2020
DOI: 10.1002/joc.6728
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Dominant factor of dry‐wet change in China since 1960s

Abstract: The dry‐wet climate conditions of an area are mainly determined by the water input (e.g., precipitation, P) and output (e.g., potential evapotranspiration, PET). Based on three dry‐wet indices and the differential equation, this study designed a new attribution method to quantify the contributions of P and PET to the dry‐wet change, so as to determine the dominant factors of the dry‐wet change over whole China (CHINA) and various hydroclimate regions (i.e., arid region, AR; semi‐arid region, SAR; semi‐humid re… Show more

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Cited by 14 publications
(9 citation statements)
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“…In order to identify the onset and terminate of dry and wet spells for 10 climate divisions of China, the SPEI was calculated based on the difference between precipitation and PET to quantify water surplus and deficit (Vicente‐Serrano et al, 2010). Although the SPEI was originally proposed for drought monitoring, it can also be used as a tool to detect flood risk (Xu et al, 2020). The SPEI is typically calculated by summing up the difference between precipitation and PET for 1 month and then by fitting the difference to a parametric probability distribution from which probability distributions are converted to the standard normal distribution (Beguería et al, 2014; McKee et al, 1993; Vicente‐Serrano et al, 2010).…”
Section: Study Region Data Sets and Methodsmentioning
confidence: 99%
“…In order to identify the onset and terminate of dry and wet spells for 10 climate divisions of China, the SPEI was calculated based on the difference between precipitation and PET to quantify water surplus and deficit (Vicente‐Serrano et al, 2010). Although the SPEI was originally proposed for drought monitoring, it can also be used as a tool to detect flood risk (Xu et al, 2020). The SPEI is typically calculated by summing up the difference between precipitation and PET for 1 month and then by fitting the difference to a parametric probability distribution from which probability distributions are converted to the standard normal distribution (Beguería et al, 2014; McKee et al, 1993; Vicente‐Serrano et al, 2010).…”
Section: Study Region Data Sets and Methodsmentioning
confidence: 99%
“…However, a recent study found that there are still uncertainties about the dry gets drier, wet gets wetter pattern; the drought/wetness trends have been overestimated [60]. These contradicting studies indicate that there are obvious regional differences in dry-wet change [61]. Aimed at this issue, negative precipitation anomalies based on atmospheric dynamics provided helpful information to recognize the drying conditions and the intensifying droughts [48].…”
Section: Climate Change Characterized By Global Warming Has Become An...mentioning
confidence: 99%
“…The spatial and temporal distribution of drought in China has changed under global climate change [4]. Owing to the vast territory and complex climate [15,61], the spatial-temporal pattern of droughts in China in the last few decades is far from fully understood [23]. Preceding studies focused on variations at the basin and local scales after the 1950s [18,59,60,[84][85][86], which poses a challenge to understanding the drywet variation in each region of China over long time periods [87].…”
Section: Climate Change Characterized By Global Warming Has Become An...mentioning
confidence: 99%
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