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Drought and Wetness Variability and the Respective Contribution of Temperature and Precipitation in the Qinghai-Tibetan Plateau

Drought and Wetness Variability and the Respective Contribution of Temperature and Precipitation... Hindawi Advances in Meteorology Volume 2021, Article ID 7378196, 13 pages https://doi.org/10.1155/2021/7378196 Research Article Drought and Wetness Variability and the Respective Contribution of Temperature and Precipitation in the Qinghai-Tibetan Plateau 1,2,3 1,2,3 Lele Zhang and Liming Gao College of Geography Science, Qinghai Normal University, Xining 810008, China MOE Key Laboratory of Tibetan Plateau Land Surface Processes and Ecological Conservation, Xining 810008, China Qinghai Province Key Laboratory of Physical Geography and Environmental Process, Xining 810008, China Correspondence should be addressed to Lele Zhang; zhang1986lele@163.com Received 29 April 2021; Revised 19 August 2021; Accepted 7 September 2021; Published 29 September 2021 Academic Editor: Roberto Coscarelli Copyright © 2021 Lele Zhang and Liming Gao. *is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Quantifying drought and wetness fluctuations is of great significance to the regional ecological environment and water resource security, especially in the fragile Qinghai-Tibetan Plateau (QTP). In this paper, the standardized precipitation evapotranspiration index (SPEI) was calculated based on the observed data and China Meteorological Forcing Dataset (CMFD) in the QTP for the period of 1979–2015, and the drought and wetness evolution based on the SPEI series and respective contribution of temperature and precipitation were also analyzed. Results indicated that meteorological stations are mainly concentrated in the eastern part of the plateau, which cannot reflect the drought and wetness trend of the whole QTP. *e linear trend and Mann–Kendall test revealed that SPEI series calculated based on CMFD data at 1-, 3-, 6-, 9-, 12-, and 24-month time scales all showed significant upward trend (p< 0.01), indicating that the QTP as a whole tended to be wetter. Spatially, the regions with significant drying (p < 0.1) and increased drought probability were mainly concentrated in the Qaidam Basin and the southern part of the QTP, and the mean contribution rates of temperature and precipitation variability to SPEI trend in these regions were 60% and −11%, respectively. *e regions with significant wetting (p< 0.1) and decreased drought probability were mainly concentrated in the northeast, central, and western parts of the plateau, and the mean contribution rates of temperature and precipitation variability to SPEI trend were −9% and 61% in these regions. From the statistics in different climatic regions, most of the arid and humid regions in the QTP tended to be drier, while the semiarid regions tended to be wetter. changing. Globally, climate change is leading dry regions to 1. Introduction become drier and wet regions to become wetter [8, 9], and Drought and wetness fluctuations, which are closely related the frequency of extreme drought and flood events would to precipitation and evapotranspiration variation [1], have further increase in the 21st century [10–12]. *ese extreme profound influences on agriculture and ecosystems. Over the events can cause billions of dollars in losses and affect more past few decades, the global climate has changed remarkably people than any other climate-related disaster [13]. *ere- [2]. Ground observation, remote sensing, and reanalysis data fore, it is of great significance to comprehensively under- revealed that temperature is rising in most regions of the stand the drought and wetness change trend. world [3, 4]. Although the spatial difference of precipitation Many meteorological drought indexes had been devel- change was relatively large [5], the frequency of extreme oped for quantifying drought and wetness fluctuations, precipitation had increased extensively in most parts of the including the Palmer drought severity index (PSDI) [14], world [6, 7]. With the changes of temperature and pre- standardized precipitation index (SPI) [15], China-Z index cipitation, the state of drought and wetness was also (CZI) [16], standardized precipitation evapotranspiration 2 Advances in Meteorology objectives of this study were to (1) assess the spatiotem- index (SPEI) [17], and soil wetness deficit index (SWDI) [18]. Among these indexes, SPI and SPEI are easy to obtain poral variation characteristics of drought and wetness in the QTP during 1979 to 2015 and (2) quantify the re- and widely used for drought and wetness changes and their impact on agriculture and ecological environment in many spective contributions of temperature and precipitation countries and regions of the world [19–25]. *e main ad- variability to drought and wetness trend in the QTP. *e vantage of the SPI is that it is calculated at multi-time scales rest of the article is arranged as follows. *e basic con- and can monitor different drought types [26, 27]. However, ditions and meteorological data in the QTP and the only precipitation is used in the calculation of the SPI [17]. methods used in this paper are introduced in Section 2. In *e index cannot reflect the influence of temperature change Section 3, the spatiotemporal evolution of drought and wetness is analyzed, and the respective contributions of on the drought and wetness conditions. Compared with SPI, SPEI is calculated based on the process of surface water temperature and precipitation variability to drought and wetness trend are calculated. *e discussion and conclu- balance and also introduces the multi-time scale character used in SPI calculation [17, 28]. Chen et al. [29] compared sions are given in Sections 4 and 5, respectively. the monitoring results of SPI, PDSI, and SPEI in China and found that the meteorological drought monitored by SPEI 2. The Study Area, Data, and Methods was better than that monitored by SPI and PDSI. *e results obtained by Zarei et al. [30] also showed that the accuracy of 2.1. Study Area and Data Sources. *e QTP lies in the SPEI in monitoring drought events is higher than that of SPI southwest of China, including most of the Qinghai and reconnaissance drought index (RDI) in Iran. Further- Province and Tibet Autonomous Region and a few parts of more, previous studies had also shown that SPEI variation is Xinjiang Uygur Autonomous Region, Gansu, Sichuan, and highly correlated with vegetation index, crop growth state, Yunnan (Figure 1). *e Altun Mountains, Qilian Moun- and soil water storage change [31–33], so it has a wide tains, and Qaidam Basin are located in the north of the application potential in water resource management, agri- plateau, and the Himalaya Mountains are located in the 4 2 cultural production, and other fields in the future. southwest. *e QTP covers a total area of 257 ×10 km , of 4 2 *e QTP is recognized as the water tower of Asia and can which 122.2 ×10 km is permafrost area [45]. With an influence the Asian monsoon through its mechanical and average elevation of more than 4500 meters, the QTP is the thermal forcing [34–37]. As a sensitive and fragile area, the highest plateau in the world, so it is