Groundwater Management in Oman by Water Metering:

Adoptive Research

WSTA 11th Gulf Water Conference "Water in the GCC...Towards Efficient Management"

Al-Bustan Hotel, Muscat, Sultanate of Oman 20-22 October 2014

S. Zekri*; K. Madani**; E. Kalbus***; H. Jayasuriya*; R. Zaier** Sultan Qaboos University, Muscat, Oman

** Imperial College of London*** German University of Technology, Muscat, Oman

WSTA 2014 1

Sultanateof

OMAN

Al-Batinahregion

53% of cropped area

Depends on groundwater

Limited recharge

Excessive pumping

Seawater intrusion

WSTA 2014 2

Ground water supplies in Oman

WSTA 2014

Source: The Ministry of Regional Municipalities and Water Resources, 2013

Figure Error! No text of specified style in document.-1: Wells licenses issued by the MRMWR during the period 2000-2010 all over the Sultanate

3564

2543

3926

2814

7129

4078

20252505

50205754

2780

0

1000

2000

3000

4000

5000

6000

7000

8000

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Number of licenses

3

Well licenses, Ground water deficit and Cropped area, in Al Batinah

Sources: Water Balance Computation for the Sultanate of Oman (2014) Final Report May 2013, Ministry of Regional Municipalities and

Water Resources, Sultanate of Oman. And Agricultural Census of Oman 2004 and 2014, Ministry of Agricultural and Fisheries

Wealth, Sultanate of Oman.

WSTA 2014

Wells licensed (2000-2010)

Ground Water Deficit MCM

(2011) Land Cropped (Feddans)

5448 -3472005 2011 Change

141432 134103 -7329

4

Wells and Salinity Association

WSTA 2014 5

WSTA 2014 6

Observed Salinity 1974 - 2010

2005

2010

2005

1974

2.0 km

3.0 km

3.4 km

salinity observationsWSTA 2014 7

Salinity effects all farmers

WSTA 2014 8

Consequences over-extraction of ground water

• Present: For self and others (intra-generational externality)

– Decreased yield– Increased cost of pumping– Reduced profits

• Future: (inter-generational externality)– Same as above and loss of nature and biodiversity

WSTA 2014 9

– subsidy program of irrigation modernization – a freeze on drilling new wells– several no-drill zones – crop substitution program – construction of recharge dams – re-use of treated wastewater

WSTA 2014 10

Water use and agricultural sustainability in Al Batinah

Oman Salinity Strategy (OSS) (2012) Main Report and Annexes, Ministry of Agriculture and Fisheries, Sultanate of Oman and

International Center For Biosaline Agriculture (ICBA) Dubai, UAE.

• Ground water reserves: 2880 Mm3 (OSS Annex1, pg.37)

• Current (2010) water abstraction: 578 Mm3/Year (OSS Annex1, pg.47)

• Sustainable extraction (aquifer recharge): 305 Mm3/Year (OSS Annex1, pg.47)

• How long can agriculture sustain?

WSTA 2014 11

Simulated optimal extent of land to be cultivated under different levels of water abstraction:

Al BatinahScenario Quantity of water

(Mm3/Year) OSS (2012) reccomendation

Technically Optimal land extent (Feddans) Crop water use as OSS

(2012)

Economically Optimal land

extent (Feddans)

Land extent 2011 (Feddans)

Current water abstraction

578 68826 87543 134103

Sustainable extraction 305 36381 43657

Sustainable extraction + water augmentation

409 48787 61959

Recommendation of OSS (2012) to reduce present water abstraction by 36%.

370 45036 55017

WSTA 2014 12

Present Farm Irrigation Practices

• Subjective decisions about when and how much to irrigate causing inefficiency• Smart irrigation will improve efficiency through automation based on field

sensors determining irrigation requirement

WSTA 2014 13

Possible Solution to Manage Groundwater

• Collective institutions• Individualized institutions

• Water metering facilitates both

WSTA 2014 14

Water metering as a solution

• In 1995 a regulations were introduced for GW quotas • Early studies suggested that:

– cost of traditional flow metering was prohibitive and that cheating is easy

– majority of Omani farmers have been found to be open to quotas, provided that:

• groundwater remains free of charge • the quota covers the crops’ water requirement • the quota is enforced without favoritism

• In this project: To overcome the major practical challenges of metering, smart electricity-water meters have been equipped to facilitate monitoring GW use without creating a financial burden.

