5-egypt-nile river flows - united nations economic and ...css.escwa.org.lb/sdpd/3639/pr-s2-5.pdf ·...
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09/06/2015
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Beirut, Beirut, 88--10 10 June June 20152015
ByBy
DoaaDoaa M. M. AminAmin, PhD, PhD
Water Recourses Research Institute (WRRI), National Water Water Recourses Research Institute (WRRI), National Water
Research Center (NWRC), Ministry of Water Resources & Research Center (NWRC), Ministry of Water Resources &
Irrigation (MWRI), EgyptIrrigation (MWRI), Egypt
Beirut, Beirut, 88--10 10 June June 20152015
�� IntroductionIntroduction
�� Hydrological Models (NFS)Hydrological Models (NFS)
�� Previous Studies Previous Studies
�� Assessment the Drought in Future Climate Change Assessment the Drought in Future Climate Change
ScenariosScenarios
–– Study Area (Blue Nile and Atbara River)Study Area (Blue Nile and Atbara River)
––MethodologyMethodology
––ResultResult
––ConclusionConclusion
––Next Step Next Step
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Results of CC Results of CC
Models Models
Preprocessing Preprocessing
MethodMethod
Impacts Impacts
Model Model
Analyzing the Analyzing the
Results and Results and
understanding understanding
the Impactsthe Impacts
Beirut, Beirut, 88--10 10 June June 20152015
Hydrological Hydrological ModelsModels
� Rainfall – Runoff Model (rainfall the source of water)
� Distributed model (to simulate the runoff variability)
� Accurate sub-basins delineation.
� Well Calibrated (to access the hydrological errors itself)
Why Nile Forecasting System ?Why Nile Forecasting System ?
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Nile Forecasting SystemNile Forecasting System
� Hydro-meteorological System
� Developed in 1992, for monitoring, Forecasting and
simulating the flow till HAD
� Upgraded several times 1994, 1998, 2000, 2006, 2009, and
2012
� Precipitation & PET is the primary inputs variable for the NFS
hydrological models
� NFS was evaluated in 2011, through the period (1989-2010)
� The Sub-basins are re-delineated in 2012
� The latest Calibration was in 2012
Beirut, Beirut, 88--10 10 June June 20152015
Rain gauge Data Satellite Images
Rainfall Estimation Models
Rainfall Estimates
Hydrological Models
Simulation and Extended Stream Flow Prediction (ESP)
Historical Climate
GIS
Water Balance
Hill Slope
Routing
Swamp
Lake
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Previous StudiesPrevious Studies
0
20
40
60
80
100
120
Base 2010s 2020s 2030s 2040s 2050s 2060s 2070s 2080s 2090s
To
tal A
nn
ua
l F
low
(B
CM
)
HadCM3 A2 HadCM3 B2
CGCM2 A2 CGCM2 B2
ECHAM4 A2 ECHAM4 B2
OBS Base
Previous Studies (Previous Studies (11))
Lake Nasser Flood & Drought Control Project (2008)
– 6 Transient scenarios (3 GCMs x 2 Emission Scenarios)
– Statistically downscaled using a spatio-temporal weather generator
– Changes at Dongolafrom 2010-2100
Beirut, Beirut, 88--10 10 June June 20152015
Previous StudiesPrevious Studies
Previous Studies Previous Studies ((22))
– 6 ensembles of scenarios (HadCM3 x A1B Emission Scenario)
– RCM downscaled
– DCFs to Modify the NFS daily rainfall
– Changes at key locations on Nile River from 2021-2050
– -19% to +29% for the Blue Nile (Diem)
– -8% to +10% for the White Nile (Malakal)
– -13% to +36% for the Whole Nile (Dongola)
Climate Change Risk Management in Egypt
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Previous StudiesPrevious Studies
Previous Studies Previous Studies ((33))
– 5ensembles of scenarios (HadCM3 x A1B Emission Scenario)
– RCM downscaled
– DCFs of Temp., Precip., PET, and Evap.
