Linking Groundwater Flow and Transport Models, GIS Technology, Satellite Images and Uncertainty Quantification for Decision Making: Buraiman Lake case study, Jeddah Saudi Arabia,

LINKING GROUNDWATER FLOW AND TRANSPORT
MODELS, GIS TECHNOLOGY, SATELLITE IMAGES
AND UNCERTAINTY QUANTIFICATION FOR
DECISION MAKING:
BURAIMAN LAKE CASE STUDY, JEDDAH, SAUDI
ARABIA
Amro Elfeki, Hatem Ewea, and Nassir Al-Amri
Dept. of Hydrology and Water Resources Management ,Faculty
of Meteorology, Environment & Arid Land Agriculture, King
Abdulaziz University , P.O. Box 80208 Jeddah 21589 Saudi
Arabia
7/19/2016Elfeki, Ewea, & Al-Amri -ICWRAE 2010

OUTLINE
 Objective of the Research
 Site Map and Data Acquisition
 Methodology
 Results
 Conclusions
 Outlook

OBJECTIVES OF THE RESEARCH
 Developing a methodology for linking flow
and transport models, satellite images, GIS
technology and uncertainty quantification for
decision making.
 Case Study: Prediction of the fate of the
sewage plume released form Buraiman
lake, Jeddah, KSA

METHODOLOGY
The methodology is an integration of
 Flow and Transport Models.
 Satellite Images.
 GIS Technology.
 Uncertainty Quantification
(Monte-Carlo Method).
For providing a risk map for
decision making
 . 0h
C C C
t
    
    
   
  
 
v D
x x x
k
 
 2
Uncertainty in Conceptual Models
. 0
. 0
Uncertainty in hydraulic conductivity, k
h
h
  
  
k
k
GIS
Monte-Carlo
Method

CLASSIFICATION OF UNCERTAINTY
Conceptual Model Uncertainty:
 Darcy’s and Fick’s Laws.
 Confined versus Unconfined aquifers
Geological Uncertainty:
 Connectivity and disconnectivity
 of the layers, geological sequence,
 boundaries between geological units.
Parameter Uncertainty:
 Hydraulic Conductivity
 porosity.
Hydro-geological Uncertainty:
 Constant head boundaries,
 impermeable boundaries,
 Plume boundaries, source area boundaries.
0 50 100 150 200 250
Horizontal Distance between Wells (m)
-50
0
Depth(m)
Well 1 Well 2
? K(x,y,z)?
(x,y,z)?
C(x,y,z)?
H=?
H=?
?
?
??
? ?
?
?
?

SEWAGE LAKE: GENERAL LOCATION
A B
Satellite Images
Buraiman Lake (Musk Lake) located in the east of Jeddah
highway.
Location: ~25 Km to
the east of Red Sea
coast.
~ 50,000 m3 /day are
discharged into the
lake.
Volume is more than
10 million m3
No outflow.
Mohorjy and Khan(2006) Preliminary Assessment of Water Quality along the Red Sea Coast near Jeddah, Saudi
Arabia, Water International, vol 31(1), pp 109-115
Basamed (2002). Hydrochemecal study and bacteriological effect on groundwater in the northern part of Jeddah district, MSc. Faculty
of Earth Sciences, kau.

BURAIMAN LAKE (SITE VISIT)

CONCEPTUAL MODEL
•The contaminant source starts from
the dam lake.
•The shape is approximated by a rectang
•The leachate and ambient groundwater
are connected.

DIRECTION OF GROUNDWATER FLOW AND GWL
About 20 degrees with the Horizontal
Al-Sefry and Sen (2006) Groundwater Rise Problem and Risk Evaluation in Major Cities of Arid Lands—
Jeddah Case in KSA, Water Resou. Management 20:91-108

SEWAGE WATER DATA
Liptak, B. G. , (1974), Environmental Engineers’ hand book: Vol.1: Water pollution. Chilton book company, Radnor, Pennsylvania.
Wastewater
Constituents
0 SOLIDS
0 SUSPENDED
125
DISOLVE
D
150 DBOD
60 NITROGEN
100
CHLORID
S
100 ALKALINITY
SUM 535 MG/L
0.535 G/L
M3=1000L
535 G/M3
0.535 KG/M3
TRUCK 20 TON 20 M3
MASS OF
WASTE= 10.7
KG/TRUC
K
#
TRUKS/DAY 800 8560 KG/DAY
3.124E+09 G/YEAR
2.57E+08 g/month
3124400 kg/year

1-D GW SURFACE PROFILES
 1 2
1
h h
h h x
L

   2 2
1 22
1
h h
h h x
L

 

ANALYTICAL SOLUTION OF TRAVEL TIME
(CONFINED AQUIFER)
 
 
 
 
1 2
1
1 2
1 2
0 1 2
L
h h
h h x
L
h hdh
q K K
dx L
h hq K
v
n n L
dx dx
v dt
dt v
dx
t
h hK
n L

