The impact of land use/cover change on extreme temperatures on the Yangtze River Delta, China

Abstract

The contribution from land use/cover change (LUCC) toward temperature in recent decades is of great concern across the globe. Although there have been many studies, most of them focus on the discussion of average temperature and lack a discussion of extreme temperatures. In this study, we first investigated the spatio-temporal changes in extreme temperatures in the Yangtze River Delta during 1980–2020 using the ensemble empirical mode decomposition (EEMD) method. Then, we explored the impact of LUCC on extreme temperatures using the observation minus reanalysis (OMR) method. Finally, the relationship between the normalized difference vegetation index (NDVI) and extreme temperatures was analyzed using the correlation analysis method. We found that: (1) extreme temperatures have a nonlinear variation characteristics on different time scales. Extremely high temperatures (EHT) clearly exhibited a monthly time scale (quasi-3-month), an interannual time scale (quasi-1-year, quasi-2-year, quasi-3-year and quasi-5-year), and an interdecadal time scale (quasi-10-year and quasi-35-year). Extremely low temperatures (ELT) also clearly exhibited a monthly time scale (quasi-3-month), an interannual scale (quasi-1-year, quasi-2-year, quasi-3-year and quasi-6-year), and an interdecadal scale (quasi-10-year and quasi-20-year). (2) EHT showed an east–middle–west staggered phase and ELT showed a southeast–northwest anti-phase characteristic in spatial distribution. (3) The contribution rates of LUCC on EHT and ELT are 53.6% and 92.4%, respectively, which are higher than for the average temperature (40%). (4) The monthly time scale response of the NDVI to extreme temperatures is more regionally concentrated and significant than that on the interannual time scale in spatial distribution. This paper makes up for the insufficiency of the impact of land use/cover changes on extreme temperature changes at multiple time scales and enriches our understanding of climate change.