The Impact of Climate Change on the Arab Region using RCM
Beirut, Beirut, 88--10 10 June June 20152015
Previous StudiesPrevious Studies
Previous Studies Previous Studies ((33))
The Impact of Climate Change on the Arab Region using RCM
Delta Change Factors for Temperature (Differences in Delta Change Factors for Temperature (Differences in °°K) for Selected Scenarios and MonthsK) for Selected Scenarios and MonthsDelta Change Factors for Precipitation (Ratios) for Selected Scenarios and MonthsDelta Change Factors for Precipitation (Ratios) for Selected Scenarios and Months
(DCF is not calculated for Black Areas, White Areas are off Scale)Delta Change Factors for PET (Ratios) for the Selected Scenarios and MonthsDelta Change Factors for PET (Ratios) for the Selected Scenarios and Months
Delta Change Factors for Evaporation (Ratios) for Selected Scenarios and Delta Change Factors for Evaporation (Ratios) for Selected Scenarios and
MonthsMonths
(DCF is not calculated for Black Areas, White Areas are off Scale)
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Assessment the Drought in Future Assessment the Drought in Future
ClimateClimate
� Meteorological droughts are periods of less than normal rainfall over a
specified region
� For assessing meteorological drought, the approach is to apply the
Standardized Precipitation Index (SPI) to different rainfall datasets to
assess its applicability in the study area.
� In terms of hydrological drought, a set of indices will be applied to the
projected Future Flow (SDI, and MWRI flood year classification)
Beirut, Beirut, 88--10 10 June June 20152015
Study Area (Blue Nile & Atbara River)Study Area (Blue Nile & Atbara River)
�� Low specific Low specific dischargedischarge
�� Variable Variable topographytopography
�� High runoff High runoff variabilityvariability
�� High Sensitivity to High Sensitivity to ClimateClimate
Roseires
Khartoum
Didessa
Angar
Temcha
Wama
Basilo
Tamaniat
Diem
Sennar
Ma lakal
Dabana
Beressa
Birr
Chacha
Fettam
Finchaa
Gummara
Guder
Megechi
Muger
Ribb
Sifa
Kesse
Atba ra
Gaba l Awlia
Mogren
Khashm
El-Girba
Hasanab
7
8
9
10
11
12
13
14
15
16
17
18
31 32 33 34 35 36 37 38 39 40
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MethodologyMethodology
RCM ResultsRCM Results
6 ensembles for
Rainfall & PET
(1961-1990)
(2021-2050)
PrePre--processingprocessing
Bias correction of
precipitation &
PET
Hydrological Hydrological
ModelsModels
Hydrological
Models in (NFS)
Assessment Assessment
Drought Drought in the in the
Eastern NileEastern Nile
Drought indices
(SPI, SDI,…)
Beirut, Beirut, 88--10 10 June June 20152015
PrePre--processingprocessing
Bias Correction
The method to bias correct precipitation is based on monthly
means, by applying a multiplicative correction factor
Corr. F = Mean Monthly Precipitation (NFS)
Mean Monthly Precipitation
(base: 1961-1990)
This is a simple bias correction that only guarantees that the mean climate
scenario is similar to the base dataset
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Meteorological Drought AssessmentMeteorological Drought Assessment
SPI was calculated for 1, 3, 6, 9, and 12 months lead times to assess
the drought using the catchment rainfall from several sources:
1. For historical rainfall, three datasets(CRU, ERa40, & NFS) are
used and results are compared in terms of drought frequency for a
period starting in 1961 and ending in 1990 or beyond (except for
the NFS dataset which starts in 1992)
2. The baseline rainfall series from the ensemble members (1961-
1990) are first compared to observed rainfall
3. Future rainfall is taken from an ensemble of 6 RCM simulations for
the period 2021-2050
Beirut, 8Beirut, 8--10 June 201510 June 2015
SPI Values and Corresponding Drought Intensity-McKee et al. (1993)