also known as the evolution of drought and wetness in the QTP has attracted “*ird Pole.” In addition, the QTP is also the headstream extensive attention. Shi and Liu [38] analyzed the charac- of many major rivers (including the Yangtze River, Yellow teristics of drought events over the continent in the Eastern River, Lancang River, Nujiang River, and so on) in the Hemisphere based on the SPEI data and found that the QTP world, and China’s largest lake (Qinghai Lake) is also had been one of the regions with high variance. Wang et al. located in the northeast of the plateau, so it is also known [39] used the high-accuracy self-calibrating Palmer drought as the “Water Tower of Asia.” *e climate on the plateau is severity index (sc_PDSI) to investigate the drought variation alternately controlled by monsoon in the summer and in China and found that northern Tibetan Plateau and westerlies in the winter [46]. Precipitation is mainly southern Tibetan Plateau experienced a wetting trend be- concentrated in the warm season, and the light precipi- tween 1961 and 2009. Feng et al. [40] investigated spatial- tation is dominant in winter [47]. Spatially, the central and temporal patterns of meteorological drought in the QTP and southern parts of the plateau are mainly controlled by the its surrounding areas and revealed that the QTP became Indian monsoon, and the annual maximum precipitation wetter in spring during 1970–2017. Chen et al. [41] divided can reach more than 1000 mm, while the western and China into six climatic regions and analyzed the charac- northern parts are controlled by the westerlies, and the teristics of drought and wetness trend based on SPEI, PDSI, annual precipitation is relatively low, and the annual and sc_PDSI and proposed that the QTP became wetter precipitation in the Qaidam Basin in the north is even less during 1961–2012. It should be noted that most of these than 100 mm [48]. *e temperature is relatively high due conclusions are based on the observed data. However, the to low latitude and altitude in the southeast of the plateau, meteorological stations are mainly concentrated in the while in the west, the temperature is relatively low due to eastern part of the plateau, while the drought and wetness high altitude [49]. conditions in the west with few stations had received little Observed monthly temperature and precipitation attention. Additionally, the precipitation and temperature were used for SPEI calculations in this study. *e observed variability is the main factor that directly affects the change data (1979–2015) for 50 sites in the QTP (Figure 1) were trends of drought/wetness, and previous studies had shown derived from the National Meteorological Information that the temperature and precipitation in the QTP both Center (https://www.data.cma.cn/). *ese data were ob- showed increasing trend [42–44]. However, it is still not tained from the daily value statistics of meteorological clear that which is the main factor that dominates the elements, and the production process strictly followed the drought and wetness trend in the plateau. standards and methods formulated by the National Me- In this study, both the observed data from 50 mete- teorological Administration of China. *e accuracy of the orological stations and CMFD high-resolution meteoro- data had been verified and also had been widely used in logical forcing data were used to analyze drought and the research of China’s climate change and hydrological wetness condition evolutions over the QTP. *e main process [50, 51]. Advances in Meteorology 3 40°N 30°N Elevation (m) 0 1,000 km 80°E 90°E 100°E Meteorological station Provincial boundarics Figure 1: Topographic map of the Qinghai-Tibetan Plateau (adapted from the geospatial data cloud at Beijing (https://www.gscloud.cn/)). Considering the sparsity of observation sites in the QTP, Table 1: Drought and wetness classification based on the SPEI value [17, 28, 30]. the CMFD data offered by the National Tibetan Plateau Data Center (https://www.data.tpdc.ac.cn/) were also used for Category SPEI value SPEI calculations. *e CMFD data constitute a near-surface Extremely wet SPEI≥ 2 meteorological forcing dataset and cover the China domain Moderately wet 1.5 ≤ SPEI< 1.99 at a 3-hourly time step and a spatial resolution of 0.1 , Slightly wet 1 ≤ SPEI< 1.49 spanning from 1979 to 2015. It was made through fusion of Near normal −0.99< SPEI< 0.99 ground-based observations of approximately 700 stations in Mild drought −1.49 < SPEI≤ −1 China with several gridded datasets including GLDAS, Moderate drought −1.99 < SPEI≤ −1.5 Extreme drought SPEI≤ −2 MERRA, GEWEX-SRB, and TRMM 3B42 v7 [52]. *e meteorological elements include near-surface temperature, pressure, specific humidity, wind speed, ground downward short-wave radiation, downward long-wave radiation, and the *ornthwaite method. Although the potential evapo- precipitation rate. *e CMFD data had been widely used in transpiration estimated by this method in winter and the study of the climate and hydrology of the QTP [53, 54]. spring in China has certain differences from other methods, the overall estimation of the potential evapotranspiration form *ornthwaite and other methods is very comparable [40, 55, 56]. By providing a text file containing temperature 2.2. SPEI for Determining Drought and Wetness Conditions. and precipitation data, geographic latitude, time, and other SPEI is an improved drought index of SPI designed by basic information, the program can automatically calculate Vicente-Serrano et al. [17]. It is calculated based on the SPEI series at different time scales. In this study, SPEI process of surface water balance and also introduces the values were calculated both based on the observed and multi-time scale character used in SPI calculation, which CMFD data in the QTP for the period of 1979–2015. fully combines the advantages of SPI and PDSI. Because of According to the classification criteria in Table 1, the its advantages, it had been widely used in drought moni- toring in many studies [40, 41]. Based on SPEI calculations, frequency of each drought and wetness category during a period can be obtained, and the probability of each category drought and wetness can be classified into seven categories as listed in Table 1. Calculation of the index is performed is calculated as follows [57]: using the computer program SPEI Calculator, which is (1) developed and maintained by the Institutional Repository � × 100%, of the Spanish National Research Council. Software and documentation are available online for downloading where p and f are the probability (%) and frequency of ith i i (https://www.digital.csic.es/). *e default method for cal- drought and wetness category during a period, respectively, culating the potential evapotranspiration in the software is and F is the total number of data points. 4 Advances in Meteorology 2.3. Statistical Methods significance test at p < 0.1, p < 0.05, and p < 0.01 levels, respectively. 