The severity of drought and precipitation prediction in the eastern fringe of the Tibetan Plateau
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.
Variables such as precipitation, evapotranspiration, temperature, and soil moisture can be used to measure drought (Xin et al. 2006; Sheffield and Wood 2008; Briffa et al. 2010). Previous analyses of drought in the EFTP have used various variables. Li et al. (2011) attributed a dry spell in the southeastern Tibetan Plateau (TP) to topographic forcing, based on precipitation, evaporation, and humidity. Fang et al. (2010) proposed, based on temperature and precipitation, that the spatial distribution of drought in the southeastern TP is influenced by tropical oceans. Deng et al. (2017) used precipitation and evapotranspiration to attribute spatial variations of drought in the eastern TP to the North Atlantic Oscillation. It is worth noting that the eastern TP has recently tended towards drought. You et al. (2012) analyzed the drying trend of the eastern TP based on two precipitation datasets. Zhao et al. (2016) indicated that precipitation in the EFTP has generally decreased across rainfall stations, indicating a drought tendency. Based on precipitation and temperature, it would appear that southeastern TP droughts have been on the increase since 2009 due to anthropogenic warming (Ma et al. 2017).
The EFTP lies in the EAM regime and is strongly influenced by precipitation throughout the year (Fig. 1b, c; Xu et al. 2008). Heavy rains often occur (Jiang et al. 2015). Precipitation in the EFTP is closely associated with local drought (Ueno and Sugimoto 2012); for example, Ren (2014) suggested that a severe rainstorm in the eastern TP during 2010 induced a severe subsequent drought. As a result of global warming, increased extreme precipitation is expected to lead to frequent drought events (IPCC 2013; Ge et al. 2017). A number of drought studies conducted in other parts of the world have also focused exclusively on precipitation (Byun and Wilhite 1999). For example, Huang and Xu (1999) analyzed the North China drought trend based on rainfall stations. Similarly, Zhai et al. (2010) reported that many river basins in north China have experienced frequent droughts since 1995, on the basis of data from rainfall stations. Paparrizos et al. (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; Zhao et al. 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 severity and duration in the EFTP, on the basis of precipitation, and to establish a statistical model to predict summer precipitation in situ. The paper is structured as follows. Section 2 introduces data and methods. Section 3 describes the spatiotemporal pattern of the EFTP drought trend. Drought severity and duration are explored in Section 4. Section 5 focuses on prediction of EFTP summer precipitation. Finally, Section 6 presents a discussion and conclusions.
2 Data and method
This study mainly analyzed observational datasets of 438 daily rainfall stations with long-term rainfall records from 2513 stations in China, with these spanning the period 1961–2014 (http://www.nmic.cn/web/index.htm). The 438 stations nearly cover the entire area considered in this study and are thus suitable for a representation of drought climate features in the EFTP. Reanalysis data, including monthly mean (2.5° × 2.5°) wind field, was acquired from the National Centers for Environmental Prediction (NCEP). EFTP elevations > 3000 m are considered to constitute the eastern platform of the TP, while elevations < 3000 m refer to the eastern slope of the TP (the 3000-m boundary line is shown in Figs. 2, 3 and 4). In this paper, summer refers to the period June–July–August (JJA).
2.2.1 The Mann-Kendall trend test
The Mann-Kendall (M-K) trend test is applied to analyze spatiotemporal positive or negative trends of variables such as rainfall (Mann 1945; Kendall 1975; Wang and Swail 2001). This method is used as a substitute for parametric linear regression analysis and to examine whether non-zero values shown in the gradient of the estimated linear regression line are correct.
2.2.2 The standardized precipitation index method
The standardized precipitation index (SPI), based only on precipitation, is widely applied for study of drought events and drought classes (Hayes et al. 1999; Pai et al. 2011). The drought analysis conducted for the EFTP focuses only on the influence of rainfall and not on the influence of evapotranspiration. The water vapor necessary for precipitation weakens with northward progression from the South China Sea (SCS) and Bay of Bengal. The SPI can be applied to confirm the severity of drought in different climate contexts and is therefore adopted in this study.
The SPI estimates precipitation deficit for multiple timescales. For purposes of this study, a 24-month timescale during the period 1961–2014 was selected. The SPI has been shown to fit a gamma distribution (Thom 1958; Livada and Assimakopoulos 2007; Almedeij 2014).
SPI classes (http://www.ncl.ucar.edu/Applications/spi.shtml)
SPI ≤ − 1.6
− 1.6 < SPI ≤ − 1.3
− 1.3 < SPI ≤ − 0.5
− 0.5 < SPI ≤ 0.5
SPI > 0.5
2.2.3 The generalized extreme value distribution method
To confirm the distribution of drought duration, the generalized extreme value (GEV) distribution is used to calculate the probability density function (PDF) distribution. The GEV is a basic extreme value statistical method that has been widely applied to extreme temperature and precipitation (Rusticucci and Tencer 2008; Schubert et al. 2008; Yang et al. 2013; Rulfová et al. 2016).
2.2.4 Meridional water vapor transport
2.2.5 Partial least squares regression
Due to its wide variable coverage and ability to overcome limitations of high collinearity and small sample size, partial least squares (PLS) regression has been widely applied in statistical prediction (Haenlein and Kaplan 2004; Smoliak et al. 2010; Yu et al. 2017). Precipitation in the EFTP is closely related to southerly moisture transport, mainly from the SCS and Philippine Sea (Xu et al. 2002). PLS regression is thus utilized to find the PLS components of preceding column-integrated meridional water vapor transport (MWVT) as predictors and to predict summer precipitation in the EFTP. In other words, the PLS regression method aims to build predictors Z that combine factors X linearly as PLS components; PLS components are then used as predictors to obtain the predictand Y (Wu and Yu 2015).
