Abstract:

We assembled a database of daily temperature data from 61 meteorological stations in the Hedong region of Gansu Province,China from 1988 to 2017.We used the MannKendall test and the Sen’s slope estimation method to analyze the patterns of spatiotemporal changes in extreme temperature indices in the Hedong region of Gansu.In addition,we explored the relationship between extreme temperatures and their influencing factors.Finally,we applied the NAR neural network and Hurst index to predict and analyze extreme temperature index changes in Hedong.Our results yielded six major findings:(1) the relative index of cold extreme values has declined over time.The absolute indices of cold and warm extreme values are greater in magnitude,and the temperature index is worse.As a result,the crop growth period is increasing.(2) The areas that are most sensitive to cold extreme changes are the alpine humid regions,while temperate semihumid zones and northern subtropical regions are the most sensitive to warm extreme changes.With the exception of the northern subtropical humid region,the crop growth season changes were significant in all areas.However,diurnal temperature differences reached significance only in temperate semihumid regions.(3) The most extreme temperature indices were significantly correlated with longitude,latitude,and altitude,but were also affected by regional environmental characteristics.(4) The intensity of the polar vortex in Asia,the northern hemisphere polar vortex intensity,and the Tibetan Plateau Index B were all closely related to changes in the extreme temperature index.(5) Our predicted extreme temperature index matched the observed downward trend for the relative index of cold extreme values.It also predicted worsening of extreme heat and cold,and temperatures.Finally,it correctly showed lengthened growing seasons.However,most of the indices deviated slightly from the 1988-2017 observational dataset.(6) Compared with other regions,most of the temperature index changes in Hedong were at intermediate levels,reflecting locally diverse responses in the region’s various climate zones.

Key words: Hedong area, extreme temperature index, temporal and spatial variation, prediction