WSTA 2014 15

Objectives of the Project

• Analyze effect of– Use of water meters and SI irrigation / fertilization

system on • groundwater resources and • farmer’s profits.

WSTA 2014 16

Method

• Since June 2013, 40 farms were equipped with water meters and SI irrigation / fertilization system and have monitored– water use– farm profit

Compared with (Optimal) based ongroundwater flow and transport model

MODFLOW / SEAWAT+

Dynamic Optimization model GAMS

WSTA 2014 17

WSTA 2014 18

Field experimental design40 Farms to be selectedSmart Water Meters to be installed

Tree crops

Vegecrops

Forage crops

EACH FARM CONTAINING

Selection of farms40 Farms

Automated Irrigation & Fertigation

Automated Irrigation Forage

cropsFlooded

irrigation –no

automation

Modern irrigation –

no automation

Data collection mechanism

SERVER

Role of SI water on pumping and productivity?

• Wireless Automated SI System developed based on a network of moisture sensors, temperature sensors and electro-valves distributed at farm level.

• Tested in Lab and in Univ. Farm • Cost: $3,600/ha. Which is 1/5th of commercial cost

• 14 Farms are being equipped since August 2014• 8 farms with fert-igation system

WSTA 2014 22

Optimization Model

• A hydro-economic model that couples an aquifer MODFLOW-SEAWAT model and a dynamic profit maximization model using GAMS

• Long-term simulation-optimization• Relatively low run-time and high accuracy for a large-scale

model• Optimization model features:

– Profit maximization under land and water constraints– Salinity is included in model– Can be run for different management institutions

• non-cooperative, • cooperative,

WSTA 2014 23

Model’s size and computation

• Gams model: A matrix with 2 Million Rows by 3.5 Million Columns and 20 Million Non-zero variables solved within 18 Minutes.

• The SEAWAT also takes 27 Minutes to simulate the model for 60 years.

WSTA 2014 24

Tested Scenarios

1. Business As Usual (BAU): current pattern and allocation of cropping continues without change (simulation)

2. Cooperative/ Central Planner Model (CPM): Long-term optimization with “perfect foresight” into consequences of pumping on salinity.

3. Non-Cooperative/Agent Based Model (ABM): Annual profit optimization with ex-post partial information on salinity

4. Exogenous Regulatory Interventions are being evaluated

WSTA 2014 25

172169

232

98

292

130

75

125

175

225

275

325

2015 2020 2025 2030 2035 2040 2045 2050 2055 2060 2065 2070 2075

Mill

ion

Cubi

c M

eter

s

Water Pumping

CPM2-Water Use ABM2-Water Use BAU-Water Use

WSTA 2014 26

6,800

5,200

5,000

4000

4500

5000

5500

6000

6500

7000

7500

8000

8500

2015 2020 2025 2030 2035 2040 2045 2050 2055 2060 2065 2070 2075

Hec

tare

s

Cropped Area

CPM2-Total Area ABM2-Total Area

BAU-Total Area

WSTA 2014 27

Results

729

472

560

100

200

300

400

500

600

700

800

900

2015 2020 2025 2030 2035 2040 2045 2050 2055 2060 2065 2070 2075

Prof

it in

Mlli

ons

$

Profit

CPM2-Profit $ ABM2-Profit $ BAU-Profit $

WSTA 2014 28

Lessons Learned

• Smart meters allowed online daily reading• at a low cost: $2/month communication cost• Resisted the high summer temperature 60° C.• Could be scaled up to the Suwaiq area with 8,000 farms

• Low cost Smart irrigation system in place• Results will require one more year

– How farmers will interact with the technology?– Maintenance and problem solving?– How fert-igation will affect productivity?

WSTA 2014 29

Way forward

• Combination of solutions for the next 20 years– Beach front land conversion to urban uses– Recharge with tertiary treated wastewater– Recharge dams– Water metering and Smart irrigation system

WSTA 2014 30

Acknowledgement and Thanks

• The Research Council of Oman for the funding of this project

• RSA electronics for the support, subsidy, provision and free installation and of the smart electricity meters.

• Co-investigators and technicians

WSTA 2014 31