Assessing Chinese flood protection and its social divergence

Dan Wang, Paolo Scussolini, Shiqiang Du

Research output: Contribution to JournalArticleAcademicpeer-review


China is one of the most flood-prone countries, and development within floodplains is intensive. However, flood protection levels (FPLs) across the country are mostly unknown, hampering the present assertive efforts on flood risk management. Based on the flood-protection prescriptions contained in the national flood policies, this paper develops a dataset of likely FPLs for China and investigates the protection granted to different demographic groups. The new dataset corresponds to local flood protection designs in 91 (53.2 %) of the 171 validation counties, and in 154 counties (90.1 %) it is very close to the designed FPLs. This suggests that the policy-based FPLs could be a valuable proxy for designed FPLs in China. The FPLs are significantly higher than previously estimated in the FLOPROS (FLOod PROtection Standards) global dataset, suggesting that Chinese flood risk was probably overestimated. Relatively high FPLs (return period of 50 years) are seen in 282 or only 12.6% of the evaluated 2237 counties, which host a majority (55.1 %) of the total exposed population. However, counties with low FPLs (return period of 50 years) host a disproportionate share (52.3 %) of the exposed vulnerable population (children and elders), higher than their share (44.9 %) of the exposed population. These results imply that to reduce social vulnerability and decrease potential casualties, investment in flood risk management should also consider the demographic characteristics of the exposed population.

Original languageEnglish
Pages (from-to)743-755
Number of pages13
JournalNatural Hazards and Earth System Sciences
Issue number2
Early online date24 Aug 2020
Publication statusPublished - 24 Feb 2021

Bibliographical note

Funding Information:
Financial support. This research has been supported by the Na-

Publisher Copyright:
© Author(s) 2021. This work is distributed under the Creative Commons Attribution 4.0 License.

Copyright 2021 Elsevier B.V., All rights reserved.


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