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Downscaling and Modeling the Climate of
Blue Nile River Basin-Ethiopia
By:
Netsanet Zelalem
Supervisors:
1. Prof. Dr. rer.nat.Manfred Koch, Kassel University
2. Dr. Solomon Seyoum, IWMI, Ethiopia
Nov9/2012
Kassel University, Germany
Statement of the Problem
• High population pressure, poor water and land
management and climate change are inducing
declining agricultural productivity and vulnerability
to climate impact [Haileslassie et al., 2008].
• In order to alleviate poverty and food insecurity, it is
widely recognized to utilize water resources such as
Blue Nile.
• So, assessment of the impact of climate change on
future water resource may provide substantial
information to the area where more than 85% of the
basin depends entirely on rain-fed agriculture.
Objective
• Evaluate the possible relationships between large scale
variables with local meteorological variables.
• Evaluate the most common statistical downscaling
methods, SDSM and LARSWG, for the assessment of the
hydrological conditions of the basin.
• Generate climate change scenarios for the basin using
different emission scenarios and AOGCMs (Atm.and Ocean).
• Investigate the possiblity of climate change on
hydrology in UBRB based on the downscaled
meteorological scenario data.
• Provide streamflow predictions of the basin for current
and downscaled future climate conditions.
Contents
•
•
•
•
•
•
Background on Climate System
Study Area
Data collection, analysis and results
Climate Modeling
Results of Climate Modeling
Conclusions
Background (Climate system)
Climate is a statistical description of weather
including averages and variability.
The earth climate system is an interaction of various
components of climate system:
 Ocean
 Land surface
 Atmosphere
 Cryospher
 Biosphere
 Anthropogenic
---Background (Climate system)
• Climate Change: refers to a statistical significant
variations that persist for an extended period, typically
decades or longer.
• The mea annual global temperature has increased by
about 0.3-0.60C since the late 19 century.
---Background (Climate change Impact )
• Today, the impact of climate change become the
biggest concern of mankind.
---Background (Climate Change Impact)
• This will impact the hydrology of the watershed systems
and hence it exhibits long-term changes.
---Background (Climate Change Impact)
• This impact needs integrated modeling to evaluate
alternate future watershed scenarios.
• IPCC findings indicate that developing countries, such as
Ethiopia, will be more vulnerable to climate change
Higher Relative
Risks
Lower Relative
Risks
---Background (Climate Model)
• Climate Models try to simulate the likely responses of
climate system to a change in any of the parameter
interactions between them mathematically.
• Generally refers as GCMs (Global Circulation Models)
• The 3-D model formulation is based on the fundamental
laws of physics:
Conservation of energy
Conservation of momentum
Conservation of mass and
The “Ideal Gas Law”
---Background (Emission Scenarios)
• Emission scenarios are
important components and
tools for the modeling of
climate change (Werner and
Gerstengarbe, 1997)
Emissions 2011-2030 2046-2065
2080-2099
A2
0.64
1.65
3.13
A1B
0.69
1.75
2.65
B1
0.66
1.29
1.79
---Background (Downscaling GCM)
• In climate change impact
studies, hydrological modeling:
Are usually required to
simulate sub-grid scale
phenomenon.
Require input data (such as
pcp, temp) at similar subgrid scale.
• Downscaling is a means of
relating the large scale
atmospheric predictor variables
to local scale so as to use for
hydrological model inputs.
---Background (Downscaling Methods)
1. Dynamic downscaling
Extract local-scale information by developing and using
regional climate models (RCMs) with the coarse GCM
data used as boundary conditions.
2. Statistical downscaling
Drive the local scale information from the larger scale
through inference from the cross-scale relationship.
It Can be categorized in to three types
 Regression downscaling
 Stochastic weather generators
 Weather typing schemes
---Background (Statistical downscaling)
1. Regression downscaling techniques:
Predicted=f(Predictors). The function f could be.
Linear or non-linear regression.
2.Stochastic weather generators:
The relationships between daily weather generator
parameters and climatic average can be used to
characterize the nature of future daily statistics (wilby,
1999).
