4/22/2015
1
Nonpoint Source Pollution Assessment:Framework, Vulnerability Analysis, and Modeling
Thomas Harter
Outline• Driver: regulatory framework
• Understanding nonpoint source transport dynamics (v. point sources)
• Vulnerability assessment
• Scoring methods
• Statistical methods
• Modeling
Dubrovsky et al., USGS, 2010 Dubrovsky et al., USGS, 2010
Nitrate: Impacted regions within the Central Valley
red dots: wells above MCL for nitrate
CVSALTS, Tasks 7 and 8 – Salt and Nitrate Analysis for the Central Valley FloorFinal Report, December 2013
Figure 7-14
All Water Systems
Estimated locations of the area’s roughly 400 regulated community public and state-documented state small water systems and of 74,000 unregulated self-supplied water systems. Source: Honeycutt et al. 2012; CDPH PICME 2010.
4/22/2015
2
Regulating Water Pollution Sources
Surface Water Quality
Ground Water Quality
Point Sources of Pollution
Nonpoint Sources of Pollution
1970s ‐ nowClean Water Act:
NPDES Permits
Regulating Water Pollution Sources
Surface Water Quality
Ground Water Quality
Point Sources of Pollution
Nonpoint Sources of Pollution
1970s ‐ nowClean Water Act:
NPDES Permits
2000s ‐ nowClean Water Act:
TMDL
Regulating Water Pollution Sources
Surface Water Quality
Ground Water Quality
Point Sources of Pollution
Nonpoint Sources of Pollution
1970s ‐ nowClean Water Act:
NPDES Permits
2000s ‐ nowClean Water Act:
TMDL
1980s ‐ nowSuperfund, TSCA, RCRA, FIFRA
RCRA Groundwater Monitoring
• Affected parties:
o TSDFs (transport, storage, and disposal facilities)• Permitted facilities vs. Interim facilities (existed prior to RCRA rules)
o MSWFs (municipal solid waste landfills)
• Detection monitoring
o 1 or more monitoring wells upgradient
o 3 or more monitoring wells downgradient
o Objective: SSI (statistically significant increase)?
• Compliance monitoring / Assessment monitoring
o Objective: groundwater protection standards exceeded?
• Corrective Action
o Treatment
o Cleanup
o Cease and desist
o ….
http://www.epa.gov/osw/hazard/tsd/td/ldu/financial/gdwater.htmhttp://www.epa.gov/solidwaste/nonhaz/municipal/landfill/financial/gdwmswl.htm
Regulatory Approaches toGroundwater Protection and Monitoring
Modified from: EOS, Transactions, AGU 2001
Regulatory Approaches toGroundwater Monitoring
from: Parker, Beth L., Cherry, John A. & Swanson, Benjamin J., 2006. A Multilevel System for High-Resolution Monitoring in Rotasonic Boreholes.Ground Water Monitoring & Remediation 26 (4), 57-73.doi: 10.1111/j.1745-6592.2006.00107
from: http://www.ems-i.com
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3
Regulating Water Pollution Sources
Surface Water Quality
Ground Water Quality
Point Sources of Pollution
Nonpoint Sources of Pollution
1970s ‐ nowClean Water Act:
NPDES Permits
2000s ‐ nowClean Water Act:
TMDL
1980s ‐ nowSuperfund, TSCA, RCRA, FIFRA
Monitoring w/ Monitoring Wells
???
Modified from: EOS, Transactions, AGU 2001
Farm Contaminant Sources: Regional Scale
• Source of N (2007) in CA:o Fertilizer use (varies with farm / farming practices) 740,000 tons
o Animal Manure 240,000 tonso Septic leach fields 27,000 tons
o Irrigation water source & mgmt.
o Treated municipal effluent 31,000 tons
Farm Contaminant Sources: Farm Scale
• Sources of N:o Feedloto Lagoono Storage areaso Manured fieldso Fertilized fieldso Various cropso Septic system
Dairy Farm Contaminant Sources: Management Units
Farm Sources: Field Scale
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4
Why is Nonpoint Source Pollution Different from Point Source Pollution of Groundwater?
