INTRODUCTION U x, V x, Z, S lp 1990-99 (CC) 2081-90 (A1B, A2) 11 13 Dataset Used • Observed daily...

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