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WP 5 Vulnerability assessment for Amman

Last edited by Katja Brinkmann

WP_5_Infosheet_Vulnerability.pdf

Summary

Flood vulnerability analysis and assessment are urgently needed to improve urban-risk management and to protect the local population. Our aim was to deliver an adapted approach for an integrated vulnerability assessment for data-scarce areas by combining different disciplinary perspectives with local knowledge.An integrated way of understanding vulnerability is by means of a social-ecological vulnerability assessment (SEVA). SEVA is defined “as the extent to which environmental degradation and climate change cause negative changes in exposure, susceptibility and in the capacity of the social-ecological system to anticipate, cope with and recover from the hazard” 1.

We use the SEVA concept as common framework 2 to assess the different vulnerability components, exposure, sensitivity and adaptive capacity, from an integrated social-ecological perspective. Our integrated SEVA is carried out at different spatial and temporal levels and summarizes the results of the various work packages, in particular the results on heavy rainfall hazards (WP 2), on flash flood risk (WP 3) and on the adaptive capacity (WP 4). Our starting point was a stakeholder analysis to determine the structural challenges in the project region and the stakeholders' expectations of CapTain Rain. Besides the vulnerability assessment for the current situation, we also explore vulnerability for possible future scenarios. Using a scenario analysis, the effects of changes in heavy rainfall and land cover changes, as well as measures to decrease flash flood damages are simulated with hydraulic and hydrologic models and assessed using vulnerability indicators. The results are incorporated into recommendations for urban planning (Fig. 1). image
Fig. 1. Vulnerability assessment cycle within the Captain Rain project

Data and Methods

Our approach is based on the SEVA concept of Thiault et al. 20211 and the general framework promoted by the Intergovernmental Panel on Climate Change (IPCC), which has been widely adopted for vulnerability assessments. In this IPCC definitions, vulnerability is defined as the propensity or predisposition to be adversely affected (see https://www.ipcc.ch/report/ar6/wg2/chapter/chapter-1/), often understood as a function of exposure, sensitivity and adaptive capacity2.

Exposure

Exposure determines the degree to which a subject (inhabitant, building, ecosystem) is exposed to flooding in case of a flash flood event. It uses a similar approach to the flash flood risk analysis, but takes into account a buffer of 10 m around the flood paths when assessing whether a building/object is exposed or not. It is calculated for the different domains using different sets of indicators:

  • Social Exposure: Describes the exposure of residents, which is determined based on the proximity of residental buildings to flood prone areas. Classification was based on the simplified risk analysis of DWA-M 119 3 using the expected water level (cm) during a flash flood event (Baseline Scenario).
Detailed description we classified all residential buildings within the analyzed watershed of Amman according to the simplified risk analysis of DWA-M 119. Using the expected water level (cm) during a flash flood event (Baseline Scenario) classification is as follows: 1 = low (< 10 cm); 2 = moderate (10 – 30 cm), 3 = high (30-50 cm) and 4 = very high (> 50 cm). The final results were transformed to a 2 m raster file.
  • Physical Exposure: Describes the exposure of infrastructure (worship place, engery and water infrastructure, cultural heritage site, school, kindergarten) which is determined based on the proximity of infrastructure to flood prone areas. Classification was based on the simplified risk analysis of DWA-M 119 3 using the expected water level (cm) during a flash flood event (Baseline Scenario with 10 m buffer).
Detailed description We classified all buildings/objects that are relevant for the infrastructure (e.g. worship place, energy and water infrastructure, critical transport network, cultural heritage site, school, bank, kindergarten) within the analyzed watershed of Amman according to the simplified risk analysis of DWA-M 119. Building/object polygons and information on their types were received from the Greater Amman Municipality (GAM) and additional OSM data (May 2024). Using the expected water level (cm) during a flash flood event (Baseline Scenario with 10m buffer) classification is as follows: 1 = low (< 10 cm); 2 = moderate (10 – 30 cm), 3 = high (30-50 cm) and 4 = very high (> 50 cm). The final results were transformed to a 2 m raster file.
  • Ecological Exposure: Describes the exposure of larger green spaces used for leisure activities and providing habitats for flora and fauna (parks, floodplain, woodland). It is determined based on their proximity to flood prone areas. Classification was based on the simplified risk analysis of DWA-M 119 3 using the expected water level (cm) during a flash flood event (Baseline Scenario with 10 m buffer).
Detailed description We classified all park and woodland areas within the analyzed watershed of Amman according to the simplified risk analysis of DWA-M 119. Park areas were received from the Greater Amman Municipality (GAM) and additional OSM data (May 2024). Woodland areas were extracted from from detailed land cover classification of Awad (2023). Using the expected water level (cm) during a flash flood event (Baseline Scenario with 10 m buffer) classification is as follows: 1 = low (< 10 cm); 2 = moderate (10 – 30 cm), 3 = high (30-50 cm) and 4 = very high (> 50 cm). The final results were transformed to a 2 m raster file.
image

