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Assessing the economic impacts of future fluvial flooding in six countries under climate change and socio-economic development

Abstract

Floods are among the most frequent and costliest natural hazards. Fluvial flood losses are expected to increase in the future, driven by population and economic growth in flood-prone areas, and exacerbated in many regions by effects of climate change on the hydrological cycle. Yet, studies assessing direct and indirect economic impacts of fluvial flooding in combination with climate change and socio-economic projections at a country level are rare. This study presents an integrated flood risk analysis framework to calculate total (direct and indirect) economic damages, with and without socio-economic development, under a range of warming levels from < 1.5 to 4 °C in Brazil, China, India, Egypt, Ethiopia, and Ghana. Direct damages are estimated by linking spatially explicit daily flood hazard data from the Catchment-based Macro-scale Floodplain (CaMa-Flood) model with country- and sector-specific depth-damage functions. These values input into an economic Input-Output model for the estimation of indirect losses. The study highlights that total fluvial flood losses are largest in China and India when expressed in absolute terms. When expressed as a share of national GDP, Egypt faces the largest total losses under both the climate change and climate change plus socio-economic development experiments. The magnitude of indirect losses also increased significantly when socio-economic development was modelled. The study highlights the importance of including socio-economic development when estimating direct and indirect flood losses, as well as the role of recovery dynamics, essential to provide a more comprehensive picture of potential losses that will be important for decision makers.

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Data availability

Data and models used are outlined in the text and listed in full in the references.

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Notes

  1. In this paper, we use bold capital letters to represent matrices (e.g. I and A), italic bold lowercase letters for vectors (e.g. x), and italic lowercase letters for scalars (e.g. n). Vectors are column vectors by default, and the transposition is denoted by an apostrophe (e.g. x'). The conversion from a vector to a diagonal matrix is expressed as italic bold lowercase letters with a circumflex (e.g. \( \hat{\boldsymbol{\alpha}} \)).

  2. Although damage to residential capital can have indirect effects on the production process as its recovery results in a non-negligible part of the total reconstruction demand, competing with industrial capital for reconstruction resources.

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Funding

K.J., Y.H., N.F., R.W., and R.J. acknowledge support from the UK government, Development for Business, Energy and Industrial Strategy. D.G. acknowledges support from the National Natural Science Foundation of China (41921005, 72091514). L.Y. acknowlwdges support from the National Key R &D Program of China (2019YFC0810705, 2018YFC0807000) and the National Natural Science Foundation of China (71771113).

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Authors and Affiliations

Authors

Contributions

Zhiqiang Yin and Dabo Guan designed the study. Zhiqiang Yin and Yixin Hu performed the analysis. Zhiqiang Yin carried out the direct damage modelling, and Yixin Hu carried out the indirect damage modelling. Zhiqiang Yin, Yixin Hu and Katie Jenkins interpreted the results and prepared the manuscript. Katie Jenkins prepared the figures and Supplementary Material. Yi He provided the flood hazard data. Nicole Forstenhäusler prepared the land cover data. Lili Yang contributed to the input-output modelling. Rhosanna Jenkins contributed to the literature review in Supplementary Material. Rachel Warren and Dabo Guan coordinated and supervised the project and reviewed the manuscript.

Corresponding author

Correspondence to Dabo Guan.

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The authors declare no competing interests.

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This article is part of the topical collection Accrual of Climate Change Risk in Six Vulnerable Countries, edited by Daniela Jacob and Tania Guillén Bolaños

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Yin, Z., Hu, Y., Jenkins, K. et al. Assessing the economic impacts of future fluvial flooding in six countries under climate change and socio-economic development. Climatic Change 166, 38 (2021). https://doi.org/10.1007/s10584-021-03059-3

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  • DOI: https://doi.org/10.1007/s10584-021-03059-3

Keywords

  • Fluvial flooding
  • Economic impacts
  • Climate change
  • Socio-economic development