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INDIAN INSTITUTE OF TECHNOLOGY ROORKEE
Assessment of Runoff Generation at Rift Valley Lakes Basin of Ethiopia for present and future
climate scenario
Dr. Manoj K. Jain
Department of Hydrology
Indian Institute of Technology, Roorkee
and Dr. Negash WageshoUrba Minch University
Ethiopia
2
INTRODUCTION
• The natural environment is under tremendous
stress as a consequence of various demands of
increasing population.
• Temporal and spatial patterns of precipitation and
extreme flows have been changing in the recent
past.
• Global water balance is highly influenced…
• Increased earth and sea surface temp., melting of
polar ice, rise in average global ocean level…
(Loaiciga et al., 1996; Singh et al., 1997; Barnett et al., 2005; Steele-
Dunne et al., 2008; IPCC,2007)
2
3
Introduction…
• Avg. temp. rise of 0.6 ± 0.2 oC (1860-2000) versus
0.74°C ± 0.18°C (1906-2000) (IPCC,2001; 2007)
3
1906 2005
T = 0.07°C ± 0.02°C per decade
1950T = 0.13°C ± 0.03°C
• Anthropogenic induced land use/cover changes havetransformed 1/3rd -1/4th of ice-free surface of our planet
(Vitousek, 1994; Vitousek et al., 1997).• Land use/land cover changes significantly altered bulk
water yield from the watershed.
4
Introduction…
• Land use changes are significant in tropical regions (Piao et al. 2007).
• Currently, 1/4th of the population of Africa is facing
high water stress and this magnitude is expected to
increase (2-3 fold ) in the next 40 years (Boko et al.,
2007)
• Adaptation mechanisms developed by African
farmers are not sufficient enough to cope up with
current and future climate variability (IPCC, 2007)
4
5
Introduction…
• The sub-Saharan region of Africa has been challenged by
both man-made and natural stresses …. (Tadross et al.,
2005; You and Ringler, 2010).
• Key Features - prolonged drought
- severe and unprecedented flood
- poor economic developments
- poor institutional developments
• Ethiopia is a victim of such global challenges
• Example: 48 flood and 12 drought events over the last
Century (EM-DAT, 2010).
5
6
Introduction…
Ethiopia: Agricultural sector accounts
• 45% of the country’s GDP
• 85% of the export revenue
• Employs 80% of labour force.
• Major source of subsistence and household
income for majority of the rural population.
• Agricultural production is mainly rain-fed.
6
ETHIOPIA
7
Introduction…
• Owing to limited data availability, little attempt has
been made, in the past, to understand the
watershed dynamics of Ethiopian basins in
response to changing climate and catchment
conditions.
• The Rift Valley lakes and rivers system of Ethiopia
has undergone major changes in recent past.
• Agricultural, water supply, and hydropower sectors
are affected by variable climate patterns.
7
8
Key Objectives
• Explore the impact of large scale present and future
climatic variables under different greenhouse gas
forcing on local climate at Rift Valley lakes basin of
Ethiopia.
• Simulate runoff at desired locations using bias
corrected precipitation and temperature in two snow
free, agricultural watersheds in the basin.
• Forecast the likely future climate implications in the
basin.
8
9
STUDY AREA
• The study mainly focuses on Rift Valley lakes Basin of
Ethiopia.
• Geog. location: 4o24’29’’ to 8o26’38‘’ N latitude and
36o35’45’’ to 39o23‘31’’ E longitude.
• Mean annual rainfall: 600-1220 mm
• Temperature: Avg. temp. varies from 10.3 oC (min.) to 30.6 oC (max.)
• Climate Condition: sub-humid to moderate tropical semi-arid.
• Gemorphology: lowland plateau & escarpments
• Study Watersheds: Bilate (5330 km2) and Hare (166.5 km2)
9
10
River Basins of Ethiopia & Study Watersheds
12 major river Basins
Bilate
Hare
Bilate (5330km2) and
Hare (166.5 km2)
Methodology
Rift Valley Lakes Basin (Study Basin)
Collection of Data and Preprocessing
Temp., PCP., Spatial Data (DEM, soil, land use)
Selection of GCMs and Emission Scenarios
Downscaling [ Temp and PCP ]
Predictands (observedTemp. and PCP.)