Sensitivity

Sensitivity determines the degree to which a subject/object is affected by a given (flash flood) exposure. It is calculated for the different domains using different sets of indicators:

  • Social Sensitivity: The social domain of sensitivity was estimated taking into account demographic (very young or very old people, disabled people, refugees) and economic factors (low income, low level of education) as well as building types (see Potter et al. (2009) 4 and Ababsa and Daher (2011) 5) assuming that disadvantaged residents are more affected by flash flood damage.
Detailed description For the calculation of the social sensitivity, we used demographic factors (Age distribution: very young and very old people, % of disabled persons, % of refugees) and economic factors (renting prices, education level) from Census data at the neighborhood scale (year 2022) received from the department of statistics in Amman. Based on this input data set, a Principal Component Analysis (PCA) was conducted to reduce data dimensions and identify the major components to characterize the sensitivity of inhabitants (main demographic and economic factors). The PCA results at the neighborhood scale were then assigned to the corresponding residential buildings. Since no information on household income was available, we used the residential types (based on a categorization of the Greater Amman Municipality) as a proxy for the distribution of relative wealth within Amman, as recommended by Potter et al. (2009) [^4] and Ababsa and Daher (2011) [^3]. The final results were transformed to a 2 m raster file.
  • Physical sensitivity: was estimated based on the damage potential of the critical infrastructure (worship places, engery and water infrastructure, cultural heritage sites, schools, kindergarten). For the classification we used the German DWA-M 119 3 standard for damage potential analysis and adopted it tot the local situation.
Detailed description Buildings and Locations within the studied area were categorized by an object use. In dependance of the object use damage potential categories were assigned from 1 = low, 2 = moderate, 3 = high and 4 = very high (critical infrastructure). The approach was based on the German DWA-M 119 standard of damage potential analysis and adopted to the local situation using expert opinion of Jordanian stakeholders (n =4). See damage potential description. The final results were transformed to a 2 m raster file.
  • Ecological Sensitivity: The ecological domain of sensitivity was estimated based on the limited capacity for water infiltration. For this we used the impermeable surfaces (build-up areas extracted from Awad, 2023 6) as a proxy, as no detailed soil map data was available.
Detailed description We used the impermeable surfaces as a proxy assuming that these areas have a higher sensitivity during flood events due to their limited water infiltration. For the classification we used the percentage of build-up areas within a 10m raster cell extracted from detailed land cover classification of Awad (2023)[^5]. Classification is as follows: 1 = low (< 25 % build-up areas within a 10m raster cell); 2 = moderate (25-50 % build-up areas within a 10m raster cell), 3 = high (50-75 % build-up areas within a 10m raster cell) and 4 (100 % build-up areas within a 10m raster cell)

Adaptive capacity

Adaptive capacity is the ability of a subject/object to adjust to the hazard event. It reduces the overall level of vulnerability and thus the effects of a flash flood. Here we assesed the adaptive capacity to implement measures taken into account the following comains:

  • Physical & Ecological adaptive capacity: The assessment was based on the availability and suitability of open space for the potential implementation of measures to decrease flash flood damages. We considered open space on public and private land.
  • Social adaptive capacity: For private land, we assessed the economic capacity of residents to implement measures on their land.
Detailed description We assessed the site suitability of a land use parcel for the implementation of measures to reduce flash flood damages (e.g. green infrastructure, bio retention, bioswales). The physical /ecological domain was calculated based on the available open space in developed and undeveloped land use parcels on public and private land within the studied watershed of Amman. The social domain was calculated based on the economic capacity to implement measures on private land. Since no information on household income was available, we used the residential types (based on a categorization of the Greater Amman Municipality) as a proxy for the distribution of relative wealth within Amman, as recommended by Potter et al. (2009) and Ababsa and Daher (2011).

