
Collecting historic flood data in data-sparse areas : a citizen approach
- Author
- Hanne Glas (UGent) and Greet Deruyter (UGent)
- Organization
- Abstract
- The plain of the river Moustiques, in the northwest of Haiti, suffers frequently from flash floods. This vulnerable and poor rural region has limited resources to minimize the costs and consequences of these inundations. Flood hazard risk assessments could offer the necessary information on the high-risk zones in the area and which measures would have the best effect. Detailed flood risk data is indispensable in such an assessment. However, the traditional acquisition measures are too expensive or impossible to execute in remote developing regions such as the catchment of the Moustiques. Therefore, this research focuses on a third method: involving citizens by conducting questionnaires to collect information on historic flood events. All 294 households that live in the plain were questioned. The questionnaire was drawn up in six sections. While the first four dealt with the day-to-day life of the household and agricultural activities, the last two sections zoom in on the knowledge of those questioned. Section 5 focused on the most recent flooding, while section 6 was composed with the same questions, but was concerning the most severe flood event in the respondents’ memory. All answers were analyzed to derive location-specific damage functions. These link the potential flood damage to the predicted water height in a certain location. For buildings, the degree of damage given by the questioned was linked to the water height indicated in his or her house. As such, a damage percentage was given for each water level, creating a damage function with a clear upward trend. These realistic percentages are useful in a quantitative flood risk assessment, proving the usefulness of this form of citizen science. However, due to the uneven distribution of the number of described flood events per water level, some damage percentages are a lot more averaged than others. Damage functions for vehicles could not be derived, due to the very low number of motorized vehicles in the study area. For livestock and farmlands, the damage degree could not be linked to water height, as the height indicated in the questionnaire was the flood level in the home of the questioned, while the agricultural lands are located in the center of the plain, away from the villages. These issues should be considered when evaluating and improving the questionnaire setup. Adaptations to the questionnaire will allow future research to derive more valuable and reliable data from this citizen-led data acquisition method.
- Keywords
- flood risk, data-sparse region, SIDS, citizen science, flooding
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8675076
- MLA
- Glas, Hanne, and Greet Deruyter. “Collecting Historic Flood Data in Data-Sparse Areas : A Citizen Approach.” Opening up the Planning Landscape : 15 Years of Actor-Relational Approaches to Spatial Planning in Flanders, the Netherlands and Beyond, edited by Beitske Boonstra et al., InPlanning, 2020, pp. 103–11, doi:10.17418/B.2020.9789491937446.
- APA
- Glas, H., & Deruyter, G. (2020). Collecting historic flood data in data-sparse areas : a citizen approach. In B. Boonstra, P. Davids, & A. Staessen (Eds.), Opening up the planning landscape : 15 years of actor-relational approaches to spatial planning in Flanders, the Netherlands and beyond (pp. 103–111). https://doi.org/10.17418/B.2020.9789491937446
- Chicago author-date
- Glas, Hanne, and Greet Deruyter. 2020. “Collecting Historic Flood Data in Data-Sparse Areas : A Citizen Approach.” In Opening up the Planning Landscape : 15 Years of Actor-Relational Approaches to Spatial Planning in Flanders, the Netherlands and Beyond, edited by Beitske Boonstra, Peter Davids, and Annelies Staessen, 103–11. InPlanning. https://doi.org/10.17418/B.2020.9789491937446.
- Chicago author-date (all authors)
- Glas, Hanne, and Greet Deruyter. 2020. “Collecting Historic Flood Data in Data-Sparse Areas : A Citizen Approach.” In Opening up the Planning Landscape : 15 Years of Actor-Relational Approaches to Spatial Planning in Flanders, the Netherlands and Beyond, ed by. Beitske Boonstra, Peter Davids, and Annelies Staessen, 103–111. InPlanning. doi:10.17418/B.2020.9789491937446.
- Vancouver
- 1.Glas H, Deruyter G. Collecting historic flood data in data-sparse areas : a citizen approach. In: Boonstra B, Davids P, Staessen A, editors. Opening up the planning landscape : 15 years of actor-relational approaches to spatial planning in Flanders, the Netherlands and beyond. InPlanning; 2020. p. 103–11.
- IEEE
- [1]H. Glas and G. Deruyter, “Collecting historic flood data in data-sparse areas : a citizen approach,” in Opening up the planning landscape : 15 years of actor-relational approaches to spatial planning in Flanders, the Netherlands and beyond, B. Boonstra, P. Davids, and A. Staessen, Eds. InPlanning, 2020, pp. 103–111.
@incollection{8675076, abstract = {{The plain of the river Moustiques, in the northwest of Haiti, suffers frequently from flash floods. This vulnerable and poor rural region has limited resources to minimize the costs and consequences of these inundations. Flood hazard risk assessments could offer the necessary information on the high-risk zones in the area and which measures would have the best effect. Detailed flood risk data is indispensable in such an assessment. However, the traditional acquisition measures are too expensive or impossible to execute in remote developing regions such as the catchment of the Moustiques. Therefore, this research focuses on a third method: involving citizens by conducting questionnaires to collect information on historic flood events. All 294 households that live in the plain were questioned. The questionnaire was drawn up in six sections. While the first four dealt with the day-to-day life of the household and agricultural activities, the last two sections zoom in on the knowledge of those questioned. Section 5 focused on the most recent flooding, while section 6 was composed with the same questions, but was concerning the most severe flood event in the respondents’ memory. All answers were analyzed to derive location-specific damage functions. These link the potential flood damage to the predicted water height in a certain location. For buildings, the degree of damage given by the questioned was linked to the water height indicated in his or her house. As such, a damage percentage was given for each water level, creating a damage function with a clear upward trend. These realistic percentages are useful in a quantitative flood risk assessment, proving the usefulness of this form of citizen science. However, due to the uneven distribution of the number of described flood events per water level, some damage percentages are a lot more averaged than others. Damage functions for vehicles could not be derived, due to the very low number of motorized vehicles in the study area. For livestock and farmlands, the damage degree could not be linked to water height, as the height indicated in the questionnaire was the flood level in the home of the questioned, while the agricultural lands are located in the center of the plain, away from the villages. These issues should be considered when evaluating and improving the questionnaire setup. Adaptations to the questionnaire will allow future research to derive more valuable and reliable data from this citizen-led data acquisition method.}}, author = {{Glas, Hanne and Deruyter, Greet}}, booktitle = {{Opening up the planning landscape : 15 years of actor-relational approaches to spatial planning in Flanders, the Netherlands and beyond}}, editor = {{Boonstra, Beitske and Davids, Peter and Staessen, Annelies}}, isbn = {{9789491937446}}, keywords = {{flood risk,data-sparse region,SIDS,citizen science,flooding}}, language = {{eng}}, pages = {{103--111}}, publisher = {{InPlanning}}, title = {{Collecting historic flood data in data-sparse areas : a citizen approach}}, url = {{http://doi.org/10.17418/B.2020.9789491937446}}, year = {{2020}}, }
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