... Hellevik (9), Rebka Fekade (10), Alemu Nebebe (10), Tekle Woldegerima (10), Liku Workalemahu (10), Abraham Workneh (10), Nebyou Yonas (10), Essete.
CLUVA
Climate change and Urban Vulnerability in Africa
SEVENTH FRAMEWORK PROGRAMME, Environment (including climate change), Call: FP7-ENV-2010, THEME [ENV.2010.2.1.5-1], [Assessing vulnerability of urban systems, population and goods in relation to natural and man-made disasters in Africa]
Temperature
RCP 8.5: 2061:2100 %incr wrt 1961:2000
RCP 8.5: 2061:2100 - 1961:2000
The contribution to the Master plan
µ
Agriculture Bare Land Community Servicel Industry & Business Mineral Recreation Residential Retail Transport Utilities & Infrastructure Vegitation
percentage change of seasonal precipitation for the time period 20212050 (RCP8.5 scenario) with respect to 1971-2000 Winter (DJF) Summer (JJA)
4 Kilometers
3) Hazard assessment 10
2
Catchment
People n° 0-5 6 - 13 14 - 23 24 - 33 34 - 44 45 - 58 59 - 69 70 - 80 81 - 93 94 - 114
10
0
log(d) − rainfall duration (h)
10
1
500
Q − discharge (m3/s)
seasonal mean temperature(2m) changes for the time period 2021-2050 (RCP8.5 scenario) with respect to 1971-2000 Winter (DJF) Summer (JJA)
300 200
Hydrograph t
20
30
40
t − time (h)
50
60
70
1
4 Kilometers
People affected by flooding in the residential area Condominium & multi−storey 9%
n° 50th τ16th 14
Top-Down Large Scale
µ Local government structure
Participatory decision - making
15
16
τ − TWI threshold
17
Income Age Land Tenure Disability
18
Villa & single−storey 24%
stakeholder meeting
Bottom-Up Small Scale
Asset Dimension
Linear Approach
Household Individual
Vulnerability Attidudinal Dimension
Physical Dimension
LIMIT STATE
For Addis Ababa city, the climate change effect induces an increase in terms of intensity. In fact, the IDF curves that take into account the climate projection are higher than those evaluated based on only historical data. It is possible to observe that the hydrographs obtained with the effects induced by the climate change are higher than the hydrographs obtained with the historical data. This results are coherent with the IDF curves.
*Exclusion of f ix/stable areas or f lood prone areas (f lood risk scenarios)
Exclusion of areas*
Nature based Factor
UMT Based Factors
Location based Factor
Slope
Proximity Transformability
Opportunity
Neighborhood Neighborhood
Transformability index (TI) calculation
Exogenous Factor Population density (defined threshold)
Required Area for Population growth
Ranking Cells based on TI
New settlements
Required Settlement Area increase
Apply transformation of cell
"Modeling Scenarios: Population Density (Scenario 1: low population Density, Scenario 2: Densification) and High risk areas scenario 3: relocation of population living in high flood risk areas into other settlement areas." Scenario 1
Population density Green area Land use/Land cover Drainage network Slope Solid waste management
Scenario 2
Scenario 3
Contextual Approach
Degree of social inclusion Data Acquisition
Methodology Probabilistic Approach: index of land use transformability is calculated based on selected impact factors. Impact Factors: Factors that have an impact on the transformation of cells into settlements based on previous studies and the judgment of local experts. Dynamic model: The excluded areas and the impact factors are updated after each iteration
End of iteration
Community
Institutional Dimension
4 Kilometers
Orthophoto
Mud/wood construction 67%
τ50th
Closing Section
0 1 2
10
1.5
What makes an area in the city of Addis Ababa vulnerable? Stakeholders from Addis Ababa picked these indicators as the most important in Addis Ababa in November 2012.