For the assessment, we then categorized land use parcels within the studied catchment of Amman based on the information provided by the Greater Amman Municipality (GAM) about the use (e.g. residential area and type, road type, commercial area and type), ownership (private, public) and level of development (developed, undeveloped) as follows:

Category Description Average open space on the parcel (m²)
1 (low) Little space for the implementation of measures (mainly built-up areas) 250
2 (moderate) Medium-sized open spaces on developed private land 950
3 (high) Large open spaces on developed parcels in public space and on private land belonging to wealthy residents (GAM class: residential A/B), as well as on undeveloped parcels in private land (GAM classes: commercial and residential C/D) 3000
4 (very high) Large open areas on undeveloped parcels in public space and on private land belonging to wealthy residents (GAM class: residential villas/A/B) 2000

The final results were transformed to a 2 m raster file.

Results and maps

Exposure

Social Exposure Physical Exposure
Social_exposure Physical_exposure
Ecological Exposure Exposure (all domains)
Ecological_exposure Exposure_all_domains

Sensitivity

Social Sensitivity Physical Sensitivity
Social_sensitivity Physical_sensitivity
Ecological Sensitivity Sensitivity (all domains)
Ecological_sensitivity Sensitivity_all_domains

Adaptive capacity

Adaptive capacity Exposure and Sensitivity combined
Adaptive_Capacity Exposure_Sensitivity_3

Key findings

  • Most vulnerable areas in the East of the studied watershed of Amman (Downtown): highest built-up area, highest sensitivity of residents (many disadvantaged inhabitants) and many exposed areas (proximity to flood path)
  • Highest adaptive capacity in the northeast and within the sub-catchment area of Marj Al Hamam: Open space in public land available, but also on private land of wealthy residents: incentives should be created here for the private implementation of measures
  • Lack of data on critical infrastructure, was compensated by OSM data, which is a promising source for data-scarce regions
  • Base data on critical infrastructure, social and ecological aspects still scarce and needs to be improved for a more detailed risk and vulnerability analysis

Acknowledgements

We would like to thank the Municipality of Greater Amman (GAM) for providing us with the basic GIS information for this analysis. The vulnerability assessment and analysis was carried out as part of the CapTain Rain project and integrated the results of the different work packages. The scientific publication of the results is still pending and will be announced here.

Contact person for further questions: Dr. Katja Brinkmann (ISOE- Institue for Social-Ecological Research)

References

  1. Depietri, Y. 2020. The social–ecological dimension of vulnerability and risk to natural hazards. In: Sustain Sci 15 (2), S. 587–604. https://doi.org/10.1007/s11625-019-00710-y ↩ ↩2

  2. Thiault, L., S. D. Jupiter, J. E. Johnson, J. E. Cinner, R. M. Jarvis, S. F. Heron, J. M. Maina, N. A. Marshall, P. A. Marshall, and J. Claudet. 2021. Harnessing the potential of vulnerability assessments for managing social-ecological systems. Ecology and Society 26(2):1. https://doi.org/10.5751/ES-12167-260201 ↩ ↩2

  3. DWA, 2016. Risikomanagement in der kommunalen Überflutungsvorsorge für Entwässerungssysteme bei Starkregen, November 2016, German Association for Water, Wastewater and Waste, Hennef, Germany 55 pp., ISBN: 978 3 88721-393-0. ↩ ↩2 ↩3 ↩4

  4. Potter, R.B., Darmame, K., Barhamb, N., Nortcliff, S., 2009. ‘Ever-growing Amman’, Jordan: Urban expansion, social polarisation and contemporary urban planning issues. Habitat International, 33, 81–92 ↩

  5. Ababsa, M., Daher, R.F., 2011. Villes, pratiques urbaines et construction nationale en Jordanie. Presses de l’Ifpo, 2011, https://doi.org/10.4000/books.ifpo.1675 ↩

  6. Awad, A., 2023. Analysis of the spatial-temporal dynamics of land-use changes using a mixed-method approach: A Case study from Amman, Jordan. M.Sc. at the faculty of life Sciences, technical university of Munich, Germany. ↩