• Scale
o Millions of acres vs. 1‐10 acres
• Intensity
o Within ~1 order magnitude above MCL vs. many orders of magnitude
above MCL
• Hydrologic Function
o Recharge vs. non‐leaky
• Frequency
o Ongoing/seasonally repeated vs. incidental
• Heterogeneity & Adjacency
CHALLENGE 1: there is ALWAYS recharge in agriculture, EVERYWHERE
r=0
q
Non-recharging source, incidental release
q
Recharging source: planned/frequent release
r
Target:UST
Target:Agriculture/Land Application
Source Area of a Monitoring Wellin a Recharge Area
Aquifer hydraulic conductivity: K
Slope of the water table: i
Monitored source length, s = d * q/r
Horizontal flow: q = K * i (Darcy’s law)
Vertical flow: r (recharge)
s
r
qd
Source Area of a Monitoring Well
Source Area of a Monitoring Wellin a Recharge Area
Horizontal flow: q = K * i (Darcy’s law)
Vertical flow: r (recharge)
Monitored source length, s = d * q/r
• Recharge rate, r: 1 ft/yr = 0.003 ft/d
• Horizontal gradient, i: 0.3% = 0.003
• Length of screen below water table, d: 20 ft
• K (ft/d) ‐ q (ft/d) ‐ s (ft)
1 0.003 2010 0.03 20020 0.06 40050 0.15 1,000
100 0.3 2,000 500 1.5 10,000
Monitoring Design for Varying Water Table Depth
(a) water level high
(b) water level intermediate
(c) water level deep
gw flow
source area
source area
source area
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CHALLENGE: There is NO floating product – redefine “First Encountered Groundwater”
regionalgw flow
monitored source area (a few feet long)
(a) Screen (length ~ 20’) located at water table, but not intersecting sand layer
(b) Screen (length ~ 20’) located in sand layer
regionalgw flow
monitored source area (several hundred feet long)
loamy clay
sand
sand
sand
regionalgw flow
MW Well Design: Varying Water Table in Heterogeneous Aquifer
regionalgw flow
monitored source area
(a) Screen (length ~ 20’) located at water table, but not intersecting sand layer
(b) Screen (length ~ 20’) located in sand layer
regionalgw flow
monitored source area
loamy clay
sand
sand
sand
regionalgw flow
CHALLENGE: There is NO PLUME to chase – nitrate, nitrate everywhere, all the time!......
• Regional variations in landuse / hydrogeology
• Farm‐to‐farm differences in management
• 1 Farm = Many Sources (Management Units)
• Within‐source/field variability (soil, irrigation)
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Farm Groundwater Monitoring:Tile Drain Monitoring
V7
V8
V9
W9
W8
V1
V2
V4
V5
V6
W1
W2
W3
W4
W5
W6
W7
W27
W28
W29
V10
W31
W30
V24
V22
W11
W10
W12
V20
V21
V19V15
V16
V17
V18
V14
V13
W23
W22
W17
W16
W18
W19W15
W14
V25 Average N of two tile drains
Nov
-19
95
Fe
b-1
99
6M
ay-1
99
6Ju
l-19
96
Se
p-1
99
6Ja
n-1
99
7A
pr-
199
7Ju
l-19
97
Oct
-19
97
Jan
-19
98
Fe
b-1
99
8M
ay-1
99
8Ju
n-1
99
8A
ug
-19
98
Nov
-19
98
Fe
b-1
99
9A
pr-
199
9Ju
n-1
99
9S
ep
-19
99
Dec
-19
99
Ap
r-20
00
Jul-
200
0O
ct-2
00
0Ja
n-2
00
1A
pr-
200
1Ju
l-20
01
Oct
-20
01
Jan
-20
02
Ap
r-20
02
Jul-
200
2S
ep
-20
02
Jan
-20
03
Ap
r-20
03
sampling date
20
30
40
50
60
70
80
90
100
tota
l N [m
g/l]
Mean
Average N of monitoring wells on dairies with tile drains
No
v-1
99
5F
eb-1
99
6M
ay-
19
96
Jul-
19
96
Sep
-19
96
Jan
-19
97
Ap
r-1
99
7Ju
l-1
99
7O
ct-1
99
7Ja
n-1
99
8F
eb-1
99
8M
ay-
19
98
Jun
-19
98
Aug
-19
98
No
v-1
99
8F
eb-1
99
9A
pr-
19
99
Jun
-19
99
Sep
-19
99
De
c-1
99
9A
pr-
20
00
Jul-
20
00
Oct
-20
00
Jan
-20
01
Ap
r-2
00
1Ju
l-2
00
1O
ct-2
00
1Ja
n-2
00
2A
pr-
20
02
Jul-
20
02
Sep
-20
02
Jan
-20
03
Ap
r-2
00
3
sampling date
20
30
40
50
60
70
80
90
100
tota
l N [m
g/l]
Mean
Wells Drains
Source Area of a Barn / Irrigation Well
source area
domestic well
regional gradient
recharge
effective gw flow direction
Domestic Well Monitoring
source area
barn well /irrigation well regional gradient
recharge
effective gw flow direction
Production Well Monitoring
Cross-section
2 milesx
200 ft
Plan-view
2 milesx
2 miles
Source Area of a Barn / Irrigation Well
• Water flow is horizontal & vertical
• Horizontal travel distances are generally MUCH longer than travel vertical distances
• Different depths of the well screen capture different water!