100 0 0
Villa & single−storey 34%
2
0
Census information Q
Mud/wood construction 51%
2.5
6) Vulnerability assessment
TR=10 Years TR=10 Years (CC) TR=30 Years TR=30 Years (CC) TR=50 Years TR=50 Years (CC) TR=100 Years TR=100 Years (CC) TR=300 Years TR=300 Years (CC)
400
Condominium & multi−storey 15%
Start of Iteration
1
10 −1 10
n° 84th
3
0.5
0 1 2
Flood prone residential area
Aim: Model the expected spatial expansion of settlement areas in Addis Ababa for different scenarios and assess the impacts of these scenarios on farmland and other vegetated areas within the city Impact Factors Slope Transformability Neighborhood Centrality Road Proximity
Update Neighborhood
TR=10 Years TR=10 Years (CC) TR=30 Years TR=30 Years (CC) TR=50 Years TR=50 Years (CC) TR=100 Years TR=100 Years (CC) TR=300 Years TR=300 Years (CC)
3.5
5
Update excluded areas
log (hr ) − rainfall height (mm)
2) Climate Change: RCM
0 1 2
µ
- Incorporation of the Green UMTs to the Addis Ababa master plan land use map -The green space component of the Addis master plan is being planned based on the concept of ecosystem services and ecological networking - Urban Growth Spatial Model for Addis Ababa presented to Addis Master plan team leaders and experts - GIS based training on Urban Spatial growth scenario given to GIS experts of Addis Master plan Office and Addis Urban Plan Institute - Toolbox containing Addis Urban growth spatial model provide to Addis Master plan office
4
x 10
ytilibisseccA
Precipitation
Climate change induced risk analysis of Addis Ababa city (Ethiopia) 7) Modeling Urban Growth Spatial Dynamics 4) UMT: Urban Morphology Types 5) Hot Spot Identification n° of people affected
1) Climate Change: GCM
e s t. 2 0 0 2
Field Survey
a)
Little Akaky: the area the field survey area Field Survey
Laboratory Test
Literature
Simulation Routine Uncertainties Characterization
Structural Model Sampling
Flood Action
Structural Analysis
b)
Set of Critical Height Values Fragility Assessment Flood Hazard
Analytic Fragility Curves
Updating Analytic Fragility Parameters
Robust Fragility
Flood Risk Class of buildings
Farmland areas lost
Other vegetated areas lost
Scenario 1
Scenario 2
Scenario 3
Scenario 1
Scenario 2
Scenario 3
3684.5 ha (24.7 %)
1834 ha (12.3 %)
2023.5 ha (13.5 %)
1567 ha (20.9 %)
1007 ha (13.4 %)
825 ha (11 %)
Fatemeh Jalayer (1), Raffaele De Risi (1), Lise Herslund (2), Gina Cavan (3,4), Hany A. Abo El Wafa (5), Andreas Printz (5), Ingo Simonis (6), Edoardo Bucchignani (7), Nathalie Jean-Baptiste (8), Siri Hellevik (9), Rebka Fekade (10), Alemu Nebebe (10), Tekle Woldegerima (10), Liku Workalemahu (10), Abraham Workneh (10), Nebyou Yonas (10), Essete Abebe Bekele (10), Kumelachew Yeshitela (10), and the CLUVA Team (1) AMRA S.c.a r.l., Napoli – Italy, (2) Danish Centre for Forest, Landscape, and Planning, University of Copenhagen, Denmark, (3) Division of Geography & Environmental Management, Manchester Metropolitan University, UK, (4) School of Environment and Development, The University of Manchester, UK, (5) Technical University of Münich, Germany, (6) Center for Scientific and Industrial Research, South Africa, (7) Euro-Mediterranean Centre for Climate Change, Italy, (8) Helmholtz Centre for Environmental Research, Germany, (9) Norwegian Institute for Urban and Regional Research, Norway, (10) Ethiopian Institute of Architecture, Building Construction and City Development, Ethiopia