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Uncertainties in exposure and vulnerability datasets

Uncertainties in exposure and vulnerability datasets#

The exposure and vulnerability datasets described are subject to a range of uncertainties that need to be considered when using these data in CRA. Here we present a list of such uncertainties.

Population datasets#

  • Population datasets are mainly derived or modeled from secondary data sources, particularly at continental to global scales. High-resolution data (both temporal and spatial) are often not consistently available for all countries. For the pan-European data assembled for the CLIMAAX project, this is particularly true for countries outside the EU where European data reporting standards are not (yet) enforced (e.g. Turkey, Ukraine, Montenegro).

  • Population data are derived from national census data and population registries, with census intervals and reporting periods varying significantly across countries (Leyk et al. 2019).

  • Disaggregation methods to raster data add another layer of uncertainty. Most available population datasets disaggregate census-based spatial population distribution to the raster cell level by using ancillary datasets, such as built-up land, land cover, or nighttime lights, which adds uncertainty (Leyk et al. 2019).

Satellite imagery#

  • Datasets derived from satellite imagery delineate settlements, built-up land, or buildings consistently across countries due to standardized algorithms. Differences in building materials, roof structures, and settlement layouts can result in missed settlements (Leyk et al. 2018).

  • Satellite-based land use and land cover data are subject to uncertainties due to the classification algorithms used (Congalton et al. 2014).

  • Some exposure and vulnerability observational data are only available in dated versions, such as Global Agro-Ecological Zones (GAEZ), updated only to 2010 values. This time lag between available observations and real-world conditions affects the accuracy of the analysis as it conceals the changes that have occurred in the last decade (Fischer et al. 2021).

OpenStreetMap (OSM)#

  • This database is a crowd-sourced geographic information database, maintained by volunteers worldwide. The crowd-sourced nature can impact the accuracy and completeness of OSM data (Herfort et al. 2023), both geospatially (Wechsler et al. 2019) and temporally (Singh 2017).

  • The level of detail varies across regions and is often less detailed in rural and economically disadvantaged areas (Zhou et al. 2022). Main OSM features are well-mapped, but smaller details may be missing or inaccurately represented (Törnros et al. 2015).

  • OSM updates over time, improving accuracy but possibly introducing inconsistencies (Singh 2017). Despite these potential inaccuracies, OSM is considered the best freely available data for building and infrastructural level at a large scale. OSM does not include projections of future changes like new construction or urban development, which affects static factors in climate risk assessments.

Damage functions#

  • Damage functions, such as depth-damage curves for flood risk assessment (Huizinga et al. 2017), are often based on local data aggregated to a global scale. Global datasets primarily represent common building styles without accounting for local variations. Improvements could be made by including additional local data, such as building types and differences between rural and urban areas.

  • Maximum damage values are represented by continent averages, but national estimates would benefit from literature reviews for more accurate local data.

Future projections#

  • Predicting future societal development is impossible, so socioeconomic scenarios must be considered to cover the range of future uncertainty (O’Neill et al. 2017).

  • Uncertainties in the underlying base data are compounded by uncertainties in future developments. Therefore, future exposure and vulnerability projections are subject to significant uncertainties in terms of both the underlying datasets and future socioeconomic developments.

References#

  • Congalton, R., Gu, J., Yadav, K., Thenkabail, P., & Ozdogan, M. (2014). Global Land Cover Mapping: A Review and Uncertainty Analysis. Remote Sensing, 6(12), 12070–12093. https://doi.org/10.3390/rs61212070

  • Fischer, G., Nachtergaele, F. O., van Velthuizen, H. T., Chiozza, F., Franceschini, G., Henry, M., Muchoney, D., & Tramberend, S. (2021). Global agro-ecological zone V4 – Model documentation. FAO. https://doi.org/10.4060/cb4744en

  • Herfort, B., Lautenbach, S., Porto De Albuquerque, J., Anderson, J., & Zipf, A. (2023). A spatio-temporal analysis investigating completeness and inequalities of global urban building data in OpenStreetMap. Nature Communications, 14(1), 3985. https://doi.org/10.1038/s41467-023-39698-6

  • Huizinga, J., de Moel, H., & Szewczyk, W. (2017). Global flood depth-damage functions: Methodology and the database with guidelines. Publications Office. https://data.europa.eu/doi/10.2760/16510

  • Leyk, S., Gaughan, A. E., Adamo, S. B., Sherbinin, A. de, Balk, D., Freire, S., Rose, A., Stevens, F. R., Blankespoor, B., Frye, C., Comenetz, J., Sorichetta, A., MacManus, K., Pistolesi, L., Levy, M., Tatem, A. J., & Pesaresi, M. (2019). The spatial allocation of population: A review of large-scale gridded population data products and their fitness for use. Earth System Science Data, 11(3), 1385–1409. https://doi.org/10.5194/essd-11-1385-2019

  • Leyk, S., Uhl, J. H., Balk, D., & Jones, B. (2018). Assessing the accuracy of multi-temporal built-up land layers across rural-urban trajectories in the United States. Remote Sensing of Environment, 204, 898–917. https://doi.org/10.1016/j.rse.2017.08.035

  • O’Neill, B. C., Kriegler, E., Ebi, K. L., Kemp-Benedict, E., Riahi, K., Rothman, D. S., Ruijven, B. J. van, Vuuren, D. P. van, Birkmann, J., Kok, K., Levy, M., & Solecki, W. (2017). The roads ahead: Narratives for shared socioeconomic pathways describing world futures in the 21st century. Global Environmental Change, 42, 169–180. https://doi.org/10.1016/j.gloenvcha.2015.01.004

  • Singh, S. K. (2017). Evaluating two freely available geocoding tools for geographical inconsistencies and geocoding errors. Open Geospatial Data, Software and Standards, 2, 1–8. https://doi.org/10.1186/s40965-017-0026-3

  • Törnros, T., Dorn, H., Hahmann, S., & Zipf, A. (2015). Uncertainties of completeness measures in OpenStreetMap–A case study for buildings in a medium-sized German city. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2, 353–357. https://doi.org/10.5194/isprsannals-II-3-W5-353-2015

  • Wechsler, S. P., Ban, H., & Li, L. (2019). The pervasive challenge of error and uncertainty in geospatial data. Geospatial Challenges in the 21st Century, 315–332. https://doi.org/10.1007/978-3-030-04750-4_16

  • Zhou, Q., Zhang, Y., Chang, K., & Brovelli, M. A. (2022). Assessing OSM building completeness for almost 13,000 cities globally. International Journal of Digital Earth, 15(1), 2400–2421. https://doi.org/10.1080/17538947.2022.2159550

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