Presentation at Kuwait Institute for Scientific Research

Scientific Research, Teaching and Society

Services Amro M. M. Elfeki, Ph.D.

Outline Main Research Interest and Achievements.

Teaching Activities.

Consultations and Societal Activies.

May 1, 2023Scientific Research, Teaching and Society

Services Amro Elfeki, PhD 2

Research Themes



Characterization of Subsurface

Heterogeneity

Modeling Space and Time Variability

in Flow and Transport in Aquifers

Modeling Rainfall-Runoff in Arid Zones

Modeling Rainfall Variability at Different Time Scales in Arid

Zones

Modeling Flash Floods in Arid Urban

Environment

Uncertainty Analysis in Water Resources and

Environmental Modeling



Characterization of Subsurface

Heterogeneity

Characterization of Subsurface Heterogeneity

May 1, 2023 5Scientific Research, Teaching and Society

Services Amro Elfeki, PhD

Geological Structure



Huesca outcrop, Spain, Courtesy Kees Geel (from Dept. of Geology, Faculty of Applied Earth Sciences, TU Delft, The Netherlands).

Characterization of Subsurface Heterogeneity (Cont.)



Characterization of Subsurface Heterogeneity (cont.)

( )

1

Markov property (One-Step transition probability)Pr( )Pr( ) : ,Marginal Distribution

lim

Pr (

N

i i -1 i - 2 i -3 0k l n pr

i i -1k l lk

Nklk

i ik l

| , , S ,..., S S S SZ Z Z Z Z | pS SZ Z

p w

| S SZ Z

( )

( 1))N i

kq lkN q N i

lq

p p, SZ

p

S So d

Markov Chain Theory:




, , 1, , 1

,

, 1, , 1 ,,

Unconditioinal Coupled Markov Chains: Pr( | , )

. 1,...

Conditioinal Coupled Markov Chains: Pr( | , , )

x

lm k i j k i j l i j m

h vlk mk

lm k h vlf mf

f

i j k i j l i j m N j qlm k q

l

p Z S Z S Z S

. p pp k n

. p p

p Z S Z S Z S Z S

p

( )

, ( ) , 1,... .x

x

h h N i vlk kq mk

m k q h h N i vlf fq mf

f

. . p p pk n

. . p p p

(Elfeki and Dekking, 2001)

Coupled Markov Chain “CMC” in 2D





1

2

3

4

5

6

7

8

0 50 100 150 200 250 300-80

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-40

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-20

0




0 50 100 150 200 250

-10

-5

0

0

1

2

3

4

5

0 5 0 1 0 0 1 5 0 2 0 0 2 5 0

- 1 0

- 5

0




Study Area The Schelluinen study area is located in the central Rhine-Meuse delta in the Netherlands. Figure 1 shows the geological cross-section, interpreted by geologists based on drillings made at 20 m apart (for details see Bierkens, 1994 and Bierkens, 1996, and Weerts, 1996). Augured drillings are used to describe the vertical sequence of the confining layers at boreholes. In addition to these drillings, a few measurements of hydraulic conductivity and porosity are performed on sediment cores. Merging soft geological information with a few hard hydraulic data yields estimation for the hydraulic properties of the entire confining layer at the scale of interest. A total number of 750 borings are available at a depth of 2 – 2.5 m below the surface

Reducing Concentration Uncertainty Using the Coupled Markov Chain Approach.

May 1, 2023 13Scientific Research Papers of Amro Elfeki, PhD

0 200 400 600 800 1000 1200 1400 1600-10

-5

0

Conditioning on half of the drillings

SIS Model Simulation

CMC Model Simulation

Geological Section






Eungyu Park

0 1 00 20 0

-20 0

-10 0

0

0 100 2 00

-2 00

-1 00

0

0 1 00 20 0

-200

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0

0 1 00 20 0

-200

-100

0

0 100 20 0

-200

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0

0 100 2 00

-2 00

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0

0 100 2 00

-2 00

-1 00

0

0 1 00 20 0

-20 0

-10 0

0

0 1 00 20 0

-200

-100

0

Large Scale Structure

Fine Scale Structure

Two Scale Structure





Modeling Space and time Variability in Flow and Transport in Aquifers

Modeling Flow and Transport in Aquifers



Simulation of Solute Transport under Oscillating Groundwater Flow in Homogeneous Aquifers

• confined aquifer• upstream water level constant• downstream water level

variable• constant thickness• constant hydraulic conductivity

K over the depth• constant specific storage SS

over the depth• aquifer modelled in a 2D

horizontal plane

The goal of the study :investigate the impact of oscillating flow conditions on solute transport in homogeneous aquifers






Governing equation of the flow:

, , , , , ,, ,xx yy

h x y t h x y t h x y tS x y x yT Tt x x y y

Principle of the finite difference method :• discretization in space• discretization in time

where h hydraulic conductivity S the storativity or storage coefficient T=Kb the transmissivity

0

( , , ) 0 , (no-flow condition)

(0, , )( , , ) ( )

h x y t for x ynh y t hh d y t h t

Flow Model:




Transport Model:




Random Walk Equations:




Transport Simulation by Random Walk Particles Method

Random Walk Model


2 2

/( )( , , )4 4

( - - ( -) )exp -4 4

o

l x t x

o ox

l x t x

HMC x y t t tV V

x t yVX Y t tV V

0

/ ( )( )

1 ( ( () )exp( ) ( )

o

x l t

t 2 2o ox

l x t x

HMC x, y,t = 4 V

x - - t y -VX Y - + d t 4 t 4 tV V






Head profiles along the aquifer length. The downstream water level is a cosine function with an amplitude of 5m and with different periods: 1, 5, 10 days. The length of the aquifer is 300m, the storativity S=0.01.




This study has confirmed the decrease in the longitudinal dispersivity that is observed in the field by Kinzelbach and Ackerer (1986) due to temporal variation




Visual Interpretation of the decrease of the longitudinal dispersivity

Modeling Flow and Transport in AquifersModeling Kinetic Adsorption in Porous Media by Two-State Random Walk Particle Method.

Plume shape and its concentration distribution is due to:- Heterogeneity of the medium.

- Soil-Contaminant Interaction “Reactivity” [Linear or Non-linear].

- Transient Conditions.



Comparison between Reactive and Non-Reactive Plumes from the Cape Cod Site, Massachusetts [LeBlanc et al., 1991] .



where C is the aqueous concentration (mobile) at time t, S is the adsorbed mass of chemical constituent on a unit mass of solid part of the porous medium (immobile) at time t, is the bulk mass density of the porous medium, ε is the effective porosity, and Vx is the Eulerian velocity field in x direction defined as follows [Bear, 1972]:

2 2

, ,2 2b

d xx d yyxC S C C C V D Dt t x x y

xK = - V

where, K is the homogenous isotropic hydraulic conductivity, is the hydraulic gradient

Dd,xx, and Dd,yy are components of pore scale (micro-level) dispersion coefficients [Bear, 1972]:

, ,| | , | |d xx d yyl t V V D D

Model Equation in a Continuous Form (Kinetic Adsorption):

ka, and kd are adsorption desorption coefficients.

b ba d

S k C k St





for mobile paricles: ( ) ( ) ( ( ), ( )). . 2 ( ( ), ( )).

( ) ( ) 2 ( ( ), ( )).

p p p p p px l x

p p p pt x

t t t t t t Z V t t t VX X X Y X Y

t t t Z V t t tY Y X Y

( ) ( )for immobile particles: X

( ) ( )

pp

p p

t t t X t t t Y Y

where, (Xp(t), Yp(t)) are the x and y coordinates of a particle at time t, (Xp(t+Δt), Yp(t+Δt)) are the x and y coordinates of a particle at time t+Δt, Δt is the time step of calculations, Z, Z´ are two independent random numbers drawn from normal distribution with zero mean and unit variance.

1

, , and ,1lk

i mi a a

p l i m k i mm b b

Simulation of Kinetic Adsorption by Markov Chain RWPM:

lkp

is the transition probability to change between state l (mobile m or immobile i ) and state k (mobile m or immobile i ).

lkp




010

2030

4050

6070

8090

100-30

-20

-10 0

010

2030

4050

6070

8090

100-30

-20

-10 0

010

2030

4050

6070

8090

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-20

-10 0

010

2030

4050

6070

8090

100-30

-20

-10 0

010

2030

4050

6070

8090

100-30

-20

-10 0

010

2030

4050

6070

8090

100-30

-20

-10 0

ABC

DEF

.

00.1

110

1

1lk

i mi a a

pm b b



Case (A) No-adsorption a= 1.0 , b= 0.0

Case (B) Highly adsorbed medium

a= 0.1, b=0.9

Case (C) Moderate kinetic reaction

a= 0.5, b=0.5

Case (D) Very slow kinetic reaction

a= 0.1, b=0.1

Case (E) Fast kinetic reaction

a= 0.9, b= 0.9

Case (F) Poorly adsorbed medium

a= 0.9, b=0.1

Influence of fine-scale heterogeneity patterns on the large-scale behaviour of miscible transport in porous media





C on cen tra tio n ( m g /l) a fte r 6 0 0 d ays from R e lea se

0 100 20 0

-20 0

-10 0

0

0 100 200

-2 00

-1 00

0

0 1 00 20 0

-20 0

-10 0

0

0 1 0 0 2 0 0

-2 0 0

-1 0 0

0

0 1 0 0 2 0 0

-2 0 0

-1 0 0

0

0 1 0 0 2 0 0

-2 0 0

-1 0 0

0

0 1 0 0 2 0 0

-2 0 0

-1 0 0

0

0.000 0.001 0.002 0.003 0.004



Uncertainty Analysis in Water Resources

Modeling

Uncertainty Analysis in Water Resources Modeling




Classification of Uncertainty: -Conceptual Model Uncertainty: Darcy’s and Fick’s Laws.

