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Climate Change
Impacts on Water Resources
Dr. Eman Sayed
Director of Water Resources
MWRI
Outline
IPCC TAR Global Impacts
IPCC AR4 Global Impacts
Examples of Global Vulnerabilities
Changes to Runoff and GW Rechange and
Water Stress
Regional Impacts
Case Study – The Nile Basin
CC Impacts on WR
There are apparent trends in streamflow volumes, both increases and decreases, in many regions
The effect of climate change on streamflow and groundwater recharge varies regionally and between scenarios, largely following projected changes in precipitation
Peak streamflow is likely to move from spring to winter in many areas due to early snowmelt, with lower flows in summer and autumn
Glacier retreat is likely to continue, and many small glaciers may disappear
Generally, water quality is likely to be degraded by higher water temperatures
Flood magnitude and frequency are likely to increase in most regions, and volumes of low flows are likely to decrease in many regions
IPCC TAR 2001 (1)
CC Impacts on WR
Globally, demand for water is increasing as a result of population growth and economic development, but is falling in some countries, due to greater water-use efficiency
The impact of climate change on water resources also depends on system characteristics, changing pressures on the system, how the management of the system evolves, and what adaptations to climate change are implemented.
Unmanaged systems are likely to be most vulnerable to climate change
Climate change challenges existing water resource management practices by causing trends not previously experienced and adding new uncertainty
Adaptive capacity is distributed very unevenly across the world
IPCC TAR 2001 (2)
CC Impacts on WR
Snowmelt-fed river basins will be affected by the seasonal shift in streamflow, an increase in the ratio of winter to annual flows, and possibly the reduction in low flows caused by decreased glacier extent or snow water storage (high confidence)
Sea-level rise will extend areas of salinisation of groundwater and estuaries, resulting in a decrease in freshwater availability for humans and ecosystems in coastal areas (very high confidence)
Increased precipitation intensity and variability is projected to increase the risks of flooding and drought in many areas (high confidence)
IPCC AR4 2007 (1)
CC Impacts on WR
Semi-arid and arid areas are particularly exposed to the impacts of climate change on freshwater (high confidence)
Higher water temperatures, increased precipitation intensity, and longer periods of low flows exacerbate many forms of water pollution, with impacts on ecosystems, human health, water system reliability and operating costs (high confidence)
Adverse effects of climate on freshwater systems aggravate the impacts of other stresses, such as population growth, changing economic activity, land-use change, and urbanisation (very high confidence)
Globally, water demand will grow in the coming decades, primarily due to population growth and increased affluence; regionally, large changes in irrigation water demand as a result of climate change are likely (high confidence)
IPCC AR4 2007 (2)
CC Impacts on WR
Climate change affects the function and operation of existing water infrastructure as well as water management practices (very high confidence)
Current water management practices are very likely to be inadequate to reduce the negative impacts of climate change on water supply reliability, flood risk, health, energy, and aquatic ecosystems (very high confidence)
Improved incorporation of current climate variability into water-related management would make adaptation to future climate change easier (very high confidence)
IPCC AR4 2007 (3)
CC Impacts on WR
Adaptation procedures and risk management practices for the water sector are being developed in some countries and regions (e.g., Caribbean, Canada, Australia, Netherlands, UK, USA, Germany) that have recognised projected hydrological changes with related uncertainties (very high confidence)
The negative impacts of climate change on freshwater systems outweigh its benefits (high confidence) in all IPCC regions – Regions with reduced runoff face water stress/scarcity while regions with increased runoff face increased rainfall variability and increases in flood risk (high confidence)
IPCC AR4 2007 (4)
Examples of current
vulnerabilities
Source: IPCC, 2007
Changes in Runoff
Source: Arnell, 2003
A1B 2050s
Changes in GW Recharge
Water Stress
World Population:
6,000 millions in 1995
9,500 millions in 2050
Water Stress:
Freshwater Resources < 1000 m3 per capita per year
Water Stress Changes – No CC
Source: Arnell, 2004
Water Stress Changes – CC
Source: Arnell, 2004
Regional Impacts
Africa:
– The most vulnerable continent
– Low adaptive capacity (poverty)
– Large predicted changes in water availability
– Complex river basins shared by many countries
Asia
– Increased stress in many stressed areas
– Rapid recession of Himalayan glaciers and Premafrost
Regional Impacts
Europe
– More winter floods (snowmelt)
– Increased Storminess
– Increased Water Stress
– Adaptation already started
Latin America
– Increased Water Stress
– Displacement of climatic regions
– Increased Vulnerability to Extreme events
Regional Impacts
North America
– High Adaptive Capacity
– Increased water stress
– Increased frequency of extreme events
Australia and New Zealand
– Becoming more arid
– Increased Water Stress
Case Study
The Nile Basin
Example: 1961-1962 Rainfall
over Equatorial Lakes
The 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
Khashm
El-Girba
Aswan
Dongola
Mogren
Hillet Doleib
Masindi
Cairo
LakeNo
TANZANIA
BURUNDI
Gabal Awlia
Paara
-5
0
5
10
15
20
25
30
35
20 25 30 35 40
EGYPT
ETHIOPIA
D.R. CONGO
RWANDA
LIBYA
CENTRAL
AFRICAN
REP.