Bias Correction for[ PCP and Temp ]
Predictors (large scale atmospheric variables)
Hydrologic Modelling[ SWAT Model ]
Runoff Simulation[ Present climate ][ Future scenario ]
- Linear correction- Non-linear correction
2 GCMs selected- BCCR-BCM2.0- CSIRO-MK3.0
Emission Scenario- A1B , A2 , CC
Predictors- Shum, Ux, Vx, Z, Slp
1990-99 (CC)2081-90 (A1B, A2)
11
13
Dataset Used
• Observed daily precipitation and temperature data from
1980-2009.
• Daily Global Climate Model (GCM) Variables for current
climate (1971-1999) and
• Daily GCM Variables for future climate condition (2081-
2090).
13
14
Data Used ( Bilate and Hare watersheds)
• Topographic Data (90m SRTM DEMs )
• Hydrometerological Data ( Daily RF, Temp., RH, Sunshine and Streamflow)
• Soil Data ( FAO soil data); Land Use Data ( Landsat image )
(a)
SoilLand use
(a)
Slope
14
15
Soil Land use and Slope classes
15
Land UseSoil
(b)
(b)
Slope
16
GCMs Selected and Scenario Used
• BCCR-BCM2.0 (Bjerknes Center for Climate Research
Version 2.0 of Norway )
- Atmospheric resolution of T63 (1.9ox1.9o)
- Oceanic resolution of 0.5o-1.5o x 1.5o
• CSIRO-MK3.0 (Commonwealth Scientific and Industrial
Research Organization of Australia)
- Atmospheric resolution of T63 (1.9ox1.9o)
- Oceanic resolution of 0.9ox1.9o.
• Emission Scenarios Used: A1B, A2 and Current Climate
condition
16
A1B – medium forcing, CO2 conc. 720 ppm in 2100 (Balanced World)
A2 – high forcing , CO2 conc. 820ppm in 2100 (Heterogeneous World)
17
Temperature downscaling
• Downscaled temperature show modest agreement to the
observed series.
• Regression , quantile plot and Q-Q plot is conducted to verify
the validity of simulated current climate condition.
17
18
Observed Temp. versus downscaled (Regression)
• .
18
19
Quantile plot of temp. for two GCM realizations
19
20
22
24
26
28
30
32
34
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
TM
AX
(oC
)
Quantiles
A1B Scenario
ObservedMK3.0BCM2.0
20
22
24
26
28
30
32
34
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
TM
AX
(oC
)
Quantiles
A2 Scenario
Observed
MK3.0
BCM2.0
20
22
24
26
28
30
32
34
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
TM
AX
(oC
)
Quantiles
Current Climate
ObservedMK3.0BCM2.0
The quantile plots are S-shaped
and are characteristic example
of bell-shaped distribution.
20
Current climate Precip. (Predicted, Observed, Bias corrected
20
0
20
40
60
80
100
120
140
160
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Pre
cip
ita
tio
n (m
m)
BCM2.0
Observed
Uncorrected
Linear corrected
Power transformed
(a)
0
20
40
60
80
100
120
140
160
180
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Pre
cip
ita
tio
n (m
m)
(b) MK3.0
Observed
Uncorrected
Linear corrected
2121
A1B scenario Precip.
0
20
40
60
80
100
120
140
160
180
200
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Prec
ipit
atio
n (m
m)
BCM2.0
ObservedUncorrectedLinear correctedPower transformed
0
20
40
60
80
100
120
140
160
180
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Prec
ipit
atio
n [m
m] MK3.0
ObservedUncorrectedLinear correctedPower transformed
A2 scenario Precip.
0
50
100
150
200
250
300
350
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Pre
cip
itat
ion
(mm
)
BCM2.0Linear correctedPower transformedObservedUncorrected
0
20
40
60
80
100
120
140
160
180
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Pre
cip
itat
ion
(m
m)
MK3.0
Observed
Uncorrected
Linear corrected
Power transformed
Future climate Precip. (Predicted, Observed, Bias corrected
22
Hydrologic Modelling
• SWAT model is calibrated and validated for observed data
at both watersheds.