2.3.1. Methods for Detecting Drought and Wetness Trends. Drought and wetness trends are indicated by the linear 2.3.2. Quantification of the Respective Contributions of trends of SPEI series in this study. *e Mann–Kendall test is Temperature and Precipitation. A simple method adapted used to determine the significance of the trends. *e based on the algorithm proposed by Wu and Chen [62] was Mann–Kendall (MK) test does not require samples to follow used for quantifying the respective contributions of the a certain distribution and is also not affected by some temperature and precipitation variability to the drought and outliers [58, 59]. In previous studies, the MK method had wetness trends, and the calculation flow of this method is as been widely used in the trend analysis of time series in follows: meteorological, hydrological, and ecological fields [19, 60]. *e method is based on theZ value to judge the change trend (1) *e monthly temperature and precipitation data of time series and is computed as follows [40, 61]. from 1979 to 2015 were divided into 12 groups by Calculate the test statistical variable S based on the SPEI month, respectively, and the linear detrending series (x , x , x , . . . , x ): 1 2 3 n method is used for these groups. *e linear n−1 n detrending method is described as follows: S � 􏽘 􏽘 sgn􏼐x − x 􏼑, (2) j i Y � y − c(i − 1979), (6) i�1 j�i+1 i i where sgn(x) represents the sign function and can be where Y is the detrended monthly precipitation or expressed as follows: temperature in the ith year (i � 1979, 1980, . . . , 2015), y is the original monthly precipitation or ⎪ 1, x − x > 0, ⎪ j i temperature in theith year, and Υ is the precipitation sgn x − x � 0, x − x � 0, (3) 􏼐 􏼑 or temperature trend during 1979–2015. j i j i −1, x − x < 0. (2) According to the results of the previous step, the j i SPEI series under 3 climate scenarios can be ob- *e variance of S can be expressed as tained. *e first SPEI time series (SPEI ) was cal- culated based on the original meteorological data, n(n − 1)(2n + 5) (4) Var S � . the second (SPEI ) was calculated using the original Dp temperature records and detrended precipitation *e standard test statistical variable Z is computed as data, and the third (SPEI ) was calculated using the Dt detrended temperature data and observed precipi- S − 1 ⎧ ⎪ √���� � ⎪ , S> 0, tation records. Var S ⎪ (3) *e factors affecting the variability of SPEI can be divided into three categories, including temperature, Z � 0, S � 0, (5) ⎪ precipitation, and other factors. *e other factors mean the factors that can affect drought and wetness S + 1 ⎪ conditions except temperature and precipitation. √���� � ⎩ , S< 0. Var S When the slope of SPEI series (S_SPEI ) is greater o o than 0, it means that this grid point tends to be wetter When the Z value is positive, it represents an increasing during 1979–2015, and the contribution rates (Cr) of trend, while negative Z value means a decreasing trend. the three factors are calculated as follows: When |Z| is greater than 1.28, 1.64, and 2.32, it means that the corresponding trend of time series has passed the S SPEI Dp 􏼌 􏼌 􏼌 􏼌 Cr T � 􏼌 􏼌 × 100%, 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 S SPEI + S SPEI + S SPEI − SPEI − S SPEI 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 Dp Dt o Dp Dp S SPEI Dt 􏼌 􏼌 􏼌 􏼌 Cr P � × 100%, 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 (7) 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 S SPEI + 􏼌S SPEI 􏼌 + S SPEI − SPEI − S SPEI 􏼌 Dp􏼌 Dt 􏼌 o Dp Dp􏼌 S SPEI − S SPEI − S SPEI o Dp Dp 􏼌 􏼌 􏼌 􏼌 Cr O � 􏼌 􏼌 × 100%, 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 S SPEI + 􏼌S SPEI 􏼌 + S SPEI − SPEI − S SPEI 􏼌 􏼌 􏼌 􏼌 Dp Dt o Dp Dp Advances in Meteorology 5 where S_SPEI , S_SPEI , and S_SPEI represent the linear IfCr< 0, it means that this factor has a negative contribution o Dp Dt slopes of SPEI , SPEI , and SPEI time series and Cr_T, to the wetting trend. If Cr � 0, it means there is no con- o Dp Dt Cr_P, and Cr_O are the Cr values of temperature, precip- tribution. When S_SPEI <0, it means that this grid point itation, and other factors. If Cr> 0, it means that this factor tends to be drier, and the contribution rates are calculated as has a positive contribution to the wetting trend, and the follows: greater the absolute value is, the greater the contribution is. −S SPEI Dp 􏼌 􏼌 􏼌 􏼌 Cr T � 􏼌 􏼌 × 100%, 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 S SPEI + 􏼌S SPEI 􏼌 + S SPEI − SPEI − S SPEI 􏼌 􏼌 􏼌 􏼌 Dp Dt o Dp Dp −S SPEI Dt 􏼌 􏼌 􏼌 􏼌 Cr P � × 100%, 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 (8) 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 S SPEI + 􏼌S SPEI 􏼌 + S SPEI − SPEI − S SPEI 􏼌 Dp􏼌 Dt 􏼌 o Dp Dp􏼌 − 􏼐S SPEI − S SPEI − S SPEI 􏼑 o Dp Dp 􏼌 􏼌 􏼌 􏼌 Cr O � 􏼌 􏼌 × 100%. 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 S SPEI + S SPEI + S SPEI − SPEI − S SPEI 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 Dp Dt o Dp Dp If Cr> 0, it means that this factor has a positive con- large time series. *e SPEI values at all time scales were tribution to the drying trend. If Cr< 0, it means that this mainly negative before 2001 and positive after 2001. *e results of the linear slope and MK test showed that all SPEI factor has a negative contribution to the drying trend. If Cr � 0, it means there is no contribution. It should be noted time series showed an obvious upward trend (p < 0.01), that only the contributions of temperature and precipitation which means that the QTP tended to be wetter as a whole variability to the drought and wetness trends were analyzed during 1979 and 2015. in this study. 3.2. Probability of Drought and Wetness Occurrences. *e 3. Results probabilities of occurrence for each drought and wetness 3.1. Spatiotemporal Variation of SPEI Series in the QTP. category at all CMFD grids in the QTP were calculated. In Based on the temperature and precipitation data recorded at order to analyze the dynamic change of occurrence prob- 50 meteorological stations and 25711 CMFD grid points ability of each drought and wetness category, the whole from 1979 to 2015, the SPEI values at 1-, 3-, 6-, 9-, 12-, and period was divided into two subperiods: the first is from 24-month time scales were calculated. *e variation trend of 1979 to 2000 (Period I) and the second is from 2001 to 2015 (Period II). As shown in Figure 4, the difference between SPEI time series at each station and grid point is given by using the linear slope and MK test (Figure 2). As can be seen probabilities of drought and wetness occurrences based on SPEI at different time scales were small in the whole plateau. from Figures 2(a)–2(f), the variation trend of SPEI time series at different time scales is consistent and the SPEI time During 1979–2000, the probability of mild drought was the series of more than 84% stations showed a downward trend. highest while the probability of slightly wet was the highest Since the meteorological stations are mainly concentrated in during 2001–2015. *e probabilities of extreme drought and the eastern part of the QTP, it is believed that the eastern part moderate drought did not change much between the two of the plateau tended to be drier during 1979 and 2015. subperiods. Compared with Period I, the probability of Figures 2(g)–2(l) show the results obtained based on CMFD extreme drought increased slightly in Period II, and the data. According to statistics, the grid points with a de- probability of moderate drought decreased slightly. How- creasing trend of SPEI are less than 38%, mainly concen- ever, the probabilities of mild drought, slightly wet, mod- erately wet, and extremely wet were different between the trated in the southeast of QTP and Qaidam Basin, which is consistent with the conclusion obtained based on meteo- two subperiods. In Period II, the probability of mild drought decreased, while the probabilities of slightly wet, moderately rological stations. However, in the central and western parts of the plateau, SPEI time series showed upward trend during wet, and extremely wet increased. 