risk-based decision analysis in ground water quality...
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RiskRisk--Based Decision Analysis in Ground Water Based Decision Analysis in Ground Water Quality ManagementQuality Management
Jagath KaluarachchiProfessorCivil and Environmental EngineeringUtah State University
BackgroundBackground
Ground water is an important natural resource providing valuable water supply to most users.
Even in abundance supply, poor ground water quality is of limited use.
Ground water quality is typically affected by land use activities producing both point and non-point source pollution.
Impacts of poor ground water quality include public health effects to economic damages.
NonNon--Point Source Pollution Point Source Pollution
Common pollutants include heavy metals, nitrogen, and organic chemicals.
Common chemicals used in agricultural activities are• nitrogen in fertilizers• pesticides, insecticides, and herbicides
Unlike on-site remediation with point-sources, best management practices (BMPs) are implemented to minimize non-point source pollution.
Nitrate in Ground WaterNitrate in Ground Water
Nitrate is commonly found in ground water in background concentrations of 1 to 5 ppm.
Excessive nitrate concentration in ground water above 10 ppm (as N) can cause health impacts including the potential for cancer.
Heavy nitrate concentrations in ground water is found due to nitrogen-rich fertilizer and septic systems.
Nitrate is a concern in agro-well areas of Sri Lanka including northern and eastern coastal aquifer regions.
What is the spatial distribution of sustainable on-ground N loading to maintain public health in a system of agricultural watersheds?
Which BMPs should be considered to reduce nitrate pollution in ground water if loadings are high?
What are the individual economic costs incurred due to the adoption of each BMP?
What is the tradeoff between competing environmental and economic goals in adopting BMPs and how to prioritize the BMPs accordingly?
Research QuestionsResearch Questions
Management OptionsManagement Options
Change of land use practicesManure applicationFertilizer applicationCrop rotationBetter designed septic systems
Change of land useAgricultural to residentialAgricultural to industrial
Conceptualization and Model Development
On-Ground Nitrogen Loading
Soil Nitrogen Dynamics
Fate and Transport of Nitrate
Nitrate Leaching
•Advection•Dispersion•Reaction
Water Table
SourcesDairy manureFertilizersSeptic systemsDairy farm lagoonsWet and dry depositionLawnsIrrigation water
LossesVolatilizationRunoff
•• Mineralization• Immobilization• Nitrification• Denitrification• Plant Uptake
Water Supply Well
Conceptual Model
SoilFertilizer
Organic N
64
4
5 5
5
7
1
3
108 8
32
9
10
11
Water Table
To Ground water
1. Mineralization 2. Nitrification 3. Immobilization4. Fertilization 5. Manure Application 6. N Fixation7. Crop Residue 8. Plant Uptake 9. Denitrification10. Volatilization 11. Leaching
N2, N2O
ManurePlant Residue
AmmoniumAmmonia
Nitrate
N2
Plant Organic nitrogen
Sustainable N-loading (for each watershed)
No
Determine potential BMPs
Multi-criteria decision analysis(decision criteria, utility theory, and ranking)
Ranking of BMPs
Select BMPs
Decision A
nalysis
Simulations
Soil-N dynamics and fate & transport of NO3
Maximize N-loadings subject to health risk constraints
Existing N-loading < sustainable N-loading
On-ground N loading
Optim
ization
Yes,no action needed
Integrated Analysis
Demonstration ExampleDemonstration Example
SumasSumas--Blaine Aquifer, WABlaine Aquifer, WA
%
%
% %
%
%
%
%
%
$
$
$
$
$
$
$SumasBlaine
Lynden
Everson
Ferndale
NooksackBirch Bay
1
4
10
313
38
6
2
33
7
516
39
23
3132
15
20
30
14
36
17
26
18
118
27
29
19
28
9
12
34
24
22
37
3521 25
C
D
E F
G
