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Development and validation of risk profiles of West African rural communities facing multiple natural hazards

View Article: PubMed Central - PubMed

ABSTRACT

West Africa has been described as a hotspot of climate change. The reliance on rain-fed agriculture by over 65% of the population means that vulnerability to climatic hazards such as droughts, rainstorms and floods will continue. Yet, the vulnerability and risk levels faced by different rural social-ecological systems (SES) affected by multiple hazards are poorly understood. To fill this gap, this study quantifies risk and vulnerability of rural communities to drought and floods. Risk is assessed using an indicator-based approach. A stepwise methodology is followed that combines participatory approaches with statistical, remote sensing and Geographic Information System techniques to develop community level vulnerability indices in three watersheds (Dano, Burkina Faso; Dassari, Benin; Vea, Ghana). The results show varying levels of risk profiles across the three watersheds. Statistically significant high levels of mean risk in the Dano area of Burkina Faso are found whilst communities in the Dassari area of Benin show low mean risk. The high risk in the Dano area results from, among other factors, underlying high exposure to droughts and rainstorms, longer dry season duration, low caloric intake per capita, and poor local institutions. The study introduces the concept of community impact score (CIS) to validate the indicator-based risk and vulnerability modelling. The CIS measures the cumulative impact of the occurrence of multiple hazards over five years. 65.3% of the variance in observed impact of hazards/CIS was explained by the risk models and communities with high simulated disaster risk generally follow areas with high observed disaster impacts. Results from this study will help disaster managers to better understand disaster risk and develop appropriate, inclusive and well integrated mitigation and adaptation plans at the local level. It fulfills the increasing need to balance global/regional assessments with community level assessments where major decisions against risk are actually taken and implemented.

No MeSH data available.


Related in: MedlinePlus

The Risk profiles of two community clusters in the Vea and Dano study                            area.                        Following the approach in the World Risk Index [25,34], the risk                            indices have been translated into five qualitative classification scheme                            of very high (5), high (4), medium (3), low (2) and very low (1).                            Classification algorithm employed is the Quantile method. In this                            figure, two levels of factors contributing to final community risk are                            presented. The first is the three major components of risk, which are                            exposure, susceptibility and lack of capacity. The second level shows                            the relative contribution of each indicator to first, the sub-component                            such as exposure and then to final risk. Only indicators contributing to                            more than 5% of the final risk are shown. Major contributory factors to                            risk are: AFIS = access to alternative food and income sources; SE-CropT                            = crop type or the proxy of crop diversification practices; ADP =                            agricultural dependent population; SS-QH = quality of housing; SE-DSD =                            length of dry season duration; CC-EMC = presence of emergency management                            committee; C-A AHHIPA = annual household income; CA-Lit = levels of                            adult population above age 15; CA-GLaM = good leadership and management                            at the community level and CIPC = caloric intake per capita.
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pone.0171921.g009: The Risk profiles of two community clusters in the Vea and Dano study area. Following the approach in the World Risk Index [25,34], the risk indices have been translated into five qualitative classification scheme of very high (5), high (4), medium (3), low (2) and very low (1). Classification algorithm employed is the Quantile method. In this figure, two levels of factors contributing to final community risk are presented. The first is the three major components of risk, which are exposure, susceptibility and lack of capacity. The second level shows the relative contribution of each indicator to first, the sub-component such as exposure and then to final risk. Only indicators contributing to more than 5% of the final risk are shown. Major contributory factors to risk are: AFIS = access to alternative food and income sources; SE-CropT = crop type or the proxy of crop diversification practices; ADP = agricultural dependent population; SS-QH = quality of housing; SE-DSD = length of dry season duration; CC-EMC = presence of emergency management committee; C-A AHHIPA = annual household income; CA-Lit = levels of adult population above age 15; CA-GLaM = good leadership and management at the community level and CIPC = caloric intake per capita.

Mentions: In Fig 9, the result of the WESCRI is presented and shows contrasting levels of risk among community clusters.