Meteorological Drought AssessmentMeteorological Drought Assessment
SPI Value Drought Intensity
2.0 or more Extremely wet
1.5 to 1.99 Very wet
1.0 to 1.49 Moderately wet
-.99 to .99 Near normal
-1.0 to -1.49 Moderately dry
-1.5 to -1.99 Severely dry
-2 and less Extremely dry
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ResultsResults1- Observed Rainfall Comparisons
0
200
400
600
800
1000
1200
1400
1600
1800
1961
1963
1965
1967
1969
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
2009
2011
To
tal A
nn
ua
l R
ain
fall
(m
m)
CRU ERA40
NFS ERA40-PRECIS
Blue Nile at Khartoum
0
200
400
600
800
1000
1200
1400
1600
1961
1963
1965
1967
1969
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
2009
2011
To
tal A
nn
ua
l R
ain
fall
(m
m)
Atbara at Atbara town
a) Annual “Observed” Rainfall Series
Beirut, 8Beirut, 8--10 June 201510 June 2015
ResultsResults1- Observed Rainfall Comparisons
Blue Nile at Khartoum Atbara at Atbara town
b) Mean Monthly Distribution of “Observed” Rainfall Series
0
50
100
150
200
250
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Me
an
Mo
nth
ly R
ain
fall
(m
m/m
on
)
CRU
ERA40
NFS
ERA40-PRECIS
0
20
40
60
80
100
120
140
160
180
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
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Beirut, 8Beirut, 8--10 June 201510 June 2015
ResultsResults2- Climate Change Impacts
0
50
100
150
200
250
300
1%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
55%
60%
65%
70%
75%
80%
85%
90%
95%
99%
Tota
l M
on
thly
Ra
infa
ll (
mm
/m
on
)
Probability of Exceedence
RCM
CRU
ERA40
NFS
ERA40-PRECIS
0
50
100
150
200
250
300
1%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
55%
60%
65%
70%
75%
80%
85%
90%
95%
99%
Tota
l M
on
thly
Ra
infa
ll (
mm
/m
on
)
Probability of Exceedence
Blue Nile - Present (1961-1990) Blue Nile - Future (2021-2050)
a) Impact of Climate Change from 6 RCM Simulations on Rainfall
Frequency Distribution of the Blue Nile and Atbara
(The yellow band indicate the range across the 6 PRECIS simulations)
0
50
100
150
200
250
300
1%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
55%
60%
65%
70%
75%
80%
85%
90%
95%
99%
Tota
l Mo
nth
ly R
ain
fall
(m
m/
mo
n)
Probability of Exceedence
0
50
100
150
200
250
300
1%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
55%
60%
65%
70%
75%
80%
85%
90%
95%
99%
Tota
l Mo
nth
ly R
ain
fall
(m
m/m
on
)
Probability of Exceedence
Atbara - Present (1961-1990) Atbara - Future (2021-2050)
Beirut, 8Beirut, 8--10 June 201510 June 2015
ResultsResults2- Climate Change Impacts
-3
-2
-1
0
1
2
3
1%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
55%
60%
65%
70%
75%
80%
85%
90%
95%
99%
An
nu
al S
PI
Probability of Exceedence
RCM
CRU
ERA40
NFS
ERA40-PRECIS
-3
-2
-1
0
1
2
3
1%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
55%
60%
65%
70%
75%
80%
85%
90%
95%
99%
An
nu
al SP
I
Probability of Exceedence
Blue Nile - Present (1961-1990)
Blue Nile - Future (2021-2050)
Hydrological Year
Hydrological Year
-3
-2
-1
0
1
2
3
1%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
55%
60%
65%
70%
75%
80%
85%
90%
95%
99%
Mo
nth
ly S
PI
Probability of Exceedence
RCM
CRU
ERA40
NFS
ERA40-PRECIS
Monthly SPI
-3
-2
-1
0
1
2
3
1%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
55%
60%
65%
70%
75%
80%
85%
90%
95%
99%
Mo
nth
ly S
PI
Probability of Exceedence
Monthly SPI
-3
-2
-1
0
1
2
3
1%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
55%
60%
65%
70%
75%
80%
85%
90%
95%
99%
3 M
on
ths S
PI
Probability of Exceedence
RCM
CRU
ERA40
NFS
ERA40-PRECIS
3-Monthly SPI
-3
-2
-1
0
1
2
3
1%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
55%
60%
65%
70%
75%
80%
85%
90%
95%
99%
3 M
on
ths S
PI
Probability of Exceedence
3-Monthly SPI
09/06/2015
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Beirut, Beirut, 88--10 10 June June 20152015
