Sustainable water supply bo olofsson

Sustainable water supply in Swedish coastal areas – possibilities and challenges

Bosse Olofsson Royal Institute of Technology, KTH

NGL Annual Meeting at Äspö 2013-11-07

• 50% of the world’s population concentrates to a 60km wide coastal zone

• Huge water stress along the coastal zone

• Swedish coast stretches >2400km

Climate change (IPCC 2013)

• Locally higher precipitation

• >2oC increase in temperature to 2100

• Dry periods occur more often

• Longer dry periods

• Most energy stored in sea

• Sea level rise (>3.2 mm/year) (IPCC 2013)

Model for precipitation and temperature changes until 2100 Source: Rossby Center, SMHI 2012

There are several model scenaries pointing towards similar direction

Swedish climate changes?

Climate change in Sweden 2050- • Increased prec.(but at least bigger variations)

• Increased evapotranspiration

• Longer vegetation season

• Longer periods of drought

• Increased competition of water

• Increased costs for water treatment

Changed number of days per year with drough to 2100

Källa: SMHI 2013

Days/year

We will need to store water for much longer periods than today

The question is where?.....

200 m

Small reservoirs

Concentration of houses

Bad existing sewage systems

Rapid flows

Increasing water demand

Attractive environment

Swedish specific coastal problems

Fertilization

Pollution

Coastal erosion

Water chemical problems (Cl, Rn, U, F)

Bare rock outcrops

High hydraulic heterogeneity

Areas with scarcity of groundwater in sweden (for water supply with sufficient quantity and quality)

(SGU 2009)

Rock

Till Clay

Sand

Sand and gravel

A bedrock with high storage capacity but sensitive to seawater intrusion

Shear fracture, partly coated with minerals

•From side

•From top

The flow possibility of each fracture depends on its •genesis •weathering conditions •mineral filling •rock stresses

Bedrock (0.001-0.05%)

Till (3-5%)

Clay (0.01-0.1%)

Water (100%)

Sand (10-40%)

Well

Well

Shear fractures

Kinematic porosity in different units

0.001 - 0.05%

Uses data from •SGU •SMHI •Lantmäteriet

Usually we have limited amount of data, especially high quality data

Till or sand and gravel

Draining tubes

Dug or drilled well

Bedrock

Clay

Bentonite or

plastic liner

Groundwater recharge

Example of method for increasing the storage

called”groundwater dams”

Figure 10. Vulnerable zones (encircled) of Boda-Kalvsvik.

Topographic Wetness Index (TWI) of Boda-Kalvsvik.

Development of methods to clarify suitable places for localization of subsurface dams

Based on water balances and aquifer deliniation in GIS

Na+

Na+

Cl-

Cl-

Cl- Na+

NO3-

Rn

Rn

Bacteria

Baltic Sea

Shortage of groundwater, often leads to deterioration

of groundwater quality

• Natural geological conditions (e.g. metals, pH, radon, alkalinity…) • Induced changes(e.g. salinization) • Pollutants (e.g. cadmium)

Water supply

Sewage

Älgö – Stockholm archipelago

What is the impact from sewage infiltration?

Bedrock (1500-2000 m3/d)

Till (15-20 m3/d)

Sand (1-2 m3/d)

=> big problems in exploitational areas. How can we get turnover time of 60 days?

Soil volume for infiltration for 1 family (ca 500 l/d)

Development of a risk assessment scenario at e.g Tynningö

Tynningö

Ramsö

Example 1: Nitrate and ammonium

Vulnerability of nitrate pollution of wells

0km 20km 40km

Radon content in wells in the county of Stockholm

N

Rn conc. (Bq/L)

0 to 100

100 to 500

500 to 1000

1000 to 640000

100

500

1000

Rn

(B

q/L

)

0km 20km 40km

Radon risk areascalculated usingkriging.

N

(White areas havetoo few wells)

Rn innehåll (Bq/l) i brunnar i Stockholms

län (n=5666)

11%

15%

47%

27%>1000

500-1000

100-500

<100

Rn innehåll (Bq/l) i brunnar i Stockholms

län (n=5666)

11%

15%

47%

27%>1000

500-1000

100-500

<100

N=5666

Stockholm county Example 2: Radon, radium and uranium

Testing of method (2209 wells)

Each point is representative of an area of 25 x 25 km2

A high correlation observed between median radon concentration and median RV- value.

RV

-val

ue

(me

dia

n v

alu

e)

Prediktion 2209 wells

Prediction of radon content in drilled wells using GIS

FRV > 0 : Low risk

-5 < FRV < 0 : Medium risk

FRV < -5 : High risk

RV-method

Example 3: Prediction of groundwater quality in private wells at Gotland

(Pirnia & Olofsson 2013)

Based on statistical analysis (ANOVA, PCA) using chemical data, geological and topographical data

Prediction of groundwater quality

(Pirnia & Olofsson 2013)

Future research need related to water supply in hard rock areas

• How to estimate storage and capacity without extensive drilling

• How to get a measure of heterogeneity and anisotropy without extensive test pumping

• How to characterize groundwater chemical quality, origin and turnover time with limited amount of data

• How to deliniate bedrock aquifer extension and set boarder conditions with sparce of data

• How to differentiate origin of compounds with many different sources (chloride, radon, lead, arsenic)

There is a strong need for robust assessment methods for planning and decision support locally and regionally

Concluding strategy

• We are convinced that the best way to develop models and techniques for generalized estimations of groundwater resources using sparce of data is to develop and test such models where there are lots of data available, such as the NGL (a.o stored in SICADA)

Thanks