ; Stroeve et al. 2007 ), sea level is rising ( Ramachandran et al. 2017 ), natural disasters such as floods, hurricanes, cyclones etc. are becoming more frequent and intense ( Easterling et al. 2000 ; Aalst 2006 ; Coumou & Rahmstorf 2012 ; Majumder et al. 2017 ), floral and faunal ranges are shifting ( Parmesan 2006 ; Wernberg et al. 2011 ), and there are also effects on agriculture and the flowering pattern of plants ( Fischer et al. 2005 ; Mall et al. 2006 ; Howden et al. 2007 ; Memmott et al. 2007 ). Almost all the ecosystems of the world are affected due to climate change. Rivers are also one of the most vulnerable ecosystems in the world. Moreover, rivers are considered as the most sensitive of all the ecosystems to the impacts of climate change, both directly and indirectly by the combination of various other stressors ( Durance & Ormerod 2007 , 2009 ). Impacts on the riverine ecosystem will not only affect the hydrology and dynamics of the river, but also pose serious threats to the survival and existence of a number of aquatic floral and faunal species, wild life, human population etc. Besides sustaining the life of organisms and providing plenty of ecosystem services, rivers are also a regulator of flood, sediment supplies, erosion, water quality and pollutant disposal etc. Mighty rivers such as the Ganga, Brahmaputra, Indus etc. also sustain many other ecosystems such as wetlands, flood plains, estuaries and riparian areas etc. by providing water, nutrients, and energy. Thus, the effects of changing climate on the river will certainly influence many other associated ecosystems too. Therefore, study of the impacts of climate change on the rivers is of utmost importance.

The impact of climate change on a river brings a plethora of consequences which affect the entire dynamics of the river, such as water resource management ( Middelkoop et al. 2001 ; Alcamo et al. 2007 ; Pahl-Wostl 2007 ; Kundzewicz et al. 2008 ), water quality ( Delpla et al. 2009 ; Whitehead et al. 2009 ), eutrophication ( Feuchtmayr et al. 2009 ; Rabalais et al. 2009 ; O'Neil et al. 2012 ), acidification ( Sabine et al. 2004 ), accumulation of toxic substances ( Gouin et al. 2013 ; Landis et al. 2013 ), hydromorphological changes ( Boon & Raven 2012 ), catchment land-use changes ( Oliver & Morecroft 2014 ), and invasion of exotic species ( Hellmann et al. 2008 ; Rahel & Olden 2008 ) etc. A number of studies on the effects of climate change on riverine systems have also been carried out ( Verghese & Iyer 1993 ; Gosain et al. 2006 , 2011 ; Boon & Raven 2012 ; Hosterman et al. 2012 ; Deshpande et al. 2016 ; Hosseini et al. 2017 ). In India, rivers play a very important role and can be even considered as the lifeline of the nation. Rivers are the main source of irrigation in the agricultural sector, upon which more than 70% of the rural population depends. Apart from this, rivers are also a major source of potable water, transportation, hydropower, aquaculture, recreational activities etc. Among the 22 river basins across the country ( CWC 2012 ), the Ganga river basin is the largest and most significant.

The River Ganga is one of the most important rivers in India. Being the largest river, Ganga supports the livelihoods of millions of people and there are many important cities and industries along its bank. However, over the years the river has faced severe negligence. Unsustainable dam construction and water diversion into the canals resulted in very low flow in the main river in several stretches. The situation further worsened due to the discharge of millions of liters of untreated industrial effluents and municipal sewage per day from nearby towns/cities. Considering the urgency of the situation and to target the escalating pollution problems, the Government of India launched an ambitious plan named the Ganga Action Plan (GAP) in 1986. However, the plan failed due to the consideration of limited issues, under-utilization of sewage treatment plants (STPs), lack of technical expertise and political will, and many other implementation issues. In 2007, the River Ganga was declared the fifth most polluted river in the world ( Rai 2013 ). To intensify the Ganga rejuvenation efforts and create more awareness regarding pollution prevention, the River Ganga was affirmed the status of ‘National River of India’ in November 2008. Later on, many other programmes were launched (e.g. Namami Gange, National Mission for Clean Ganga etc.) and various authorities were established (e.g. National Ganga River Basin Authority etc.) to revive the river, but to date the situation has not improved ( Das & Tamminga 2012 ). In such circumstances, impacts of climate change may prove to be a major setback for the river. Further, these effects may be more stringent and influential for the River Ganga considering the large area under impact. Interaction of the increasing temperature and changing discharge patterns owing to climate change, along with other existing pressures on River Ganga, will affect the survival of millions of people and various floral and faunal species. Negative impacts on other hydrological and ecological processes also cannot be neglected. Therefore, in this paper efforts have been made to assess the impacts of climate change on the various aspects of the riverine dynamics of River Ganga.

River Ganga originates from Gaumukh in the Gangotri glacier at 30°55′ N and 79°7′ E at about 4,100 m above mean sea level in the Uttarkashi district of Uttarakhand, India. The Gangetic ecosystem is one of the most vivid and complex ecosystems, on which approximately 445 million people are dependent either directly or indirectly ( Lokgariwar et al. 2014 ). The Ganga river basin is the largest among all the river basins in India, and the fourth largest in the world. The basin is part of the Ganga–Brahmaputra–Meghna basin, which drains through China (4%), Nepal (13%), India (79%) and Bangladesh (4%) ( Nepal & Shrestha 2015 ). In India, the Ganga basin lies in the states of Uttarakhand, Uttar Pradesh, Bihar, Jharkhand, West Bengal, Rajasthan, Madhya Pradesh, Haryana, Himachal Pradesh and Delhi. In northern India, the catchment of the Ganga basin is one of the largest water catchments, draining an area of approximately 1.09 million km 2 ( Bharati et al. 2011 ). The 2,525 km river, flowing through the states of Uttarakhand and Uttar Pradesh (1,425 km), Bihar and Jharkhand (475 km) and West Bengal (625 km), touches 44% of the Indian population before flowing through Bangladesh and emptying into the Bay of Bengal ( Figure 1 ). Originating in the mighty Himalayas, the Ganga has a very fertile and large basin that accounts for 30% of India's cultivable land. In addition to its economic and ecological relevance, it also has a strong cultural presence among Indians. Nevertheless, the River Ganga is an integral part of Indian spirituality. There are many mythological statements on this river and hence, millions of Ganga devotees throng to the river just to have a holy dip ( Lokgariwar et al. 2014 ; Sanghi 2014 ). However, there is a lack of effort to understand how this massive river is responding to climate change along its basin. Climate change will have a considerable impact on the dynamics of the river Ganga. This will directly affect a major portion of northern India, which depends on the river for meeting domestic, agricultural, and industrial water needs.

Location of River Ganga.

Temperature and precipitation patterns

An increasing trend in the temperature of the Indian subcontinent is evident from the pattern shown in Figure 2 . In the coming years, the rise in temperature will be much more severe and rapid ( Diffenbaugh & Field 2013 ). According to an estimate, the average global temperature is likely to increase by 1.8–4.0 °C ( IPCC 2007 ). Rising atmospheric temperature is expected to increase the water temperature as well. Since rivers are turbulent and are in close contact with atmospheric air, they respond to atmospheric warming very quickly. It is supposed that there will be an increase in the surface water temperature of streams. Regional climate model studies in the Ganges basin predict an increase in the mean annual temperature of 1–4 °C between 2010 and 2050 ( Moors et al. 2011 ). A study on the Koshi river basin, a sub-basin of the Ganges, also reveals increasing trends in the seasonal maximum and minimum temperatures ( Shrestha et al. 2017 ). Due to the warming of climate, there may also be a change in winter precipitation from snow to rain. This eventually may lead to a change in the flow of the river ( Kundzewicz et al. 2008 ).

Trend of average annual temperature in the Indian subcontinent during 1900–2015 ( Data source: https://data.gov.in/ ).

There are other factors which also influence the river temperature and heat budget. The heating of river water depends on the amount of solar insolation received and radiated back; resistance for the heat flow offered by the river banks to the river bed; heat flow transfer between river and surrounding air, river bank, and land; evaporation and condensation mechanisms etc. Moreover, rivers also receive heat from the water coming from their catchment areas ( Webb et al. 2008 ). A combination of all these factors will influence the hydrological cycle. In response to warming climate, the hydrological cycle is expected to intensify. Since the warm air can hold more water vapor, the resulting precipitation will be much more intense. Nevertheless, due to warming climate, rainfall will replace snowfall. As drier atmospheric conditions will result in increased drying of the land surface, it will put additional pressure on the river to fulfill the water demand.

Affected by the temperature variation, precipitation is also expected to show changes ( Shrestha et al. 2017 ). Studies prove that there will be a mixed pattern of changes in precipitation – in some seasons higher rainfall may result in floods while in others extreme drought conditions may prevail due to high temperatures. Changing precipitation patterns during summer and winter are also expected to increase the nutrient loading in rivers from increased erosion of agricultural soils and associated catchment areas ( Rahman et al. 2016 ). As the pattern and intensity of precipitation will change, flow in the rivers is also expected to change. This may have a significant effect on the habitats and communities of the river ecosystem. Another impact of diminished flows is on the concentration of dissolved oxygen (DO) levels. Reduced flow conditions result in a marked decrease in DO concentration, which ultimately enhances the chances of eutrophic conditions in rivers ( Bocaniov et al. 2016 ). Higher evaporation rates coupled with reduced precipitation will lead to the continued lowering of water tables, which will eventually also lead to a reduction in groundwater levels.

High concentration of atmospheric CO 2

Exceedingly high concentrations of CO 2 may enhance the carbonic acid concentration in the river waters. The upper safe limit for CO 2 concentration in the atmosphere is considered to be 350 ppm. However, higher emission from various anthropogenic sources had increased atmospheric CO 2 concentrations to 406.82 ppm by 2017 ( Figure 3 ). Research suggests that excess CO 2 present in the atmosphere is dissolved in rainwater, resulting in the formation of carbonic acid (H 2 CO 3 ). Upon contact with rock surfaces, such waters enhance the dissolution of rock surfaces, thereby speeding up the chemical weathering processes ( Beaulieu et al. 2012 ). In the long term, this may result in much higher sediment loads on the river.

Carbon dioxide concentration in the atmosphere (1960–2017) ( Data source: Mauna Loa Observatory, Hawaii).

River Ganga is thought to carry approximately 403–660 × 10 6 tonnes of sediments annually ( Subramanian & Ramanathan 1996 ), of which 88% of the annual sediment load is restricted to only during monsoons ( Subramanian 1996 ). Therefore, further enhancement in the sediment loads will have an impact on the overall ecological characteristics as well as the water quality of the river.

Glacial retreat, runoff and river responses

The fast melting of mountain glaciers results in changes in the discharge regimes of rivers originating from glaciers ( Milner et al. 2009 ; Bliss et al. 2014 ). Glaciers influence the flow of water in rivers, as they are the natural keepers and controllers of fresh water flow to the rivers. As the increasing temperature is resulting in fast melting of glaciers, it may result in floods and other disasters in the rivers ( Lutz et al. 2014 ). Gangotri glacier in the Uttarkashi district of Garhwal Himalaya, which is the feeding glacier of River Ganga, is retreating at a fast rate owing to increasing temperature and climate change. It has been reported that the average rate of glacier retreat is 19 m/year ( Naithani et al. 2001 ). Remote sensing images reveal that this glacier has been receding since 1780; however, the retreat has increased since 1971 ( Figure 4 ). In the last 25 years of the 20th century, it has shrunk more than 850 m ( Sharma & Owen 1996 ). This will have a serious impact on the flow characteristics of the Ganga ( Bolch et al. 2012 ; Immerzeel et al. 2012 ; Kääb et al. 2012 ). It has been reported that between 2003 and 2009, approximately 174 gigatonnes of water was lost by Himalayan glaciers, which led to severe floods in the Indus, Ganga, and Brahmaputra rivers affecting millions of lives ( Gardner et al. 2013 ; Laghari 2013 ).

Retreat of Gangotri glacier (1780–2001) ( Image source: https://earthobservatory.nasa.gov/ ).