H
I
B
A
US/Canada border
1 0 1 2 Miles
N
1 Bertrand2 Blaine3 Breckenridge4 California5 Cherry Point6 Dale7 Deer8 Fazon9 Fingalson10 Fishtrap
11 Fourmile12 Haynie13 Johnson14 Jordan15 Kamm16 Lake Terrell17 Lower Anderson18 Lower Dakota19 Lummi Peninsula East20 Lummi Peninsula West
21 Lummi River Delta22 Nooksack Channel (water)23 Nooksack Deming to Everson24 Nooksack River Delta25 North Fork Anderson26 North Fork Dakota27 Saar28 Sandy Point29 Schell30 Schneider
31 Scott32 Semiahmoo33 Silver34 Smith35 South Fork Anderson36 South Fork Dakota37 Swift38 Ten Mile39 Wiser Lake/Cougar Creek
Sumas-Blaine AquiferModel domain
$Cities% Boundary condition points
BackgroundBackground
Area of 963 square kmMostly agriculture but scattered residential and industrial activities. Serious nitrate contamination over the past two decades; sometimes more than 150 ppm. Low water table and high vulnerability to nitrate leaching.Heavy agricultural activities
8th in the US for dairy production5th in the world for raspberry production
Land Cover ClassificationLand Cover Classification(NLCD from the USGS)(NLCD from the USGS)
1 0 1 Miles
N
NLCD grid11 Open Water12 Perennial Ice/Snow21 Low Intensity Residential22 High Intensity Residential23 Commercial/Industrial/Transportation31 Bare Rock/Sand/Clay32 Quarries/Strip Mines/Gravel Pits33 Transitional41 Deciduous Forest42 Evergreen Forest43 Mixed Forest51 Shrubland61 Orchards/Vineyards/Other71 Grasslands/Herbaceous81 Pasture/Hay82 Row Crops83 Small Grains84 Fallow85 Urban/Recreational Grasses89 Dairy91 W oody W etlands92 Emergent Herbaceous W etlands
Selected ResultsSelected Results
NLCD class
Dai
ry m
anur
e
Wet
dep
osit
ion
Dry
dep
osit
ion
(reg
iona
l)
Dry
dep
osit
ion
(dai
ry)
Irri
gati
on
Fert
ilize
r
Law
ns
Legu
mes
Low Intensity Residential • • • High Intensity Residential • • • Commercial/Industrial/Transportation Bare Rock/Sand/Clay • • Quarries/Strip Mines/Gravel Pits • • Transitional • • • • Deciduous Forest • • Evergreen Forest • • Mixed Forest • • Shrubland • • Orchards/Vineyards/Other • • • • Grasslands/Herbaceous • • • • Pasture/Hay • • • • • Row Crops • • • • Small Grains • • • • Fallow • • • • Urban/Recreational/Grasses • • • Dairy Farms • • • • • Woody Wetlands • • Emergent Herbaceous Wetlands • •
Simulation ModelsSimulation Models
On-ground loading of N • actual land use information
Soil-N transformations• in-house model similar to the NLEAP
Flow in ground water• MODFLOW
Fate and transport in ground water• MT3D
Optimization• Genetic algorithm combined with artificial neural network
Multi-criteria decision analysis• Importance order of criteria method
OnOn--ground N Loadingground N Loading
23%
20%
9%7%
5%
4%
4%
4%
24% Bertrand
Fishtrap
Johnson
Breckenridge
Kamm
Ten Mile
South Fork Dakota
California
Remaining drainages
Dairy Manure (56%)
Fertilizers (31%)
Atmospheric deposition (7%)
Legumes (2%)
Irrigation (1%)
Dairy Lagoon (2%)
Septic Systems (1%)
Total Nitrogen LoadingTotal Nitrogen Loading
0
1
2
3
4
5
6
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Time (months)
Mas
s of n
itrog
en (1
0^6)
On-ground Leaching
Transient soil nitrogen balanceTransient soil nitrogen balance
0
18
36
54
72
90
108
126
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Time (months)
Mas
s of n
itrog
en (1
03 lbs)
0.0
0.4
0.8
1.2
1.6
2.0
2.4
2.8On-ground Leaching Recharge
Health Risks for different NHealth Risks for different N--LoadingsLoadings
5
7
9
11
13
15
1 6 11 16 21 26 31 36 41 46 51 56
Receptor
NO
3-N
(mg/
L)
Existing OptimalMCL
Existing and Existing and optimal Noptimal N--loadings from loadings from fertilizer and fertilizer and manuremanure
0.0
0.2
0.4
0.6
0.8
1.0
Four
mile
TenM
ile
Cal
iforn
ia
Sout
h Fo
rk D
akot
a
Schn
eide
r
Lum
mi P
enin
sula
Wes
t
Fish
trap
Bre
cken
ridge
