Sustainable water supply bo olofsson
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Transcript of Sustainable water supply bo olofsson
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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
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• 50% of the world’s population concentrates to a 60km wide coastal zone
• Huge water stress along the coastal zone
• Swedish coast stretches >2400km
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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)
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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?
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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
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Changed number of days per year with drough to 2100
Källa: SMHI 2013
Days/year
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We will need to store water for much longer periods than today
The question is where?.....
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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
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Areas with scarcity of groundwater in sweden (for water supply with sufficient quantity and quality)
(SGU 2009)
Rock
Till Clay
Sand
Sand and gravel
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A bedrock with high storage capacity but sensitive to seawater intrusion
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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
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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%
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Uses data from •SGU •SMHI •Lantmäteriet
Usually we have limited amount of data, especially high quality data
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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”
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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
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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)
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Water supply
Sewage
Älgö – Stockholm archipelago
What is the impact from sewage infiltration?
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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)
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Development of a risk assessment scenario at e.g Tynningö
Tynningö
Ramsö
Example 1: Nitrate and ammonium
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Vulnerability of nitrate pollution of wells
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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
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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)
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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
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Example 3: Prediction of groundwater quality in private wells at Gotland
(Pirnia & Olofsson 2013)
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Based on statistical analysis (ANOVA, PCA) using chemical data, geological and topographical data
Prediction of groundwater quality
(Pirnia & Olofsson 2013)
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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
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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)
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Thanks