Structure of the presentation

(1)

Unit 6:

Identification and assessment of pollution and uncertainties in

groundwater modelling

Hans Peter Nachtnebel

Inst. of Hydrology, Water Management and Hydraulic Engineering Hans_peter.nachtnebel@boku.ac.at

(2)

GW Pollution Risk H.P. Nachtnebel, IWHW-BOKU

Structure of the presentation

• Objectives

• Introduction and background

• Methodology

• Application

• Conclusion and results

(3)

Objectives

• A regional groundwater system suffers from nitrate pollution originating from different

sources

• The groundwater system is used for regional drinking water supply

• The pollution sources have to be identified and the spatio-temporal variability of nitrate

concentration has to be assessed to assist in water supply management

(4)

From exposure to dose: environmental transport processes

(5)

Introduction and background

(6)

Introduction and background

River Mur Project area Observation wells

Slovenija

(7)

Introduction and Background

Detailed analysis

(8)

Introduction and background

• A shallow alluvial aquifer along the river Mur is utilized for regional drinking water supply

• Intensive agricultural land use (corn, lifestock farming) has led to a continuous increase in

nitrate loads and subsequently to an increase of nitrate concentrations of the groundwater

• The concentrations show a large spatio-

temporal variabilty and the water managers would like to improve their knowledge about peaks in nitrate concentration

(9)

Tasks of the study

• Origin of pollutants

• Flow of pollutants

• Uncertainty in estimates of nitrate concentratrions

(10)

Available data

(11)

Hydrogeology of the groundwater system

Artesian wells Porous aquifer (gravel and sand) Porous aquifer (sand)

Tertiary sediments (silt)

(12)

Land use

(13)

Data base

• Groundwater table is regularly monitored at 26 stations in a biweekly interval

• Groundwater quality at 22 locations monthly 195 sampling points within the initial campaign

• Hydrogeological data

3 pumping test locations in the project area 8 from outside but near by

12 boreholes and geoelectric data

• Soil map, land use data

(14)

The project area

2 of several water supply wells in the region

(15)

Methodology

• Analysis of sampling data to identify sources

• Geostatistical analysis of concentration data

(16)

Methodology

• Analysis of sampling data to identify sources

• Geostatistical analysis of concentration data

• Application of a 2D-solute transport groundwater model

What could it help ?

Which additional conclusions can be drawn ?

(17)

Geostatistical analysis

• The nitrate concentrations vary in time and space

• The sampling points are quite irregularly distributed over the region

• The nitrate concentrations show a trend from North to South

• An extension of kriging „External Drift Kriging“ is applied

(18)

Detailed monitoring program

Within a monitoring campaign about 105 wells were sampled

Irregularly distributed (clustering) 25 wells are regularly monitored

(19)

Water quality monitoring

• Temperature

• Electrical conductivity

• NH4

• NO3-Nitrate

• NO2-Nitrite

• Phosphorous (diss., particulate, Ortho)

• TOC

• Diss. Oxygen

• Hardness of water

(20)

Some results from the monitoring program

Leaking Septic Tank or Sewer Fertilizer and manure application

Kalium

(21)

Some results from the monitoring program

Leaking Septic Tank or Sewer

Fertilizer and manure application

Kalium Nitrate

(22)

Geostatistical analysis

• Nitrate data from first sampling campaign were statistically analysed

• Declustering (Journel, 1983) was applied due to irregular locations (many stations within a short distance)

(23)

Geostatistical analysis

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(24)

Geostatistical analysis

• Kriging is a BLUE estimator and it provides both estimation of the expectation value

estimation of the uncertainty (estimation variance)

(25)

Geostatistical analysis

• The data set may exhibit a spatial trend Universal or External Drift Kriging

• The data set may exhibit spatio-temporal features

seasonal variograms plus trends

(26)

Geostatistical analysis

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Spatial trend

Index t because time dependent

(27)

Spatial distribution of nitrate concentration

April April September

(28)

Results from geostatistical analysis

Low uncertainty (14mg/l)²

High uncertainty (22-26 mg/l)²

(29)

Conclusions from the statistical analysis

• Pollution sources could be identified

• Nitrate pollution is highly variable in space and time

• The estimation uncertainty is very large

(30)

Conclusions from the statistical analysis

• Pollution sources could be identified

• Nitrate pollution is highly variable in space and time

• The estimation uncertainty is very large

• How could we improve our knowledge about the system ??