1979 and 2015, indicating that these areas tended to be *e spatial differences of drought and wetness proba- wetter. bility variations in the QTP were also analyzed. Considering In order to characterize the drought and wetness trend in that the probabilities calculated by different time scales were the whole QTP, the SPEI series at 1-, 3-, 6-, 9-, 12-, and 24- close, only SPEI calculated at 12-month time scales was used month time scales were averaged over the 25711 CMFD for statistics. Figure 5 shows the probability of each drought pixels, and the temporal evolution of these SPEI series is and wetness category of all CMFD pixels in 2001–2015 displayed in Figure 3. It can be seen from Figure 3 that the minus that in 1979–2000. If the value obtained by sub- traction is positive, it means that the probability in fluctuation of SPEI series calculated by small time scale is relatively large, while the fluctuation is relatively small by 2001–2015 has increased compared with that in 1979–2000; 6 Advances in Meteorology 40°N 40°N 40°N 35°N 35°N 35°N 30°N 30°N 30°N 25°N 25°N 25°N 80°E 90°E 100°E 80°E 90°E 100°E 80°E 90°E 100°E Decreased significantly (p < 0.1) Decreased significantly (p < 0.1) Decreased significantly (p < 0.1) Decreased but not significantly (p > 0.1) Decreased but not significantly (p > 0.1) Decreased but not significantly (p > 0.1) Increased but not significantly (p > 0.1) Increased but not significantly (p > 0.1) Increased but not significantly (p > 0.1) Increased significantly (p < 0.1) Increased significantly (p < 0.1) Increased significantly (p < 0.1) (a) (b) (c) 40°N 40°N 40°N 35°N 35°N 35°N 30°N 30°N 30°N 25°N 25°N 25°N 80°E 90°E 100°E 80°E 90°E 100°E 80°E 90°E 100°E Decreased significantly (p < 0.1) Decreased significantly (p < 0.1) Decreased significantly (p < 0.1) Decreased but not significantly (p > 0.1) Decreased but not significantly (p > 0.1) Decreased but not significantly (p > 0.1) Increased but not significantly (p > 0.1) Increased but not significantly (p > 0.1) Increased but not significantly (p > 0.1) Increased significantly (p < 0.1) Increased significantly (p < 0.1) Increased significantly (p < 0.1) (d) (e) (f) 40°N 40°N 40°N 35°N 35°N 35°N 30°N 30°N 30°N 25°N 25°N 25°N 80°E 90°E 100°E 80°E 90°E 100°E 80°E 90°E 100°E Decreased significantly (p < 0.1) Decreased significantly (p < 0.1) Decreased significantly (p < 0.1) Decreased but not significantly (p > 0.1) Decreased but not significantly (p > 0.1) Decreased but not significantly (p > 0.1) Increased but not significantly (p > 0.1) Increased but not significantly (p > 0.1) Increased but not significantly (p > 0.1) Increased significantly (p < 0.1) Increased significantly (p < 0.1) Increased significantly (p < 0.1) (g) (h) (i) 40°N 40°N 40°N 35°N 35°N 35°N 30°N 30°N 30°N 25°N 25°N 25°N 80°E 90°E 100°E 80°E 90°E 100°E 80°E 90°E 100°E Decreased significantly (p < 0.1) Decreased significantly (p < 0.1) Decreased significantly (p < 0.1) Decreased but not significantly (p > 0.1) Decreased but not significantly (p > 0.1) Decreased but not significantly (p > 0.1) Increased but not significantly (p > 0.1) Increased but not significantly (p > 0.1) Increased but not significantly (p > 0.1) Increased significantly (p < 0.1) Increased significantly (p < 0.1) Increased significantly (p < 0.1) (j) (k) (l) Figure 2: Spatial distributions of the Mann–Kendall trend statistic for SPEI at different time scales for the period 1979–2015. (a)–(f) are the SPEI trends based on observed data, and (g)–(l) are the SPEI trends based on the CMFD data. (a) SPEI-1. (b) SPEI-3. (c) SPEI-6. (d) SPEI-9. (e) SPEI-12. (f) SPEI-24. (g) SPEI-1. (h) SPEI-3. (i) SPEI-6. (j) SPEI-9. (k) SPEI-12. (l) SPEI-24. otherwise, the probability has decreased. As shown in Southern Xinjiang, and the southeast of Tibet, the increased Figure 5(a), the probability of extreme drought in most areas probabilities of extreme drought in other regions were less of the southeastern, southwestern, and northwestern parts of than 10%. In the central and northeastern parts of the the QTP increased during 2001–2015 compared with that plateau, the probability of extreme drought decreased, with a during 1979–2000. However, except for the Qaidam Basin, decrease of less than 10% in almost all regions. From Advances in Meteorology 7 1.5 1.5 1.5 Slope: 0.0016/a Slope: 0.0018/a Slope: 0.0015/a 1.0 M-K test: p<0.01 1.0 1.0 M-K test: p<0.01 M-K test: p<0.01 0.5 0.5 0.5 0.0 0.0 0.0 -0.5 -0.5 -0.5 -1.0 -1.0 -1.0 -1.5 -1.5 -1.5 1979 1986 1993 2000 2007 2014 1979 1986 1993 2000 2007 2014 1979 1986 1993 2000 2007 2014 Year Year Year (a) (b) (c) 1.5 1.5 Slope: 0.0014/a Slope: 0.0016/a 1.0 Slope: 0.0014/a 1.0 1.0 M-K test: p<0.01 M-K test: p<0.01 0.5 M-K test: p<0.01 0.5 0.5 0.0 0.0 0.0 -0.5 -0.5 -0.5 -1.0 -1.0 -1.0 -1.5 -1.5 -1.5 1979 1986 1993 2000 2007 2014 1979 1986 1993 2000 2007 2014 1979 1986 1993 2000 2007 2014 Year Year Year (d) (e) (f) Figure 3: Temporal variability of the SPEI values at 1-, 3-, 6-, 9-, 12-, and 24-month time scales for the period 1979–2015 in the QTP. 18 16 18 14 16 12 12 6 6 3 2 0 0 Before 2001 Before 2001 Before 2001 After 2001 After 2001 After 2001 (a) (b) (c) 18 18 20 16 16 2 2 0 0 Before 2001 Before 2001 Before 2001 After 2001 After 2001 After 2001 (d) (e) (f) Figure 4: Changes of drought and wetness probability before and after 2001. (a) SPEI-1. (b) SPEI-3. (c) SPEI-6. (d) SPEI-9. (e) SPEI-12. (f) SPEI-24. SPEI-9 SPEI-1 Probability (%) Probability (%) Extreme drought Extreme drought Moderate drought Moderate drought Mild drought Mild drought Slightly wet Slightly wet Moderately wet Moderately wet Extremely wet Extremely wet SPEI-12 SPEI-3 Probability (%) Probability (%) Extreme drought Extreme drought Moderate drought Moderate drought Mild drought Mild drought Slightly wet Slightly wet Moderately wet Moderately wet Extremely wet Extremely wet Probability (%) Probability (%) SPEI-24 SPEI-6 Extreme drought Extreme drought Moderate drought Moderate drought Mild drought Mild drought Slightly wet Slightly wet Moderately wet Moderately wet Extremely wet Extremely wet 8 Advances in Meteorology 40°N 40°N 40°N 30°N 30°N 30°N 80°E 90°E 100°E 80°E 90°E 100°E 80°E 90°E 100°E <-20 [0, 10) <-20 [0, 10) <-20 [0, 10) [-20, -10) [10, 20) [-20, -10) [10, 20) [-20, -10) [10, 20) [10, 0) >20 [10, 0) >20 [10, 0) >20 (a) (b) (c) 40°N 40°N 40°N 30°N 30°N 30°N 80°E 90°E 100°E 80°E 90°E 100°E 80°E 90°E 100°E <-20 [0, 10) <-20 [0, 10) <-20 [0, 10) [-20, -10) [10, 20) [-20, -10) [10, 20) [-20, -10) [10, 20) [10, 0) >20 [10, 0) >20 [10, 0) >20 (d) (e) (f) Figure 5: Drought and wetness probability variation before and after 2001 in the QTP (after 2001 values minus before 2001). *e red