Föredrag på Grundvattendagarna 2013

1. 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

2. • 50% of the world’s population concentrates to a 60km
wide coastal zone
• Huge water stress along the coastal zone
• Swedish coast stretches >2400km

3. 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)

4. Swedish climate changes?
Model for precipitation and temperature changes until 2100
Source: Rossby Center, SMHI 2012
There are several model scenaries pointing towards similar direction

5. 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

6. Changed number of days
per year with drough to
2100
Days/year
Källa: SMHI 2013

7. We will need to store water for
much longer periods than today
The question is where?.....

8. Swedish specific coastal problems
Bare rock outcrops
Small reservoirs
Concentration of houses
Bad existing sewage systems
High hydraulic heterogeneity
Water chemical problems (Cl, Rn, U, F)
Rapid flows
Increasing water demand
Attractive environment
200 m
Fertilization
Pollution
Coastal erosion

9. Areas with scarcity of
groundwater in
sweden (for water
supply with sufficient
quantity and quality)
Clay
Sand
(SGU 2009)
Till
Rock
Sand and gravel

10. A bedrock with high storage
capacity but sensitive to
seawater intrusion

11. Shear fracture, partly coated with minerals
The flow possibility of each
fracture depends on its
•genesis
•weathering conditions
•mineral filling
•rock stresses
•From top
•From side

12. Kinematic
porosity in
different units
Bedrock (0.001-0.05%)
Clay (0.01-0.1%)
Till (3-5%)
Well
Sand (10-40%)
Water (100%)
Well
0.001 - 0.05%
Shear fractures

13. Usually we
have limited
amount of
data,
especially
high quality
data
Uses data from
•SGU
•SMHI
•Lantmäteriet

14. Example of method for increasing the storage
called”groundwater dams”
Groundwater recharge
Draining tubes
Bentonite or
plastic liner
Dug or drilled well
Bedrock
Clay
Till or sand and gravel

16. Development of
methods to clarify
suitable places for
localization of
subsurface dams
Figure 10. Vulnerable zones (encircled) of Boda-Kalvsvik.
Based on water
balances and
aquifer deliniation
in GIS
Topographic Wetness Index (TWI) of Boda-Kalvsvik.

17. Shortage of groundwater,
often leads to deterioration
of groundwater quality
• Natural
Rn
NO3-
Bacteria
geological conditions (e.g.
metals, pH, radon, alkalinity…)
• Induced changes(e.g. salinization)
Na+• Pollutants (e.g. cadmium)
Cl-
Rn
ClNa+
Na+ Cl-
Baltic Sea

18. Älgö – Stockholm archipelago
Water supply
Sewage
What is the impact from sewage infiltration?

19. Soil volume for infiltration for 1 family (ca 500 l/d)
Sand
(1-2 m3/d)
Till
(15-20 m3/d)
=> big problems in
exploitational areas.
How can we get
turnover time of 60
days?
Bedrock (1500-2000 m3/d)

20. Example 1: Nitrate and ammonium
Development of a risk assessment scenario at e.g Tynningö
Ramsö
Tynningö

21. Vulnerability of nitrate pollution of wells

22. Example 2: Radon, radium and uranium
N
län (n=5666) N
Stockholm county
Radon content in
wells in the county
of Stockholm
11%
Radon risk areas
calculated using
kriging.
27%
20km
40km
15%
nnar i Stockholms
666)
%
>1000
(White areas have
too few wells)
0km
20km
40km
500-1000
100-500
<100
>1000
47%
N=5666
500-1000
100-500
<100
Rn conc. (Bq/L)
0 to 100
100 to 500
500 to 1000
1000 to 64000
1000
Rn (Bq/L)
0km
500
100
0

23. Testing of method
(median value)
RV-value
(2209 wells)
A high correlation observed
between median radon
concentration and median
RV- value.
Each point is representative of an area of 25 x 25 km2

24. Prediction of radon content in drilled wells using GIS
RV-method
Prediktion 2209 wells
FRV > 0
: Low risk
-5 < FRV < 0 : Medium risk
FRV < -5
: High risk

25. Example 3: Prediction of groundwater quality in private wells at Gotland
(Pirnia & Olofsson 2013)

26. Prediction of groundwater quality
Based on statistical analysis (ANOVA, PCA) using chemical
data, geological and topographical data
(Pirnia & Olofsson 2013)

27. Future research need related to water
supply in hard rock areas
There is a strong need for robust assessment methods for
planning and decision support locally and regionally
• 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)

28. 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)

29. Thanks