UGANDA
SUDAN
ERITRIA
CHAD
KENYA
Historical Rainfall Simulation
Source: LNDFC 2005
CC Impacts on Rainfall
Statistical Downscaling Modelling
Global Climate Models’ Output:
– Monthly Rainfall Data
– Large Gridboxes (e.g. 2.5º x 3.75º)
SDM Model developed to get:
– Daily Data
– Fine Resolution (5 x 5 km) NFS Scale
SDM is Stochastic – Ensembles are
used to sample the variability
Baseline Simulations
0
5
10
15
20
25
30
35
40
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Mean
Mo
nth
ly F
low
(B
CM
)
A2 Mean
Observed
HadCM3 – A2
0
5
10
15
20
25
30
35
40
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Mean
Mo
nth
ly F
low
(B
CM
)
A2 Mean
Observed
HadCM3 – B2
0
5
10
15
20
25
30
35
40
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Mean
Mo
nth
ly F
low
(B
CM
)
A2 Mean
Observed
CGCM2 – A2
0
5
10
15
20
25
30
35
40
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Mean
Mo
nth
ly F
low
(B
CM
)
B2 Mean
Observed
CGCM2 – B2
0
5
10
15
20
25
30
35
40
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Mean
Mo
nth
ly F
low
(B
CM
)
A2 Mean
Observed
ECHAM4 – A2
0
5
10
15
20
25
30
35
40
Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Mean
Mo
nth
ly F
low
(B
CM
)
B2 Mean
Observed
ECHAM4 – B2
Future Simulations (1)
0
20
40
60
80
100
120
140
Base 2010s 2020s 2030s 2040s 2050s 2060s 2070s 2080s 2090s
Decad
al
Mean
An
nu
al
Flo
w (
BC
M)
HadCM3 – A2
0
20
40
60
80
100
120
140
Base 2010s 2020s 2030s 2040s 2050s 2060s 2070s 2080s 2090s
Decad
al
Mean
An
nu
al
Flo
w (
BC
M)
HadCM3 – B2
0
20
40
60
80
100
120
Base 2010s 2020s 2030s 2040s 2050s 2060s 2070s 2080s 2090s
Decad
al
Mean
An
nu
al
Flo
w (
BC
M)
CGCM2 – A2
0
20
40
60
80
100
120
Base 2010s 2020s 2030s 2040s 2050s 2060s 2070s 2080s 2090sD
ecad
al
Mean
An
nu
al
Flo
w (
BC
M)
CGCM2 – B2
0
20
40
60
80
100
120
140
160
180
200
Base 2010s 2020s 2030s 2040s 2050s 2060s 2070s 2080s 2090s
Decad
al
Mean
An
nu
al
Flo
w (
BC
M)
ECHAM4 – A2
0
20
40
60
80
100
120
140
160
180
200
Base 2010s 2020s 2030s 2040s 2050s 2060s 2070s 2080s 2090s
Decad
al
Mean
An
nu
al
Flo
w (
BC
M)
ECHAM4 – B2
Future Simulations (2)
0
50
100
150
200
250
2010
2014
2018
2022
2026
2030
2034
2038
2042
2046
2050
2054
2058
2062
2066
2070
2074
2078
2082
2086
2090
2094
2098
To
tal
An
nu
al
Flo
w (
BC
M)
A2 Base
HadCM3
0
50
100
150
200
250
2010
2014
2018
2022
2026
2030
2034
2038
2042
2046
2050
2054
2058
2062
2066
2070
2074
2078
2082
2086
2090
2094
2098
To
tal
An
nu
al
Flo
w (
BC
M)
A2 Base
CGCM2
0
50
100
150
200
250
2010
2014
2018
2022
2026
2030
2034
2038
2042
2046
2050
2054
2058
2062
2066
2070
2074
2078
2082
2086
2090
2094
2098
To
tal
An
nu
al
Flo
w (
BC
M)
A2 Base
ECHAM4
Figure 1 Simulated Annual Flow Series at Dongola from A2 Experiments (2010-2099)
Comparing Scenarios
0
20
40
60
80
100
120
Base 2010s 2020s 2030s 2040s 2050s 2060s 2070s 2080s 2090s
To
tal A
nn
ua
l F
low
(B
CM
)
HadCM3 A2 HadCM3 B2
CGCM2 A2 CGCM2 B2
ECHAM4 A2 ECHAM4 B2
OBS Base
IPCC AR4 Scenarios
A1B scenario data obtained for:
– CGCM3
– HadCM3
– ECHAM5
Rainfall data has been analyzed
PET inputs (Temperature, Humidity, etc.)