---Background (Statistical downscaling)
3. Weather typing schemes
 Involve grouping local, meteorological variables in
relation to different classes of atmospheric circulation.
 Future regional climate scenarios are constructed by:
Resembling from observed variable distribution
 Climate change is then estimated by determining the
change of the frequency of weather classes.
Study area
---Study Area
Features of Upper Blue Nile watershed
The total area=176,000 km2
Latitude: 7° 45’ and 12° 45’N and longitude: 34°
05’ and 39° 45’E
Altitude: Min. 485m to Max. 4,257m asl
UBNB has 14 sub-basins
It contributes 40% of Ethiopia surface water
resources [World Bank 2006]
87% of the Nile flow at Aswan dam is from
Ethiopia from this UBNB contributes 60% and
the Atbara (13%) and the Sobat (14%)
Data sources
Data Name
Sources
Precipitation
Maximum Temperature
Minimum Temperature
NMA
www.ncep.noaa.gov
NCEP
WCRP CMIP3 Multi-Modal data set
http://esg.llnl.gov:8080/index.jsp
GCMs
World Climate Data Center
http://www.mad.zmaw.de/wdc-forclimate/cera-data-model/index.html
Data Collection and Quality Checking
• After collection of precipitation data from 53 stations and
temperature from 33 stations  for 1970-2000 period at
daily time scale, data quality( Such as, filling missing data
and consistency check) control has been conducted.
• Areal precipitation and temperature based on Thiessen
Polygon method: Stn.
Results: 
Sub-Basin Results of Observed Data
Large Scale Data
 Criterion to chose GCMs
1. Based on outputs of
MAGICC-SCENGEN
2. Based on data availability
3. Based on their participation
IPCC-AR4
4. Allowable number of GCMs
ECHAM-5, GFDLCM21 and
SCIRO-MK3
Data of selected GCMs
GCM
Emission
Scenario of
A1B and A2
Current
Condition
Scenario
65 years
Into Future
Scenario
100 years Atmospheric
Into Future Resolutions
Scenario
(Deg)
Echam5
1970-2000 2046-2065
2081-2100 1.9x1.9
GFDLCM2.1
1970-2000 2046-2065
2081-2100 2.0x2.5
CSIRO-MK3
1970-2000 2046-2065
2081-2100 1.9x1.9
NCEP
1970-2000
2.5X2.5
• A1b and A2 emission scenarios are considered to account
the worst (A2) and the middle(A1B).
• Re-griding has been done using Xconv package.
Large-scale Predictor Variables
S
No
Predictor variables
Design
ation
S
No
Predictor variables
Designat
ion
1
Air pressure at sea level
mslp
11
Northward wind @850mpa
p8_v
2
Precipitation flux
prat
12
Northward wind @500mpa
p5_v
3
Minimum air temperature
tmin
13
Meridional surface wind speed
p_v
4
Maximum air temperature
tmax
14
Specific humidity @850mpa
s850
5
Surface air tempratur@2m
temp
15
Specific humidity @500mpa
s500
6
Air temperature @850mpa
t850
16
Geopotential height @850mpa
p850
7
Air temperature@500mpa
t500
17
Geopotential height @500mpa
p500
8
Eastward wind@850mpa
p8_u
18
Relative humidity @500mpa
r500
9
Eastward wind@500mpa
p5_u
19
Relative humidity @850mpa
r850
10
Zonal surface wind speed
p_u
Large Scale Data
 Re-analysis grid lines covering the study area
Name of
Grid box
Name of
Grid box
Subbasin
considered
sub basin
considered
Tana
22 and 23
Anger
12and 22
Belles
12,13,
Wonbera
12 and 22
12
Muger
22 and 32
11,12,
Beshilo
22,23,
22 and 23
Dabus
D idessa
21and 22
32 and 33
Guder
22
Wolaka
22 and 32
Fincha
22
N/Gojam
22 and 23
S/Gojam
22
Jimma
22 and 32
Statistical Downscaling Tools
• Two statistical downscaling tools:
• *SDSM: A regression based statistical downscaling
model (wilby, et al., 2002)
• *LARS-WG: Long Ashton Research Station Stochastic
Weather Generators (Semenov et al, 1998).
