Assessing the impacts of climate change on Atbara flows using bias-corrected

GCM scenarios

Assessing the impacts of climate change on Atbara flows using bias-corrected

GCM scenarios

SIGMED and MEDFRIEND International Scientific Workshop

Relations man / environment and sediment transport: a spatial

approach

Algeria 7&8 June 2011Mohamed Elshamy

OutlineOutline

1. Uncertainty Cascade1. Uncertainty Cascade

2. The Nile Basin & Previous CC studies2. The Nile Basin & Previous CC studies

3. Study Area & Methodology3. Study Area & Methodology

4. Results 4. Results

5. Conclusions 5. Conclusions

Uncertainty CascadeUncertainty Cascade

Emissions Emissions

Concentrations Concentrations

Radiative Forcing Radiative Forcing

Global Climate Models Global Climate Models

Regional Details (Downscaling) Regional Details (Downscaling)

Impact Models (e.g. Hydrology) Impact Models (e.g. Hydrology)

Ob

serv

atio

ns

Ob

serv

atio

ns

The Nile BasinThe Nile Basin

Large area (2.9 x 106 km2) Low specific discharge Spans several climate

regions Variable topography High runoff variability High Sensitivity to Climate

Mongalla

Jinja

Pakwach

Diem

Roseires

Sennar

Khartoum

Malakal

Atbara

KhashmEl-Girba

Aswan

Dongola

Mogren

Hillet Doleib

Masindi

Cairo

LakeNo

TANZAN IA

BU RU NDI

Gabal Awlia

Paara

-5

0

5

10

15

20

25

30

35

20 25 30 35 40

EGY PT

ETH IOPIA

D.R. CONGO

RWANDA

L IBY A

CENTRAL

AF RICAN

REP.

U GANDA

SU DAN

ERITRIACHAD

K ENY A

Lake Nasser Flood & Drought Control Project (2008)

Previous Studies (1)Previous Studies (1)

– 6 Transient scenarios (3 GCMs x 2 Emission Scenarios)

– Statistically downscaled using a spatio-temporal weather generator

– Changes at Dongola from 2010-2100

0

20

40

60

80

100

120

Base 2010s 2020s 2030s 2040s 2050s 2060s 2070s 2080s 2090s

To

tal A

nn

ua

l Flo

w (

BC

M)

HadCM3 A2 HadCM3 B2

CGCM2 A2 CGCM2 B2ECHAM4 A2 ECHAM4 B2

OBS Base

Elshamy, M.E., Sayed, M.A.-A. and Badwy, B., 2009. Impacts of climate change on Nile flows at Dongola using statistically downscaled GCM scenarios. Nile Water Science & Engineering Magazine 2: 1-14

Previous Studies (2)Previous Studies (2)

Elshamy et al. (2009)– 17 GCMs x A1B

scenario– Statistically

downscaled using Bias Correction Method

– Blue Nile Flow Changes: -60% to +45%

0

2

4

6

8

10

12

14

16

18

20

22

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Flo

w (

BC

M)

Ensemble Mean 2081-98

Observed 1961-90

Elshamy, M.E., Seierstad, I.A. and Sorteberg, A., 2009. Impacts of climate change on Blue Nile flows using bias-corrected GCM scenarios. Hydrol. Earth Syst. Sci., 13(5): 551-565.

The Atbara BasinThe Atbara Basin

Climate: Semi-Arid/Arid

Area:200 000 km2

Mean Rainfall: 500 mm/yr

Mean PET: 1730 mm/yr

Mean Flow: 8.5 BCM/yr

(1961-1990)

Very Sensitive to Climate

The Atbara BasinThe Atbara Basin

Sensitivity to ClimateSensitivity to Climate

-200

-150

-100

-50

0

50

100

150

200

-40 -30 -20 -10 0 10 20 30 40

Preciptation Change %Flo

w C

ha

ng

e %

Atbara

Kagera

Gilgel Abbay

-100

-80

-60

-40

-20

0

20

40

60

80

100

-40 -30 -20 -10 0 10 20 30 40

PET Change %

MethodologyMethodologyCoarse Daily GCM

Rainfall

Coarse Daily GCMRainfall

Bias Correction DownscalingBias Correction Downscaling

Fine-Scale Daily Rainfall Fine-Scale Daily Rainfall

Hydrological Model Hydrological Model

Flow at Atbara17 GCMs

x 1 Scenario

Flow at Atbara17 GCMs

x 1 Scenario

Compatible PET ScenariosCompatible PET Scenarios

Bias Correction DownscalingBias Correction Downscaling

Bias correction for downscaling rainfall (based on fitting the gamma distribution to daily rainfall)

Simple bias correction for PET (ratio)NFS & HBV for hydrological modelingAn ensemble approach (17 GCMs – A1B)Baseline 1961-90, Future 2081-2098,

