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Transcript
Sustainability Considerations
in the Design of Big Dams:
Merowe, Nile Basin
Mentor: Prof. El Fatih Eltahir
Group: Anthony Paris, Teresa Yamana,
Suzanne Young
Outline
 Introduction
and motivation
 Nile hydrology
 The model
 Climate
 Sedimentation
 Public health
 Future Work
Goals and Motivation



Simulate the role of environmental
engineers in large scale projects
Analyze the effect the Dam will have on
the environment and local population,
and make recommendations to mitigate
effects
Assess whether long-term effects will
significantly decrease Dam’s lifetime and
plan accordingly
Introduction

Sudan needs Energy



Merowe Dam



19-year old Civil War
Frequent power blackouts
Utilizing hydropower
Creating hope
Dam Design Details



Ten turbines – 1,250 MW Capacity
Long in relation to height
Active reservoir storage 8.3 bcm
General Layout
Average Longterm Monthly Nile flows, 1872-1986
25
Discharge (km^3/month)
20
15
10
5
0
January
February
March
April
May
June
July
August
September
October
November December
The
New
Model
“The
Model”
Storage to Elevation Relationship
Reservoir Characteristics
350
340
330
Elevation (m)
320
310
300
290
280
270
260
0
1E+09
2E+09
3E+09
4E+09
5E+09
6E+09
7E+09
Sur face Ar e a (m ^2)
Reservoir Characteristics
350
340
330
Elevation (m)
320
310
300
290
280
270
260
0
2E+10
4E+10
6E+10
8E+10
Storage (m^3)
1E+11
1.2E+11
1.4E+11
Matlab Model
dS/dt = inflow – evap – Q_out(turbines) –
Q_out(overflow)
 Determines what volume to make available to
turbines





Pessimistic Model – use as much water as possible
Gradual Release Model – ration storage in dry
season
Constant Head Model – Q_out=Q_in
Determines the number of turbines to turn on
 Calculates volume, area, Power
Pessimistic
Gradual Release
Constant Head
The Effect of Climate Change on Dam
Performance
Suzanne Young
Climate change
 Changes
in chemical composition of
atmosphere  global warming
 Temperatures increase, precipitation?
 Literature review: Predictions of Nile flows
confounded by different simulations giving
conflicting results
Range of discharges for major points along the Nile
(Summary of Yates 1998b results)
Two numbers on ends of each line represent extreme discharges of six GCM scenarios, whereas
boxed number is historic average; Additional tick marks on each line are remaining GCM scenarios,
which indicate range of climate change induced flows of Nile Basin.
Climate scenarios
Climate scenario
Years
Average flow
Deviation from long term
[km3/yr]
average 88 km3/yr
No change
1943-1969
88
--
Wetter climate
1872-1898
102
+15%
Drier climate
1979-1986
74
-15%
Also varied maximum storage height of reservoir from 294 m to 298 m
Nile discharge, 1872-1986
130
120
Longterm annual average = 88.1 km^3/year
Annual discharge (km^3/year)
110
100
90
80
70
60
50
40
1870
1880
1890
1900
1910
1920
1930
1940
1950
1960
1970
1980
Potential Hydropower
Power = γQh
γ = ρg
ρ = density of water = 1000 [kg/m3]
g = gravity = 9.8 [m/s2]
Q = flow at dam [m3/s]
h = drop in head between intake to powerhouse and outlet to
river [m]
Results
 Wetter
climate = highest power (~30%
higher than no change in climate)
 Reservoir storage height increase gives
linear increase in power (~10%/m)
 Pessimistic model > Gradual release
model
 Drier climate power yields higher than no
change in climate (!)
Pessimistic model
No change in climate
Wetter climate
Gradual release model
Drier climate
Pessimistic
model yields
higher power
than Gradual
Release
model
Pessimistic model: Comparison of climate scenarios
Average annual power [Watts]
2.40E+11
2.35E+11
2.30E+11
No change
2.25E+11
Wetter climate
Drier climate
2.20E+11
2.15E+11
2.10E+11
2.05E+11
294
295
296
297
Maximum storage height of reservoir [m]
298
Seasonal variations
Climate scenario
Wetter
No change Drier
January
4.95
3.82
3.30
February
3.44
2.84
2.60
March
2.79
2.21
2.17
April
2.01
1.91
3.20
May
1.71
1.95
2.81
June
2.04
2.55
2.74
July
6.02
6.92
4.87
August
20.84
18.50
14.79
September
25.05
20.77
18.24
October
17.30
14.12
9.95
November
9.53
7.62
5.36
December
6.70
4.88
3.97
Annual
102.39
88.09
73.54
Recommendations
 Use
pessimistic model as basis for
operating parameters
 Increase height of maximum reservoir
storage pending economic analysis
Sedimentation into the Reservoir
Anthony Paris
Erosion: Sources of Nile
Sediments