• Runoff is simulated for current climate and future scenarios
at two watersheds.
• Simulated daily runoff is aggregated to monthly series for
further analysis.
22
23
Model Calibration
0
200
400
600
800
10000
10
20
30
40
50
60
Jan-9
2
May-9
2
Sep
-92
Jan-9
3
May-9
3
Aug
-93
Dec-9
3
Ap
r-94
Aug
-94
Dec-9
4
Ap
r-95
Aug
-95
Dec-9
5
Ap
r-96
Aug
-96
Dec-9
6
RF
(mm
)
Ru
no
ff (m
3/s
)
observedsimulated
0
200
400
600
8000
2
4
6
8
10
Jan
-95
Ap
r-95
Jun
-95
Sep
-95
De
c-95
Mar
-96
Jun
-96
Sep
-96
De
c-96
Mar
-97
Jun
-97
Sep
-97
De
c-97
Mar
-98
Jun
-98
Sep
-98
De
c-98
Mar
-99
Jun
-99
Sep
-99
De
c-99
Mar
-00
Jun
-00
Sep
-00
De
c-00
Rai
nfa
ll (
mm
)
Dis
char
ge (
m3 /
s)
observed
simulated
Bilate
Hare
Qsim = 0.8687*Qobs + 0.207R² = 0.81
0
1
2
3
4
5
0 1 2 3 4 5 6Si
mu
late
d d
isch
arge
(m3 /
s)observed discharge (m3/s)
(b)
Qsim = 0.963Qobs- 0.674R² = 0.92
0
10
20
30
40
50
0 10 20 30 40 50
sim
ula
ted
dis
char
ge
(m3/s
)
observed discharge (m3/s)
(a)
23
1992-96
1995-00
24
Model Validation
0
200
400
600
8000
20
40
60
80
Jan
-98
Ma
y-9
8
Sep
-98
De
c-9
8
Ap
r-9
9
Au
g-9
9
De
c-9
9
Ap
r-0
0
Au
g-0
0
De
c-0
0
Ap
r-0
1
Au
g-0
1
De
c-0
1
Ap
r-0
2
Au
g-0
2
De
c-0
2
RF
(mm
)
Dis
cha
rge
(m3
/s)
observed
Simulated
0
200
400
600
8000
2
4
6
Jan
-03
Ap
r-0
3
Jul-
03
Oct
-03
Jan
-04
Ap
r-0
4
Jul-
04
Oct
-04
Jan
-05
Ap
r-0
5
Jul-
05
Oct
-05
Jan
-06
Ap
r-0
6
Jul-
06
Oct
-06
Ra
infa
ll (
mm
)
Dis
cha
rge
(m
3/s
)
observed
simulated
(a)
(b)
Bialte Watershed
Hare Watershed
24
Qsim = 1.0679*Qobs - 0.3046R² = 0.81
0
1
2
3
4
0 1 2 3 4
sim
ula
ted
dis
charg
e (m
3/s
)observed discharge (m3/s)
Qsim = 0.95*Qobs + 1.30R² = 0.82
0
10
20
30
40
50
0 10 20 30 40 50sim
ula
ted
dis
char
ge (m
3/s
)
observed discharge (m3/s)
2003-06
1998-02
25
Model efficiency
indices Calibration Validation Calibration Validation
( 1995-2000) (2003-2006) ( 1994-2000) (2003-2006)
R2 0.92 0.82 0.88 0.81
bR2 0.89 0.78 0.71 0.86
NSE 0.91 0.79 0.87 0.96
PBIAS 8.93 -9.05 1.36 8.83
RSR 0.09 0.21 0.19 0.32
p-factor 0.82 0.78 0.81 0.78
r-factor 0.72 0.88 1.40 1.80
Hare-basinBilate basin
Model performance indices
25
26
Runoff Simulated – Current climate condition ( 1990-99)
26
0
20
40
60
80
100
Jan
-90
Jul-
90
Jan
-91
Jul-
91
Jan
-92
Jul-
92
Jan
-93
Jul-
93
Jan
-94
Jul-
94
Jan
-95
Jul-
95
Jan
-96
Jul-
96
Jan
-97
Jul-
97
Jan
-98
Jul-
98
Jan
-99
Jul-
99
Jan
-00
Ru
no
ff (
mm
)
Observed MK3.0 BCM2.0(a)
0
20
40
60
80
100
120
Jan
-90
Jul-
90
Jan
-91
Jul-
91
Jan
-92
Jul-
92
Jan
-93
Jul-
93
Jan
-94
Jul-
94
Jan
-95
Jul-
95
Jan
-96
Jul-
96
Jan
-97
Jul-
97
Jan
-98
Jul-
98
Jan
-99