Dal
e
John
son
Jord
an
Ber
trand
(Can
ada)
Fish
trap
(Can
ada)
Sum
as R
iver
(Can
ada)
Drainage
Fert
ilize
r (1
0^6
lbs
N)
ExistingOptimal
0.0
0.5
1.0
1.5
2.0
2.5
Four
mile
Cal
iforn
ia
Schn
eide
r
Fish
trap
Dal
e
Jord
an
Fish
trap
(Can
ada)
Drainage
Man
ure
(10^
6 lb
s N
)
ExistingOptimal
110.0750.075240.2960.391Johnson (Canada)
210.2400.240221.0931.406Fishtrap (Canada)290.2310.231261.1251.516Bertrand (Canada)40.0300.030130.3110.359Jordan
280.1600.160181.1051.344Johnson00.0900.090180.5670.694Dale
110.0950.107251.7502.347Breckenridge
40.3320.346271.3451.838Fishtrap00.0280.028180.0690.085Lummi Peninsula00.0830.083280.1150.159Schneider
00.0320.032241.0401.376S. Fork Dakota00.1100.110200.5230.653California170.1030.124420.6191.070Tenmile
00.0600.060220.3190.410Fourmile
Reduction (%)
SustainableExistingReduction (%)
SustainableExisting
Fertilizer Loading (x106 lbs.yr)Manure Loading (x106 lbs.yr)Drainage
Mean Cost(x106 $)DescriptionBMP
14.4Manure composting/exporting + fertilizer application reduction + adopt a feeding strategy for dairy cattle
9
2.1Manure composting/exporting + adopt a feeding strategy for dairy cattle
8
21.0Manure composting/exporting + fertilizer application reduction7
10.6Adopt a feeding strategy for dairy cattle + fertilizer application reduction
6
-1.7Adopt a feeding strategy for dairy cattle5
12.3Fertilizer application reduction4
8.7Manure composting/exporting3
43.8Dairy cattle head reduction2
0Do-nothing1
MultiMulti--criteria decision analysiscriteria decision analysis
Criteria• Summation of concentration deviations above MCL• Number of receptors exceeding MCL• Net cost• Cost per unit concentration reduction• Nitrate buildup in the ground water• Nitrogen buildup in the soil• Cumulative nitrate flux to the surface water• Nitrate leaching• Total on-ground nitrogen loading• On-ground nitrogen runoff losses• On-ground nitrogen volatilization losses
-100
100
300
500
700
1 2 3 4 5 6 7 8 9BMP
CPC
R
0
14
28
42
56
1 2 3 4 5 6 7 8 9
BMP
EMC
L
Values of Decision Criteria
Efficiency of BMPsEfficiency of BMPs
9.0
10.0
11.0
12.0
0 20 40 60 80 100 120
Time (months)
Nitr
ate
conc
entr
atio
n (m
g/L
)
12, 3, or 84567 or 9MCL
Importance Order of Criteria MethodImportance Order of Criteria Method
0.475
0.905 0.905
0.354
0.7030.780
1.0000.905
1.000
0.237
0.659
0.818
0.265
0.555
0.685
0.8890.838
0.922
0.000
0.412
0.731
0.175
0.406
0.589
0.779 0.7710.843
0.00.10.20.30.40.50.60.70.80.91.0
Alt. 1 Alt. 2 Alt. 3 Alt. 4 Alt. 5 Alt. 6 Alt. 7 Alt. 8 Alt. 9
BMP
Tot
al u
tility
scor
e
Ranking of BMPsRanking of BMPs
1419
4148
2557
5626
6265
3334
8883
7772
9991MinimumAverageMaximum
Utility Score of BMPRanking
DecisionDecision--Support SystemSupport System
BenefitsBenefits
Site-independent soil nitrogen dynamics model provides spatial and temporal distribution of nitrate leaching to ground water.
The decision model predicts the sustainable on-ground nitrogen loading that satisfies the health risk constraints.
The decision model can be used in predicting aquifer vulnerability to nitrates under a variety of land use classes and practices.
Evaluate and prioritize management options under a variety of economic and environmental decision criteria.
ConclusionsConclusions
In agriculture-dominated watersheds, high nitrate leaching is due mainly to agricultural practices. The difference between the nitrate leaching and on-ground nitrogen loading is usually substantial and signifies a soil buildup of nitrogen.
Accounting for the spatial distribution of on-ground nitrogen loadings and nitrate leaching is essential for reliable modeling of nitrate fate and transport in ground water.
The proposed integrated modeling framework allows for the accurate simulation of the outcome of the current land use practices and the proposed BMPs.