area represents that the probability in 2001–2015 has increased compared with that in 1979–2000, while the blue area represents that the probability has decreased. (a) Extreme drought. (b) Moderate drought. (c) Mild drought. (d) Slightly wet. (e) Moderately wet. (f) Extremely wet. Figures 5(b) and 5(c), it can be seen that the probabilities of rates for the SPEI trend, respectively, in the drought-prone moderate drought and mild drought in the Qaidam Basin regions. However, in the regions that tend to be wetter, the and the southeast of plateau increased obviously during increase of the temperature and precipitation results in 2001–2015, while the probability mainly decreased in other negative contribution and positive contribution rate for the SPEI trend, respectively. Figures 6(a) and 6(b) show the Cr regions, but the decreased value of the probability of mild drought was higher than that of moderate drought. During values of the precipitation and temperature variability in 2001–2015, the probabilities of slightly wet and moderately drought-prone regions. It can be seen that the contribution wet showed the opposite trend. *e probabilities of slightly rates of temperature in these regions range from −33% to wet and moderately wet decreased in the Qaidam Basin and 100%, with an average value of 60%. *e contribution rates the southeast of the plateau but increased in other regions of precipitation are between −100% and 50%, and the av- (Figures 5(d) and 5(e)). It can be seen from Figure 5(f) that erage is −11%. *e increase of temperature was the main the probability of extremely wet increased in most other reason that the Qaidam Basin and southeast and southwest regions except the Qaidam Basin. According to this result, of Qinghai-Tibetan Plateau tended to be drier during the probability of drought had increased and the probability 1979–2015. Spatially, the Cr values of temperature to SPEI of wetness had decreased obviously in the regions that trend in the Qaidam Basin were positive, while the con- tributions of precipitation were negative. However, in some tended to be drier. However, the results were opposite in the regions that tended to be wetter. areas of the southeast and southwest of the QTP, the con- tribution rates of temperature and precipitation to the drought trend were both positive. Figures 6(c) and 6(d) show 3.3. 6e Respective Contributions of Temperature and Pre- theCr values of the precipitation and temperature variability cipitation Variability to the Drought and Wetness Trend. in the regions that tend to be wetter. *e Cr values of Using the method introduced in Section 2.3.2., the Cr values temperature in these regions ranged from −50% to 74%, with of the temperature and precipitation variability to the an average value of −9%. *e contribution rates of pre- drought and wetness trends for 25711 grids in the QTP were cipitation were between −46% and 100%, and the average is calculated. Generally, the increase of the temperature and 61%. According to this result, the variation of precipitation precipitation results in positive and negative contribution was the main reason that the northeast, central, and western Advances in Meteorology 9 N N 40°N 40°N 30°N 30°N 20°N 20°N 80°E 90°E 100°E 80°E 90°E 100°E -0.33 ~ 0 0.33 ~ 0.66 -1 ~ -0.66 0 ~ 0.33 0 ~ 0.33 0.66 ~ 1 -0.66 ~ -0.33 0.33 ~ 0.50 -0.33 ~ 0 (a) (b) N N 40°N 40°N 30°N 30°N 20°N 20°N 80°E 90°E 100°E 80°E 90°E 100°E -0.5 ~ -0.33 0.33 ~ 0.66 -0.46 ~ -0.33 0.33 ~ 0.66 -0.33 ~ 0 0.66 ~ 0.74 -0.33 ~ 0 0.66 ~ 1 0 ~ 0.33 0 ~ 0.33 (c) (d) Figure 6: *e respective contribution rate of the precipitation and temperature variability to the SPEI trend in the QTP. (a) and (b) are the contribution rates of the precipitation and temperature variability in the regions tending to be drier, and (c) and (d) are the contribution rates in the regions tending to be wetter. (a) Cr_temperature. (b) Cr_precipitation. (c) Cr_temperature. (d) Cr_precipitation. parts of the QTP tended to be wetter, which was different found that Tibet was showing a wetting trend in the period of from the regions that tend to be drier. Spatially, theCr values 1979–2015. It should be noted that Li et al. [64] assessed the drought condition over Tibet only based on meteorological of temperature and precipitation are both positive in the northeast and western parts of the QTP. However, in most of data from 38 stations, and these stations are mainly con- the central parts, Cr values of precipitation are positive, but centrated in the south of Tibet. In our study, both stations Cr values of temperature are negative. and CMFD pixels showed that drought conditions were aggravating in the same regions. However, in the north and central Tibet, most areas showed obvious wetting trends, 4. Discussion which means that only the observed data cannot truly reflect the overall drought and wetness trend in Tibet. 4.1. Comparison with Previous Studies. In previous studies, Li et al. [63] applied SPEI to characterize the drought conditions in the southeast part of the QTP during 1982–2012, and the results explicitly showed a drying trend. 4.2. Drought and Wetness Variability in Different Climate Regions. Previous studies have shown that most wet regions Chen et al. [41] calculated six drought indexes with mete- orological grid data at 0.5 degree resolution, analyzed the were becoming wetter and dry regions were becoming drier change trend of drought and wetness in different climatic under global warming [9, 65], and the trend of drought and regions of China, and concluded that the QTP had an ob- wetness in different climatic zones of the QTP is not clear. vious trend of wetting. *e above conclusions are highly *e China Meteorological Administrations takes the cu- consistent with ours, indicating that the conclusions ob- mulative temperature with the daily average temperature of tained in this study are credible. In the western part, Li et al. no less than 10 C and the multi-year average value of the [64] concluded that dryness conditions were aggravating temperature in the coldest month as the heat index and the during the period 1971–2014 across Tibet. However, we dryness as the moisture index and divides China into 32 10 Advances in Meteorology WE 40°N 35°N 30°N 25°N 0 500 km 75°E 80°E 85°E 90°E 95°E 100°E 105°E IIID1 VA3 HB1 HD2 HB2 HD1 HC3 HA1 II C2 HC2 HVA1 II D1 IVA2 HC1 VA5 Figure 7: *e climate regionalization in the QTP. secondary climate regions [66], including 15 climate regions IIC2 HC1 in the QTP (Figure 7). *ese 15 climate regions are Southern HC2 Xinjiang (IIID1), Northern Tibet (HD2), Southern Tibet HC3 (HC3), Central Tibet (HC2), Qinba (IVA2), Sichuan (VA3), HB1 Changdu (HB2), Bomi and Western Sichuan (HA1), HB2 Dawang and Zayu (HVA1), Qilian and Qinghai Lake (HC1), VA5 Southern Qinghai (HB1), Qaidam (HD1), Central Inner HVA1 HA1 Mongolia (IIC2), Inner Mongolia and Gansu (IID1), and VA3 North Yunnan (VA5). Among these climatic zones, Qinba, IVA2 Sichuan, Bomi and Western Sichuan, Dawang and Zayu, and IID1 North Yunnan belong to the humid regions, Changdu and HD1 Southern Qinghai belong to the semihumid regions, HD2 Southern Xinjiang, Northern Tibet, Qaidam, and Inner IIID1 Mongolia and Gansu belong to the arid regions, and 0 