In warmer climates it is also expected that a large amount of precipitation will occur in the form of rain, rather than snow ( Trenberth 2011 ). This can have serious implications on the river water and basin area. Precipitation in the form of snow results in slow melting of ice and therefore, allows a continuous water supply to the river stream. However, rains will immediately fill up the water bodies, resulting in floods in the basin area. It has many other related consequences. Frequent rains will also result in increased soil erosion, thereby silting the rivers, which will further aggravate the flood situation ( Ghosh & Mistri 2015 ). Faster run-off from catchment areas to the river may also result in a decline in the groundwater table and soil moisture, as the over-flowing water will not have sufficient time to recharge the groundwater. In the long term, this could have very serious implications for the overall groundwater availability, soil productivity, agricultural output, and therefore on the entire population depending upon the river ( Kumar 2012 ; Kidmose et al. 2013 ; Taylor et al. 2013 ).

The changing temperature and precipitation patterns, fast glacial retreat, and increasing CO 2 concentration will have a number of subsidiary effects on the river, as discussed below.

Changes in the fundamental ecological processes

Fundamental ecological processes in any freshwater aquatic ecosystem include production and decomposition patterns, nutrient cycling, and energy flow etc. Temperature change affects the productivity of the aquatic ecosystem to a significant extent. Primary productivity may increase in response to an increase in the length of the growing season and increase in nutrient release from catchment soils. This will alter the food-web structure in river water, leading to higher phytoplankton biomass and thereby a decrease in benthic oxygen concentrations as well as an increase in nutrient release from sediments ( Jeppesen et al. 2009 ). Higher suspended sediment loads will also increase the turbidity of the river body ( Miller et al. 2015 ), therefore changing the underwater light regime. It will have adverse impacts on the growth of submerged aquatic plants. However, after some time this increasing productivity trend will begin to decline, because the rate of respiration also increases along with the increasing temperature, which again becomes a reason for increasing CO 2 . Decomposition processes also become enhanced due to higher temperatures. Once these productivity and decomposition patterns are altered, this also affects the nutrient cycling as well as the energy flow. Thus, the increasing temperature will have a significant impact on the fundamental processes of the river ecosystem.

Changes in the hydrologic characteristics of aquatic systems

Erratic rainfall patterns are not only the cause of severe floods and droughts, but are also responsible for disturbances in the discharge pattern of the river. The flow conditions of the river determine many of the characteristics, of which water quality is one. During very high discharge periods, water quality shows variations due to the mixing of waters of different origins, such as surface run-off, underground water, and water which circulates within the soil. All these waters have many different characteristics; for instance, water coming through surface run-off usually carries suspended solids along with other impurities. Water which circulates within the soil is the source of dissolved organic carbon, nitrogen, and phosphorus. Moreover, groundwater is mostly the source of silicate, calcium, magnesium, sodium, and potassium. Therefore, discharge changes lead to a change in the ambient water quality to a great extent.

River water quality

There will be colossal changes in river water quality owing to increasing temperature and global warming ( Hassan et al. 1998 ; Jun et al. 2010 ; Rehana & Mujumdar 2011 ; Todd et al. 2012 ). Increased temperature of the river water will influence the growth rate of phytoplankton, macrophytes, aquatic organisms, and other species, as many of the chemical and biological processes run at a faster pace at high temperatures. As per the Arrhenius relation, the kinetics of a given chemical reaction can be doubled for a temperature increase of 10 °C. Thus, it is expected that dissolution, solubilization, complexation, degradation and many other such effects may take place at a faster pace owing to increased temperature due to global warming. Increased temperature will also influence the growth rate of phytoplankton and bacteria ( Whitehead & Hornberger 1984 ; Wade et al. 2002 ; Sakyi & Asare 2012 ), which in turn will stimulate the process of eutrophication, thus causing the river water quality to deteriorate. Faster kinetics will also lead to faster dissociation of water molecules, thus making water more acidic. In the dry season, due to the high rate of evaporation and faster reactions, biochemical oxygen demand (BOD) will be high ( Figure 5 ). Further, irregular and intense rainfall may also result in higher run-off from the catchment area, thus resulting in higher loads of suspended solids and sediments, contaminants, and increased soil erosion ( Leemans & Kleidon 2002 ; Lane et al. 2007 ).

Impact of climatic change on water quality.

There are several water quality parameters on which climate change will have a significant impact, such as temperature, pH, DO, dissolved organic matter, micropollutants, various microorganisms etc. As stated above, an increase in temperature will lead to faster kinetics of several biochemical reactions, which will result in a concentration increase of dissolved substances and decrease in DO content. The saturation concentration of DO decreases almost 10% with a 3 °C increase ( Delpla et al. 2009 ). A decrease in DO can be linked to an enhancement in the microbial assimilation process of biodegradable organic matter, which ultimately results in dissolved organic carbon ( Prathumratana et al. 2008 ).

Dissolved organic matter is also an important factor to be considered as it affects the ecosystem functioning by influencing sunlight absorbance, energy and nutrient supply, acidity etc. Higher dissolved organic matter will result in low transparency and thus less solar penetration up to the depth of the river. It will have implications for the growth of benthic flora and fauna. In warmer climates, blue-green algae (cyanobacteria) may also flourish. It has been reported that global warming increases the total abundance and proportions of warm water species such as green algae and diatoms in the water ( Daufresne & Boët 2007 ). An increase in water temperature may also increase the pesticide concentration, as surface waters are the immediate receptors of pesticide contamination from agricultural fields. Warming climate and changes in rainfall patterns as well as intensity may also influence pesticides' ultimate fate ( Bloomfield et al. 2006 ).

Effect on species composition

As the atmospheric temperature rises, the water temperature increases as well, which ultimately affects the species diversity. Climate change is expected to affect all the levels of riverine biodiversity, from species to biome levels ( Learmonth et al. 2006 ; Bellard et al. 2012 ). As many of the species in the Gangetic ecosystem are already threatened, climate change effects will further complicate the situation. Habitat loss is expected for the species which have comparatively narrower distribution and in the locations where the temperature increase will be higher ( Eaton & Scheller 1996 ). Temperature change will also affect species composition and abundance, as well as the occurrence of scarce and/or non-native species ( Daufresne et al. 2009 ). According to Bergmann's rule of thermal regulation, species tend to be smaller in warmer climates. Therefore, a reduction in the body size of the species is also expected owing to increased water temperature.

Endemic taxa will be threatened both by habitat loss and as a result of reduced connectivity between habitats, especially if water-flow connections are lost. There is also the risk of deoxygenation due to increased temperatures. This problem can be further aggravated if there is enhancement in plant growth due to high water temperature and unlimited nutrient supply, which can lead to further low levels of oxygen and risk for the aquatic faunal species ( Whitehead et al. 2009 ). These environmental changes will further result in significant modifications in the distribution of species, higher susceptibility to alien species invasion, and overall biodiversity reduction that may eventually lead to impaired ecosystem services.

Climate change also has very negative effects on the specific biota of the River Ganga. Ganga is home to a number of fish species, reptiles, birds, and mammals. Endangered species such as the Gangetic Dolphin ( Platanista gangetica gangetica ), Ganges softshell turtle ( Nilssonia gangetica ), Gharial ( Gavialis gangeticus ), Himalayan Mahseer ( Tor putitora ) etc. are already under severe threat. These species are heading towards a higher extinction risk every passing year ( Figure 6 ). The Gangetic Dolphin, which was ‘vulnerable’ up to 1990, was moved into the ‘endangered’ category in 2004. Likewise, Gharial moved to the ‘critically endangered’ category from ‘endangered’ in 2007. Changing climate will further diminish their chances of survival. Increased temperature affects the prey population for dolphins. Changing climatic patterns may also alter the water current and flow characteristics of the river, which will further intensify the problem due to changed prey distribution, feeding grounds, changes in trophic relationships, community structure, migratory pathways, and lower reproductive success, ultimately leading to lower chances of survival ( Smith et al. 2009 ; Simmonds & Eliott 2009 ; Smith & Reeves 2012 ). For example, dolphins depend upon echolocation for finding their food; hence, changes in river flow and depth will adversely affect their distribution and survival. Warmer water may also affect the health of the river dolphins due to thermoregulatory issues and increase in exposure to toxic algal blooms.

Extinction risk of fauna in the Gangetic ecosystem.

Phenology and predator–prey interactions in riverine ecosystems

Increasing global temperature will also affect the phenology of the vegetation community as well as animals in the freshwater ecosystem ( Walther et al. 2002 ; Visser & Both 2005 ; Cleland et al. 2007 ; Anderson et al. 2013 ). While the floral community may flourish more due to rising temperatures, the consumption pattern of faunal species will also change accordingly. Temperature increase may alter the growing season, pollination, and flowering pattern of many species ( Khanduri et al. 2008 ). It will have a direct or indirect impact on the overall plant fitness ( Galen & Stanton 1991 , 1993 ). The warming temperature of water may also produce a change in the interaction patterns of algae and herbivores. Range shift is another consequence of a disturbed ecosystem. Due to changes in the surrounding water temperature, species tend to shift their ranges to a more habitable region. All the aquatic faunal species which are dependent on the phytoplankton predator–prey may not move at a similar pace to that of the phytoplankton. This mismatch may lead to a decline in some of the species ( Winder & Schindler 2004 ; Walther 2010 ). Further, over-consumption of autotrophs by herbivores can disturb the entire ecosystem, as climate change can also result in changes in the feeding pattern of many species owing to the changes in food availability and requirements ( Stenseth et al. 2002 ). Increased water temperature will also lead to changes in the food-web structure, with higher winter survival of fish and a general switch from dominance of zooplankton and aquatic macrophytes to fish and phytoplankton. In rivers, increased temperature will cause stress for fish and invertebrates with high oxygen requirements, leading to changes in community composition. The population of some of the aquatic faunal species may also face the danger of extinction if the physiological processes are not able to be in sync with the phenological changes.

A change in predator–prey interactions is another significant repercussion of climate change ( Abrahams et al. 2007 ; Broitman et al. 2009 ). In the Gangetic ecosystem, the fish community, fish-eaters, and their prey are ectothermic. Therefore, a change in the surrounding water temperature will influence the energy needs or food requirements of these species. Higher temperature results in faster metabolism, leading to a higher energy requirement; this refers to increased interactions between predator and prey. The influencing parameters of the water affecting predator–prey interactions will be temperature, DO, and turbidity ( Abrahams et al. 2007 ). It has been shown in Figure 5 that under elevated water temperature conditions, DO will be decreased while turbidity will be enhanced. In the reduced DO concentration conditions, many of the prey species would try to avoid being predated by deliberately moving towards hypoxic environments. Thus, a hypoxic environment will act as a shelter for the prey species and a barrier for the predator ( Figure 7 ). Turbidity will have its impact on the process of prey detection by predators and subsequent survival efforts of the prey species. Due to the turbid water conditions, prey will become unable to mark the presence of its predator well ahead of time ( Figure 7 ), and hence the chances of its survival will reduce ( Miner & Stein 1996 ).

Predator–prey interaction: (a) effect of DO, (b) effect of turbidity.

Hydropower potential of the river

As hydroenergy is solely dependent on water resources, climate change impacts on rivers will certainly affect the hydroenergy potential of India ( Pathak 2010 ). India is the seventh largest producer of hydroelectric power in the world. By 2017, India's installed hydroelectric power capacity was about 13.5% of the total power generation capacity ( GoI Report 2017 ). The River Ganga is known to support a number of hydropower projects in approximately 10 states of the country, the highest being in the state of Uttarakhand. Moreover, the Ganga basin will have the highest dam density in the Himalayan region if all the ongoing and proposed dams are constructed ( Pandit & Grumbine 2012 ). Therefore, it is necessary to understand the impacts of climate change on the hydropower potential of River Ganga. In hydropower production, the amount of electricity generated in a dam/reservoir is dependent on the quantity of the water passing through the turbine, the water head, and the mechanical efficiency of the turbine. Water passing through the turbine further depends on the seasonal and quantitative changes in precipitation pattern and evapotranspiration ( Koch et al. 2011 ). Therefore, changes in climate resulting in variability in temperature, precipitation and run-off will certainly have an impact on the hydropower potential of the dams and reservoirs. Besides reservoirs, the run-off-river hydropower potential will also be disturbed to a great extent. A number of case studies have been carried out in the past depicting the negative impacts of climate change on overall hydropower production ( Madani & Lund 2010 ; Chiang et al. 2013 ; Kachaje et al. 2016 ; Tarroja et al. 2016 ). Basically, climate change will influence hydropower production through two variables: discharge and head. Variation in the discharge pattern of the river may disturb the continuous water availability to power stations. Whereas less discharge is expected to reduce the power production, high discharge for a few months followed by a longer dry period may result in increased spill, thereby again rendering decreased power generation. Reduced precipitation events will also reduce the required head level in the reservoirs, as low inflow will result in lower water levels. Moreover, variation in temperature and precipitation intricately influences a number of factors, which ultimately result in reduced hydropower production, as depicted in Figure 8 . Negative impacts on the hydropower generation owing to variable climate will certainly have an effect on the entire scenario of the energy sector. In such situations, dependency on existing conventional fossil fuels may increase, which will further contribute to climate change.

