Improving our understanding

of the aquifer systems in

Sundarbans

Dr. Gopal Krishan

(NIH)

Image: Prasari

Mr. Andrew McKenzie

(BGS)

Duration June to December, 2019

Dr. Purnabha Dasgupta

(PRASARI)

CONTENTS

Ground Water Concerns of Indian Sundarbans

Geographical Area

Objectives

Methodology

Findings

Probable Solutions: Five Square Model

ASR Model

Impact on Government

Ground Water Concerns in

Indian Sundarbans

• Increased pressure on sweet water sources lead toground water exploitation by farmers (Ground waterdepletion rate is 3m and 2.5m per annum in 1st and

2nd aquifer, respectively) (CGWB Report, 2019)

• Increased salinity (2.2-4.1 dS/m) in shallow aquiferswith increased abstraction

• Alteration of the nature of surface water from sweetto brackish (>15-20ppm) during summer and winterseasons

• Toxic effect of salinity on farm lands impactingtowards crop loss (34-40% crop loss occur duringsummer seasons)

• Presence of sodic-soil layer at 6-8ft depth (CSSRI

Report, 2016) in majority of the islands

• Periodic inundation by saline water in every 5 yearsleads to river embankment failure in IndianSundarbans (IPCC Dissemination Report, 2019)

• Piezometric heads failing from as early as Mid-February to Mid-June every year

• Trading of irrigation water taking place from groundwater and surface water sources (during Februaryto June every year)

Change in nature of surface water

High salinity and crop loss

Dry and Saline soil

Periodic Inundation

Geographical area• Sundarbans – the mangrove delta of Ganga - Bramhaputra

• Spread across India and Bangladesh – it constitutes 9 % of total area of West Bengal

• Two districts- North 24 Parganas (9 blocks) and South 24 Parganas (13 blocks) –

54 habituated islands

• Mangrove coverage 4463.89 sq. Km. in Indian Sundarbans

• High Rural Poverty (> 50% places); high population density

• 1500-2000 mm monsoon rains

Haldar & Debnath 2014

Reserve Forest

Reserve Forest

Reserve Forest

Objectives

• Assessment of groundwater stress in

selected parts of Indian Sundarbans

• Developing community driven conceptual

models of saline aquifers of the selected

study area

• Proposing simulated models on Aquifer

Storage and Recovery (ASR) for the area

Methodology

• Determination of the groundwater stressed area under Indian

Sundarbans through secondary data

• Selection of the study area through purposive sampling (non-

probabilistic method)

• Socio-hydrological survey of the study area (to measure

irrigation and drinking water stress on the community,

lithology and aquifer properties)

• Participatory salinity mapping (pre & post monsoon),

participatory water flow mapping, participatory aquifer

mapping and water-resource mapping

• Field level data collection on five-square

• Field level data collection to develop simulated model on

Aquifer Storage and Recovery

• Capacity building of the Para-hydrogeologists to collect data,

disseminate information and act on the solutions in

collaboration with relevant state departments

Pre M Pond

Pre

M P

on

dP

re M

Po

nd

Post M Pond

Po

st

M P

on

dP

os

t M P

on

d

Post M D TW

Po

st M

D T

WP

os

t M

D T

W

Pre M D TW

Pre

M D

TW

Pre

M D

TW

Pre M SW

Pre

M S

WP

re M

SW

Post M SW

Po

st

M S

WP

os

t M S

W

Correlation among water sources - salinity

Salinity correlations in Indian Sundarbans

Case 1: Inference from Paired Sample Correlation of pond water: There is a

high positive correlation between pre and post monsoon salinity of the

pond water implying that salinity can be reduced with management

Case 2: Inference from Paired Sample Correlation of Deep Tubewell water:

There is a weak positive correlation between pre and post monsoon salinity

of the deep tube well implying that the aquifer system is stable and salinity

is tends to constant in deeper aquifers

Case 3: Inference from Paired Sample Correlation of shallow well water:

There is a weak positive correlation between pre and post monsoon salinity

of the shallow well implying that salinity is stable in the shallow aquifers as

well

One Sample T-test on salinity variations

Statistical inference from above table is that pre and post monsoon salinity is

statistically higher than the normal salinity test value i.e., 1.1dS/m

therefore, implies that the water available from majority of the sources are

higher than the threshold limit for cultivation and to be used as drinking

purposes of human beings and livestock.

Also it implies long term exposure from these water sources may have

detrimental health effects

Economics of Surface Water Management

• Land use management cost for 0.34acre land is INR

90,000/- at current price

• Benefit out of the farming system is expect INR 65000-

70000/- per annum

• Break Even in one and half years

• Annual maintenance of the system is as low as INR

5000/-

• Benefit cost Ratio for the system is 2.88 per Ha

• Increase in cropping intensity for the system is

expected 30-35% per Ha

• Increase in total production is expected 35-40% per Ha

• Expected incremental net income per Ha is INR 2-2.5

Lacs

Credit:

PRASARI

Ground Water Stress and

Probable Solutions

• Increasing availability ofsurface water

• Aquifer storage and recoveryto increase sweet wateravailability in a saline aquifer

• Salinity management throughsurface water management

• Crop water management

• Desalination of river water andsupply through pipes (it is notpossible in Indian Sundarbansdue to high silt content in thewater)

• Protection of river embankment to reduce cyclone/flood impact

ASR in the coastal area; Source: BGS

Desalination in the coastal area;

Source: BGS

Why ASR?

• Water crisis during dry seasonslike Rabi and Pre-kharif

• Common Property Resources like-ponds and creeks available as afresh water source for ASR

• Trained para-hydrogeologists

• Agricultural patch of land undercommon property resources

• Availability of technical agenciesand required knowledge

• Community platform ready to takepart in the research

• State Govt. Departments ready toreplicate the model after fieldexperimentation

Modelling

Performance

• USGS SEAWAT model

• A mathematical

simulation of the

injection and recovery

cycle

• Run on an aquifer 20

metres thick – with a

well in centre of a 1 ha

block

• Pump in for 100 days,

rest for 50, pump out for

100 days

Experimental model developed at NIH

Economics of ASR

• Sweet water production

cost/ cost calculation for

ASR

• Cost of water harvesting

structure+ solar pump (0.1-

0.25HP)+ injection and

recovery pipes+ cost of

valves, taps etc = (annual

operation cost+ maintenance

cost) / annual total

freshwater production

• Benefit Cost Ratio per Ha =

160000 / 60000 = 2.66

Proposed benefits

Maximization of sweet water availability for

critical irrigation in summer and winter

Increased access to fresh drinking water

Storage of ground water in ASR against

adverse climatic conditions common in Indian

Sundarbans

Expected increase in cropping intensity

Expected increase in overall farm production

Improved net income of the farmers

Image: PRASARI

• Cadre of 12 para hydrogeologists, trained

• Comprehensive inventory and socio

hydrogeological

• Quarterly monitoring at 120 handpumps

• Water quality samples

• Interviews with drillers

ACKNOWLEDGEMENTS

Funding

IUKWC

NIH

Director, Dr. Sharad K. Jain

Sh. C.P. Kumar, Head, GWHD

IITM, Pune

Dr. S.K. Sahai, Ms. Priya Joshi

CEH, Wallingford

Dr. Harry Dixon , Dr. Sunita Sarkar

PRASARI

Mr. Saikat and 24 paragnas para hydrogeologists

ধন্যবাদ

Image: PRASARI

Thanks for your patience