(31)

Flow and transport

• A groundwater model

– Types of groundwater systems – Flow equation

– Numerical solution

(32)

Types of groundwater systems

Environmental Risk Analysis Data Analysis

confined unconfined

(33)

Types of groundwater systems

porous

karstic

rock

(34)

Flow and balance equation

Environmental Risk Analysis Data Analysis

Q_z(t)

S(t)

Q_A(t)

x h

(35)

Application of a 2D solute transport groundwater model

• The Konikov-Bredehoeft model was used

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Groundwater flow equation

Changes in horizontal flows (left side) = storage and vertical flows (recharge or abstraction) (right side)

(36)

Application of a 2D solute transport groundwater model

• The Konikov-Bredehoeft model was used

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Groundwater flow equation

Transport equation of pollutants with concentration c

Dispersion introduces additional uncertainty

(37)

Flow and dispersion

Groundwater flow trajectory Particle movement follows the gradient of groundwater table and concentration gradient and

irregularities in the underground Thus particles injected at the same location exhibit different flow paths (yellow and black)

Speading plumes are generated

(38)

Parameter estimation

• Based on the local data plus from outside data variograms were estimated for

hydraulic conductivity K storage capacity S

Dispersion D

bottom layer or thickness of groundwater b heads (initial and boundary conditions) h

(39)

Parameter estimation

• Based on the few local data plus from outside data variograms were estimated for

hydraulic conductivity K, storage capacity S,

Dispersion D,

bottom layer or thickness of groundwater b, heads (initial and boundary conditions) h

• Due to limited data there is a large uncertainty in

(40)

Model calibration

• The model parameters (mainly K and S) are uncertain and can be changed within a

reasonable range to fit the observations

• Time series of heads and concentrations should be well reproduced

• The spatial pattern should be well reflected

(41)

Model calibration

• Observed heads and concentrations in time

LOC2

LOC1

(42)

Model calibration with respect to pattern

Contour lines of same concentration level From model

From observation (interpolation)

Ratio of overlapping area to outside areas

(43)

Uncertainty in model parameters

Reality (unknown) Observation

Interpolation

X Parameter Value

(44)

Uncertainty in model parameters

Reality (unknown) Observation

Interpolation Conditional simulation

X Parameter Value

We assume that measurements are perfect

(45)

Conditional simulation

• Hundreds of different bottom layers b, hydraulic conductivity fields K, and initial conditions are generated C(x,y,t=0), h(x,y,t=0)

• All of them have the same probability and fit the observations

• Each input results in a different GW model output

(flow field and concentration pattern)

(46)

Results from simulation

• For each grid point we get hundreds of NO3 time series

• We can estimate the uncertainty in the model output

• We can see the importance (significance) of an input parameter for the output (sensitivity)

(47)

Estimation uncertainty of nitrate (mg/l)

²

in the gw-model

4 3 2 9 8

7 6

(48)

Estimation uncertainty of nitrate (mg/l)

²

in the gw-model

8 9 10 11

14 12 10

(49)

Comparison of geostatistics with model output (mg/l)

²

14 12 10

22 26

14

(50)

Conclusions

• The overall uncertainty in nitrate concentrations is larger in the geostatistical analysis than in the GW-model output

• Why ?

(51)

Conclusions

• The overall uncertainty in Nitrate concentrations is larger in the geostatistical analysis than in the GW-model output

• Why ?

• We have included additional information (data and knowledge e.g. flow and transport model)

(52)

Conclusions

• Here, heads (initial and boundary conditions) are the largest source of uncertainty

• Then hydraulic conductivity

• Bottom layer information is not so relevant (smaller uncertainty)

(53)

Assesing the impacts

• Until now the probability of exceeding a pollution level has been estimated

• Possible consequences:

– New wells have to be drilled

– Water purification systems have to be developed – Water transfer from another region

– ….

(54)

Summary

• A groundwater pollution problem was analysed by geostatistical methods and by a physically based approach

• Estimates of pollution level as well as the respective uncertainties are available

• The incorporation of a model had reduced the overall uncertainty

(55)

Structure of the presentation

Unit 6:

Identification and assessment of pollution and uncertainties in

groundwater modelling

Structure of the presentation

Objectives

Introduction and background

Introduction and background

Introduction and Background

Introduction and background

Tasks of the study

Available data

Hydrogeology of the groundwater system

Land use

Data base

The project area

Methodology

Methodology

Geostatistical analysis

Detailed monitoring program

Water quality monitoring

Some results from the monitoring program

Some results from the monitoring program

Geostatistical analysis

Geostatistical analysis

 

 

   

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Geostatistical analysis

Geostatistical analysis

Geostatistical analysis

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Spatial distribution of nitrate concentration

Results from geostatistical analysis

Conclusions from the statistical analysis

Conclusions from the statistical analysis

Flow and transport

Types of groundwater systems

Types of groundwater systems

Flow and balance equation

Application of a 2D solute transport groundwater model

Application of a 2D solute transport groundwater model

Flow and dispersion

Parameter estimation

Parameter estimation

Model calibration

Model calibration

Model calibration with respect to pattern

Uncertainty in model parameters

Uncertainty in model parameters

Conditional simulation

Results from simulation

Estimation uncertainty of nitrate (mg/l)

in the gw-model

Estimation uncertainty of nitrate (mg/l)

in the gw-model

Comparison of geostatistics with model output (mg/l)

Conclusions

Conclusions

Conclusions

Assesing the impacts

Summary

Thank you for your attention

_   

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