20406080 100 Southern Tibet, Central Tibet, Qilian and Qinghai Lake, and Percentage (%) Central Inner Mongolia belong to the semiarid regions. Figure 8: Percentages of areas tending to be drier (red bar) and Previous studies had shown that the SPEI at 12-month wetter (blue bar) significantly (p< 0.1) in different climate regions time scale (SPEI-12) is suitable for describing the long-term based on the trend of SPEI-12 in the QTP during 1979–2015. drought and wetness trend [67]. In this study, we also se- lected SPEI-12 to analyze the characteristics of drought and Zayu, Qilian and Qinghai Lake, Southern Qinghai, and wetness changes in different climatic regions of the QTP. Central Inner Mongolia. According to this result, most of Following the variation trend of SPEI-12 from each CMFD the arid and humid regions in the QTP tended to be drier, grid, the percentages of areas tending to be drier and wetter while the semiarid regions tended to be wetter. significantly (p < 0.1) in different climate zone were also calculated (Figure 8). As shown in Figure 8, climate regions tending to be drier significantly include Southern Xinjiang, 5. Conclusion Qinba, Sichuan, Changdu, Bomi and Western Sichuan, Qaidam, Inner Mongolia and Gansu, and North Yunnan. *is study investigated the drought and wetness trend and Climate regions tending significantly to be wetter include the respective contributions of temperature and precipita- Northern Tibet, Southern Tibet, Central Tibet, Dawang and tion variability by using SPEI calculated based on the Climate regions Advances in Meteorology 11 observed and CMFD dataset in the QTP during 1979–2015. downloaded from the Resource and Environment Data *e main results are summarized as follows: Science Cloud Platform and Data Center (https://www. resdc.cn). *e observed data were provided by the Na- (1) *e SPEI series based on the observed and CMFD tional Meteorological Information Center (https://data.cma. data showed that the southeast of the QTP and the cn/). Qaidam Basin in the northeast tended to be drier during 1979–2015, but most areas in the middle and Conflicts of Interest west of the plateau tended to be wetter. *e SPEI sequence obtained by averaging all CMFD grid *e authors declare that they have no conflicts of interest. points revealed that the whole QTP tends to be wetter during the study period. Acknowledgments (2) On the whole, the probability of drought was de- *is research was supported by the National Natural Science creasing and the probability of wetness was in- Foundation of China (42171467, 42001060, and 41705139) creasing in the QTP during 1979–2015. In regions and the Basic Research Project of Qinghai Province (2021- that tended to be drier, the probabilities of mild ZJ-947Q). *e authors also thank the National Tibetan drought, moderate drought, and extreme drought Plateau Data Center, Resource and Environment Science were also increasing, while in areas that tend to be and Data Center in Beijing, and China Meteorological wetter, the probabilities of all l grades of drought Administration for providing the meteorological data for were decreasing. this study. (3) In the drier regions, temperature is the dominant factor controlling the change trend of SPEI, the References average contribution rate of temperature is 60%, and the contribution rate of precipitation is only −11%. [1] D. J. Lorenz, J. A. Otkin, M. Svoboda, C. R. Hain, In the wetter regions, the contribution rate of pre- M. C. Anderson, and Y. 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Drought and Wetness Variability and the Respective Contribution of Temperature and Precipitation in the Qinghai-Tibetan Plateau

Advances in Meteorology , Volume 2021 – Sep 29, 2021

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Copyright © 2021 Lele Zhang and Liming Gao. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Hindawi Advances in Meteorology Volume 2021, Article ID 7378196, 13 pages https://doi.org/10.1155/2021/7378196 Research Article Drought and Wetness Variability and the Respective Contribution of Temperature and Precipitation in the Qinghai-Tibetan Plateau 1,2,3 1,2,3 Lele Zhang and Liming Gao College of Geography Science, Qinghai Normal University, Xining 810008, China MOE Key Laboratory of Tibetan Plateau Land Surface Processes and Ecological Conservation, Xining 810008, China Qinghai Province Key Laboratory of Physical Geography and Environmental Process, Xining 810008, China Correspondence should be addressed to Lele Zhang; zhang1986lele@163.com Received 29 April 2021; Revised 19 August 2021; Accepted 7 September 2021; Published 29 September 2021 Academic Editor: Roberto Coscarelli Copyright © 2021 Lele Zhang and Liming Gao. *is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Quantifying drought and wetness fluctuations is of great significance to the regional ecological environment and water resource security, especially in the fragile Qinghai-Tibetan Plateau (QTP). In this paper, the standardized precipitation evapotranspiration index (SPEI) was calculated based on the observed data and China Meteorological Forcing Dataset (CMFD) in the QTP for the period of 1979–2015, and the drought and wetness evolution based on the SPEI series and respective contribution of temperature and precipitation were also analyzed. Results indicated that meteorological stations are mainly concentrated in the eastern part of the plateau, which cannot reflect the drought and wetness trend of the whole QTP. *e linear trend and Mann–Kendall test revealed that SPEI series calculated based on CMFD data at 1-, 3-, 6-, 9-, 12-, and 24-month time scales all showed significant upward trend (p< 0.01), indicating that the QTP as a whole tended to be wetter. Spatially, the regions with significant drying (p < 0.1) and increased drought probability were mainly concentrated in the Qaidam Basin and the southern part of the QTP, and the mean contribution rates of temperature and precipitation variability to SPEI trend in these regions were 60% and −11%, respectively. *e regions with significant wetting (p< 0.1) and decreased drought probability were mainly concentrated in the northeast, central, and western parts of the plateau, and the mean contribution rates of temperature and precipitation variability to SPEI trend were −9% and 61% in these regions. From the statistics in different climatic regions, most of the arid and humid regions in the QTP tended to be drier, while the semiarid regions tended to be wetter. changing. Globally, climate change is leading dry regions to 1. Introduction become drier and wet regions to become wetter [8, 9], and Drought and wetness fluctuations, which are closely related the frequency of extreme drought and flood events would to precipitation and evapotranspiration variation [1], have further increase in the 21st century [10–12]. *ese extreme profound influences on agriculture and ecosystems. Over the events can cause billions of dollars in losses and affect more past few decades, the global climate has changed remarkably people than any other climate-related disaster [13]. *ere- [2]. Ground observation, remote sensing, and reanalysis data fore, it is of great significance to comprehensively under- revealed that temperature is