Source Area of a Barn / Irrigation Well
• Water flow is horizontal & vertical
• Horizontal travel distances are generally MUCH longer than travel vertical distances
• Different depths of the well screen capture different water!
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Source Area of a Barn / Irrigation Well
• Water flow is horizontal & vertical
• Horizontal travel distances are generally MUCH longer than travel vertical distances
• Different depths of the well screen capture different water!
Source Area of a Barn / Irrigation Well
Deeper Groundwater= Older Groundwater
Recharge Rate (color map) & Reverse Particle Paths from 100m wellpoints
Groundwater Age [Years]at 30 m (top), 100 m (bottom)
Future Impacts Likely Increase:Delay of Impact due to GW Age
Age at 100 ft (30 m) depth
Age at 300 ft (100 m) depth
Harter et al., 2009
Measured Groundwater Age in Multilevel Groundwater WellsTritium/Helium‐3 Groundwater Age (2‐20 yrs)
one-half mile
N
1
2
3
4
5
6
7
1B: 2 yr
2B: 5 yr
3B: 20 yr
6B: <2 yr
4: 4-20 yr
The DairyDTW: 80-120 ft bgsWells: multi-level
Observations Young groundwater present Age increases with depth in
both multi-level wells & across the site
No significant saturated-zone denitrification in monitor wells Courtesy, Brad Esser & Jean Moran, LLNL, 2009
Regulating Water Pollution Sources
Surface Water Quality
Ground Water Quality
Point Sources of Pollution
Nonpoint Sources of Pollution
1970s ‐ nowClean Water Act:
NPDES Permits
2000s ‐ nowClean Water Act:
TMDL
1980s ‐ nowSuperfund, TSCA, RCRA, FIFRA
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Focus: Enforcement Monitoring
Example of Working with a Regulation: Speed Limit
Management Tool:Brakes
Feedback:Speedometer
Enforcement:Radar Controls
Responsible Party:Driver
Focus: Enforcement Monitoring
Applying Point Source Approach to Nonpoint Source:
Management Tool:$$$ “agronomic”
Feedback:missing
Enforcement:Monitoring Wells
Responsible Party:Landowner
Focus: Enforcement Monitoring
Alternative Monitoring Approach to Nonpoint Source:
Management Tool:Water and Nutrient Management
Feedback:Nutrient/Water Monitoring
& Assessment
Enforcement:Annual Nitrogen Budget
+Management Practice
Assessment+
Regional Trend Monitoring
Responsible Party:Landowner
Regulating Water Pollution Sources
Surface Water Quality
Ground Water Quality
Point Sources of Pollution
Nonpoint Sources of Pollution
1970s ‐ nowClean Water Act:
NPDES Permits
1980s – nowCA pesticide contamination
prevention act 2010s ‐ futureCA Porter‐Cologne:
Dairy OrderILRP/Ag OrdersCV‐SALTS
2000s ‐ nowClean Water Act:
TMDL
1980s ‐ nowSuperfund, TSCA, RCRA, FIFRA
Source Area of a Barn / Irrigation Well
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Assessment: Field Trials & Modeling Transport and Fate of Nitrate and Salts=> improved management practices
0
200
400
600
800
1000
1200
1993 1994 1995 1996 1997 1998 1999 2000
Crop Year
lbs
N p
er a
cre
fertilizer N (commercial)
org-N (manure)