ResultsResults2- Climate Change Impacts
Blue Nile - Present (1961-1990)
Blue Nile - Future (2021-2050)
9-Monthly SPI
9-Monthly SPI
12-Monthly SPI
12-Monthly SPI
-3
-2
-1
0
1
2
31%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
55%
60%
65%
70%
75%
80%
85%
90%
95%
99%
6 M
on
ths S
PI
Probability of Exceedence
RCM
CRU
ERA40
NFS
ERA40-PRECIS
6-Monthly SPI
-3
-2
-1
0
1
2
3
1%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
55%
60%
65%
70%
75%
80%
85%
90%
95%
99%
6 M
on
ths
SP
I
Probability of Exceedence
6-Monthly SPI
-3
-2
-1
0
1
2
3
1%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
55%
60%
65%
70%
75%
80%
85%
90%
95%
99%
9 M
on
ths S
PI
Probability of Exceedence
RCM
CRU
ERA40
NFS
ERA40-PRECIS
-3
-2
-1
0
1
2
3
1%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
55%
60%
65%
70%
75%
80%
85%
90%
95%
99%
9 M
on
th
s S
PI
Probability of Exceedence
-3
-2
-1
0
1
2
3
1%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
55%
60%
65%
70%
75%
80%
85%
90%
95%
99%
12
Mo
nth
s S
PI
Probability of Exceedence
RCM
CRU
ERA40
NFS
ERA40-PRECIS
-3
-2
-1
0
1
2
3
1%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
55%
60%
65%
70%
75%
80%
85%
90%
95%
99%
12
Mo
nth
s S
PI
Probability of Exceedence
Beirut, Beirut, 88--10 10 June June 20152015
ResultsResults2- Climate Change Impacts
Atbara - Present (1961-1990)
Atbara - Future (2021-2050)
Hydrological Year
Hydrological Year
Monthly SPI
Monthly SPI
3-Monthly SPI
3-Monthly SPI
-3
-2
-1
0
1
2
3
1%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
55%
60%
65%
70%
75%
80%
85%
90%
95%
99%
An
nu
al
SP
I
Probability of Exceedence
-3
-2
-1
0
1
2
3
1%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
55%
60%
65%
70%
75%
80%
85%
90%
95%
99%
An
nu
al S
PI
Probability of Exceedence
-3
-2
-1
0
1
2
3
1%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
55%
60%
65%
70%
75%
80%
85%
90%
95%
99%
Mo
nth
ly S
PI
Probability of Exceedence
-3
-2
-1
0
1
2
3
1%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
55%
60%
65%
70%
75%
80%
85%
90%
95%
99%
Mo
nth
ly S
PI
Probability of Exceedence
-3
-2
-1
0
1
2
3
1%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
55%
60%
65%
70%
75%
80%
85%
90%
95%
99%
3 M
on
th
s S
PI
Probability of Exceedence
-3
-2
-1
0
1
2
3
1%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
55%
60%
65%
70%
75%
80%
85%
90%
95%
99%
3 M
on
ths
SPI
Probability of Exceedence
09/06/2015
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Beirut, Beirut, 88--10 10 June June 20152015
ResultsResults2- Climate Change Impacts
Atbara - Present (1961-1990)
Atbara - Future (2021-2050)
9-Monthly SPI
9-Monthly SPI
12-Monthly SPI
12-Monthly SPI
6-Monthly SPI
6-Monthly SPI
-3
-2
-1
0
1
2
31%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
55%
60%
65%
70%
75%
80%
85%
90%
95%
99%
6 M
on
ths S
PI
Probability of Exceedence
-3
-2
-1
0
1
2
3
1%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
55%
60%
65%
70%
75%
80%
85%
90%
95%
99%
6 M
on
ths
SP
I
Probability of Exceedence
-3
-2
-1
0
1
2
3
1%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
55%
60%
65%
70%
75%
80%
85%
90%
95%
99%
9 M
on
ths
SP
I
Probability of Exceedence
-3
-2
-1
0
1
2
3
1%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
55%
60%
65%
70%
75%
80%
85%
90%
95%
99%
9 M
on
ths S
PI
Probability of Exceedence
-3
-2
-1
0
1
2
3
1%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
55%
60%
65%
70%
75%
80%
85%
90%
95%
99%
12
Mo
nth
s S
PI
Probability of Exceedence
-3
-2
-1
0
1
2
3
1%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
55%
60%
65%
70%
75%
80%
85%
90%
95%
99%
12
Mo
nth
s S
PI
Probability of Exceedence
Beirut, Beirut, 88--10 10 June June 20152015
ConclusionConclusion
� CRU has higher flood probabilities than NFS, but similar drought
probabilities.