SDSM: A regression based Statistical
Downscaling models
• Identify predictand relationships using
multiple linear regression techniques.
• The predictor variables provide daily
information concerning the large-scale state of
the atmosphere,
• The predictand describes condition at the site
scale.
LARS-WG
• Generate precipitation, min and
max temperature.
• Semi-empirical distributions are
used to state a day as wet/dry
series.
• Semi-empirical distributions are
used for precipitation amounts,
dry/wet series.
• Semi-empirical distributions are
used for Temperature. It is
conditioned on wet/dry status
of a day.
Cases considered
• Three cases are employed in climate modeling
Type
Case-1
Case-2
Case-3
GCMs
echam5
echam5
Echam5, gfdl21
& csiro-mk3
Emission
Period
Tools
a1b, a2
2050s, 2090s SDSM
a1, a2
2050s, 2090s LARS-WG
a1b, a2
2050s, 2090 LARS-WG
• All the cases are applied for each of 14 sub-basins in
UBNB.
Climate modeling-Case1
 SDSM reduces the task into a
number of discrete processes as
follows:
 1. Quality control of data and
transformation.
 2. Selection of appropriate predictor
variables for model calibration.
 3. Calibrate Model.
 4. Generate the daily data.
 5. Analyze the outputs.
 6. Scenario generation: Then
analysis of climate change scenarios
Selecting predictor variables
• Predictor is selected based on correlation analysis off-line of
SDSM and using SDSM screening methods in the software.
SDSM Calibration Approach
 Model calibration is performed in two approaches:
 Unconditional: It assumes a direct link between the
regional-scale predictors and the local predictand.
• Maximum and minimum temperature
 Conditional: depend on an intermediate variable such as
the probability of wet-day occurrence, intensity, amount
etc.
• Precipitation
The performance of calibration result for each sub basin 
Relative change (%)
Results-Case1
150
Simulated RF change from observed at Muger
100
50
0
2050s_A1B
2050s_A2
2090s_A1B
2090s_A2
Jan
57
58
109
111
Feb
51
41
73
84
Mar
19
16
42
46
Apr
21
18
35
39
May
18
16
42
45
Jun
30
29
82
91
Jul
50
43
102
109
Aug Sep Oct
53 9 19
48 9 21
100 15 32
107 21 32
Nov
45
38
67
72
Dec
72
59
110
117
Trend line of simulated RF at Muger
3000
2500
1500
Observed
2050s_A2
1000
500
Control
2090s_A1B
2050s_A1B
2090s_A2
2099
2096
2093
2090
2087
2084
2081
2063
2060
2057
2054
2051
2048
2000
1997
1994
1991
1988
1985
1982
1979
1976
1973
0
1970
RF (mm)
2000
Relative change (%)
Results-case1
0
-20
-40
-60
-80
Jan
2050s_A1B -12
2050s_A2 -4
2090s_A1B -20
2090s_A2 -31
2500
Feb Mar Apr May Jun Jul Aug Sep
-32 -39 -36 -48 -46 -42 -23 -11
-34 -38 -28 -42 -43 -34 -25 -17
-53 -63 -66 -66 -65 -58 -52 -55
-55 -66 -66 -68 -68 -61 -50 -49
Trend line of simulated RF at Wonbera
Observed
2050s_A2
Control
2090s_A1B
Oct
-32
-24
-38
-43
Nov
-21
-21
-32
-45
Dec
-10
-10
-24
-24
2050s_A1B
2090s_A2
1500
1000

500
0
1970
1973
1976
1979
1982
1985
1988
1991
1994
1997
2000
2048
2051
2054
2057
2060
2063
2081
2084
2087
2090
2093
2096
2099
RF (mm)
2000
Simulated RF change from observed at Wonbera
Climate Modeling –Case2
 The weather generator consists of three main sections:
 Model calibration
Analysis of observed station data in order to calculate the
weather generators.
 Model validation
Qtest is used for determining how well the model is
simulating observed conditions.
The statistical characteristics of the observed data are
compared with those of the synthetic data.
 Model use
Generating the synthetic weather based on the available data
parameter generated during model calibration or by
combining scenario file with the generated parameter to
account climate change.