2.2.6 Calculation of apparent heat source Q 1
3 Spatial and temporal drought trends in the EFTP
Based on the above, drought trends in major parts of the EFTP emerge clearly and distinctly through the spatiotemporal characteristics of precipitation variations. However, the severity degree of drought in situ remains uncertain.
4 Severity degree of drought in the EFTP based on SPI analysis
The numbers of drought months, based on SPI classes
Number of months
Extreme dry (SPI ≤ − 1.6)
Severely dry (− 1.6 < SPI ≤ − 1.3)
Dry (− 1.3 < SPI ≤ − 0.5)
Nearly normal (− 0.5 < SPI ≤ 0.5)
Wet (SPI > 0.5)
5 Summer precipitation prediction in the EFTP based on a statistical PLS prediction model
Precipitation always occurs during summer in the EFTP, leading to local drought (Jiang et al. 2015). Precipitation prediction is clearly crucial for drought research in this region, and for this purpose, we select stations on the eastern slope of the TP, at which precipitation declines significantly (at the 90% confidence interval) in summer (Fig. 4 inverted triangle). Dryness and wetness conditions in the EFTP are closely related to the southerly moisture transport characteristics of the TP-monsoon. Moisture from the Bay of Bengal is transported eastward towards the SCS and Philippine Sea from spring to summer, eventually affecting the TP (Xu et al. 2002). Zhu et al. (2011) also demonstrated that southerly water vapor from the SCS and the western Pacific bypasses the TP, resulting in the summer TP drought.
PLS regression is employed to identify the dominant PLS components of MJJ-averaged column-integrated MWVT variations (hereafter referred to as PLS modes) preceding summer rainfall as the predictors. The PLS modes can best explain the covariance between MJJ-averaged column-integrated MWVT variations and summer rainfall variations simultaneously; conversely, the empirical orthogonal function (EOF) mode can only explain MJJ-averaged column-integrated MWVT.
Our results suggest that southerly moisture transport from the SCS, Philippine Sea, and tropical western Pacific in early summer could be a precursor of summer rainfall in the EFTP. Improved predictive abilities are expected to contribute to summer drought prevention in the EFTP.
6 Discussion and conclusions
This study aims to elucidate the spatiotemporal distribution of drought in the EFTP, focusing on an analysis of drought severity and duration and on improving the ability to predict droughts using the rainfall variable.
First, spatiotemporal drought distribution in the EFTP is studied. An apparent decreasing rainfall trend is observed in 1991–2014, indicating recent drought. Areas with a decreasing rainfall trend are mainly concentrated on the eastern slope of the TP, with drought here attributed to local downward air flows induced by the TP heat source. Second, drought severity and duration are analyzed based on SPI classes and the GEV method, respectively. Results indicate that drought has become more severe, with severe and extreme droughts tending to occur on the eastern slope of the TP. Extreme droughts are also becoming more frequent.
To help in preventing drought hazards, the early summer column-integrated MWVT from the SCS, Philippine Sea, and tropical western Pacific is used to predict summer precipitation in the EFTP. A statistical PLS prediction model is built to predict summer rainfall, using the PLS components of preceding MJJ-averaged column-integrated MWVT. Due to its excellent forecasting ability, wide variable coverage, and multiple correlation solving capabilities, the PLS prediction model is widely used in statistical prediction (Ye et al. 2017; Yu et al. 2017); for example, Wu and Yu (2015) analyzed the key role of the mega-El Niño Southern Oscillation (ENSO) in EAM seasonal prediction utilizing the PLS model. In this research, a PLS model is used for rainfall prediction in the EFTP. This shows excellent performance in reconstructing summer rainfall for 1961–2006. Promising forecast abilities are also shown for the period 2007–2014. Correlation coefficients of the observation with the reconstructed and forecast summer rainfall are 0.81 and 0.67, respectively. Results obtained suggest that southerly moisture transport in early summer would enable summer drought prevention in the EFTP.
Previous studies have observed a decreasing rainfall trend in the EFTP, noting that rainfall reduction often occurs on the TP slope (Yang et al. 2011, 2014; Zhao et al. 2016). Our results confirm these conclusions and we attribute rainfall reduction on the eastern slope of the TP to local downward air flows induced by the TP heat source.
This study indicates a trend towards more severe drought and provides a model for reliable summer precipitation prediction in the EFTP on an inter-annual timescale, which would provide a powerful supplement for drought research in situ. However, seasonal and intra-annual drought characteristics still require further investigation. In addition, only station observations and reanalysis datasets are employed in this research. In future, use of more accurate satellite data and current climate models is warranted.
We acknowledge useful comments from the anonymous reviewers. We thank the National Meteorological Information Center (http://www.nmic.cn/web/index.htm) for providing observational precipitation dataset. The authors received financial support from the following: (1) the Third TP Scientific Experiment, a project supported by the Special Scientific Research Fund for Public Welfare Sectors (Meteorology) by the Ministry of Finance (GYHY201406001); (2) the science development fund of the Chinese Academy of Meteorological Sciences (2018KJ019); (3) a major project supported by the National Natural Science Foundation of China (NSFC) (91644223 and 91337000); (4) a high-level innovative talent cultivation project by the Department of Science and Technology of Guizhou Province ((2016)4026); and (5) the Jiangsu postgraduate research and innovation program project (KYCX17_0869 and 1344051701002).
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