Daily rainfall data & Monthly PET data

Rain gauge DataRain gauge Data Satellite ImagesSatellite Images

Rainfall Estimation ModelsRainfall Estimation Models

Rainfall EstimatesRainfall Estimates

Hydrological Models

Hydrological Models

Simulation and Extended Stream Flow

Prediction (ESP)

Simulation and Extended Stream Flow

Prediction (ESP)

Water BalanceWater Balance

Hill SlopeHill Slope

RoutingRouting

SwampSwamp

LakeLake

Historical Climate

Historical Climate

GISGIS

Nile Forecast System (NFS)Nile Forecast System (NFS)

HBV Hydrological ModelHBV Hydrological ModelPrecipitation on lakes

Lakeevaporation

EA=EPOT

P

From soil moisture routinedUZ

UPPER ZONE

LOWER ZONE

KUZ1

KUZ

UZ1

UZ

Q11

Q10

Q2

LZKLZ

PERCLake area in % (LA)

Runoff, Q

KLZKUZKUZ1

UZ

PERC

: Time constant, lower zone, 1/t: Time constant, upper zone, 1/t: Time constant, upper zone, 1/t

: Water level, upper zone

: Percolation to lower zone, mm/day

QQ10Q11

Q2

= Q10 + Q11 + Q2= MIN (UZ, UZ1)*KUZ= MAX (0, (UZ - UZ1)*KUZ1))

= KLZ*LZ

PARAMETERS IN THE RESPONSEFUNCTION : RUNOFF COMPONENTS :

d UZ = dUZ - PERC - Q11 - Q10

Water balance equation, upper zone:

d LZ = PERC + (P – EPOT)*LA/100 - Q2

Water balance equation, lower zone:

LZ : Water level, lower zone

UZ1 : Threshold for quick flow, mm

Model PerformanceModel Performance

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

Jan-19

61

Jan-19

62

Jan-19

63

Jan-19

64

Jan-19

65

Jan-19

66

Jan-19

67

Jan-19

68

Jan-19

69

Jan-19

70

Jan-19

71

Jan-19

72

Jan-19

73

Jan-19

74

Jan-19

75

Jan-19

76

Jan-19

77

Jan-19

78

Jan-19

79

Jan-19

80

Jan-19

81

Jan-19

82

Jan-19

83

Jan-19

84

Jan-19

85

Jan-19

86

Jan-19

87

Jan-19

88

Jan-19

89

Jan-19

90

Mon

thly

Flo

w (B

CM/m

on)

OBS NFS HBV

Monthly NSE = 0.69 & 0.83 for NFS & HBV respectively

The GLUE FrameworkThe GLUE Framework

• GLUE: Generalized Likelihood Uncertainty Estimation

• GLUE rejects the concept of a single optimal model and parameter set

• Assumes all model structures and parameter sets have a likelihood of being accepted

• Likelyhood depends performance as measured by a selected criteria

Results: Rainfall ChangesResults: Rainfall Changes

0

20

40

60

80

100

120

140

160

180

200

220

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Rai

nfa

ll (

mm

/mo

nth

)

Mean 1961-1990

OBS

0

20

40

60

80

100

120

140

160

180

200

220

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Rai

nfa

ll (

mm

/mo

nth

)

Mean 2081-98

Mean 1961-90

Results: PET ChangesResults: PET Changes

280

285

290

295

300

305

310

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Tem

per

atu

re (

°K)

Mean 1961-1990

OBS

280

285

290

295

300

305

310

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Tem

per

atu

re (

°K)

Mean 2081-98

Mean 1961-90

Results: Flow Changes - NFSResults: Flow Changes - NFS

0

1

2

3

4

5

6

7

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Mea

n M

on

thly

Flo

w (

BC

M)

Mean

Median

Max

Min

OBS

0

1

2

3

4

5

6

7

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Mea

n M

on

thly

Flo

w (

BC

M)

Results: Flow Changes - HBVResults: Flow Changes - HBV

0

1

2

3

4

5

6

7

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Mea

n M

on

thly

Flo

w (

BC

M)

0

1

2

3

4

5

6

7

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Mea

n M

on

thly

Flo

w (

BC

M)

Conclusions Conclusions• GCMs agree on Temperature rise (2-5.3 °C) leading

to 3-17% increase in PET• GCMs disagree on precipitation changes (-36% to

+39%)• High Sensitivity of Basin leads to extreme flow

change ranges: -76% to +97% from both NFS & HBV• Ensemble mean flow is reduced by 25% & 6% for

NFS & HBV respectively• Hydrological models add another uncertainty• GLUE provides a framework to propagate the

uncertainty from scenarios to impacts• Probabilities are now attached to the uncertainty

bounds• Small sample size lead to small difference between

GLUE bounds and max/min bounds