Transcript Characterizing the uncertainty of climate change impacts

Projected Changes in Nile
Flows: RCM Results
Mid-Term Workshop
Climate Change Risk Management
Programme
Forecasting & IWRM Component
Prepared by: Nile Forecast Center
Outline
1. Modeling Climate Change Impacts
2. Pervious Studies on the Nile
3. Study Methodology
4. Results
5. Conclusions
Modeling Climate Change Impacts
Methodology and Uncertainty Cascade
Emissions
Concentrations
Observations
Radiative Forcing
Global Climate Models
Regional Details (Downscaling)
Impact Models (e.g. Hydrology)
The Nile Basin
35
30
 Large area (2.9 x 106 km2)
 Low specific discharge
 Spans several climate
regions
 Variable topography
 High runoff variability
 High Sensitivity to Climate
Cairo
EGYPT
LIBYA
25
Aswan
20
Dongola
Atbara
CHAD
Mogren
Gabal Awlia
15
Khashm
El-Girba
Sennar
SUDAN
ERITRIA
Khartoum
Roseires
Diem
10
Malakal
Hillet Doleib
LakeNo
CENTRAL
ETHIOPIA
AFRICAN
REP.
Mongalla
5
Paara UGANDA
Pakwach
Masindi
D.R. CONGO
KENYA
Jinja
0
RWANDA
BURUNDI
TANZANIA
-5
20
25
30
35
40
Previous Studies (1)
Lake Nasser Flood & Drought Control Project
(2008)
120
100
Total Annual Flow (BCM)
– 6 Transient scenarios
(3 GCMs x 2 Emission
Scenarios)
– Statistically
downscaled using a
spatio-temporal
weather generator
– Changes at Dongola
from 2010-2100
80
60
40
20
HadCM3 A2
CGCM2 A2
ECHAM4 A2
OBS Base
HadCM3 B2
CGCM2 B2
ECHAM4 B2
0
Base 2010s 2020s 2030s 2040s 2050s 2060s 2070s 2080s 2090s
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)
Elshamy et al. (2009)
20
18
Ensemble Mean 2081-98
Observed 1961-90
16
Flow (BCM)
– 17 GCMs x A1B
scenario
– Statistically
downscaled using Bias
Correction Method
– Blue Nile Flow
Changes: -60% to +45%
22
14
12
10
8
6
4
2
0
Jan Feb Mar Apr May Jun
Jul
Aug Sep Oct Nov Dec
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.
Methodology
Coarse Scale GCM Boundary Conditions
RCM Downscaling
Fine-Scale Climate
(Baseline 1950-2000 & Future 2020-2050)
Calculate Delta Change Factors (DCFs)
Modify Baseline Data (1989-2007) using DCFs
Determine Hydrological Impacts (NFS)
Methodology: Why RCM?
RCM
GCM
• Higher Resolution: Better Representation of shoreline and terrain
• Physical Model: Consistent Climate Elements
Methodology: Ensemble Selection
Emission Scenario A1B
Results: Rainfall Changes - Ratios
1
2
3
4
5
6
Black: Ratio cannot be calculated
Jan
White: Off Scale (>3)
Results: Rainfall Changes - Ratios
1
2
3
4
5
6
Black: Ratio cannot be calculated
Aug
White: Off Scale (>3)
Results: Temperature Changes - Diffs
1
2
3
4
5
6
Jan
Results: Temperature Changes - Diffs
1
2
3
4
5
6
Aug
Results: PET Changes
NFS
Selected Scenario
Hydrological Changes: Blue Nile@Diem
50%
40%
Flow
Rainfall
PET Changes
30%
20%
10%
0%
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
-10%
-20%
-30%
-40%
-50%
Hydrological Changes: White Nile@Malakal
40%
30%
Rainfall
Flow
PET Changes
20%
10%
0%
Jan Feb
Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Jan
-10%
-20%
-30%
-40%
Conclusions
• Expected ranges for changes in rainfall,
temperature, and PET are smaller than
previous studies
• Changes in flows:
-19% to +29% for the Blue Nile (Diem)
-8% to +10% for the White Nile (Malakal)
• RCM provides a viable downscaling
methodology
• RCM results confirm the uncertainty regarding
the direction of change for rainfall and flow
• RCM reduced the uncertainty bandwidth but
care must be taken that not all sources are
included
Way Forward
 Collaboration within the Nile Basin to exchange data
and experience
 Nile countries need to Adapt to Flow Changes – In
addition to population growth
– Flexibility in Water Management to face uncertainty
– No regret step-wise adaptation plans
 Translating Climate impacts into hydrological 
agricultural  socio-economic, hydropower, …
impacts
 Further research: Expansion to other Emission
Scenarios, RCMs, etc to better characterize the
uncertainty, uncertainty propagation to decision
making – adaptation planning