Impact of climate change on hydropower production.

The above discussion reveals that undoubtedly, climate change impacts will prove to be disastrous not only for the river but also for the millions of people who are directly or indirectly dependent on it. In order to attenuate the impacts, significant efforts are required to be undertaken. A proactive approach is essential to find suitable ways of dealing with the situation. Research into the dynamic aspects of a riverine ecosystem, stormwater management, river catchment management, aquatic ecology and related features is essential. Further, accepting the fact that climate change is already taking place, adaptive approaches also need to evolve. Although rivers, being dynamic entities, are continuously adapting themselves, current climate changes are occurring at a much faster rate than the adaptive capability of the rivers. Moreover, a reactive approach is the ultimate solution to overcome the impacts due to climate change-induced disasters. Thus, a combination of these approaches will hopefully bring resilience to the dynamics of River Ganga ( Figure 9 ).

Management strategy for the River Ganga.

Changes in the basic hydrological aspects of the river would influence many of the related ecological processes such as energy flow pattern, biogeochemical cycles, productivity in the riverine ecosystem, decomposition processes, predator–prey interactions, inter-specific competition etc. ( Traill et al. 2010 ). In order to sustain ecological processes, it is imperative to identify the key species that are responsible for the ecosystem's resilience. It has been found that high species richness is able to sustain the ecosystem very well under stressed conditions ( Loreau et al. 2001 ); therefore, those species which are known to have lesser functions may prove to be highly beneficial in the longer term. Considering these facts, more research needs to focus on the assessment of species diversity in the Gangetic ecosystem. In order to assess the threats to the Gangetic ecosystem, continuous research is also necessary for finding the response of aquatic floral and faunal species towards various extrinsic factors. More emphasis should be given to finding the processes and mechanisms through which changes are taking place in the behaviour, physiology, and evolution of species along with mechanisms affecting intra- and inter-specific relations. In order to avoid range shifting of various micro- and macrospecies of the Gangetic ecosystem, research is required to understand the life history and ecology of the species ( Palmer et al. 2009 ). It is imperative to say that in dynamic riverine ecosystems, the effects of change may sometimes also be reversed by various ecosystem processes ( Suttle et al. 2007 ).

In order to attenuate the adverse impacts of climate change, a number of adaptive management strategies also need to be adopted in the Ganga basin. Existing causes of stress in the river, such as point and non-point pollution, water abstraction, increasing number of dams/reservoirs etc., need to be curtailed. There is a need to enhance and strengthen the conservation measures also, not only for the river but also for the adjacent catchment area. Plantations along the riparian area may help to slow down the silting and flood situation. The establishment of drought tolerant plant species may also help to prevent erosion of the river bank in high temperature conditions. Moreover, appropriate environmental flow needs to be maintained in the Ganga, especially in the middle and lower reaches, so that the river may recuperate. Invasive plant species which can threaten the native species should be removed. There is also a need to know the features at gene, population, community, and ecosystem levels which may provide better chances of survival to the organisms. Apart from adopting various protective measures for the river, significant efforts are also required to control allied activities such as forestry and related land uses, grazing, farming, dam/reservoir management etc. ( Arthington et al. 2006 ).

As already stated, severe rainfall resulting from extreme weather events may lead to floods and subsequently significant erosion of river banks and catchment areas. Therefore, river restoration projects need to be executed well ahead of time to avoid grave consequences. In order to reduce high energy flow and for improvement in river water quality, additional water bodies may be created as storage basins, which will be adjacent to the Ganga river and have linkages with the main channel. Wetland creation and the development of storm water infrastructure may also result in a positive outcome ( Poff 2002 ). For better preparedness and quick response action, strengthening of water monitoring networks and weather forecasting systems is essential. River flow monitoring on a regular basis is also essential to determine the climate change-induced alterations in flow pattern ( Palmer et al. 2009 ). There should also be efforts for the management of nearby land areas, and anthropogenic activities should be minimized. Infrastructure development and industrial agglomeration along the river need to be curtailed in order to lessen the pollution load in the river.

Another very significant aspect in the management of the river is effective policy planning. Past experiences show that due to lack of coordination among various monitoring agencies involved, the expected results could not be achieved. As the River Ganga flows through five states, coordinated efforts are required to execute any plan. Besides the involvement of a central agency, state boards and municipal authorities should also be involved. Public participation is also essential in governing water-related issues. Thus, a decentralized approach to planning is vital for the effective implementation of schemes and positive outcomes.

Conservation and management of the River Ganga is a national priority ( Figure 10 ). Since time immemorial it has been the most revered river in the country. Besides providing a host of ecosystem services, the River Ganga has also played a very important role in the growth and development of the economy by contributing to the agricultural sector, industrial sector, hydropower generation, tourism, and other recreational activities. However, due to increased urbanization and industrialization, there has been a colossal loss in the pristine quality of the river. The effects of climate change will further add to the pitiable condition of the river and in coming years, these risks will be much more intense. Climate change impacts will not only result in severe disasters of floods and drought, but will also reduce the carrying capacity as well as assimilative capacity of the river by affecting its abiotic and biotic components. Therefore, immediate efforts are required in order to reduce any further degradation of the river ecosystem. Despite a number of action plans executed in the past few decades, there is still much to be done. More concerted efforts are required in the direction of climate change management. Plans related to the control of pollution in the River Ganga need to be intermingled with climate change management efforts by employing better policy planning. There is also a need to learn from the experiences gained in the past. Moreover, bridging the gap between scientific/technical interventions and spiritual consciousness can be an effective step for the rejuvenation of the River Ganga.

Impact of climate change on the dynamics of River Ganga.

The authors are grateful to the Department of Science and Technology, New Delhi, India for financial support (Grant No. DST/SPLICE/CCP/NMSHE/TF-4/NIH/2015-G).

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Real-time assessment of the Ganga river during pandemic COVID-19 and predictive data modeling by machine learning

School of Biochemical Engineering, IIT (BHU) Varanasi, Uttar Pradesh, Varanasi, 221005 India

Associated Data

In this study, four water quality parameters were reviewed at 14 stations of river Ganga in pre-, during and post-lockdown and these parameters were modeled by using different machine learning algorithms. Various mathematical models were used for the computation of water quality parameters in pre-, during and post- lockdown period by using Central Pollution Control Board real-time data. Lockdown resulted in the reduction of Biochemical Oxygen Demand ranging from 55 to 92% with increased concentration of dissolved oxygen at few stations. pH was in range of 6.5–8.5 of during lockdown. Total coliform count declined during lockdown period at some stations. The modeling of oxygen saturation deficit showed supremacy of Thomas Mueller model ( R 2  = 0.75) during lockdown over Streeter Phelps ( R 2  = 0.57). Polynomial regression and Newton’s Divided Difference model predicted possible values of water quality parameters till 30th June, 2020 and 07th August, 2020, respectively. It was found that predicted and real values were close to each other. Genetic algorithm was used to optimize hyperparameters of algorithms like Support Vector Regression and Radical Basis Function Neural Network, which were then employed for prediction of all examined water quality metrics. Computed values from ANN model were found close to the experimental ones ( R 2  = 1). Support Vector Regression-Genetic Algorithm Hybrid proved to be very effective for accurate prediction of pH, Biochemical Oxygen Demand, Dissolved Oxygen and Total coliform count during lockdown.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13762-022-04423-1.

Introduction

With the outbreak of the coronavirus pandemic, the life of people is adversely affected. COVID-19 came into light in December 2019 from Wuhan city in Hubei Province of China (Hasnain et al. 2020 ). It affects the respiratory tract and spreads from person to person through physical contact. As researchers are not sure about its source, having not discovered a vaccine to date, no specific treatment is known yet (Chakraborty and Maity 2020 ). The only options left with the public are social distancing, lockdown and personal hygiene. COVID-19 pandemic has severely affected countries like Italy, the USA, Pakistan, China, Germany and India etc. and their respective Government applied lockdown strictly (Paul et al. 2020 ).

As a consequence, people remained indoors and commercial activities were shut down (Wray 2020 ). India was also under lockdown in the wake of coronavirus pandemic. Restrictions on industrial activities during lockdown significantly lowered air and water pollution. This resulted in the substantial rejuvenation of rivers with a positive impact on stable marine life. During lockdown, the water quality of the Ganga river has improved significantly (Singh 2020 ). Lockdown has caused a reduction in the disposal of hazardous wastes not only in the Ganga but also in other rivers. The Ganga or Ganges is a 1,680 miles long river in India that originates from the Gangotri Glacier of the western Himalayas in Uttarakhand and the river flows from the northwest to the southeast, merges into the Bay of Bengal. In India, it covers states such as Uttarakhand, Uttar Pradesh, Bihar and West Bengal (Chaturvedi 2012 ). The Ganga is the lifeline of millions who live along the way. Approximately 43% of India's population lives in the Ganga basin, which is over 860,000 km 2 and covers 26.3% of the country's total geographical area (Trivedi 2010 ). It is a sacred river, worshipped as the goddess Ganga in the Hinduism, which witnesses high religious and cultural tourism on its banks. In 2008, the Ganga river declared was the ‘National River’ of India (Sati 2021 ). There are over 29 cities, 97 towns and thousands of villages on the banks of the Ganga River (Dutta et al. 2020 ).

It hosts about 140 species of fish and 90 species of amphibians. For most of its course, it is a wide and sluggish stream that flows through one of India's most fertile and densely populated regions. The major contributors of pollution are tanneries in Kanpur, distilleries, paper mills and sugar mills in the Yamuna, Ramganga, Kosi and Kali river catchments (Dutta et al. 2020 ). There has been a decrease in fish population along the river, indicating a lack of supportive habitat and water quality degradation. Fishermen report destructive fishing, overfishing and the construction of Farakka barrage as the significant reasons for the decline in fish population from the river-floodplain in Bihar (Dey et al. 2019 ). In 2017, the river Ganga was considered to be sixth most polluted river in the world (Paul 2017 ). Lots of steps have been taken to clean the river, but the desired results have not been achieved to date. Drew ( 2017 ) mentioned that there are numerous hydropower stations, dams and barrages in the main stem of the Ganga river and its tributaries that are harming and obstructing the flow of the river. Apart from this, construction and widening of roads and tunnels in the upper Ganga region affects the flow of water and leaves the river bed dry. The author termed this as “destructive model of development” and added that the continuous inflow of untreated wastewater in the Ganga, including untreated sewage and hazardous waste from the industry as well as agricultural runoff, is worsening the water quality of the river (Drew 2017 ).

The river Ganga passes through states that serve the various subsistence needs of people living in the surrounding areas, such as drinking, bathing, fishing and agriculture. Despite being one of the most functionally important rivers in the world, serving an estimated 500 million people, the Ganga is contaminated in large amounts by the discharge of untreated wastewater and untreated industrial waste (Postel and Richter 2012 ). High population density at the basin, several festive celebrations at the shore, garbage disposals and dumping of corpses directly into the river Ganga have contributed most to its pollution. The river also serves the agriculture in the surrounding region and therefore ends up with a vast amount of chemical fertilizers, pesticides and insecticides that worsen its quality (Chakraborty 2021 ). A non-point category source of pollution, that is, open defecation, is a significant and worrying cause of the disease-causing microorganisms that dwell in the river Ganga. In the river beyond Kanpur, fecal coliform levels have crossed the acceptable bathing standard (Srinivas et al. 2020 ). High pollution level increases the chances of obstructions, ultimately leads to stagnant water condition which breeds diseases such as dengue, malaria and chikungunya. These deadly diseases take millions of lives and cost the country colossal capital every year. The harmful microorganisms originating from fecal pollution are also suspected of having a pivotal role in antibiotic resistance (Lockwood 2016 ). The government has focused on pollution point source control policies (Srinivas et al. 2020 ), but no significant improvement has not yet been seen so far.