have been processed to calculate PET
(ongoing)
Delta approach will be used to develop
scenarios
A1B Rainfall – CGCM3
A1B Rainfall – ECHAM5
A1B Rainfall – HadCM3
Bias Corrected Scenarios
Rainfall and PET Scenarios from 17 GCMs
Daily Rainfall for 1961-1990 and 2081-2098
periods
Bias Correction for Downscaling (a
distribution mapping approach)
Focus on Blue Nile at Diem
0
50
100
150
200
250
300
350
400
450
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Rain
fall
(m
m)
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
BCM
CGCM
CGCM63
CNRM
CSIRO30
CSIRO35
CM20
CM21
AOM
GOAL
INMCM
MIROCH
MIROCM
ECHAM
MRI
CCSM
PCM
NFS 92-06
Rainfall Distributions (Baseline)
0
20
40
60
80
100
120
140
160
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Evap
ora
tio
n (
mm
)
0
2
4
6
8
10
12
14
16
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Flo
w (
BC
M)
BCM
CGCM
CGCM63
CNRM
CSIRO30
CSIRO35
CM20
CM21
AOM
GOAL
INMCM
MIROCH
MIROCM
ECHAM
MRI
CCSM
PCM
OBS/PET
Evaporation and Flow (Basline)
0
50
100
150
200
250
300
350
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Rain
fall
(m
m)
0
20
40
60
80
100
120
140
160
180
200
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
PE
T (
mm
)Ensemble Mean 2081-98 Baseline 1961-90
Rainfall and PET (Future)
0
20
40
60
80
100
120
140
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
AE
T (
mm
)
Ensemble Mean 2081-98
Baseline Mean 1961-90
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
AET and Flow (Future)
ΔQ = 3.0108 ΔR - 0.1474
R2 = 0.9378
-100%
0%
100%
-20% 0% 20%
Mean Annual Rainfall Change (ΔR)
Mean
An
nu
al
Flo
w C
han
ge (
ΔQ
)
BCM
CGCM
CGCM63
CNRM
CSIRO30
CSIRO35
CM20
CM21
AOM
GOAL
INMCM
MIROCH
MIROCM
ECHAM
MRI
CCSM
PCM
ΔQ = 3.2475 ΔR - 0.1002
R2 = 0.9702
-100%
0%
100%
-20% 0% 20%
Mean JJAS Rainfall Change (ΔR)
Mean
JA
SO
Flo
w C
han
ge (
ΔQ
)
BCM
CGCM
CGCM63
CNRM
CSIRO30
CSIRO35
CM20
CM21
AOM
GOAL
INMCM
MIROCH
MIROCM
ECHAM
MRI
CCSM
PCM
Flow vs Rainfall Changes
ΔPET = 0.0375 ΔT - 0.037
R2 = 0.69
0%
5%
10%
15%
20%
25%
2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5
Mean Annual Temperature Change (ΔT °C)
Mean
An
nu
al
PE
T C
han
ge (
ΔP
ET
)
BCM
CGCM
CGCM63
CNRM
CSIRO30
CSIRO35
CM20
CM21
AOM
GOAL
INMCM
MIROCH
MIROCM
ECHAM
MRI
CCSM
PCM
ΔPET = 0.056 ΔT - 0.06
R2 = 0.74
0%
5%
10%
15%
20%
25%
2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5
Mean JJAS Temperature Change (ΔT °C)
Mean
JJA
S P
ET
Ch
an
ge (
ΔP
ET
)
BCM
CGCM
CGCM63
CNRM
CSIRO30
CSIRO35
CM20
CM21
AOM
GOAL
INMCM
MIROCH
MIROCM
ECHAM
MRI
CCSM
PCM
PET vs Temperature Changes
Conclusions
The Uncertainty is still high
Changes in precipitation range between -14% and
+15%
Changes in Temperature range between 2-5°
Changes in PET range between +2-14%
Changes in Flow range between -60% to +45%
Simple linear relationships can be used as a fast-
track method to assess the impacts
RCMs for the Nile Basin
RACMO: based on the HIRLAM model. The land surface scheme is based the Tiled ECMWF Scheme for Surface Exchanges over Land (TESSEL). RACMO was applied to the Nile Basin by Yasir Mohamed (2005) to study the effect of Draining the Sudd marshes on the Nile Hydroclimatology.
PRECIS is developed by the UK Met Office Hadley Center and is configurable to any area, boundary conditions can be obtained freely for developing countries from Hadley Center – a project has just started to develop climate scenarios for the Nile basin using PRECIS
The Way Forward
Better research to quantify/reduce uncertainty
Integrated Cross-Sectoral Development Plans
Integration of CC into Development Policies Increase System Flexibility
Start with adaptation measures needed anyway (e.g. Efficiency Improvements, Coastal Protection, etc.)
Adaptation Options
Agriculture: Cropping pattern changes, practice changes
Coastal Zones: beach nourishment, construction of groins and breakwaters, tightening legal regulations, integrated coastal zone management and changing land use
Water Resources: rainfall harvesting, increasing abstraction of ground water, water recycling, desalination, efficiency improvements, rationalization, minimizing the need for water and optimizing the economic return of its unit volume
THANK YOU
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