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The Severity of Drought and Precipitation Prediction in the Eastern Fringe of the Tibetan Plateau

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The Severity of Drought and Precipitation Prediction in the Eastern Fringe of the Tibetan Plateau Theoretical and Applied Climatology https://doi.org/10.1007/s00704-018-2564-8 ORIGINAL PAPER The severity of drought and precipitation prediction in the eastern fringe of the Tibetan Plateau Yang Zhao1,2 & Xiangde Xu 2 & Liufeng Liao3 & Yuhong Wang4 & Xiaoping Gu3 & Rui Qin5 & Yunyun Guo6 & Zhaoping Kang7 & Fang Wang2 & Min Wang8 Received: 31 January 2018 /Accepted: 10 July 2018 # The Author(s) 2018 Abstract The trend, severity, and duration of drought in the eastern fringe of the Tibetan Plateau (EFTP) have been investigated using the Mann-Kendall (M-K) trend test, standardized precipitation index (SPI), and generalized extreme value (GEV), using data obtained from 438 rainfall stations and reanalysis datasets for the period 1961–2014. A recent drought trend is evident from a decrease in rainfall, with this mainly occurring on the eastern slope of the TP (< 3000-m elevation); this is attributed to downward air flows over the eastern slope of the Tibetan Plateau (TP) induced by TP heating. Recent droughts have also been more severe, with these again mostly occurring on the eastern slopes. The duration of drought illustrates that extreme droughts are becoming more frequent. The study also predicted summer precipitation, due to its crucial role in drought research in the EFTP. Results show that the preceding May–June–July (MJJ) averaged column-integrated meridional water vapor transport (MWVT) from the South China Sea (SCS), Philippine Sea, and tropical western Pacific is a vital predictor of summer precipitation in the EFTP. A partial least squares (PLS) regression prediction model is therefore constructed, using the leading PLS components of preceding MJJ-averaged column- integrated MWVT. Compared to the observed summer rainfall, the PLS prediction model performs an excellent reconstructed skill with a correlation of 0.81 (1961–2006) and exhibits a promising forecast skill with a correlation of 0.67 (2007–2014). Results suggest that southerly moisture transport in early summer would help prevent summer drought in the EFTP. 1 Introduction Drought is one of the most important meteorological factors, affecting agriculture, water resources, and ecosystems (Dai et * Xiangde Xu al. 2004). Severe droughts cause millions of casualties and [email protected] large economic losses each year (Eisensee and Strömberg 2007; Hao et al. 2012). The eastern fringe of the Tibetan 1 Nanjing University of Information Science & Technology, Plateau (EFTP) (23°–40° N, 99°–109° E) is located on the Nanjing 210044, Jiangsu, China western boundary of the East Asia monsoon (EAM) area in 2 State Key Laboratory of Severe Weather, Chinese Academy of China and includes provinces such as Sichuan, Guizhou, Meteorological Sciences, Beijing 100081, China Yunnan, and Gansu (Gao et al. 2017;Fig.1). Frequent drought 3 Guizhou Key Laboratory of Mountainous Climate and Resources, events in the EFTP lead to landslides, rockfalls, and debris Guizhou Institute of Mountainous Environment and Climate, flows. For instance, a prolonged drought event resulted in Guiyang 550002, China the displacement of over 18 million people in 2006 (Zhang 4 Service Center of Public Meteorology, China Meteorological et al. 2015). During 2009–2010, an unprecedented drought in Administration, Beijing 100081, China Sichuan led to 21 million residents suffering water shortages, 5 Institute of Urban Meteorology, China Meteorological with about 4 million crops devastated (Lu et al. 2011;Maetal. Administration, Beijing 100081, China 2017). A better understanding of drought in the EFTP would 6 Sichuan Meteorological Observatory, Chengdu 610071, China improve drought forecasting and water resource management 7 Hubei Key Laboratory for Heavy Rain Monitoring and Warning and could help improve regional responses to drought. Research, Institute of Heavy Rain, CMA, Wuhan 430074, China Variables such as precipitation, evapotranspiration, 8 Tianjin Meteorological Information Centre, Tianjin 300074, China temperature, and soil moisture can be used to measure Y. Zhao et al. Fig. 1 a Altitude of terrain in East Asia is color shaded (unit, m), with the red solid box showing the location of the EFTP. b U and V wind components at 850 hPa (in summer) during 1961–2014. c U and V wind components at 850 hPa (annual) during 1961– 2014. Red arrows indicate wind in a and b drought (Xin et al. 2006; Sheffield and Wood 2008; Briffa since 2009 due to anthropogenic warming (Ma et al. et al. 2010). Previous analyses of drought in the EFTP 2017). have used various variables. Li et al. (2011) attributed a The EFTP lies in the EAM regime and is strongly in- dry spell in the southeastern Tibetan Plateau (TP) to topo- fluenced by precipitation throughout the year (Fig. 1b, c; graphic forcing, based on precipitation, evaporation, and Xu et al. 2008). Heavy rains often occur (Jiang et al. humidity. Fang et al. (2010) proposed, based on 2015). Precipitation