rising in most regions of the stand the drought and wetness change trend. world [3, 4]. Although the spatial difference of precipitation Many meteorological drought indexes had been devel- change was relatively large [5], the frequency of extreme oped for quantifying drought and wetness fluctuations, precipitation had increased extensively in most parts of the including the Palmer drought severity index (PSDI) [14], world [6, 7]. With the changes of temperature and pre- standardized precipitation index (SPI) [15], China-Z index cipitation, the state of drought and wetness was also (CZI) [16], standardized precipitation evapotranspiration 2 Advances in Meteorology objectives of this study were to (1) assess the spatiotem- index (SPEI) [17], and soil wetness deficit index (SWDI) [18]. Among these indexes, SPI and SPEI are easy to obtain poral variation characteristics of drought and wetness in the QTP during 1979 to 2015 and (2) quantify the re- and widely used for drought and wetness changes and their impact on agriculture and ecological environment in many spective contributions of temperature and precipitation countries and regions of the world [19–25]. *e main ad- variability to drought and wetness trend in the QTP. *e vantage of the SPI is that it is calculated at multi-time scales rest of the article is arranged as follows. *e basic con- and can monitor different drought types [26, 27]. However, ditions and meteorological data in the QTP and the only precipitation is used in the calculation of the SPI [17]. methods used in this paper are introduced in Section 2. In *e index cannot reflect the influence of temperature change Section 3, the spatiotemporal evolution of drought and wetness is analyzed, and the respective contributions of on the drought and wetness conditions. Compared with SPI, SPEI is calculated based on the process of surface water temperature and precipitation variability to drought and wetness trend are calculated. *e discussion and conclu- balance and also introduces the multi-time scale character used in SPI calculation [17, 28]. Chen et al. [29] compared sions are given in Sections 4 and 5, respectively. the monitoring results of SPI, PDSI, and SPEI in China and found that the meteorological drought monitored by SPEI 2. The Study Area, Data, and Methods was better than that monitored by SPI and PDSI. *e results obtained by Zarei et al. [30] also showed that the accuracy of 2.1. Study Area and Data Sources. *e QTP lies in the SPEI in monitoring drought events is higher than that of SPI southwest of China, including most of the Qinghai and reconnaissance drought index (RDI) in Iran. Further- Province and Tibet Autonomous Region and a few parts of more, previous studies had also shown that SPEI variation is Xinjiang Uygur Autonomous Region, Gansu, Sichuan, and highly correlated with vegetation index, crop growth state, Yunnan (Figure 1). *e Altun Mountains, Qilian Moun- and soil water storage change [31–33], so it has a wide tains, and Qaidam Basin are located in the north of the application potential in water resource management, agri- plateau, and the Himalaya Mountains are located in the 4 2 cultural production, and other fields in the future. southwest. *e QTP covers a total area of 257 ×10 km , of 4 2 *e QTP is recognized as the water tower of Asia and can which 122.2 ×10 km is permafrost area [45]. With an influence the Asian monsoon through its mechanical and average elevation of more than 4500 meters, the QTP is the thermal forcing [34–37]. As a sensitive and fragile area, the highest plateau in the world, so it is also known as the evolution of drought and wetness in the QTP has attracted “*ird Pole.” In addition, the QTP is also the headstream extensive attention. Shi and Liu [38] analyzed the charac- of many major rivers (including the Yangtze River, Yellow teristics of drought events over the continent in the Eastern River, Lancang River, Nujiang River, and so on) in the Hemisphere based on the SPEI data and found that the QTP world, and China’s largest lake (Qinghai Lake) is also had been one of the regions with high variance. Wang et al. located in the northeast of the plateau, so it is also known [39] used the high-accuracy self-calibrating Palmer drought as the “Water Tower of Asia.” *e climate on the plateau is severity index (sc_PDSI) to investigate the drought variation alternately controlled by monsoon in the summer and in China and found that northern Tibetan Plateau and westerlies in the winter [46]. Precipitation is mainly southern Tibetan Plateau experienced a wetting trend be- concentrated in the warm season, and the light precipi- tween 1961 and 2009. Feng et al. [40] investigated spatial- tation is dominant in winter [47]. Spatially, the central and temporal patterns of meteorological drought in the QTP and southern parts of the plateau are mainly controlled by the its surrounding areas and revealed that the QTP became Indian monsoon, and the annual maximum precipitation wetter in spring during 1970–2017. Chen et al. [41] divided can reach more than 1000 mm, while the western and China into six climatic regions and analyzed the charac- northern parts are controlled by the westerlies, and the teristics of drought and wetness trend based on SPEI, PDSI, annual precipitation is relatively low, and the annual and sc_PDSI and proposed that the QTP became wetter precipitation in the Qaidam Basin in the north is even less during 1961–2012. It should be noted that most of these than 100 mm [48]. *e temperature is relatively high due conclusions are based on the observed data. However, the to low latitude and altitude in the southeast of the plateau, meteorological stations are mainly concentrated in the while in the west, the temperature is relatively low due to eastern part of the plateau, while the drought and wetness high altitude [49]. conditions in the west with few stations had received little Observed monthly temperature and precipitation attention. Additionally, the precipitation and temperature were used for SPEI calculations in this study. *e observed variability is the main factor that directly affects the change data (1979–2015) for 50 sites in the QTP (Figure 1) were trends of drought/wetness, and previous studies had shown derived from the National Meteorological Information that the temperature and precipitation in the QTP both Center (https://www.data.cma.cn/). *ese data were ob- showed increasing trend [42–44]. However, it is still not tained from the daily value statistics of meteorological clear that which is the main factor that dominates the elements, and the production process strictly followed the drought and wetness trend in the plateau. standards and methods formulated by the National Me- In this study, both the observed data from 50 mete- teorological Administration of China. *e accuracy of the orological stations and CMFD high-resolution meteoro- data had been verified and also had been widely used in logical forcing data were used to analyze drought and the research of China’s climate change and hydrological wetness condition evolutions over the QTP. *e main process [50, 51]. Advances in Meteorology 3 40°N 30°N Elevation (m) 0 1,000 km 80°E 90°E 100°E Meteorological station Provincial