Jul-
99
Jan
-00
Ru
no
ff (
mm
)
Observed BCM2.0 MK3.0(b)
Bilate
Hare
2727
0
20
40
60
80
100
Jan-
2081
Jul-2
081
Jan-
2082
Jul-2
082
Jan-
2083
Jul-2
083
Jan-
2084
Jul-2
084
Jan-
2085
Jul-2
085
Jan-
2086
Jul-2
086
Jan-
2087
Jul-2
087
Jan-
2088
Jul-2
088
Jan-
2089
Jul-2
089
Jan-
2090
Jul-2
090
Runo
ff (
mm
)
MK3.0 (A1B) BCM2.0 (A1B)
0
20
40
60
80
100
Jan-
2081
Jul-2
081
Jan-
2082
Jul-2
082
Jan-
2083
Jul-2
083
Jan-
2084
Jul-2
084
Jan-
2085
Jul-2
085
Jan-
2086
Jul-2
086
Jan-
2087
Jul-2
087
Jan-
2088
Jul-2
088
Jan-
2089
Jul-2
089
Jan-
2090
Jul-2
090
Runo
ff (m
m)
MK3.0 (A2) BCM2.0 (A2)
0
20
40
60
80
100
120
Jan-
2081
Jul-2
081
Jan-
2082
Jul-2
082
Jan-
2083
Jul-2
083
Jan-
2084
Jul-2
084
Jan-
2085
Jul-2
085
Jan-
2086
Jul-2
086
Jan-
2087
Jul-2
087
Jan-
2088
Jul-2
088
Jan-
2089
Jul-2
089
Jan-
2090
Jul-2
090
Runo
ff (m
m)
MK3.0 (A1B) BCM2.0 (A1B)
0
20
40
60
80
100
120
Jan-
2081
Jul-2
081
Jan-
2082
Jul-2
082
Jan-
2083
Jul-2
083
Jan-
2084
Jul-2
084
Jan-
2085
Jul-2
085
Jan-
2086
Jul-2
086
Jan-
2087
Jul-2
087
Jan-
2088
Jul-2
088
Jan-
2089
Jul-2
089
Jan-
2090
Jul-2
090
BCM2.0 (A2) MK3.0 (A2)
Runoff Simulated – Future climate condition (2081-90)
Bilate Hare
2828
Ave
rage
mo
nth
ly R
un
off
Sim
ula
ted
Cu
rre
nt
clim
ate
A1
B s
cen
ario
A2
sce
nar
io
Bilate Hare
Bilat Hare
29
Box plots of simulated Runoff (mm)
29
A B
30
Conclusion
• Runoff simulated for the current climate (1990-1999) using
bias corrected precipitation and temperature modestly
reproduced effects similar to that of observed weather
variables at both watersheds.
• The overall NSE and coefficient of determination model
performance indices ranges between 0.79 and 0.96 during
calibration and validation period at both watersheds; other
indices are at acceptable limit.
• The simulated annual water yield is within ±3.4% error to the
observed annual stream flow volume at the same outlet.
30
31
Conclusions
• Significant amount of variability is observed in downscaled
precipitation for current climate whereas the associated
variability for temperature is very less.
• Increased extreme precipitation and temperature events
prevail for future scenarios.
• Average dry-spell length found to increase between October
and February whereas it remains stable from March –
September months for both emission scenarios.
• Bias correction improved both the mean and CV to a
reasonable degree
31
32
Conclusions…
• Simulated average annual water yield shows slight
variation between GCMs.
• The simulated future runoff is characterized by
higher number of extreme events.
32
33
Thanks