Abstract

AbstractDrought has caused serious damage to the water resource system and agricultural production in Shandong Province, China. This study calculated the standardized precipitation evapotranspiration index (SPEI) based on the monthly precipitation and average temperature data of 25 meteorological stations in Shandong Province from 1961 to 2017. The trend analysis method and ArcGIS software were utilized to analyze the multi-scale (SPEI-1, SPEI-3, and SPEI-12) spatiotemporal changes of drought. The results revealed that (1) The intensity of drought showed an increasing trend in Shandong Province from 1961 to 2017; (2) The main periods of the drought on the seasonal scale (spring, summer, autumn, and winter) and annual scale were 8 years, 4 years, 15 years, 4 years, and 4 years, respectively; (3) Of the four seasons, the frequency of drought in autumn and winter were the highest. At the annual scale, the high-frequency drought areas were mainly concentrated in the southern mountainous regions; (4) In terms of the spatial change trend of drought, Shandong Province as a whole displayed a trend of becoming wet in the central and southwest regions and dry in the eastern region; and (5) Droughts were discovered to be simultaneously influenced by multiple atmospheric circulation indices in Shandong Province.

Highlights

  • Global climate change has become one of the most complex challenges facing mankind in the 21st century and is seriously impacting both the regional ecological environment and the sustainable development of human society (Schär et al )

  • Based on the data from 25 meteorological stations in Shandong Province for the period 1961–2017, this study attempted to analyze the spatiotemporal changes of drought and the drought cycle in Shandong Province by calculating the standardized precipitation evapotranspiration index (SPEI) at different time scales and to discuss the major global and regional atmospheric circulation indices relative to Shandong Province, with the expectation of providing decision-making suggestions for the development of drought early warning systems, as well as agricultural and animal husbandry advancement in Shandong Province

  • SPEI-1 exhibited the largest fluctuations during the 1961–2017 study period, and the fluctuation period of SPEI-3 was longer than that of SPEI-1, reflecting the seasonal change law of Shandong’s dry and wet periods

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Summary

INTRODUCTION

Global climate change has become one of the most complex challenges facing mankind in the 21st century and is seriously impacting both the regional ecological environment and the sustainable development of human society (Schär et al ). Based on the data from 25 meteorological stations in Shandong Province for the period 1961–2017, this study attempted to analyze the spatiotemporal changes of drought and the drought cycle in Shandong Province by calculating the SPEI at different time scales and to discuss the major global and regional atmospheric circulation indices relative to Shandong Province, with the expectation of providing decision-making suggestions for the development of drought early warning systems, as well as agricultural and animal husbandry advancement in Shandong Province. The meteorological data in this study came from the ‘Monthly Dataset of Chinese Surface Climate Data (3.0)’ (https://data.cma.cn/) compiled by the Meteorological Information Center of the China Meteorological Administration This dataset includes 15 meteorological elements from 31 meteorological stations in Shandong Province,. The large-scale ocean atmospheric circulation indices selected for use in this study included the Arctic Oscillation (AO) (http://www.cpc.ncep.noaa.gov/), Southern Oscillation (SOI) (https://psl.noaa.gov/gcos_wgsp/Timeseries/SOI/), East Asian Summer Monsoon (EASM) (http://ljp.gcess.cn/dct/page/1), North Atlantic Oscillation (NAO) (http://ljp.gcess.cn/dct/ page/1), Pacific Decadal Oscillation (PDO) (http://www. esrl.noaa.gov/psd/data/correlation/pdo.data), sunspot number (SN) (http://www.sidc.be/silso/datafiles), and multivariate ENSO index (MEI) (https://www.esrl.noaa. gov/psd/enso/mei/index.html)

Research methods
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CONCLUSIONS

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