In this study, changes in water quality of the river Ganga have been evaluated during the lockdown phase and compared with pre-lockdown statistics. Bioinspired mathematical models such as Streeter Phelps, Thomas Mueller, Support Vector Regression with Genetic Algorithm (SVR-GA), Lasso regression, Artificial neural network (ANN), Newton’s divided difference (NDD) and Polynomial regression model have been used for the computation of water quality parameters in the river water under both pre-lockdown and during lockdown conditions. Streeter Phelps and Thomas Mueller model were utilized for predicting oxygen saturation deficit in the river Ganga . In addition to this, SVR-GA, Lasso regression and ANN were implemented to model levels of DO, BOD, pH and TC in the Ganga river. Finally, NDD and Polynomial regression models have been used to predict water quality parameters (DO, BOD, pH and TC) in the present condition and future changes in the water quality of the river Ganga such as after unlocking phase-I in India, i.e., 30th June 2020 based on the past trends. SVR-GA is a hybrid algorithm which uses a hyperparameter optimization algorithm (GA) along with a modeling algorithm (SVR) (Jiang et al. 2013 ). The ability of SVR marked by its margin approach is well suited for all kinds of data and has been successfully used for the modeling of pH and DO before. Lasso Regression model, which has a shrink or reject feature is advantageous when dealing with regression data. This model originates from Ridge regression and is a robust regression algorithm which was also used for lockdown data prediction.

ANN is an oversimplified version of the inter-neuron communication process that takes place in the brain. Their architecture depends on the number of hidden layers and the activation functions, thus leaving a room for improvisation and experimentation (Ahmed 2017 ). A highly interconnected neural network is very effective for accurate predictions. Still, it tends to over fit on the training data, that is why smaller and effective neural network models have been developed (Sarkar and Pandey 2015 ). One such model is the Radical Basis Function Neural Network (RBF-NN) is a simple one hidden layer ANN which uses a radical basis as its activation function. In the present study, the RBF-NN model, Levenberg–Marquardt algorithm (LMA) and a two hidden layer Multi-Layer Perceptron (MLP) model for prediction of water quality data have been applied. The RBF-NN model was used with GA as the optimizer of its hyperparameters. GA selects a random population based on the specified constraints and picks out the best possible pair of parameters which have the highest fitness. The GA fitness function has been represented with mean squared error (MSE) in the present work. The present study will be useful in developing technologies for reducing the pollution level in the river Ganga and other rivers, preventing it from returning to the previous state based on the data available from these models. This study is also helpful in formulating/revising the laws dealing with a permissible limit of discharge of industrial effluents in the river Ganga and other natural water resources. The entire analytical study of the Ganga river by using CPCB data was conducted at IIT (BHU) Varanasi (Co-ordinate 25° 15′ 30″ N 82° 59′ 39″ E) Varanasi, India.

Ganga river (literature survey before and during lockdown)

Before lockdown, the river Ganga was not suitable for bathing from Uttar Pradesh to West Bengal with the exception of certain places in Uttarakhand (Webdesk 2020 ). Figure  1 shows the sources of pollution in the river Ganga.

Ganga pollution overview

Over 500 water samples from April to June were analyzed for two consecutive years, i.e., 2017 and 2018 (Haider Naqvi 2020 ). The amount of DO decreased to less than 2 mg/L due to the hypoxic state of the river bed, which made the river unable to sustain aquatic life. The river Ganga has been used for dumping of industrial and domestic waste in industrial towns that contaminated the river. For instance, 400 tanning units contribute 50 MLD (million liters per day) of hazardous waste and 140 MLD of domestic waste in Kanpur (Haider Naqvi 2020 ). The water at Haridwar and Rishikesh was found unfit for drinking and bathing. The river water was in class B ever since the foundation of Uttarakhand was laid (Srivastava 2020 ).

It was reported that only 18 spots were fit while 62 spots were unfit for bathing and the river was almost unfit for drinking with a high level of coliform bacteria in the river. River water from 7 spots out of 86 monitoring stations was drinkable only after disinfection. The spots which were found suitable for drinking purpose after disinfection have been classified as ‘class A’ (Bhagirathi at Gangotri, Rudraprayag, Devprayag, Raiwala-Uttarakhand, Rishikesh, Bijnor and Diamond Harbor in West Bengal). Water at 78 monitoring stations was not suitable for drinking and bathing in Bhusaula in Bihar, Kanpur, Gola Ghat in Varanasi, Dalmau in Raebareli, Sangam in Allahabad, Ghazipur, Buxar, Patna, Bhagalpur, Howrah-Shivpur in West Bengal and many others. Thus, water available in pre-lockdown condition of the river Ganga was not suitable for drinking and bathing.

The industrial and commercial activities almost ceased during the lockdown, allowing the Ganga river to breathe again. In India, a total of four phases of lockdowns were observed for 68 days (Lockdown 1.0 (21 days)—25th March, 2020 to 14th April, 2020, Lockdown 2.0 (19 days)—14th April, 2020 to 3rd May, 2020, Lockdown 3.0 (14 days)—3rd May, 2020 to 17th May, 2020 and Lockdown 4.0 (14 days)—18th May, 2020 to 31st May, 2020).

Amid of lockdown, the CPCB, India reported on April 28, 2020 that the Ganga water has improved significantly for bathing purposes in most of the surveillance centers. Observations recorded during lockdown were as follows:

• Rise in DO level from 22nd March, 2020 to15th April, 2020.
• Level of BOD showed a significant decline. The lower range indicated the better health of the river.
• A gradual rise in BOD level toward downstream stretches of the river Ganga.

Singh ( 2020 ) has made a remarkable observation that the level of DO increased from 25 to 30% at five ghats in Varanasi, while the level of BOD decreased up to 35%. Detailed information on changes in water quality parameters during lockdown is tabulated in Table S2 of supporting material.

Materials and methods

The total length of the Ganga river (measured along the Hooghly) from source to mouth is 2, 525 km. The Ganges originates near the Gangotri and travels about 350 km before entering into the village Balawali (district Bijnor) of Uttar Pradesh. It flows from Balawali approximately 1,150 km in Uttar Pradesh and enters the village Sitab Diara, Bihar. It flows 450 km from Sitab Diara and arrives into the West Bengal in Manikchak village (district Malda town). At the Farraka barrage, the Indian government controls water of the Ganga in distributaries namely Hooghly and Padma in the West Bengal and Bangladesh, respectively. It flows 550 km in West Bengal from village Manikchak to Haldia (near Calcutta) before merging into the Bay of Bengal. The 14 real-time stations from Anoopshahar, Uttar Pradesh to Howrah bridge, West Bengal have been considered in the present study for data modeling.

Water quality data set

The data sets of the pre-lockdown condition were collected from the system software ‘Suitability of river Ganga water’ designed by the Central Pollution Control Board (CPCB), India. This is a real-time water quality monitoring system established by CPCB, which helps in monitoring changes in the river at any given time. In India, CPCB has classified water into five classes (A to E), defining different treatment levels for the various purposes (Table S1 of supporting material shows the classes of water defined by CPCB). This classification helps managers and planners of the water quality monitoring system to set targets for water quality and to design appropriate rehabilitation programs for different water bodies. In India, water quality standards are established by CPCB in terms of the primary water quality criteria.

Water quality parameters

The parameters of water quality considered in the present study were pH, BOD, DO and TC. The pH is a measure of how acidic the water is and about 7.4 is considered as the optimum pH for the river water (Azad 2020 ). Wastewater from sewage treatment plants comprises of organic matter which is decomposed by the microorganisms and in return the dissolved oxygen is consumed. When more oxygen is consumed than produced, the concentration of DO decreases proportionately and possibly the population of a few susceptible organisms may move away, weaken or die. The DO level fluctuates in every 24 h and seasonally. It varies with the temperature of the water and altitude (APHA 1992 ). BOD influences the amount of DO in rivers and streams. Higher is the BOD value, faster is depletion of the oxygen in the stream, which means that there is less oxygen available for higher aquatic life forms. High level of BOD has similar effects as low DO concentration such as suffocation and death of aquatic organisms. A test for TC is the most basic measure for bacterial contamination of a water body. TC counts provide a general indication of a water supply's sanitary conditions. The risk of waterborne infection is increased when coliform bacteria are found in drinking water. Several types of malfunctions can cause TC contamination like seepage through the well casing, faulty well cap and well flooding. In order to cope with bacterial contamination, many long-term solutions are available such as inspection, repair of defective wells and installation of continuous disinfection equipment.

Mathematical models

Streeter phelps model.

Streeter and Phelps in 1925 developed a water quality model based on field data from the Ohio river, which was initially used by the US Public Health Service (Digvijay Kumar 2017 ).

In the present study, the Streeter Phelps model has been used to model DO in 14 real-time stations of the Ganga river.

Considering a mixed system (no in-/out flow) (Fig.  2 ) with the state variables Z and X ,

A mixed system with no inflow/outflow

where Z is degradable organic matter (mg/L) and X is the DO level (mg/L).

• Aerobic decay of organic matter ‘ Z ’ by bacteria suspended in the water column (1st order kinetics)
• Consumption of oxygen ‘ X ’ during mineralization of ‘ Z ’
• Exchange of oxygen between water and atmosphere

Differential equations and parameters involved in the model are

where k d is decay rate (1/Time), k a is aeration rate (1/Time), s is a stoichiometric factor (Mass X /mass Z ) and X sat is O 2 saturation level (mg/L).

These equations are valid only when X  >  > 0.

Re-definition of state variables leads to simplified form at boundary conditions:

where L is BOD (biochemical oxygen demand) and Stoichiometric factor ‘s’ equals 1 → omitted.

Thus, Eqs.  1 and 2 can be rewritten as:

Equation 3 may be expanded by separation of variables for the initial condition L ( t  = 0) =  L o .

Integration of Eq.  3 yields Eq.  5 .

Substituting the value of L from Eq.  5 in Eq.  4 results in Eq.  6

Now, using the method of integrating factor, re-ordering of Eq.  6 yields

Multiplication with the factor “exp ( k a · t )” mimics Eq.  8

Applying the product rule, Eq.  9 was obtained as

Equation  10 was achieved after separation of variables and integration

Equation  10 is O 2 saturation deficit Streeter Phelps model.

Thomas and Mueller model

Thomas ( 1948 ) accounted for settle able BOD in the dissolved oxygen sag equation of Streeter Phelps model. Analytical solutions for simple initial and boundary conditions were developed by Thomann and Mueller ( 1987 ). The model includes changes in DO concentrations due to distributed sources (non-point sources) within the stream. Equation  11 illustrates the model of Thomas and Mueller (TM):

where L d  = non-point source BOD (mg/L).

It is apparent from Eq.  11 that the soluble concentration of the DO generated in range by non-point sources was combined at the entry point with the attenuation phenomenon of the DO entering into the cell.

Polynomial interpolation determines a polynomial of order n that passes through n  + 1 point. The NDD model is of interest due to its clarity and precision. This model shows where a function will go, based on its y -values at respective x -values (Das and Chakrabarty 2016 ). Newton’s polynomial possesses the permanence property, which means that new data values can be represented by ( n  + 1)th degree polynomial and the term can be added to previously obtained n th degree polynomial. Accuracy of the polynomial interpolation depends on how close the interpolated point is to the middle of x -values used. It generates only one polynomial of least possible degree that passes through all the data points. Equation  19 depicts NDD model

Newton’s divided difference interpolation method has been used to generate the function depicting water quality of the Ganga river from pre-lockdown to lockdown period. After obtaining interpolating polynomial, it was extrapolated to predict water quality parameters (BOD, DO, pH and TC) till 7th August, 2020 (200th day from 20th January). In the present study, 20th January, 2020 has been marked as 0th day (pre-lockdown data). Using this model, polynomials were obtained for BOD, DO, pH and TC separately for each of the 14 stations and these were plotted to extrapolate values for upcoming months. This model was trained using python programming language.

