Spatial-temporal characteristics of drought detected from meteorological data with high resolution i

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The spatial patt of meteorological factors cannot be accurately simulated by using observations from meteorological stations (OMS) that are distributed sparsely in complex terrain. It is expected that the spatial-temporal characteristics of drought in regions with complex terrain can be better represented by meteorological data with the high spatial-temporal resolution and accuracy. In this study, Standard Precipitation Evapotranspiration Index (SPEI) calculated with meteorological factors extracted from ITPCAS (China Meteorological Forcing Dataset produced by the Institute of Tibetan Plateau Research, Chinese Academy of Sciences) was applied to identify the spatial-temporal characteristics of drought in Shaanxi Province of China, during the period of 1979–2016. Drought areas detected by SPEI calculated with data from ITPCAS (SPEI-ITPCAS) on the seasonal scale were validated by historical drought records from the Chinese Meteorological Disaster Canon-Shaanxi, and compared with drought areas detected by SPEI calculated with data from OMS (SPEI-OMS). Drought intensity, trend and temporal ranges for mutations of SPEI-ITPCAS were analyzed by using the cumulative drought intensity (CDI) index and the Mann-Kendall test. The results indicated that drought areas detected from SPEI-ITPCAS were closer to the historical drought records than those detected from SPEI-OMS. Severe and exceptional drought events with SPEI-ITPCAS lower than –1.0 occurred most frequently in summer, followed by spring. There was a general drying trend in spring and summer in Shaanxi Province and a significant wetting trend in autumn and winter in north Shaanxi Province. On seasonal and annual scales, the regional and temporal ranges for mutations of SPEI-ITPCAS were different and most mutations occurred before the year 1990 in most regions of Shaanxi Province. The results reflect the response of different regions of Shaanxi Province to climate change, which will help to manage regional water resources.
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