4.6 Article

Drought Risk Assessment Based on Vulnerability Surfaces: A Case Study of Maize

Journal

SUSTAINABILITY
Volume 8, Issue 8, Pages -

Publisher

MDPI
DOI: 10.3390/su8080813

Keywords

vulnerability surfaces; drought risk assessment; EPIC; maize

Funding

  1. National Basic Research Program of China (973 Project): Relationship between Global Change and Environmental Risks and its Adaptation Paradigm [2012CB955403]
  2. National Key Research and Development Program [2016YFA0602402]

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Agriculture is a sector easily affected by meteorological conditions. Crop yield reduction, even regional conflicts, may occur during a drought. It is extremely important to improve the state of our knowledge on agricultural drought risk. This study has proposed a new method (vulnerability surfaces) for assessing vulnerability quantitatively and continuously by including the environmental variable as an additional perspective on exposure and assessed global maize drought risk based on these surfaces. In this research, based on the Environmental Policy Impact Climate (EPIC) model, irrigation scenarios were adopted to fit Loss rate-Drought index-Environmental indicator (L-D-E) vulnerability surfaces by constructing a database suitable for risk assessment on a large scale. Global maize drought risk was quantitatively assessed based on its optimal vulnerability surface. The results showed an R-2 for the optimal vulnerability surface of 0.9934, with coarse fragment content as the environmental indicator. The expected global average annual yield loss rate due to drought was 19.18%. The global average yield loss rate due to drought with different return periods (10a, 20a, 50a, and 100a) was 29.18%, 32.76%, 36.89%, and 38.26%, respectively. From a global perspective, Central Asia, the Iberian Peninsula, Eastern Africa, the Midwestern United States, Chile, and Brazil are the areas with the highest maize drought risk. The vulnerability surface is a further development of the vulnerability curve as a continuous expression of vulnerability and considers differences in environmental factors. It can reflect the spatial heterogeneity of crop vulnerability and can be applied in large-scale risk assessment research.

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