 

  

 
  



 
2
1 2
nL
t
K h h



ANALYTICAL SOLUTION OF TRAVEL TIME
(UNCONFINED AQUIFER)
 
 
 
 
 
 
 
2 2
1 22
1
2 2
1 2
2 2
1 22
1
2 2
1 2
2 2
1 22
1
2 2
1 22
12 2
01 2
2
2
2
L
h h
h h x
L
h h
dh Lq K K
dx h h
h x
L
h h
q K Lv
n n h h
h x
L
dx dx
v dt
dt v
h hn
t h x dx
Lh h
K
L

 

  



 


  

 


 
 
 
3
2 2
1 2 2
1
3
2 2
1 22 3
1 12
2 2
1 2
2 ( )
3
,
4
3
ax b
ax bdx
a
h h
a b h
L
h hn
t h h
Lh h
K
L

 

  
 
             
 
 


TRAVEL TIME FOR CONFINED AND UNCONFINED
AQUIFERS
t = 37 years
t = 49 years
 
2
1 2
nL
t
K h h


 
 
3
2 2
1 22 3
1 12
2 2
1 2
4
3
h hn
t h h
Lh h
K
L
 
             
 
 
R = 0m/day
k = 120m/day
L = 21800m
h1 = 73m
h2 = 0m
n 0.25-

GROUNDWATER LEVELS

VARIABILITY IN HYDRAULIC CONDUCTIVITY
120 /
302 /
500
( )
Log-
k
K m day
m day
m
s
s Exp
K Normal Distribution







  
  
 
Alquhtani, M.B. and W.M. Shehata, 2003,
Vulnerability map of the groundwater rise in Jeddah,
Saudi Arabia: 12th Asian regional conf. on soil
mechanics and geotechnical engineering, 4-8
August, 2003, Singapore, chapter 3-3, pp 357,
Estimated Parameters

2-D GROUNDWATER MODELS
Un-Confined Aquifer
0
h h
K K
x x y y
      
    
      
2 2
0
h h
K K
x x y y
      
    
      
Confined Aquifer

GWL CONTOUR OF A SINGLE REALIZATION

TRANSPORT MODEL
Governing equation of solute transport :
C is concentration
Vx and Vy are pore velocities, and
Dxx , Dyy , Dxy , Dyx are pore-scale dispersion coefficients
x y xx xy yx yy
C C C C C C CV V D D D D
t x y x x y y x y
   
   
   
   
   
             
        
* - i j
mij ijL L T
VV
D V D
V
   
       
     
*mD
ij
L

T

is effective molecular diffusion,
is delta function,
is longitudinal dispersivity, and
is lateral dispersivity.

RANDOM WALK METHOD
    1 22 2xy yxx x
p p x L T
D VD V
X t t X t V t Z V t Z V t
x y V V
 
 
 
 
 

          
 
    1 22 2yx yy y x
p p y L T
D D V V
Y t t Y t V t Z V t Z V t
x y V V
 
 
 
 
 
 
          
 
The displacement is a normally distributed random variable, whose
mean is the advective movement and whose deviation from the mean
is the dispersive movement.
EFFECTIVE POROSITY 0.3
LONGITUDINAL DISPERSIVITY 2m
TRANSVERSE DISPRSIVITY 1m
∆x 100m
∆y 100m
LENGTH OF AQUIFER 21800m
DEPTH OF AQUIFER 20m
SOURCE DIMENSIONS 100x100 m
∆t 30.14days
INJECTED MASS 2.57E+08grams/month
NUMBER OF PARTICLES 1000

PLUMES ENVELOPE AND SINGLE PLUME
Confined
Un-
Confined

SINGLE PLUME AND ENSEMBLE PLUMES (C/U)
ConfinedUn-
Confined
t = 37 yearst = 49 years

CONCLUSIONS
 GW and transport models can be connected with GIS
and satellite images to visualize the flow pattern and
pollution transport.
 The Monte-Carlo methodology together with GIS and
satellite images are useful tools to account for
uncertainty and providing maps of uncertainty
(Envelop).
 Well data showed quasi linear aquifer response when
compared with quadratic aquifer response. A point
that needs in depth investigation in arid zones.

OUTLOOK
 Perform elaborated study with more reliable
data.
 Validation of the results by collecting samples
from locations indentified by this study and
check for the level of pollution.
 Check the validity of the aquifer response in
arid zones (linear versus quadratic
response).

Linking Groundwater Flow and Transport Models, GIS Technology, Satellite Images and Uncertainty Quantification for Decision Making: Buraiman Lake case study, Jeddah Saudi Arabia,

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Linking Groundwater Flow and Transport Models, GIS Technology, Satellite Images and Uncertainty Quantification for Decision Making: Buraiman Lake case study, Jeddah Saudi Arabia,