Incorporating Climate Scenario
• Climate changes derived from GCMs can be incorporated
in stochastic weather generator by applying climate
change scenarios expressed on a monthly basis in the
relevant climate variable.
e5ab_2050
month
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
m.rain
1.66
2.20
0.91
1.11
0.85
0.80
1.00
1.24
1.17
1.01
1.33
2.93
e5a2_2090
wet
dry
min
max
tsd
rad
1.04
0.97
0.98
1.05
1.30
0.98
1.44
1.54
1.62
1.24
0.98
1.02
0.97
1.02
1.01
1.00
1.17
0.84
1.18
1.80
1.98
1.31
0.98
1.01
2.31
2.60
2.39
2.19
2.73
2.97
2.96
2.27
2.15
2.56
2.92
3.17
1.85
1.89
2.60
1.95
3.06
4.06
3.59
1.71
1.63
2.23
2.00
2.21
1.31
1.13
1.53
1.10
1.27
1.23
1.18
1.22
1.52
1.32
1.33
1.54
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
m.rain
2.94
1.20
1.05
1.12
0.87
0.87
1.02
1.20
1.10
1.05
1.16
2.33
wet
dry
min
max
tsd
rad
1.01
1.01
0.99
1.03
0.74
0.89
1.48
1.76
1.52
1.03
1.02
1.00
0.98
1.00
0.99
1.01
1.03
1.04
1.06
1.28
1.34
0.84
1.03
1.00
2.54
2.08
2.00
1.85
2.33
2.70
2.56
2.18
1.90
2.26
2.49
2.55
1.51
1.99
2.36
1.49
2.43
3.63
2.91
1.80
1.56
1.79
1.85
1.87
1.06
1.04
1.26
1.06
1.17
1.12
1.24
1.13
1.19
1.16
1.07
1.04
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
Relative change (%)
Results-Case2
150
100
50
0
-50
pcpa1b_2050s
pcpa2_2050s
pcpa1b_2090s
pcpa2_2090s

Temperature Change
5.0
4.0
3.0
2.0
1.0
UBNB Seasonal pcp
Winter
126
75
5
-18
Spring
0
1
-11
-4
UBNB Seasonal Temprature Change
Winter
Spring
Summer
Autumn
Summer
1
1
0
-2
Autumn
-1
-4
10
11
Climate Modeling: Case-3
• The methodology is same as case-2.
• The climate change scenario is constructed from 3GCMs.
UBNB Seasonal pcp
Relative change (%)
10
5
0
-5
-10
-15
-20
Winter
Spring
Summer
Autumn
pcpa1b_2050s
0
-3
-5
-6
pcpa2_2050s
4
-2
-4
-7
pcpa1b_2090s
-15
-9
-6
4
pcpa2_2090s
-11
-8
-6
5

Comparison of Mono-Modal and
Multi-Modal Approaches
• Multi-modal approach under estimated pcp prediction
and this is more apparent in 2050s than 2090s.
• Annual relative % change in pcp increases due to
relatively high increase in dry periods.
• Tmx and Tmn change has no significant difference
between two approaches in 2050s.
• Multi-modal approach underestimates both Tmx and
Tmn during 2090s
• Summer season in the case of mono-modal is warmer
while spring season is warmer in multimodal approach.
Comparison of Mono-Modal and
Multi-Modal Approaches
Mono/Multi-modal Comparisons
Comparisons of SDSM and
LARS-WG outputs
• Generally, downscaled precipitation results from
SDSM and LARS-WG show marked difference.
• Both downscaling tools illustrate an increase in
maximum and minimum temperature in both 2050s
and 2090s time compare with the base line period.
SDSM and LARS-WG Comparison
SDSM and LARS-WG Comparison
Conclusions
• LARS-WG performs better in precipitation prediction
than SDSM.
• simulation of future precipitation using SDM has
significant spatial variation than LARS-WG.
• LARS-WG illustrate similar trend across each subbasins in the simulation of precipitation, maximum
and minimum temperature.
• LARS-WG shows better performance over the study
area than SDSM.
THANK YOU.