NH4-N (manure)
Plant N uptakeN surplus
106 mg/l NO3-N
43 mg/l
10 mg/l
0
20
40
60
80
100
120
140
5/1
/93
10
/30
/93
5/1
/94
10
/31
/94
5/1
/95
10
/31
/95
5/1
/96
10
/30
/96
5/1
/97
10
/31
/97
5/2
/98
10
/31
/98
5/2
/99
11
/1/9
9
5/1
/00
10
/31
/00
Sampling Date
Ave
rag
e sh
allo
w g
rou
nd
wat
er n
itra
te-N
[m
g/l]
Measured
Modeled
VanderSchans et al., Ground Water, 2009for further publications: http://groundwater.ucdavis.edu/gw_201.htm
Focus: Enforcement Monitoring
Alternative Monitoring Approach to Nonpoint Source:
Management Tool:Water and Nutrient Management
Feedback:Nutrient/Water Monitoring
& Assessment
Enforcement:Annual Nitrogen Budget
+Management Practice
Assessment+
Regional Trend Monitoring
Responsible Party:Landowner
Groundwater Assessment / Vulnerability Analysis => prioritize planning/enforcement
Vulnerability Analysis: Overview Vulnerability Analysis: Some Modeling Examples
Another Vulnerability Scheme: Nitrate Hazard Index
Dzurella, Pettygrove et al.,Journal Soil Water Conservvation, 2015
Based on:
SoilCropIrrigation
Nitrate Contamination Study Area
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Explaining the Mass Balance Approach to Estimate N Leaching to Groundwater
Synthetic fertilizer N+
Wastewater effluent N+
Biosolids N+
Dairy manure N+
Atmospheric deposition N+
Irrigation water N
=
Atmospheric losses N+
Harvested N+
Surface runoff N+
Leaching N to groundwater+
Storage Change in N in root zone
Mass balance requires that:
Explaining the Mass Balance Approach to Estimate N Leaching to Groundwater
Synthetic fertilizer N+
Wastewater effluent N+
Biosolids N+
Dairy manure N+
Atmospheric deposition N+
Irrigation water N
=Atmospheric losses N
+Harvested N
+Surface runoff N
After setting “storage change in N” to zero and rearranging the mass balance equation, we obtain the following formula to estimate N leaching to groundwater:
Leaching N to
groundwater-
+ +
+ +
+
-
=
+● 0.9
Scale:50 m
50 m (1 acre) scale
1940 1950 1960 1970 1980 1990 2000 2010
1M ac
2M ac
3M ac
4M ac
110,000
220,000
330,000
440,000
Cropland Area
Cropland Area(without Alfalfa)
tons N/yr
study area scalePrevious Slide: spatial resolution ‐ 50 m x 50 m (~1 acre)Below: spatial resolution ‐ study area total
Irrigation water
Atmosphere
SyntheticFertilizer
Biosolids
Effluent
Poultry, Swine
Dairy Manure
Atmosphere
Runoff
Leaching to Groundwater
Harvest
18
Total Nitrogen Inputs:420,000 tons N/yr
Total Nitrogen Outputs:420,000 tons N/yr
Scale:Study Area
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11
Role of thick unsaturated soils/sediments in nonpoint source transport & travel time
Alluvial Fan Stratigraphy
Modeling Approach
• Model 2 “Heterogeneous ‐SF”
– Given the information for the spatial correlation structure of the scaling factors, a value of scaling factor at each grid is generated
Results (Velocity)
Homogeneous Hetero - SF Hetero - VG
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Results (Conc.)
