Cropland Exposure to Extreme Dryness and Wetness in China Under Shared Socioeconomic Pathways
ABSTRACT Global warming by human activities have exacerbated the occurrence of extreme climatic events, which have taken a huge toll on human production and livelihoods. Predicting future changes in extreme wetness and dryness, along with the extent of cropland exposure to these conditions under various scenarios, is essential for effective climate adaptation and achieving sustainable development goals. This study employs the Standardised Antecedent Precipitation Evapotranspiration Index (SAPEI) method to identify extreme dryness and extreme wetness in China for future projections (2021–2100), and to analyse the characteristics of changes in the pixel‐day, intensity, and affected area by extreme dryness and wetness, as well as the cropland exposures to them under different shared socioeconomic pathways (SSPs). We find that the intensity and spatial extent of extreme dryness and wetness significantly increase in future climate projections, especially under high‐emission scenario compared to low‐emission scenario. Under the scenarios with increased emissions, the cropland exposure increased in most parts of China. Therefore, it is particularly urgent to keep the low‐emission scenario in order to minimise the cropland damage caused by extreme drought and wetness in China in the future.
Authors
- Ruowen Yang (ORCID: https://orcid.org/0000-0002-8373-3669)
- Shijie Li (ORCID: https://orcid.org/0000-0002-2419-9988)
- Pedro Cabral (ORCID: https://orcid.org/0000-0001-8622-6008)
- Guojie Wang (ORCID: https://orcid.org/0000-0002-8613-0003)
- Peng Zhang (ORCID: https://orcid.org/0000-0002-7518-4743)
- Yunxia Zhang
Institutions
- Nanjing University of Information Science and Technology (CN)
- China National Commission for Disaster Reduction (CN)
- University of Florence (IT)
Publication Details
- Journal
- International Journal of Climatology
- Published
- 2024-12-03
- DOI
- https://doi.org/10.1002/joc.8715
- Citations
- 5
- Primary Topic
- Climate variability and models
- Type
- article
- Field-Weighted Citation Impact
- 1.41
Funders
- National Natural Science Foundation of China