Polynomial regression model

Polynomial regression determines nonlinear relationship between the value of ‘ x ’ and the corresponding conditional mean of ‘ y ’ (Ostertagová 2012 ). The expected value of ‘ y ’ can be modeled as n th degree polynomial, yielding a general polynomial regression model (Eq.  13 )

In this study, the polynomial regression model was used to model values of DO, BOD, pH and TC as a function of time to analyze and predict the Ganga water quality till 7th August, 2020. The model was trained to generate polynomials of degree 2, 3 and 4 for DO, BOD, pH and TC at real-time stations. Just to maintain consistency in results, this model was also trained using python programming language.

Radical basis function kernel support vector regression with genetic algorithm (SVR-GA)

Vapnik et al. ( 1997 ) developed an algorithm that used the earlier work of Support Vector Machines to address regression problems, which was then known as Support Vector Regression (SVR). The most powerful aspect of SVR is that it takes into account the error limit of epsilon, which means that an error between the predicted and the true value is allowed to lie within the range of [−  ε , ε ] and that no error greater than that is accepted. Using this rule, a function ‘ f ’ is generated that would be able to fulfill this condition. In linear form, function ‘ f ’ can be estimated as:

where w , x is the dot product of w and x.

Flatness in Eq.  14 would mean to obtain a small value of w by minimizing the norm (Smola and Schölkopf 2004 ).

Usually, it is not always possible to search for a function ‘ f ’ which would produce data pairs which lie in the epsilon margin. Therefore, soft margin like approach is used, where slack variables ξ i , ξ i ∗ representing the distance between the true values and the epsilon tunnel are introduced. This addition helps in making the optimization problem feasible. Thus, a risk function ‘ R ’ is defined by incorporating an epsilon insensitive loss function with a constant ‘ C ’. The regularized convex optimization problem (Smola and Schölkopf 2004 ) can be written as:

where C is a positive constant that plays a role in determining the extent to which a deviation from the error tunnel is tolerated.

This can be seen as a trade-off between the model flatness and empirical risk (Smola and Schölkopf 2004 ). Lagrange construction of the primary function gives a quadratic optimization problem that is solved for α i , α i ∗ (Vapnik and Vapnik 1998 ):

Here, ( α i , α i ∗ ) are Lagrange multipliers.

The vectors x i corresponding to non-zero Lagrange multipliers are then called as support vectors (Vapnik et al. 1997 ). After performing optimization, f ( x ) can be obtained as:

A kernel K x , x i is defined for a nonlinear regression model. The kernel generates an inner product in some feature space and solves the corresponding dual optimization problem (Vapnik et al. 1997 ). Some examples of kernels are Polynomial, Gaussian, Radical basis function. In the present study, Radical basis function (RBF) kernel has been used. The kernel and the nonlinear objective function can then be written as:

The variables C , ε , γ are user-defined while implementing SVR. Since these hyperparameters are crucial for the proper functioning of the algorithm, their right selection is of utmost importance. Genetic Algorithm (GA) was used to meet this requirement. It was first introduced by Holland ( 1992 ) and is a natural evolution-based technique that seeks inspiration from Darwin’s theory of survival of the fittest. The GAs are being applied successfully in a number of areas such as job shop problems (Falkenauer and Bouffouix 1991 ; Nakano and Yamada 1991 ), control system optimization (Krishnakumar and Goldberg 1992 ), pipeline optimization (Goldberg and Kuo 1987 ), molecular geometry optimization (Deaven and Ho 1995 ) and feature subset selection (Yang and Honavar 1998 ).

Goldberg ( 2006 ) has outlined the differences between GAs and other optimization techniques. Some of the advantages include the use of the coding of parameter set and not the parameters themselves, search from a population of points, using payoff information when binding to auxiliary information and the use of probabilistic transition rules over deterministic rules. These four advantages give GAs an edge over other commonly used traditional optimization techniques. GA can be broken down into four steps where the GA selects a population of individuals and computes the fitness function for each individual. Individuals with the highest fitness function are chosen to produce offsprings. The second and third steps involve crossovers and mutations between the selected individuals, which lead to the formation of a new generation. Finally, the fitness function for this new generation is calculated and the process repeats from step one unless the goal of the algorithm is reached.

The combination of SVR with a real-valued GA has been used as the optimization algorithm for SVRs hyperparameters ( C , ε , γ ). Liu et al. 2013 used this hybrid model for water quality estimation (DO and temperature) and compared it with traditional SVR and BP neural network models. Their RGA-SVR model outperformed over the traditional models. Similarly, Wang et al. ( 2011 ) used SVR model with GA automated SVR parameter selection for the prediction of permanganate index (CODMn), ammonia–nitrogen (NH 3 –N) and chemical oxygen demand (COD) and found this superior to MLR algorithm.

Lasso regression

The lasso regression (LR) model was developed by Tibshirani ( 1996 ), which is built upon the robustness of ridge regression. It preserves the quality features of ridge regression and subset selection by shrinking some coefficients and setting others to zero. For data x i , y i , i  = 1, 2, … n . where, x i = x i 1 , … x ik are the predictor variables and y i are the responses.

The lasso optimization problem can be solved by minimizing Eq. ( 20 ).

An assumption is made that x ij are standardized to avoid any dependence on the measurement scale. Here, t ≥ 0 is a prespecified tuning parameter which controls the amount of shrinkage applied (Tibshirani 1996 ). Lasso regression has been previously used as a predictor algorithm for water quality estimates (Ahmed et al. 2019 ; Brooks et al. 2016 ).

Artificial neural network (ANN)

ANN is a very powerful algorithm whose architecture is inspired by the process of communication of neuronal cells. ANN can take many forms and in the present study the LMA, MLP and RBF-NN have been focused. ANN work immensely well with water quality data (El-Shafie et al. 2011 ). Authors compared the ANN model with the linear regression model and found that ANN has high accuracy as compared to the other models. Najah et al. ( 2013 ) performed a comparative study with different ANN models like RBF-NN, MLP-NN and Linear Regression model (LRM) for water quality estimation and found RBF-NN superior to MLP-NN and LRM. Authors showed that RBF-NN could be a reliable water quality predictor model. Both of these studies used a trial and error basis for determining the number of hidden layers and neuron units in the layers.

ANN with LMA

The chosen ANN for the pH, DO, BOD and TC models consisted of one input layer with fourteen input variables, one hidden layer and one output layer. In addition to this, TC consisted of a similar number of hidden and output layers except for 12 input variables. The designed ANN models (pH, DO, BOD and TC) were trained for utilizing LMA as it rapidly solves and tunes the model parameters in comparison with other algorithms (Singh et al. 2009 ). The model simulation has been done by ANN tool in MATLAB 2017a.

The MLP is a neural network with completely connected layers that are stacked against each other. Each layer is activated using a particular activation feature. In order to construct an MLP, two fully connected hidden dense layers were superimposed and activated by the function ‘rectified linear unit’ (RELU) from the python library ‘Keras.’ Data were then iterated over sufficient epochs until it converged to produce the lowest MSE (Gardner and Dorling 1998 ).

The RBF is a feedforward neural network with one hidden layer between the input and output layer. In an RBF-NN, all neurons from a layer are connected to all neurons in the next layer. Harpham et al. ( 2004 ) highlighted the advantages of applying GAs to RBF-NN, thus creating a hybrid. This addition eliminates the test and error approach since GA automatically produces an optimal solution for hyperparameters. In the present study, a GA-based search algorithm has been applied to find optimal hyperparameters for RBF-NN model.

Results and discussion

Statistics of the river ganga: pre-lockdown and during lockdown.

As shown in Table ​ Table1, 1 , the parameters (pH, DO, BOD and TC) of the river Ganga varied in the lockdown period.

Water quality parameters of the river Ganga during pre-lockdown and lockdown period

*Pre-L is Pre-Lockdown and L is Lockdown

In the present study, 14 stations namely Anoopshahar; Farrukabad; Rajghat, Kannauj; Bithoor, Kanpur; Jajmau, Kanpur; Assi ghat, Varanasi; Malviya Bridge, Varanasi; Patna; Bhagalpur; Berhampore; Monipurghat, Nadia; Palta, Barrackpore; Serampore, Hooghly and Howrah bridge, West Bengal were analyzed. The changes in the parameters at these stations have been listed below.

At Anoopshahar, pH increased by 0.1, followed by an increment in BOD and DO with no detectable change in the values of TC. The increment was in the range as delineated by CPCB, India (shown in Table S1 of supporting material). Thus, this water quality at Anoopshahar permitted all the uses of water.

In the Farrukabad and Kannauj, there has been a decrease in pH, TC and DO with the simultaneous increase in BOD level. Though these changes were not positive yet the variation in pH, DO, TC and BOD were in the permissible range of CPCB (Table S1 of supplementary information).

In Bithoor and Jajmau Kanpur, there was a decrease in pH, DO and BOD and water at these stations were considered pollution-free which can be used for drinking, bathing, irrigation and other purposes. Considering TC, its level was increased in Bithoor but declined in Jajmau, Kanpur but it was in the range given by CPCB in Bithoor but not in Jajmau. Thus, the river ganga water can be used for all purpose in Bithoor but not in Jajmau, Kanpur.

In Assi ghat and Malaviya Bridge, Varanasi, a decrease in pH and DO level together with increase in BOD and TC was observed. These changes were not in an acceptable range of CPCB, India.

In Patna, the water quality was found unsuitable owing to a slight decrease in pH and DO and significant augmentation in BOD indicated a high level of pollution. But TC was found to decline here and it was within the acceptable range given by CPCB. At Bhagalpur, Bihar water sample was found unfit for drinking, bathing and irrigation.

In Berhampore, Monipurghat, Nadia; Palta, Barrackpore; Serampore, Hooghly and Howrah bridge, West Bengal a decrease in the pH, DO and BOD was observed with increase in TC and it was much higher than the acceptable range given by CPCB. The decrement in pH, DO and BOD was in the range of permissible limit demarcated by CPCB. Thus, these stations also possessed some positive changes similar to Anoopshahar, Farrukabad, Rajghat and Varanasi. The changes in pH, DO, BOD and TC during lockdown were studied and compared with pre-lockdown data as shown in Table ​ Table1 1 .

As shown in Table ​ Table1, 1 , after lockdown pH in all stations was within an acceptable range of 6.5–8.5. Before lockdown, only two stations, namely Malviya Bridge, Varanasi and Serampore, Hooghly exceeded this range. But during the lockdown, these stations were within the standard range as depicted by CPCB. These changes replenished the Ganga river after a long gap.

It is appropriate to mention that there had been an insignificant change in water quality parameters during lockdown 3.0 and 4.0 as the time difference was of 14 days only.

Specifically, the health indicators of the Ganga's water improved significantly such as increased DO (in Anoopshahar), reduced BOD (in Bithoor, Kanpur; Jajmau, Kanpur; Malviya Bridge, Varanasi; Berhampore; Monipurghat, Nadia; Palta, Barrackpore; Serampore, Hooghly and Howrah bridge) and reduction in TC (Farrukabad, Rajghat, Jajmau, Patna and Palta, Barrackpore) during the lockdown.

Streeter–Phelps model

Streeter Phelps model equation was used to find O 2 saturation deficit ( D ) for 14 real-time stations of the river Ganga (Table ​ (Table2). 2 ). The value of ‘ D ’ was experimentally determined and compared with the theoretical value derived from the model (Fig.  3 ).

Comparison of experimental and theoretical O 2 saturation deficit values with reference to the Streeter Phelps model

Comparison of experimental and theoretical values of D for 14 real-time stations with reference to Streeter Phelps model

It was observed from Table ​ Table2 2 that this model was not accurate for predicting the value of ‘ D ’ as it showed a very high percentage of error for each real-time station of the river Ganga together with a sluggish coefficient of regression ( R 2  = 0.57).

Bhargava ( 1986 ) revealed that Streeter Phelps models could not precisely predict DO sag of a stream instantly after sewage outfalls as model does not take bio-flocculation and sedimentation of the adjustable BOD into account. Jha et al. ( 2007 ) applied Streeter Phelps models for analyzing one of the most polluted rivers in India, i.e., the river Kali and showed the negative outcome with under and over-prediction. Kaushik et al. ( 2012 ) modified Streeter Phelps model by considering the settle able component of BOD and the effect of storage zones on river’s DO. Authors found that the modified model was able to predict parameters of rivers more accurately.

Thomas and Mueller model was used to find ‘ D ’ including non-point sources in the river water for 14 real-time stations. The theoretical results did not show a close agreement with the experimental values (Fig.  4 , Table ​ Table3). 3 ). However, this model had a slightly better fit as compared to Streeter Phelps model based on the value of R 2 (= 0.75).