Homogeneous Hetero - SF Hetero - VG
Nitrate in a 16 m thick alluvial unsaturated zoneFresno, California
Results (High subplot in kg N)
Homogeneous Hetero - SF Hetero - VG
Input N 2400 2400 2400
Root Uptake 835 838 827Leaching to GW 956 839 916
Stored in RZ 93 95 89Stored in Deep VZ 515 565 543
Measured N = 87 Kg/ha
Predicted N (MB) = 478 kg/ha
• Depth to the water table+
• Cropland water budgets (deep percolation)+
• Soil type d
Vadose Zone Residence Time of Nitrate
Vadose Zone Travel Time Vadose Zone Travel Time
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Nonpoint Source Groundwater Modeling:Key Challenge (why MODPATH is not enough)
spatio‐temporally distributed sources:loading to water table
spatially distributed sinks: wells
High Resolution Flow Field
adaptive mesh grid refinement
Kourakos et al., Water Resour. Res, 2012Kourakos and Harter, Env. Simulation, 2014Kourakos and Harter, Comp. Geosciences, 2014
Adaptive Mesh Refinement Finite Element Grid
Kourakos et al., Water Resour. Res, 2012Kourakos and Harter, Env. Simulation, 2014Kourakos and Harter, Comp. Geosciences, 2014
Water Table Distribution
Kourakos et al., Water Resour. Res, 2012Kourakos and Harter, Env. Simulation, 2014Kourakos and Harter, Comp. Geosciences, 2014 Kourakos and Harter, 2012, 2014, 2014
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Streamlines: Coarse vs. Fine Discretization
COARSEFINE
“NPS Assessment Tool”
Kourakos et al., WRR, 2012
“mSim” NPS Assessment Tool
Matlab code available at:
http://groundwater.ucdavis.edu/mSim(to be updated soon with adaptive mesh refinement code)
TRILINOS
Adaptive Mesh Refinement (DEAL.II)
Kourakos et al., Water Resour. Res, 2012Kourakos and Harter, Env. Simulation, 2014Kourakos and Harter, Comp. Geosciences, 2014
Unit Response Functions: Examples
Source Loading + Transfer Function to Wells =Simulated Nitrate Concentration History
spatio‐temporally distributed sources:loading to water table
spatially distributed sinks: wells
Validation
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Predictions UsingGroundwater Nitrate Loading
Exceedance Probability,Nitrate above 45 mg/L (MCL)
Eastern Tulare Lake Basin
Deep Percolation of Salinity
N
San Joanquin river
Alluvial Fan Boundary
Location of study area.
A
A’
Zhang, 2005; Zhang, Harter, and Sivakumar, WRR 2006
Hydrogeologic cross section of study area [Belitz and Phillips,1995]
Zhang, 2005; Zhang, Harter, and Sivakumar, WRR 2006
1
2
3
45
1—upper fan 2—inter-fan3—distal fan II4—distal fan I5—Sierran Sand
Multi-unit division of the study area.
Zhang, 2005; Zhang, Harter, and Sivakumar, WRR 2006 Zhang, 2005; Zhang, Harter, and Sivakumar, WRR 2006
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Upper fan realization.
Zhang, 2005; Zhang, Harter, and Sivakumar, WRR 2006 Zhang, 2005; Zhang, Harter, and Sivakumar, WRR 2006
-1
-0.5
0
0.5
1
1.5
0 500 1000 1500 2000
time
cum
ula
tive d
istr
ibution c
urv
e
averaged breakthroughcurve of wells in upfanmean+1.96stdev
mean-1.96stdev
-1
-0.5
0
0.5
1
1.5
0 500 1000 1500 2000
averaged breakthrough curve of wells ininterfanmean+1.96stdev
mean-1.96stdev
-0.5
0
0.5
1
1.5
0 500 1000 1500 2000
averaged breakthrough curve ofwells in distal fan Imean+1.96stdev
mean-1.96stdev
-0.5
0
0.5
1
1.5
0 500 1000 1500 2000
averaged breakthrough curve ofwells in distal fan Imean+1.96stdev
mean-1.96stdev
-0.5
0
0.5
1
1.5
0 500 1000 1500 2000 2500
averaged breakthrough curve ofwells in Sierra Sandmean+1.96stdev
mean-1.96stdev
0
100
200
300
400
500
0 50 100 150 200 250
depth of top screen
arri
val
tim
e o
f fi
rst
10%
mas
s
upfan
inter-fan
distal fan I
distal fan II
Sierra Sand
0
100
200
300
400
500
0 30 60 90 120
length of well screen
arri
val
tim
e o
f fi
rst
10%
mas
s
upfan
inter-fan
distal fan I
distal fan II
Sierra Sand
Zhang, 2005; Zhang, Harter, and Sivakumar, WRR 2006
Breakthrough curves of salinity: probability of arrival
Zhang, 2005; Zhang, Harter, and Sivakumar, WRR 2006
Microbial nonpoint source pollution
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Fallow Field During Pre‐Irrigationwith Manure
Upgradient monitoring well
Downgradient monitoring well
Groundwater flow direction
Fallow Field During Pre‐Irrigationwith Manure
Upgradient monitoring well
Downgradient monitoring well
Groundwater flow direction
Conceptual Model
Transport Equation
Advection(Flow)
Filtration
Dispersion
Modeling Source Loading
HOMOGENEOUS
HETEROGENOUS:GAUSSIAN
Modeling Source Loading
HETEROGENOUS:POISSON
(High Intensity)
HETEROGENOUS:POISSON
(IntermediateIntensity)
HETEROGENOUS:POISSON
(Low Intensity)
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Principle of Poisson Loading
• Defined by 4 simple Poisson distributed parameters
• High, intermediate and low density considered (10 realizations each).