Geological Uncertainty: Connectivity and dis-connectivity of the layers, geological sequence, boundaries between geological units.

Parameter Uncertainty: K, porosity.

Hydro-geological Uncertainty: Constant head boundaries, impermeable boundaries, Plume boundaries, source area

boundaries.


0 50 100 150 200 250

Horizontal D istance between W ells (m )

-50

0

Dept

h (m

)

W ell 1 Well 2

? K(x,y,z)?

(x,y,z)?

C(x,y,z)?H=?

H=?

?

???

? ?

?

?

?





0 50 100 150 200-10

-5

0

0 50 100 150 200-10

-5

0

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0

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-5

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0

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-5

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0

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0 50 100 150 200-10

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-5

0

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0

0

0.1

1

10

3 4

Litho logy C oding

6 5

T= 82 years

# drillings

2

3

5

9

25

31




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-5

0

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-5

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0 0.1 1 10

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-5

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-5

0

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-5

0

0 50 100 150 200-10

-5

0

CactualC C

T = 4.1 years

T = 82.2 years

T = 136.9 years

mg/lit

2 boreholes




0 50 100 150 200-10

-5

0

0 50 100 150 200-10

-5

0

0 50 100 150 200-10

-5

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0

0 50 100 150 200-10

-5

0

0 50 100 150 200-10

-5

0

0 0.1 1 10

actualC C C

T = 4.1 years

T = 82.2 years

T = 136.9 years

5 boreholes

mg/lit




0 50 100 150 200-10

-5

0

0 50 100 150 200-10

-5

0

0 50 100 150 200-10

-5

0

0 50 100 150 200-10

-5

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0 50 100 150 200-10

-5

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0 50 100 150 200-10

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0 50 100 150 200-10

-5

0

0 50 100 150 200-10

-5

0

0 50 100 150 200-10

-5

0

0 50 100 150 200-10

-5

0

actualC C C

T = 4.1 years

T = 82.2 years

T = 136.9 years

31 boreholes



Reduction of Uncertainty in Geological Formations, Groundwater Flow and Transport Models



Prediction of Contaminant Plumes (Shapes, Spatial Moments and Macro-dispersion) in Aquifers with insufficient Geological Information


0 0.1 1 10 100

-10

-5

0

1 2 3 4 5

-10

-5

0

-10

-5

0

49 days

-10

-5

0

279 days

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-10

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0

594 days

-10

-5

0

-10

-5

0

49 days

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279 days

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594 days

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-10

-5

0

49 days

-10

-5

0

279 days

0 50 100 150 200 250

-10

-5

0

594 days

Lithology Cod ing

Concentration Scale (m g/L)

Simulation of the MADE 1 Exp



Prediction of Contaminant Plumes (Shapes, Spatial Moments and Macro-dispersion) in Aquifers with insufficient Geological Information


Simulation



Design of Groundwater Monitoring System at Landfill Sites





Modeling Rainfall-Runoff in Arid Zones

Location of the Study Area



Rainfall-Runoff Characteristics

15 16 17 18 190

0.51

1.52

2.53

3.5storm at station J-418

Time(hr)Rain

fall

dept

h (m

m)

15 16 17 18 190

1

2

3

4

5 storm at station J-417

Time(hr)Rain

fall

dept

h (m

m)

Event 14 June 1987

0 5 10 15 20 25 300

100

200

300

400

500

Runoff Simulation Station J-418

DataSMADA

Time (Hr)

Disc

harg

e (m

3/se

c)

0 5 10 15 20 25 300

100200300400500600700

Runoff Simulation Station J-417

smadaData

Time (Hr)

Disc

harg

e (m

3/se

c)



Rainfall-Runoff Modeling

Al-Ahmdi, F.(2005).Rainfall-runoff modeling in arid regions using geographical information systems and remote sensing: case study;western region of saudi arabia, MSc. KAU.