� SPI reduce the bias from RCM model because it involves
normalization of rainfall distributions.
� The current set of climate simulations indicate a general increase in
rainfall, but this does not exclude the increase of drought probability
for some lead times
� The uncertainty bandwidth increases near the ends of the SPI
probability distributions but not for all lead times
� The SPI proved to be a useful way to characterize meteorological
drought across different catchments and at different time scales
09/06/2015
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Beirut, Beirut, 88--10 10 June June 20152015
Next Step Next Step
Assessment the Hydrological Drought:
1. Identify the suitable hydrological drought Indices
2. Applying these indices on historical data (Observed &
NFS_sim)
3. Applying these indices on the baseline projected flow series
(1961-1990)
4. Applying these indices on the future projected flow that is taken
from an ensemble of 6 RCM simulations for the period 2021-
2050
Beirut, Beirut, 88--10 10 June June 20152015
Suggested Drought IndicesSuggested Drought Indices
1- Standardized Discharge Index (SDI)
This Index SDI requires stream flow volume values
The formula of the SDI is:
SDI = (V-Vmean)/Std
Category SDI Range
Non drought ≥ 0.0
Mild drought -1.0 to 0.0
Moderate drought -1.5 to -1.0
Severe drought -2.0 to -1.5
Extremely Dry < -2.0
09/06/2015
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Beirut, Beirut, 88--10 10 June June 20152015
Suggested Drought IndicesSuggested Drought Indices
2- MWRI Drought Classification
The total annual flow of the Nile
over the hydrologic year (1st August
– 31st July), categorized based on
the annual naturalized flow of the
Nile at Aswan.
The average annual natural flow of
the Nile is 84 BCM, therefore the
categories are centred around this
figure
Category Natural Flow at Aswan
Extremely dry < 56
Very dry 56 - 70
Dry 70 - 77
Lightly dry 77 - 81
Near normal 81 - 87
Lightly wet 87 - 91
Wet 91 - 98
Very wet 98 – 112
Extremely Wet > 112
Beirut, Beirut, 88--10 10 June June 20152015
MWRI-Based Flow Classification and Corresponding Naturalized Flow
Ranges (BCM) at Key Locations along the Blue Nile, and the Atbara
Category Khartoum Diem Atbara
Extremely dry < 24.5 < 27.5 < 6.5
Very dry 24.5 – 32.7 27.5 – 36.8 6.5 – 8.7
Dry 32.7 - 36.9 36.8 – 41.5 8.7 – 9.8
Lightly dry 36.9 – 39.2 41.5 – 44.1 9.8 – 10.4
Near normal 39.2 – 42.8 44.1 – 48.1 10.4 – 11.4
Lightly wet 42.8 – 45.1 48.1 – 50.7 11.4 – 12.0
Wet 45.1 – 49.3 50.7 – 55.4 12.0 -13.1
Very wet 49.3 – 57.5 55.4 – 64.7 13.1 -15.3
Extremely Wet > 57.5 > 64.7 > 15.3
09/06/2015
15
Beirut, Beirut, 88--10 10 June June 20152015
DoaaDoaa M. M. AminAmin