Development and validation of risk profiles of West African rural communities facing multiple natural hazards
The Risk profiles of two community clusters in the Vea and Dano study                            area.                        Following the approach in the World Risk Index [25,34], the risk                            indices have been translated into five qualitative classification scheme                            of very high (5), high (4), medium (3), low (2) and very low (1).                            Classification algorithm employed is the Quantile method. In this                            figure, two levels of factors contributing to final community risk are                            presented. The first is the three major components of risk, which are                            exposure, susceptibility and lack of capacity. The second level shows                            the relative contribution of each indicator to first, the sub-component                            such as exposure and then to final risk. Only indicators contributing to                            more than 5% of the final risk are shown. Major contributory factors to                            risk are: AFIS = access to alternative food and income sources; SE-CropT                            = crop type or the proxy of crop diversification practices; ADP =                            agricultural dependent population; SS-QH = quality of housing; SE-DSD =                            length of dry season duration; CC-EMC = presence of emergency management                            committee; C-A AHHIPA = annual household income; CA-Lit = levels of                            adult population above age 15; CA-GLaM = good leadership and management                            at the community level and CIPC = caloric intake per capita.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC5382969&req=5

pone.0171921.g009: The Risk profiles of two community clusters in the Vea and Dano study area. Following the approach in the World Risk Index [25,34], the risk indices have been translated into five qualitative classification scheme of very high (5), high (4), medium (3), low (2) and very low (1). Classification algorithm employed is the Quantile method. In this figure, two levels of factors contributing to final community risk are presented. The first is the three major components of risk, which are exposure, susceptibility and lack of capacity. The second level shows the relative contribution of each indicator to first, the sub-component such as exposure and then to final risk. Only indicators contributing to more than 5% of the final risk are shown. Major contributory factors to risk are: AFIS = access to alternative food and income sources; SE-CropT = crop type or the proxy of crop diversification practices; ADP = agricultural dependent population; SS-QH = quality of housing; SE-DSD = length of dry season duration; CC-EMC = presence of emergency management committee; C-A AHHIPA = annual household income; CA-Lit = levels of adult population above age 15; CA-GLaM = good leadership and management at the community level and CIPC = caloric intake per capita.
Mentions: In Fig 9, the result of the WESCRI is presented and shows contrasting levels of risk among community clusters.

View Article: PubMed Central - PubMed

ABSTRACT

West Africa has been described as a hotspot of climate change. The reliance on rain-fed agriculture by over 65% of the population means that vulnerability to climatic hazards such as droughts, rainstorms and floods will continue. Yet, the vulnerability and risk levels faced by different rural social-ecological systems (SES) affected by multiple hazards are poorly understood. To fill this gap, this study quantifies risk and vulnerability of rural communities to drought and floods. Risk is assessed using an indicator-based approach. A stepwise methodology is followed that combines participatory approaches with statistical, remote sensing and Geographic Information System techniques to develop community level vulnerability indices in three watersheds (Dano, Burkina Faso; Dassari, Benin; Vea, Ghana). The results show varying levels of risk profiles across the three watersheds. Statistically significant high levels of mean risk in the Dano area of Burkina Faso are found whilst communities in the Dassari area of Benin show low mean risk. The high risk in the Dano area results from, among other factors, underlying high exposure to droughts and rainstorms, longer dry season duration, low caloric intake per capita, and poor local institutions. The study introduces the concept of community impact score (CIS) to validate the indicator-based risk and vulnerability modelling. The CIS measures the cumulative impact of the occurrence of multiple hazards over five years. 65.3% of the variance in observed impact of hazards/CIS was explained by the risk models and communities with high simulated disaster risk generally follow areas with high observed disaster impacts. Results from this study will help disaster managers to better understand disaster risk and develop appropriate, inclusive and well integrated mitigation and adaptation plans at the local level. It fulfills the increasing need to balance global/regional assessments with community level assessments where major decisions against risk are actually taken and implemented.

No MeSH data available.


Related in: MedlinePlus