Modeling Flash Floods in Arid Urban Environment

Simulation of Urban Flooding by Diffusive Wave Model: Jeddah Flood 2009 Case Study



Images of Flood on the 25th Nov. 2009 at KAU Campus and Surrounding Streets

Scientific Research, Teaching and Society Services Amro Elfeki, PhDMay 1, 2023 59

Objectives Developing an integrated framework with geomatics

techniques for modelling urban flooding based on a newly proposed hydrodynamic model that has the following features:

Conceptually simple. Efficient in terms of computer cost (Time and Storage).

Using the developed model to simulate the flood event on the 25th of Nov. 2009 in Jeddah city.

Scientific Research, Teaching and Society Services Amro Elfeki, PhDMay 1, 2023 60

Analogy with 2-D Advection Diffusion Model

Scientific Research, Teaching and Society Services Amro Elfeki, PhD

xx xy

yx yy

h h h h hu v D Dt x y x x y

h hD Dy x y

2

2 22

13

2

2 22

13

( ) ( ) 0 .

( ) ( ) ( )

( ) ( ) ( )

b

b

h hu hv continuity eqt x y

zhu hu huv hgh ght x y x x

u vgn u x momentum eqh

zhv huv hv hgh ght x y y y

u vgn v y momentum eqh

Dxx= is the dispersion coefficient in x-direction due to flow in x-directionDyy= is the dispersion coefficient in y-direction due to flow in y-directionDXy= is the dispersion coefficient in x-direction due to flow in y-directionDyX= is the dispersion coefficient in x-direction due to flow in x-direction

0.5

0.5

1003.281

1003.281

k zux

k zvy

∆z = the difference in bed elevation between two cells

∆x and ∆y = are the cell size in x and y directions

k is roughness coefficient given by McCuen (1989) and SCS (1972) provide values of k for several flow situations .

-L L Tij ijuvD VV

May 1, 2023 61

Visual Comparison

Scientific Research, Teaching and Society Services Amro Elfeki, PhD

526000 527000 528000 529000 530000 531000 532000

2376000

2377000

2378000

2379000

0 0.1 0.5 1

526000 527000 528000 529000 530000 531000 532000

2376000

2377000

2378000

2379000

526000 527000 528000 529000 530000 531000 532000

2376000

2377000

2378000

2379000

W ater D epth (m )

Time = .5 Hr

Time = 1.5 Hr

Time = 4.5 Hr

May 1, 2023 62



Modeling Rainfall Variability at Different Time Scales in Arid

Zones

Modeling Monthly Rainfall Records in Arid Zones Using Markov Chains: Saudi Arabia Case Study

Objectives: Modeling monthly rainfall records in arid

zones for future predictions of monthly rainfall.

Application on a case study in Saudi Arabia (Three Stations).



May 1, 2023 Scientific Research, Teaching and Society Services Amro Elfeki, PhD

Stations Considered in the Study

65


Visualization of a Monthly Rainfall Record (Khules Station)

66


Spreadsheet Model for Statistical Analysis (Khules Statistics)

An Excel Sheet has been developed to calculate the descriptive statistics and perform hypothesis testing to fit a distribution

67


Spreadsheet Model for Markov Chain Analysis and Predictions

An Excel Sheet has been developed to

perform coding of the station record,

calculate the transition probability of the sequence,

and

perform simulationsof the sequence based on the data and the parameters estimated from the other sheet.

68


Results of Tabouk Station

ExponentialLog-normal

69

IDF curves





0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00

T/D

R/R

tot

0.00

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0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00

T/D

R/R

Tot

First Quartile Median Storm

second Quartile Median Storm

0

0.05

0.1

0.15

0.2

0.25

0.3

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0 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00

T/D

R/R

tot

Teaching Activities Introduction to Hydrology Surface water Hydrology Watershed Hydrology Applied Groundwater Hydrology Open channel Hydraulics Fluid Mechanics Numerical Methods in Water Resources. Numerical Methods in Hydrology. Statistical Hydrology Stochastic Hydrology Selected topics (Advanced topics in Rainfall-Runoff Modeling) Selected topics (Hydrodynamic models for surface water) Special topics: Scientific writing Geostatistics Stochastic Modeling of Flow and transport



Community Services



Business and Knowledge Alliance at KAU: Expert House :Arabian Shield for Hydro-consultation

Meteorology and Environment• Preliminary design of 170 dams (surface and subsurface) in the Kingdom of Saudi Arabia.• Hydrological reports for flood protection and Storm water drainage system.• Check the Suitability of the diversion dam at Umm Al-Qura University campus, Mekkah KSA for protecting the camps from flash floods.• Contamination by diesel at Dellah campus.• Contamination by diesel at a farm near a petrol station.• EIA of oilspill at Yunboa RC.

Thank You!

Presentation at Kuwait Institute for Scientific Research

Engineering

Transcript of Presentation at Kuwait Institute for Scientific Research