SPATIOTEMPORAL VARIATION OF EXTREME CLIMATE AND ITS CORRELATION WITH COTTON PRODUCTION IN XINJIANG
This study aims to explore the temporal and spatial variation of extreme climate and its correlation with cotton production in Xinjiang.Utilizing data from 53 meteorological stations,22 extreme climate indicators,and comprehensive cotton production data(including total production,unit yield,and planting area)spanning from 1985 to 2021,this study employed climate tendency rate,Mann-Kendall,meteorological production decomposition,and gray correlation methods to investigate the spatiotemporal changes of extreme climate in Xinjiang and its correlation with cotton production.(1)The research findings underscored a significant increase in the warm and humid indices of extreme climate in Xinjiang,contrasted by a decline in the cold and drought indices,thereby accentuating the warm and humid characteristics.The mutation of extreme temperature and the secondary mutation of extreme precipitation were observed from the late 1990s to the early 21st century.(2)The climate tendency rate of extreme precipitation revealed a distinct east-to-west increasing trend,while the extreme temperature exhibited a significant rise across Xinjiang.(3)The cotton trend planting area,trend unit yield,and trend total yield all demonstrated a significant linear upward trend;the meteorological planting area and meteorological total yield displayed a fluctuating upward trend;the meteorological unit yield presented a fluctuating trend.Over the past 37 years,normal years had been the most common.The cotton total yield,unit yield,meteorological unit yield,and total planting area were significantly influenced by extreme temperature,whereas the meteorological planting area and meteorological total yield were primarily affected by extreme precipitation.Therefore,the trend of extreme climate change in the Xinjiang region is significant,and its correlation with cotton production is very high.It is therefore recommended that adaptation measures such as monitoring weather forecasts,conducting climate zoning work,and increasing investment in agricultural infrastructure be implemented.
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