Comparison of experimental and theoretical of D for 14 real-time stations with reference to the Thomas and Mueller model

Comparison of theoretical and experimental D values with reference to the Thomas and Muller model

The water quality parameters were predicted for 7th August, 2020, i.e., the 200th day starting from 20th January, 2020. Table S3 of supporting material shows the value of predicted parameters on 7th August, 2020.

Assuming that the conditions do not return to original pre-lockdown conditions, this model analyzed the situation from pre-lockdown to lockdown and predicted the possible values for the near future. It also provided incorrect results for 3 stations, i.e., Rajghat, Patna and Bhagalpur, which do not seem to be possible. It was inferred from this model that the actual values were close to predicted values (pH, BOD, DO and TC) for 7th August, 2020.

Water quality parameters were predicted using 2, 3 and 4 degree polynomials on 30th June, 2020 (i.e., on day 162 starting on 20 January 2020) and these values are shown in Tables S4, S5, S6, S7 and S8 of the supporting material. For prediction, 30th June, 2020 was selected as it falls close to 31st May, 2020, and reduces the chance of error that could increase if one moves away from the 31st May, 2020 data values. Considering the range of values from these polynomials, it can be predicted that the water quality parameters (BOD, DO, pH and TC) will fall within the range of values that were predicted for 30th June, 2020.

The actual value of these parameters will depend on how the level of pollution goes back to the previous one. The values will more likely to fall in the ranges stated in Table S4, S5, S6, S7 and S8 of the supporting material.

This model analyses the situation from pre-lockdown to lockdown statistics and predicts somewhat possible values for near future. From the graphs, it was clinched that all values fall in acceptable range except BOD at Patna and Bhagalpur. Also, the DO levels at Rajghat, Patna and Bhagalpur show steep changes. The quality of the Ganga water appeared to be improved from pre-lockdown situation. Since the values and curves for polynomial second degree were the same as for NDD model, this implied that the NDD model was the reliable one.

The polynomial regression model was better than NDD as it provided the range (generated by 2nd- , 3rd- , and 4th-degree polynomial) in which the predicted parameters would lie. The polynomial regression model fitted better than NDD as most of the actual values lie in or near the predicted range. This is due to the fact that NDD is an interpolation method; however, in the present work it predicts the future values by extrapolating the curve. Also, NDD resulted in the second-degree polynomial, which does not correspond to the actual variation in the parameters in due course of the time.

The SVR model, a kernel-based regression model was used and its parameters, i.e., C , ε , γ were optimized for each water quality parameter with the help of a simple GA. Here, GA was employed using a one-point crossover function having mutation with a root mean square as the fitness measure. The algorithm was performed on a population of 50 randomly selected individuals iterated upon 30 generations with a crossover probability of 0.5 and a mutation probability of 0.02. Upon running, the algorithm first randomly selects 50 individuals with their ranges being, C  = [1, 100], γ = 0.1 , 1 , ε = 0.001 , 0.01 . Each of these individuals undergoes crossover and mutation, after which the fitness of an individual is calculated. This process runs over a set of 30 generations with each generation producing a slightly better generation than itself. From the last generation, the individual with the highest fitness function is chosen as the best individual.

The model showed overfitting with zero MSE upon running. To solve this, fivefold cross-validation was used wherein the data were split into test and train set five times. This helped in solving overfitting. The model reported different MSE for pH, DO, BOD and TC in Table ​ Table4 4 .

Mean absolute error using different models

The R 2 value for the pH, DO and TC approached unity signifying a perfect fit. BOD, however, showed a low R 2 value (Table ​ (Table5 5 ).

R 2 value for pH, DO, BOD and TC for different models

These values show that out of the three parameters studied, the SVR—GA model works best for the pH, DO with R 2 value approaching unity (Table ​ (Table6, 6 , Fig.  5 ).

SVR-GA error for 14 real-time stations

SVR-GA predicted values of BOD, pH, DO and TC

For TC analysis, data from January were paired with other parameters (pH, DO, BOD and TC). This was used as the input data set for the prediction of TC during the lockdown. SVR-GA gave an R 2 value of 0.99, pointing toward a high goodness of fit.

In this model, a ‘ t ’ value of 0.01 was used. Trial and error basis were used and alpha values have been modified and tested. The alpha value of 0.01 was finally selected. The model provided R 2 values leaning toward zero for pH, DO, BOD and TC and failed to predict the data correctly (Tables ​ (Tables4, 4 , ​ ,5, 5 , ​ ,7, 7 , Fig.  6 ).

Lasso regression error for 14 real-time stations

Lasso predicted values of BOD, pH, DO and TC

Apart from this, Lasso regression performed robustly for TC prediction and gave R 2 values of 0.93.

In the present study, a nonlinear transfer function (TANSIG) in the hidden layer was used for ANNs. The ANN predicted output and error in pH, DO, BOD and TC model for real-time stations of the river Ganga are shown in Table ​ Table8 8 .

ANN predicted output and error using L–M algorithm for pH, DO and BOD models for 14 stations of the river Ganga

The plots between experimental and theoretical values of pH, DO, BOD and TC values are shown in Fig.  7 .

Comparison of the experimental and theoretical a pH, b DO, c BOD and d TC levels in the river Ganga

The best validation performance in ten neurons was 0.08877, 0.38177, 34.7517 and 16,371,716.42 at epoch 3, 3, 2 and 7 for pH, DO, BOD and TC, respectively, with the lowest MSE (Fig.  8 ).

Performance plot for modeling of a pH, b DO, c BOD and d TC levels in the river Ganga

The linear R 2 values for training, validation and test data sets used for all the models (pH, DO, BOD and TC) are represented in Figure S1 of supporting material. The selected ANN generated the most trustworthy models for all three data sets. The experimental and theoretical values pH, DO, BOD and TC derived through these models were in close agreement ( R 2  = 0.92–1.0). This suggested that the model fitted well with the experimental data sets. ANNs have also been used to estimate and forecast the water quality variables like modeling of DO and BOD in the river water (Singh et al. 2009 ).

Similarly, Shamseldin ( 2010 ) used ANN for forecasting the flow of rivers in the developing countries. The chlorine concentration in the water distribution network has been assessed through ANN by Cordoba et al. ( 2014 ). ANN has been used for the prediction of water quality index (Bansal and Ganesan 2019 ; Gupta et al. 2019 ). The results of ANN-based modeling have shown significant accuracy over other traditional modeling techniques. Shakeri Abdolmaleki et al. ( 2013 ) applied ANN for predicting copper concentration in the drinking water reservoir of Iran. Authors found that predicted values were very close to the real concentration of copper. The BOD, DO and other water quality parameters were forecast by using ANN in the Karoon river (Emamgholizadeh et al. 2014 ). The predicted values were close to the real ones, which proved ANN, an effective modeling technique for predicting water quality variables in the river. Gomolka et al. ( 2018 ) used ANN to estimate the BOD level and for controlling rate of aeration in river.

Two RELU activated hidden layers were used and epochs were performed until full convergence of loss function was observed.

The MLP showed excellent results for pH, DO and BOD with R 2 values very close to one (Tables ​ (Tables4, 4 , ​ ,5, 5 , ​ ,9, 9 , Fig.  9 ) but it's prediction for TC was not at par with its performance for the other indices.

MLP error for 14 real-time stations

MLP predicted values of BOD, pH, DO and TC

An RBF-NN was applied with GA to optimize the hyperparameters like learning rate (lr) and several kernels (k). A multi-feature input algorithm was constructed which picked the hyperparameters using a GA where MSE was chosen as the fitness function. The initial population was picked out where the kernel number and learning rate constrained to a range of [1, 7] and [0.0001, 0.02], respectively. An initial population size of 50 was chosen. The algorithm was run for 30 generations with a crossover and a mutation probability of 0.7 and 0.02, respectively. The model ran for 100 epochs each time. The results of the model showed poor performance for BOD, DO and TC. The model’s goodness of fit for pH is better than Lasso regression but not SVR and MLP (Tables ​ (Tables4, 4 , ​ ,5, 5 , ​ ,10, 10 , Fig.  10 ).

RBF-NN error for 14 real-time stations

RBF-NN predicted values of BOD, pH, DO and TC

Comparative study

Several studies conducted by other researchers on the quality of the Ganga's water during lockdown have been discussed in detail in Table ​ Table11. 11 . The outcomes of their work with the technique involved in the estimation of water quality parameters are included and have been compared with the present study.

Comparative assessment of the present work with that of other researchers to ascertain changes in the Ganga river's water quality characteristics during lockdown

In the present study, the water quality of the river Ganga has been evaluated during the lockdown and predicted for post lockdown conditions. It was found that the pH of all stations was within the standard range 6.5–8.5 in lockdown period. An increment in DO has been observed in Anoopshahar. Apart from that, all stations had DO > 5 mg/L except Patna and Bhagalpur. It was noted that Patna and Bhagalpur stations had very high BOD levels compared to other stations that signified a substantial level of pollution. During the lockdown, Anoopshahar, Farrukabad, Rajghat, Kannauj and Assi ghat, Varanasi had BOD exactly as 3 mg/L. The decrement in TC was observed in Farrukabad, Rajghat, Jajmau, Patna and Palta during the lockdown period. In the present study, bioengineered mathematical models, namely Streeter Phelps, Thomas Mueller, SVR-GA, Lasso Regression, ANN, NDD and Polynomial regression, were attempted to predict the water quality parameters. Polynomial regression and NDD model were able to predict pH, BOD, DO and TC levels from 20th January, 2020 to 30th June, 2020 and 07th August, 2020. Thus, NDD and polynomial regression models were used to predict the near future values of the water quality parameters (BOD, DO, pH and TC) of the river Ganga. But NDD model was not able to predict TC values. However, the NDD model is simply an interpolation method, which can be further extrapolated to predict the values. On the other hand, polynomials of 2, 3 and 4 degrees were generated in polynomial regression model to obtain the range of predicted values. The NDD model is verified by the polynomial degree 2 regression that appeared to be acceptable after comparison. Overall, polynomial regression model was better than NDD model. In ANN models using LMA, the best validation performance was observed with ten neurons as 0.08877, 0.38177, 34.7517 and 16,371,716.42 at epoch 3, 3, 2 and 7 for pH, DO, BOD and TC, respectively. Additionally, SVR-GA hybrid was superior compared to its counterparts such as Lasso Regression and RBF-NN in the prediction of real-time water quality data indices such as pH, DO of the river Ganga. It also produced the best results for TC forecast during the lockdown period. It was unable to predict the lockdown BOD values correctly. MLP was the second-best algorithm after SVR-GA, which showed accurate fits for three (pH, DO, BOD) of the indices but couldn’t accurately predict TC levels. SVR-GA and MLP showed a nearly perfect fit for the pH and TC data with significantly lesser MSE values. The R 2 value for pH modeled by SVR-GA ( R 2  = 0.99) and MLP ( R 2  = 0.99) was near unity, pointing to a perfect fit. Similarly, the R 2 value for TC modeled by SVR-GA is 0.99. The abnormal high deviations in BOD modeling in all the models except MLP ( R 2  = 0.99) can be due to the presence of outliers. It can, therefore, be stated that SVR and MLP are relatively quicker and better choices as the modeling techniques for predicting values of water quality parameters of the river Ganga. Thus, in the present study, SVR-GA, MLP and polynomial regression model were found superior to NDD for the prediction of water quality parameters in the long run. Moreover, as these models are fitted with the least error, there are numerous applications where their use is highly recommended. Like, SVR-GA algorithm can be effectively implemented to estimate parameters of water, MLP is capable of modeling a sequencing batch reactor that will treat municipal wastewater. The comparison of different models showed their applicability in predictive modeling of river flow and wastewater treatment.

Below is the link to the electronic supplementary material.

Acknowledgements

The authors are thankful to the School of Biochemical Engineering, IIT (BHU) Varanasi, Varanasi for financial and technical support of the present research work.

Author contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by JS, SS and PS. The final draft of the manuscript was reviewed by VM. All authors read and approved the final manuscript.

The authors did not receive support from any organization for the submitted work.

Declarations

The authors have no conflicts of interest to declare that are relevant to the content of this article.

