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## Summary
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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_” [^2].
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We use the **social-ecological vulnerability concept** as common framework [^1] to assess the different vulnerability components, exposure, sensitivity and adaptive capacity, from an integrated social-ecological perspective. Besides the vulnerability assessment for the current situation, we also explore vulnerability for possible future pathways with regard to climate (heavy rainfall) and land cover changes (exposure and sensitivity), as well as measures to decrease flash flood damages (adaptive capacity). Using a scenario analysis, the effects of changes in heavy rainfall and land use changes, as well as measures to decrease flash flood damages are simulated with hydraulic and [hydrologic models](https://gitlab.pik-potsdam.de/peterh/captainrain/-/wikis/Home/Products/WP-3-Flash-flood-risk-analysis/Hydrological-modelling-using-HEC-HMS) and assessed using vulnerability indicators.
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Download infosheet
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## Data and Methods
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Our approach is based on the SEVA concept of Thiault et al. 2021[^2] 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 capacity[^1].
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### Exposure
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Exposure determines the degree to which a subject (inhabitant, building, ecosystem) is exposed to flooding in case of a flash flood event. It is calculated for the different domains using different sets of indicators:
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- **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 [^6] using the expected water level (cm) during a flash flood event (Baseline Scenario).
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<details><summary>Detailed description</summary>
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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.
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</details>
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- **Physical Exposure**: Describes the exposure of critical infrastructure (worship place, engery and water infrastructure, cultural heritage site, school, kindergarten) which is determined based on the proximity of critical infrastructure to flood prone areas to flood prone areas. Classification was based on the simplified risk analysis of DWA-M 119 [^6] using the expected water level (cm) during a flash flood event (Baseline Scenario).
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<details><summary>Detailed description</summary>
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We classified all buildings/objets with critical 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 polygons and information on building 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) 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.
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</details>
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- **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 [^6] using the expected water level (cm) during a flash flood event (Baseline Scenario).
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<details><summary>Detailed description</summary>
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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) 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.
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</details>
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### Sensitivity
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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:
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- **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) [^3]) assuming that disadvantaged residents are more affected by flash flood damage.
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<details><summary>Detailed description</summary>
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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.
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</details>
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- **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 [^6] standard for damage potential analysis and adopted it tot the local situation.
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<details><summary>Detailed description</summary>
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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 =
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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](Home/Products/WP 3 Damage Potential). The final results were transformed to a 2 m raster file.
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</details>
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- **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 [^5]) as a proxy, as no detailed soil map data was available.
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<details><summary>Detailed description</summary>
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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 =
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moderate (25-50 % build-up areas within a 10m raster cell), 3 = high (50-75 % build-up
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areas within a 10m raster cell) and 4 (100 % build-up areas within a 10m raster cell)
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</details>
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### Adaptive capacity
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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:
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- **Physical & Ecological adaptive capacity**: The assessment was based on the availability and suitability of open space for the potential implementation of measur4es to decrease flash flood damages. We considered open space on public and private land.
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<details><summary>Detailed description</summary>
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</details>
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- **Social adaptive capacity**: For private land, we assessed the economic capacity of residents to implement measures on their land.
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## Results and maps
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## Exposure
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## Sensitivity
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### Social sensitivity
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<details><summary>See map</summary>
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</details>
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## Adaptive capacity
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## References
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[^1]: 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.
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https://doi.org/10.5751/ES-12167-260201
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[^2]: 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
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[^3]: 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
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[^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
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[^5]:Awad, A., 2023. Analysis of the spatial-temporal dynamics of land-use changes using a
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mixed-method approach: A Case study from Amman, Jordan. M.Sc. at the faculty of life
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Sciences, technical university of Munich, Germany.
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[^6]: 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.
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