• Poisson loading consistent with zones of preferential infiltration, or multiple distributed point sources
Poisson Pulse DensityParameter Description High Inter-
mediateLow
Λc Expected number of Clusters
50 25 10
γn Expected number of pulses per
cluster
10 5 2
ρr Expected radius of
pulse from cluster centre
(m)
3 3 3
φr Radius of a pulse (m)
1 1 1
Mean spatial coverage (%)
53 22 4
Heterogeneous Aquifer Model
1 of 10 random realizations
Facies‐Specific Parameters
FaciesGravel Coarse Sand Medium Sand Sandy Loam Clay
Volumetric Proportion (%)
21 17 31 26 5
Mean Length (m) 2.56 1.7 Background 2.47 1.69
Hydraulic Conductivity (m/day)
100 50 10 1 0.001
Collector diameter (mm) 10 1.0 0.3 0.03 3.3 x 10-4
Collector Efficiency ηc*
Enterococcus 1.01 0.85 1.80 14.01 716.4Escherichia Coli 5.98 3.70 5.89 25.44 1.36 x 104
Salmonella 4.38 2.75 4.44 20.38 1.23 x 104
Campylobacter 3.99 2.66 4.58 24.93 1.46 x 104
Collision Efficiency αc 4.5 x 10-5 1 x 10-4 1 x 10-3 5 x 10-3 0.5
Filtration Coefficient λ* (m-1)
Enterococcus 4.75 x 10-
3
8.93 x 10-2 6.31 2453 1.02 x 1010
Escherichia Coli 2.83 x 10-
2
0.39 20.63 4452 2.13 x 1010
Salmonella 2.07 x 10-
2
0.29 15.56 3566 1.93 x 1010
Campylobacter 1.88 x 10-
2
0.28 16.05 4363 2.29 x 1010
Heterogeneous Aquifer Model
1 of 10 random realizations
One of 14,040 hypothetical monitoring well locations within this modeling domain (excluding areas near the simulation domain boundary).
High loading rate
Medium loading rate
Low loading rate
Homogeneous Aquifer Heterogeneous Aquifer
Probability of Prevalence in Time
Homogeneous Aquifer
Heterogeneous Aquifer
Loading:Homogeneous
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Probability of Prevalence in Time
Homogeneous Aquifer
Heterogeneous Aquifer
Loading:Gaussian
Probability of Prevalence in Time
Homogeneous Aquifer
Heterogeneous Aquifer
Loading:Poisson
- high intensity
Generation of additional transport “modes” owing to downgradienttransport and secondary loading for a spatially discontinuous source. For repeated loading, observed concentration at any point is a function of the aquifer transport properties and loading density
Probability of Prevalence in Time
Homogeneous Aquifer
Heterogeneous Aquifer
Loading:Poisson
- high intensity
Probability of Prevalence in Time
Homogeneous Aquifer
Heterogeneous Aquifer
Loading:Poisson
- intermediateintensity
Probability of Prevalence in Time
Homogeneous Aquifer
Heterogeneous Aquifer
Loading:Poisson
- low intensity
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http://www.whymap.org/
Conclusions
• fine grained alluvial aquifers: strong attenuation of
fecal microorganism, disease outbreaks events
most likely occur from loading immediately
adjacent to wells / faulty well construction
• High permable/low attenuation strata: large
transport distances of fecal microorganisms
• Poisson pulse source term - consistent with
sporadic observations of fecal microorganisms in
groundwater, even adjacent to persistent sources of
contamination
• Further research: spatial variation in fecal sources;
remobilization of microbes at field/farm scale =>
better risk model parametrization
• Additional processes: vadose zone, non-ideal
behavior