in the EFTP is closely associated with temperature and precipitation, that the spatial distribution local drought (Ueno and Sugimoto 2012); for example, of drought in the southeastern TP is influenced by tropical Ren (2014) suggested that a severe rainstorm in the east- oceans. Deng et al. (2017) used precipitation and ern TP during 2010 induced a severe subsequent drought. evapotranspiration to attribute spatial variations of As a result of global warming, increased extreme precip- drought in the eastern TP to the North Atlantic itation is expected to lead to frequent drought events Oscillation. It is worth noting that the eastern TP has (IPCC 2013;Geetal.2017). A number of drought studies recently tended towards drought. You et al. (2012)ana- conducted in other parts of the world have also focused lyzed the drying trend of the eastern TP based on two exclusively on precipitation (Byun and Wilhite 1999). For precipitation datasets. Zhao et al. (2016) indicated that example, Huang and Xu (1999) analyzed the North China precipitation in the EFTP has generally decreased across drought trend based on rainfall stations. Similarly, Zhai et rainfall stations, indicating a drought tendency. Based al. (2010) reported that many river basins in north China on precipitation and temperature, it would appear that have experienced frequent droughts since 1995, on the southeastern TP droughts have been on the increase basis of data from rainfall stations. Paparrizos et al. The severity of drought and precipitation prediction in the eastern fringe of the Tibetan Plateau (2016) found that, based only on precipitation, drought in Greece appears to be becoming more severe. Precipitation could be easily applied in drought research (Hayes et al. 1999). However, there is limited drought research on the EFTP that uses precipitation as a meteorological variable. Though the above-mentioned studies indicated a drought tendency in the EFTP (You et al. 2012;Zhaoetal.2016; Ma et al. 2017), both the likely severity and duration of drought remain unclear. Precipitation prediction is further necessary to enable drought hazard prevention. The goals of this study are therefore to investigate spatiotemporal variations in drought trends, and their se- verityanddurationintheEFTP,onthebasisofprecipi- tation, and to establish a statistical model to predict sum- Fig. 2 Locations of all stations (green dots) in the EFTP are shown in the – – mer precipitation in situ. The paper is structured as fol- red solid box (23° 40° N, 99° 109° E). North and south blue solid lines denote the Yellow and Yangtze Rivers, respectively. The gray-shaded lows. Section 2 introduces data and methods. Section 3 colors indicate terrain height (unit, m) describes the spatiotemporal pattern of the EFTP drought trend. Drought severity and duration are explored in Section 4.Section5 focuses on prediction of EFTP sum- mer precipitation. Finally, Section 6 presents a discussion non-zero values shown in the gradient of the estimated and conclusions. linear regression line are correct. 2 Data and method 2.2.2 The standardized precipitation index method 2.1 Data The standardized precipitation index (SPI), based only on precipitation, is widely applied for study of drought This study mainly analyzed observational datasets of 438 events and drought classes (Hayes et al. 1999; Pai et al. daily rainfall stations with long-term rainfall records 2011). The drought analysis conducted for the EFTP from 2513 stations in China, with these spanning the focuses only on the influence of rainfall and not on period 1961–2014 (http://www.nmic.cn/web/index.htm). the influence of evapotranspiration. The water vapor The 438 stations nearly cover the entire area necessary for precipitation weakens with northward pro- considered in this study and are thus suitable for a gression from the South China Sea (SCS) and Bay of representation of drought climate features in the EFTP. Bengal. The SPI can be applied to confirm the severity Reanalysis data, including monthly mean (2.5° × 2.5°) of drought in different climate contexts and is therefore wind field, was acquired from the National Centers for adoptedinthisstudy. Environmental Prediction (NCEP). EFTP elevations > The SPI estimates precipitation deficit for multiple 3000 m are considered to constitute the eastern timescales. For purposes of this study, a 24-month time- – platform of the TP, while elevations < 3000 m refer to scale during the period 1961 2014 was selected. The the eastern slope of the TP (the 3000-m boundary line is SPI has been shown to fit a gamma distribution showninFigs.2, 3 and 4). In this paper, summer refers (Thom 1958; Livada and Assimakopoulos 2007; to the period June–July–August (JJA). Almedeij 2014). The probability density function for the gamma distribution g(x) is shown as follows: 2.2 Method 1 gxðÞ¼ xα−1e−x=β ð1Þ 2.2.1 The Mann-Kendall trend test βΓðÞα The gamma function Γ(α) is defined as follows: The Mann-Kendall (M-K) trend test is applied to analyze ∞ spatiotemporal positive or negative trends of variables ΓðÞ¼α ∫ α−1 −x ð Þ 0 x e dx 2 such as rainfall (Mann 1945; Kendall 1975;Wangand Swail 2001).
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