• Ahmed AAM. Prediction of dissolved oxygen in Surma River by biochemical oxygen demand and chemical oxygen demand using the artificial neural networks (ANNs) J King Saud Univ Eng Sci. 2017; 29 :151–158. doi: 10.1016/j.jksues.2014.05.001. [ CrossRef ] [ Google Scholar ]
• Ahmed U, Mumtaz R, Anwar H, Shah AA, Irfan R, García-Nieto J. Efficient water quality prediction using supervised machine learning. Water. 2019; 11 :2210. doi: 10.3390/w11112210. [ CrossRef ] [ Google Scholar ]
• APHA . Standard methods for the examination of water and wastewater. 18. Washington, DC: American Public Health Association; 1992. [ Google Scholar ]
• Azad S (2020) For 1st time in decades, tests show Ganga water in Haridwar fit to drink
• Bansal S, Ganesan G. Advanced evaluation methodology for water quality assessment using artificial neural network approach. Water Resour Manag. 2019; 33 :3127–3141. doi: 10.1007/s11269-019-02289-6. [ CrossRef ] [ Google Scholar ]
• Bhargava DS. DO sag model for extremely fast river purification. J Environ Eng. 1986; 112 :572–585. doi: 10.1061/(ASCE)0733-9372(1986)112:3(572). [ CrossRef ] [ Google Scholar ]
• Brooks W, Corsi S, Fienen M, Carvin R. Predicting recreational water quality advisories: a comparison of statistical methods. Environ Model Softw. 2016; 76 :81–94. doi: 10.1016/j.envsoft.2015.10.012. [ CrossRef ] [ Google Scholar ]
• Chakraborty SK. River pollution and perturbation: perspectives and processes. In: Chakraborty SK, editor. Riverine ecology. Berlin: Springer; 2021. pp. 443–530. [ Google Scholar ]
• Chakraborty I, Maity P. COVID-19 outbreak: migration, effects on society, global environment and prevention. Sci Total Environ. 2020; 728 :138882. doi: 10.1016/j.scitotenv.2020.138882. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Chaturvedi MC. Ganga–Brahmaputra–Meghna waters: advances in development and management. CRC Press; 2012. [ Google Scholar ]
• Cordoba GC, Tuhovčák L, Tauš M. Using artificial neural network models to assess water quality in water distribution networks. Procedia Eng. 2014; 70 :399–408. doi: 10.1016/j.proeng.2014.02.045. [ CrossRef ] [ Google Scholar ]
• Das B, Chakrabarty D. Newton’s divided difference interpolation formula: representation of numerical data by a polynomial curve. Int J Math Trend Technol. 2016; 35 :26–32. doi: 10.14445/22315373/IJMTT-V35P528. [ CrossRef ] [ Google Scholar ]
• Deaven DM, Ho K-M. Molecular geometry optimization with a genetic algorithm. Phys Rev Lett. 1995; 75 :288. doi: 10.1103/PhysRevLett.75.288. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Dey S, Choudhary S, Dey S, Deshpande K, Kelkar N. Identifying potential causes of fish declines through local ecological knowledge of fishers in the Ganga River, eastern Bihar, India. Fish Manag Ecol. 2019; 27 :140–154. doi: 10.1111/fme.12390. [ CrossRef ] [ Google Scholar ]
• Dhar I, Biswas S, Mitra A, Pramanick P, Mitra A (2020) COVID-19 Lockdown phase: a boon for the River Ganga water quality along the city of Kolkata. NUJS J Regul Stud Spec 53–57
• DigvijayKumar TABAS. D.O. modelling and water quality analysis of river Brahmaputra in Guwahati, Assam. J Civ Eng Environ Technol. 2017; 4 :64–67. [ Google Scholar ]
• Drew G. River dialogues: Hindu faith and the political ecology of dams on the sacred Ganga. Tucson: University of Arizona Press; 2017. [ Google Scholar ]
• Dutta V, Dubey D, Kumar S. Cleaning the River Ganga: impact of lockdown on water quality and future implications on river rejuvenation strategies. Sci Total Environ. 2020; 743 :140756. doi: 10.1016/j.scitotenv.2020.140756. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Duttagupta S, et al. Impact of Covid-19 lockdown on availability of drinking water in the arsenic-affected Ganges River Basin. Int J Environ Res Public Health. 2021; 18 :2832. doi: 10.3390/ijerph18062832. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• El-Shafie A, Mukhlisin M, Najah AA, Taha MR. Performance of artificial neural network and regression techniques for rainfall-runoff prediction. Int J Phys Sci. 2011; 6 :1997–2003. [ Google Scholar ]
• Emamgholizadeh S, Kashi H, Marofpoor I, Zalaghi E. Prediction of water quality parameters of Karoon River (Iran) by artificial intelligence-based models. Int J Environ Sci Technol. 2014; 11 :645–656. doi: 10.1007/s13762-013-0378-x. [ CrossRef ] [ Google Scholar ]
• Falkenauer E, Bouffouix S (1991) A genetic algorithm for job shop. In: ICRA. Citeseer, pp 824–829
• Gardner MW, Dorling S. Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences. Atmos Environ. 1998; 32 :2627–2636. doi: 10.1016/S1352-2310(97)00447-0. [ CrossRef ] [ Google Scholar ]
• Garg V, Aggarwal SP, Chauhan P. Changes in turbidity along Ganga River using Sentinel-2 satellite data during lockdown associated with COVID-19 Geomatics. Nat Hazards Risk. 2020; 11 :1175–1195. doi: 10.1080/19475705.2020.1782482. [ CrossRef ] [ Google Scholar ]
• Goldberg DE. Genetic algorithms. Delhi: Pearson Education India; 2006. [ Google Scholar ]
• Goldberg DE, Kuo CH. Genetic algorithms in pipeline optimization. J Comput Civ Eng. 1987; 1 :128–141. doi: 10.1061/(ASCE)0887-3801(1987)1:2(128). [ CrossRef ] [ Google Scholar ]
• Gomolka Z, Twarog B, Zeslawska E, Lewicki A, Kwater T. Using artificial neural networks to solve the problem represented by BOD and DO indicators. Water. 2018; 10 :4. doi: 10.3390/w10010004. [ CrossRef ] [ Google Scholar ]
• Gupta R, Singh A, Singhal A. Application of ANN for water quality index. Int J Mach Learn Comput. 2019; 9 :688–693. doi: 10.18178/ijmlc.2019.9.5.859. [ CrossRef ] [ Google Scholar ]
• Haider Naqvi SK (2020) Lockdown does what decades of schemes couldn’t: clean Ganga, Published on 4 April 2020 in Hindustan Times
• Harpham C, Dawson CW, Brown MR. A review of genetic algorithms applied to training radial basis function networks. Neural Comput Appl. 2004; 13 :193–201. doi: 10.1007/s00521-004-0404-5. [ CrossRef ] [ Google Scholar ]
• Hasnain M, Pasha MF, Ghani I. Combined measures to control the COVID-19 pandemic in Wuhan Hubei, China: a narrative review. J Biosaf Biosecur. 2020; 2 :51–57. doi: 10.1016/j.jobb.2020.10.001. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Holland JH. Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. Cambridge: MIT Press; 1992. [ Google Scholar ]
• Jha R, Ojha C, Bhatia K. Critical appraisal of BOD and DO models applied to a highly polluted river in India. Hydrol Sci J. 2007; 52 :362–375. doi: 10.1623/hysj.52.2.362. [ CrossRef ] [ Google Scholar ]
• Jiang M, Jiang S, Zhu L, Wang Y, Huang W, Zhang H. Study on parameter optimization for support vector regression in solving the inverse ECG problem. Comput Math Methods Med. 2013; 2013 :158056. doi: 10.1155/2013/158056. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kaushik N, Tyagi B, Jayaraman G. Modeling of the dissolved oxygen in a river with storage zone on the banks. Appl Math. 2012; 3 :699–704. doi: 10.4236/am.2012.37103. [ CrossRef ] [ Google Scholar ]
• Krishnakumar K, Goldberg DE. Control system optimization using genetic algorithms. J Guid Control Dyn. 1992; 15 :735–740. doi: 10.2514/3.20898. [ CrossRef ] [ Google Scholar ]
• Liu S, Tai H, Ding Q, Li D, Xu L, Wei Y. A hybrid approach of support vector regression with genetic algorithm optimization for aquaculture water quality prediction. Math Comput Model. 2013; 58 :458–465. doi: 10.1016/j.mcm.2011.11.021. [ CrossRef ] [ Google Scholar ]
• Lockwood AH. Heat advisory: Protecting health on a warming planet. Cambridge: MIT Press; 2016. [ Google Scholar ]
• Muduli P, et al. Water quality assessment of the Ganges River during COVID-19 lockdown. Int J Environ Sci Technol. 2021; 18 :1645–1652. doi: 10.1007/s13762-021-03245-x. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Mukherjee P, Pramanick P, Zaman S, Mitra A (2020) Eco-restoration of River Ganga water quality during COVID-19 lockdown period using Total Coliform (TC) as proxy. NUJS J Regul Stud Spec 69–74
• Najah A, El-Shafie A, Karim O, El-Shafie AH. Application of artificial neural networks for water quality prediction. Neural Comput Appl. 2013; 22 :187–201. doi: 10.1007/s00521-012-0940-3. [ CrossRef ] [ Google Scholar ]
• Nakano R, Yamada T (1991) Conventional genetic algorithm for job shop problems. In: ICGA, pp 474–479
• Ostertagová E. Modelling using polynomial regression. Procedia Eng. 2012; 48 :500–506. doi: 10.1016/j.proeng.2012.09.545. [ CrossRef ] [ Google Scholar ]
• Paul D. Research on heavy metal pollution of river Ganga: a review. Ann Agrar Sci. 2017; 15 :278–286. doi: 10.1016/j.aasci.2017.04.001. [ CrossRef ] [ Google Scholar ]
• Paul A, Chatterjee S, Bairagi N. Prediction on Covid-19 epidemic for different countries: focusing on South Asia under various precautionary measures. Medrxiv. 2020; 395 :689. [ Google Scholar ]
• Postel S, Richter B. Rivers for life: managing water for people and nature. Washington: Island Press; 2012. [ Google Scholar ]
• Roy MB, Ghosh M, Roy PK. Assessment of water quality of river ganga during covid-19 lockdown. Desalination Water Treat. 2021; 223 :26–33. doi: 10.5004/dwt.2021.27155. [ CrossRef ] [ Google Scholar ]
• Sarkar A, Pandey P. River water quality modelling using artificial neural network technique. Aquat Procedia. 2015; 4 :1070–1077. doi: 10.1016/j.aqpro.2015.02.135. [ CrossRef ] [ Google Scholar ]
• Sati VP. Ganges. Berlin: Springer; 2021. [ Google Scholar ]
• Shakeri Abdolmaleki A, Gholamalizadeh Ahangar A, Soltani J. Artificial neural network (ANN) approach for predicting Cu concentration in drinking water of Chahnimeh1 reservoir in Sistan-Balochistan, Iran. Health Scope. 2013; 2 :31–38. doi: 10.17795/jhealthscope-9828. [ CrossRef ] [ Google Scholar ]
• Shamseldin A. Artificial neural network model for river flow forecasting in a developing country. J Hydroinform. 2010; 12 :22–35. doi: 10.2166/hydro.2010.027. [ CrossRef ] [ Google Scholar ]
• Singh K, Jha R. Critical appraisal of water quality model parameters for an urban city in lower Ganga basin during pre-and post-COVID19 Lockdown in India. Indian J Sci Technol. 2021; 14 :2439–2447. doi: 10.17485/IJST/v14i29.242. [ CrossRef ] [ Google Scholar ]
• Singh K, Basant A, Malik A, Jain G. Artificial neural network modeling of the river water quality—a case study. Ecol Model. 2009; 220 :888–895. doi: 10.1016/j.ecolmodel.2009.01.004. [ CrossRef ] [ Google Scholar ]
• Singh B (2020) Lockdown gift: gleaming Ganga lays new conservation roadmap. Times of India. https://m.timesofindia.com/city/varanasi/lockdown-gift-gleaming-ganga-lays-new-conservation-roadmap/amp_articleshow/75571128.cms . Accessed 06/05/2020
• Smola AJ, Schölkopf B. A tutorial on support vector regression. Stat Comput. 2004; 14 :199–222. doi: 10.1023/B:STCO.0000035301.49549.88. [ CrossRef ] [ Google Scholar ]
• Srinivas R, Singh AP, Shankar D. Understanding the threats and challenges concerning Ganges River basin for effective policy recommendations towards sustainable development. Environ Dev Sustain. 2020; 22 :3655–3690. doi: 10.1007/s10668-019-00361-0. [ CrossRef ] [ Google Scholar ]
• Srivastava NSaS (2020) Lockdown impact: Ganga water in Haridwar becomes 'fit to drink' after decades. India Today
• Thomann RV, Mueller JA. Principles of surface water quality modeling and control. New York: Harper & Row Publishers; 1987. [ Google Scholar ]
• Thomas H (1948) The pollution load capacity of streams, Water and sewage works GICAL survey Fohn Knox Rd-Suite F 240 [ PubMed ]
• Tibshirani R. Regression shrinkage and selection via the Lasso. J R Stat Soc: Ser B (Methodol) 1996; 58 :267–288. [ Google Scholar ]
• Trivedi R. Water quality of the Ganga River—an overview. Aquat Ecosyst Health Manag. 2010; 13 :347–351. doi: 10.1080/14634988.2010.528740. [ CrossRef ] [ Google Scholar ]
• Vapnik V, Golowich SE, Smola AJ (1997) Support vector method for function approximation, regression estimation and signal processing. In: Advances in neural information processing systems, pp 281–287
• Vapnik V, Vapnik V. Statistical learning theory. New York: Wiley; 1998. p. 624. [ Google Scholar ]
• Wang X, Fu L, He C. Applying support vector regression to water quality modelling by remote sensing data. Int J Remote Sens. 2011; 32 :8615–8627. doi: 10.1080/01431161.2010.543183. [ CrossRef ] [ Google Scholar ]
• Webdesk N (2020) Rivers heal during lockdown. National Herald India. https://www.nationalheraldindia.com/videos/rivers-heal-during-lockdown
• Wray M ( 2020) Coronavirus lockdown eases pollution, Venice canal runs clear. Globalnews. https://globalnews.ca/news/6683226/climate-change-coronavirus/ . Accessed 16/03/2020
• Yang J, Honavar V. Feature subset selection using a genetic algorithm. In: Liu H, Motoda H, editors. Feature extraction, construction and selection. Berlin: Springer; 1998. pp. 117–136. [ Google Scholar ]