Ethiopian Highlands
(~90%)
 Travels through the
Blue Nile and Atbara
 The sediment load is
most significant
during flood season
(July-Oct.)
 50-228 million tones
per year
Sedimentation Analysis
 1)
How much sediment will settle in the
reservoir?
 2) Where will the sediment settle?
 3) How long is the economic life of the
project?
 4) What things can be done to improve
the situation?
Hand Calculations
Calculating Trapping Efficiency – 1st Round
 Brune’s Curve
 C = Capacity
 I = Inflow

C
 T
I
Hand Calculations
Calculating VS – 1st Round
 β = Bulk density of clay loam
 QC = sediment load [tons/yr]
 VS = Volume of sediment retained [m3/yr]

VS  T QC 
Borland & Miller Reservoir
Classification




H = any water lvl.
HO = lowest bed lvl.
VH = res. Vol. at H
α = coef.
M = coef. (slope)
log H vs log C
10.2
10
y = 4.4794x + 2.2173
9.8
9.6
log C

9.4
9.2
9
8.8
8.6
1.45
1.5
1.55
1.6
1.65
1.7
1.75
log H-Ho

Lake


65% dead storage
35% active storage
V H   H  H O 
M
1.8
Economic Life of Reservoir
Scenarios
Flow Rate
Suspended Load
Estimated Bed Load
Economic Life
1
44 billion m3/yr
30 million
5%
350 yrs
2
63.7 billion m3/yr
50 million
15%
205 yrs
3
44 billion m3/yr
77 million
5%
105 yrs
4
63.7 billion m3/yr
158 million
15%
65 yrs
5
44 billion m3/yr
137 million
5%
70 yrs
6
63.7 billion m3/yr
228 million
15%
45 yrs
Improvements

1) Trapping


2) Sluicing


Opening low level-lying sluices to flush out
sediments, only effects local area
3) Dredging


Creating dams upstream to catch sediment
$$$ May be cost effective towards end of life
4) Flushing

Allow the high sediment filled flood waters to flush
through the system
The Effect of the Dam on Public Health
Teresa Yamana
Dams’ Threat to Public Health
 As
a development project, obligation to
protect public health
 Merowe Dam expected to increase
incidence of Malaria, Schistosomiasis,
River Blindness and Rift Valley Fever
 Stagnant water in reservoirs and irrigation
ditches provide habitat for vectors
 Constant supply of water - Dry season no
longer limits vectors
Malaria

Protozoa Plasmodium
transmitted by Anopheles
mosquitoes
 A. funestus breeds in
illuminated shoreline
throughout the year
 A. gambiae breeds in
reservoir drawdown area in
dry season (November –
June)
Drawdown area:
129 km2
Illuminated shoreline:
2-48 km2
Malaria Control Strategies
 Reduce
Mosquito habitat through
operating parameters
 Chemical or biological control strategies
 Reduce bites by using window screens,
bednets
 Provide vaccination and treatment for at
risk or infected population
Schistosomiasis
 Parasite
carried by snails living in
illuminated shore line
 Reduce human contact to water – piped
water supply
 Provide sanitation services – break link in
life cycle
 Control snail population
River Blindness
Transmitted by black fly – fast moving water
 Water-washed – provide piped water supply
 Stop flow through dam 2 days per 2 weeks July
– September

Annual Power Generated
normal
with RB control
Percent
reduction
Var 1
2.05E+11
1.96E+11
4.39
Var 2
1.99E+11
1.89E+11
5.03
Var 3
1.87E+11
1.77E+11
5.35
River Blindness – Variation 2
Rift Valley Fever
 Transmitted
from livestock to humans via
mosquitoes
 Occurs when reservoirs are filled
 Vaccinate or remove livestock
 Quarantine contaminated livestock and
meat
 Warn livestock and meat workers
 Control mosquito habitat
Model Preferences
A. gambiae – Variation 3
 A. funestus and Schistosomiasis snails –
Variation 1
 River Blindness blackfly – add control
 Which is Most Important?





Need more data!
What diseases will cause the most problems?
Formulate strategy based on regional priority
GOAL – no increase in disease caused by dam
Future Work

Integrate 3 Climate, Sedimentology and Public
Health concerns
 Thorough cost-benefit analysis
 Climate


Sedimentation


More experimentation with various climate scenarios
2-D and 3-D models to predict delta formations and
identify problem spots
Public Health

Prioritize between diseases to find optimal operating
parameters
THANK YOU!!
 Prof.
El Fatih Eltahir
 Prof. Dennis McLaughlin & Sheila Frankel
 Profs. Ole Madsen & Dara Entekhabi
 Dr. Sadeqi of the Kuwait Fund
 Valeri Ivanov
 1E seniors!