Pollution of River Ganga, Case Study

Pollution of the Ganges (or Ganga), the largest river in India, poses significant threats to human health and the larger environment. Severely polluted with human waste and industrial contaminants, the river provides water to about 40% of India's population across 11 states, serving an estimated population of 500 million people which is more than any other river in the world. Today, the Ganges is considered to be the sixth-most polluted river in the world. Raghubir Singh, an Indian photographer, has noted that no one in India spoke of the Ganges as polluted until the late 1970s. However, pollution has been an old and continuous process in the river as by the time people were finally speaking of the Ganges as polluted, stretches of over six hundred kilometres were essentially ecologically dead zones. A number of initiatives have been undertaken to clean the river but failed to deliver as desired results. After getting elected, India's Prime minister Narendra Modi affirmed to work in cleaning the river and controlling pollution. Subsequently, the Namami Gange project was announced by the government in the July 2014 budget.[10] An estimated Rs 2,958 Crores (US$460 million) have been spent until July 2016 in various efforts in cleaning up of the river. Background of the case Ganga is a trans-boundary river of Asia flowing through India and Bangladesh. It is one of the most sacred rivers to the Hindus and a lifeline to a billion Indians who live along its course. One of the most populated cities along its course is Kanpur. This city has a population of approx. 29.2 lakhs (2.9 million). At this juncture of its course Ganga receives large amounts of toxic waste from the city´s domestic and industrial sectors, particularly the leather tanneries of Kanpur. In 1985, M.C. Mehta filed a writ petition in the nature of mandamus to prevent these leather tanneries from disposing off domestic and industrial waste and effluents in the Ganga river. This writ petition was bifurcated by the Supreme Court into two parts known as Mehta I and Mehta II. Mehta I [ M.C. Mehta v. Union of India , [1987] 4 SCC 463]: Proceedings and Orders passed before final judgment: In this petition the petitioner requested the court to request the Supreme Court (the Court) to restrain the respondents from releasing effluents into the Ganga river till the time they incorporate certain treatment plants for treatment of toxic effluents to arrest water pollution. At the preliminary hearing the Court directed the issue of notice under Order I Rule 8 of the CPC, treating this case as a representative action by publishing a small gist of the petition in the newspapers calling upon all the industrialists, municipal corporations and the town municipal councils having jurisdiction over the areas through which the river Ganga flows to appear before the Court and to show cause as to why directions should not be issued to them. In pursuance of this notice many industries and local authorities appeared before the Supreme Court. The Court highlighted the importance certain provisions in our constitutional framework which enshrine the importance and the need for protecting our environment. Article 48-A provides that the State shall endeavor to protect and improve the environment and to safeguard the forests and wild life of the country. Article 51-A of the Constitution of India, imposes a fundamental duty on every citizen to protect and improve the natural environment including forests, lakes, rivers and wild life. The Court stated the importance of the Water (Prevention and Control of Pollution) Act, 1974 (the Water Act). This act was passed to prevent and control water pollution and maintaining water quality. This act established central and stated boards and conferred them with power and functions relating to the control and prevention of water pollution. Section 24 of the Act prohibits the use of the use of any stream for disposal of polluting matter. A stream under section 2(j) of the Act includes river, water course whether flowing or for the time being dry, inland water whether natural or artificial, sub-terrene waters, sea or tidal waters to such extent or as the case may be to such point as the State Government may by the notification in the official gazette may specify. The Act permits the establishment of Central Boards and State Boards. Section 16 and Section 17 of the Act describe the power of these boards. One of the functions of the State Board (the Board) is to inspect sewage or trade effluents, plants for treatment of sewage and trade effluents, data relating to such plants for the treatment of water and system for the disposal of sewage or trade effluent.

What is a Trade Effluent?

Mehta ii (m.c. mehta v. union of india decided on 12th january, 1988).

• (iii) the collection and removal of sewage, offensive matter and rubbish and treatment and disposal thereof including establishing and maintaining farm or factory:
• (vii) the management and maintenance of all Mahapalika waterworks and the construction or acquisition of new works necessary for a sufficient supply of water for public and private purposes.
• (viii) guarding from pollution water used for human consumption and preventing polluted water from being so used.

The Court also relied on Section 251, 388, 396, 398, 405 and 407 of the Adhiniyam which provide provisions for disposal of sewage, prohibition of cultivation, use of manure, or irrigation injurious to health, power to require owners to clear away noxious vegetation and power of the Mukhya Nagar Adhikari to inspect any place at any time for the purpose of preventing spread of dangerous diseases. These provisions deal with the duties of the Nagar Mahapalika or the Mukhya Nagar Adhikari appointed under the Adhiniyam with regard to the disposal of sewage and protection of the environment. These provisions governing the local bodies indicate that the Nagar Mahapalikas and the Municipal Boards are primarily responsible for the maintenance of cleanliness in the areas of their jurisdiction. The Court also relied on the provisions of the Water Act which provide the meaning of pollution, sewage effluent, stream and trade effluents. Sections 3 and 4 of the Water Act provide for the establishment of the Central and State Boards. A State Board was constituted under Section 4 of the Water Act in the State of Uttar Pradesh. Section 16 of the Water Act sets out the functions of the Central Board and Section 17 of the Water Act lays down the functions of the State Board. The functions of the Central Board are primarily advisory and supervisory in character. The Central Board is also required to advise the Central Government on any matter concerning the prevention and control of water pollution and to co-ordinate the activities of the State Boards. The Central Board is also required to provide technical assistance and guidance to the State Boards, carry out and sponsor investigations and research relating to problems of water pollution and prevention, control or abatement of water pollution. The functions of the State Board are more comprehensive. In addition to advising the State Government on any matter concerning the prevention, control or abatement of water pollution, the State Board is required among other things:

• to plan a comprehensive programme for the prevention, control or abatement of pollution of streams and wells in the State and to secure the execution thereof;
• to collect and disseminate information relating to water pollution and the prevention, control or abatement thereof;
• to encourage, conduct and participate in investigations and research relating to problems of water pollution and prevention, control or abatement of water pollution;
• to inspect sewage or trade effluents, works and plants for the treatment of sewage and trade effluents;
• to review plans, specifications or other data relating to plants set up for the treatment of water, works for the purification thereof and the system for the disposal of sewage or trade effluents or in connection with the grant of any consent as required by the Water Act;
• to evolve economical and reliable methods of treatment of sewage and trade effluents, having regard to the peculiar conditions of soils, climate and water resources of different regions and more especially the prevailing flow characteristics of water in streams and wells which render it impossible to attain even the minimum degree of dilution; and
• to lay down standards of treatment of sewage and trade effluents to be discharged into any particular stream taking into account the minimum fair weather dilution available in that stream and the tolerance limits of pollution permissible in the water of the stream, after the discharge of such effluents.

Sections 20, 21 and 23 of the Water Act confer power on the State Board to obtain information necessary for the implementation of the provisions of the Water Act, to take samples of effluents and to analyze them and to follow the procedure prescribed in connection therewith and the power of entry and inspection for the purpose of enforcing the provisions of the Water Act. Section 24 of the Water Act prohibits the use of stream or well for disposal of polluting matters etc. contrary to the provisions incorporated in that section. Section 32 of the Water Act confers the power on the State Board to take certain emergency measures in case of pollution of stream or well. Where it is apprehended by a Board that the water in any stream or well is likely to be polluted by reason of the disposal of any matter therein or of any likely disposal of any matter therein, or otherwise, the Board may under Section 33 of the Water Act make an application to a court not inferior to that of a Presidency Magistrate or a Magistrate of the first class, for restraining the person who is likely to cause such pollution from so causing. The Court relied on a common law principle which states that Municipal Corporation can be restrained by an injunction in an action brought by a riparian owner who has suffered on account of the pollution of the water in a river caused by the Corporation by discharging into the river insufficiently treated sewage from discharging such sewage into the river. In the case of Pride of Derby and Derbyshire Angling Association v. British Celanese Ltd [3], the Derby Corporation admitted that it had polluted the plaintiffs fishery by discharging into it insufficiently treated sewage. According to the Derby Corporation Act, 1901 it was under a duty to provide a sewerage system, and that the system which had accordingly been provided had become inadequate solely from the increase in the population of Derby. The Court noted that M.C. Mehta is not a riparian owner. Nevertheless he is a person interested in protecting the lives of the people who make use of the water flowing in the river Ganga. Therefore, his right to maintain the petition cannot be disputed. The nuisance caused by the pollution of the river Ganga was held to be a public nuisance. Final judgment:

• The Court directed the Kanpur Nagar Mahapalika to take appropriate action under the provisions of the Adhiniyam for the prevention of water pollution in the river. It was noted that a large number of dairies in Kanpur were also polluting the water of the river by disposing waste in it. The Supreme Court ordered the Kanpur Nagar Mahapalika to direct the dairies to either shift to any other place outside the city or dispose waste outside the city area.
• Kanpur Nagar Mahapalika was ordered to increase the size of sewers in the labour colonies and increase the number of public latrines and urinals for the use of poor people.
• Whenever applications for licenses to establish new industries are made in future, such applications shall be refused unless adequate provision has been made for the treatment of trade effluents flowing out of the factories.

The above orders were made applicable to all Nagar Mahapalikas and Municipalities which have jurisdiction over the area through which the Ganga river flows. In addition to this, the Supreme Court further relied on Article 52A (g) on the Constitution of India, which imposes a fundamental duty of protecting and improving the natural environment. The Court order that:

• It is the duty of the Central Government to direct all the educational institutions throughout India to teach at least for one hour in a week lessons relating to the protection and the improvement of the natural environment including forests, lakes, rivers and wildlife in the first ten classes.
• The Central Government shall get text books written for the said purpose and distribute them to the educational institutions free of cost. Children should be taught about the need for maintaining cleanliness commencing with the cleanliness of the house both inside and outside, and of the streets in which they live. Clean surroundings lead to healthy body and healthy mind.

Training of teachers who teach this subject by the introduction of short term courses for such training shall also be considered. This should be done throughout India. Written By:- Navnit Kumari

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