boundarics Figure 1: Topographic map of the Qinghai-Tibetan Plateau (adapted from the geospatial data cloud at Beijing (https://www.gscloud.cn/)). Considering the sparsity of observation sites in the QTP, Table 1: Drought and wetness classification based on the SPEI value [17, 28, 30]. the CMFD data offered by the National Tibetan Plateau Data Center (https://www.data.tpdc.ac.cn/) were also used for Category SPEI value SPEI calculations. *e CMFD data constitute a near-surface Extremely wet SPEI≥ 2 meteorological forcing dataset and cover the China domain Moderately wet 1.5 ≤ SPEI< 1.99 at a 3-hourly time step and a spatial resolution of 0.1 , Slightly wet 1 ≤ SPEI< 1.49 spanning from 1979 to 2015. It was made through fusion of Near normal −0.99< SPEI< 0.99 ground-based observations of approximately 700 stations in Mild drought −1.49 < SPEI≤ −1 China with several gridded datasets including GLDAS, Moderate drought −1.99 < SPEI≤ −1.5 Extreme drought SPEI≤ −2 MERRA, GEWEX-SRB, and TRMM 3B42 v7 [52]. *e meteorological elements include near-surface temperature, pressure, specific humidity, wind speed, ground downward short-wave radiation, downward long-wave radiation, and the *ornthwaite method. Although the potential evapo- precipitation rate. *e CMFD data had been widely used in transpiration estimated by this method in winter and the study of the climate and hydrology of the QTP [53, 54]. spring in China has certain differences from other methods, the overall estimation of the potential evapotranspiration form *ornthwaite and other methods is very comparable [40, 55, 56]. By providing a text file containing temperature 2.2. SPEI for Determining Drought and Wetness Conditions. and precipitation data, geographic latitude, time, and other SPEI is an improved drought index of SPI designed by basic information, the program can automatically calculate Vicente-Serrano et al. [17]. It is calculated based on the SPEI series at different time scales. In this study, SPEI process of surface water balance and also introduces the values were calculated both based on the observed and multi-time scale character used in SPI calculation, which CMFD data in the QTP for the period of 1979–2015. fully combines the advantages of SPI and PDSI. Because of According to the classification criteria in Table 1, the its advantages, it had been widely used in drought moni- toring in many studies [40, 41]. Based on SPEI calculations, frequency of each drought and wetness category during a period can be obtained, and the probability of each category drought and wetness can be classified into seven categories as listed in Table 1. Calculation of the index is performed is calculated as follows [57]: using the computer program SPEI Calculator, which is (1) developed and maintained by the Institutional Repository � × 100%, of the Spanish National Research Council. Software and documentation are available online for downloading where p and f are the probability (%) and frequency of ith i i (https://www.digital.csic.es/). *e default method for cal- drought and wetness category during a period, respectively, culating the potential evapotranspiration in the software is and F is the total number of data points. 4 Advances in Meteorology 2.3. Statistical Methods significance test at p < 0.1, p < 0.05, and p < 0.01 levels, respectively. 2.3.1. Methods for Detecting Drought and Wetness Trends. Drought and wetness trends are indicated by the linear 2.3.2. Quantification of the Respective Contributions of trends of SPEI series in this study. *e Mann–Kendall test is Temperature and Precipitation. A simple method adapted used to determine the significance of the trends. *e based on the algorithm proposed by Wu and Chen [62] was Mann–Kendall (MK) test does not require samples to follow used for quantifying the respective contributions of the a certain distribution and is also not affected by some temperature and precipitation variability to the drought and outliers [58, 59]. In previous studies, the MK method had wetness trends, and the calculation flow of this method is as been widely used in the trend analysis of time series in follows: meteorological, hydrological, and ecological fields [19, 60]. *e method is based on theZ value to judge the change trend (1) *e monthly temperature and precipitation data of time series and is computed as follows [40, 61]. from 1979 to 2015 were divided into 12 groups by Calculate the test statistical variable S based on the SPEI month, respectively, and the linear detrending series (x , x , x , . . . , x ): 1 2 3 n method is used for these groups. *e linear n−1 n detrending method is described as follows: S � 􏽘 􏽘 sgn􏼐x − x 􏼑, (2) j i Y � y − c(i − 1979), (6) i�1 j�i+1 i i where sgn(x) represents the sign function and can be where Y is the detrended monthly precipitation or expressed as follows: temperature in the ith year (i � 1979, 1980, . . . , 2015), y is the original monthly precipitation or ⎪ 1, x − x > 0, ⎪ j i temperature in theith year, and Υ is the precipitation sgn x − x � 0, x − x � 0, (3) 􏼐 􏼑 or temperature trend during 1979–2015. j i j i −1, x − x < 0. (2) According to the results of the previous step, the j i SPEI series under 3 climate scenarios can be ob- *e variance of S can be expressed as tained. *e first SPEI time series (SPEI ) was cal- culated based on the original meteorological data, n(n − 1)(2n + 5) (4) Var S � . the second (SPEI ) was calculated using the original Dp temperature records and detrended precipitation *e standard test statistical variable Z is computed as data, and the third (SPEI ) was calculated using the Dt detrended temperature data and observed precipi- S − 1 ⎧ ⎪ √���� � ⎪ , S> 0, tation records. Var S ⎪ (3) *e factors affecting the variability of SPEI can be divided into three categories, including temperature, Z � 0, S � 0, (5) ⎪ precipitation, and other factors. *e other factors mean the factors that can affect drought and wetness S + 1 ⎪ conditions except temperature and precipitation. √���� � ⎩ , S< 0. Var S When the slope of SPEI series (S_SPEI ) is greater o o than 0, it means that this grid point tends to be wetter When the Z value is positive, it represents an increasing during 1979–2015, and the contribution rates (Cr) of trend, while negative Z value means a decreasing trend. the three factors are calculated as follows: When |Z| is greater than 1.28, 1.64, and 2.32, it means that the corresponding trend of time series has passed the S SPEI Dp 􏼌 􏼌 􏼌 􏼌 Cr T � 􏼌 􏼌 × 100%, 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 S SPEI + S SPEI + S SPEI − SPEI − S SPEI 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 Dp Dt o Dp Dp S SPEI Dt 􏼌 􏼌 􏼌 􏼌 Cr P � × 100%, 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 (7) 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 S SPEI + 􏼌S SPEI 􏼌 + S SPEI − SPEI − S SPEI 􏼌 Dp􏼌 Dt 􏼌 o Dp Dp􏼌 S SPEI − S SPEI − S SPEI o Dp Dp 􏼌 􏼌 􏼌 􏼌 Cr O � 􏼌 􏼌 × 100%, 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 S SPEI + 􏼌S SPEI 􏼌 + S SPEI − SPEI − S SPEI 􏼌 􏼌 􏼌 􏼌 Dp Dt o Dp Dp Advances in Meteorology 5 where S_SPEI , S_SPEI , and S_SPEI represent the linear IfCr< 0, it means that this factor has a negative contribution o Dp Dt slopes of SPEI , SPEI , and SPEI time series and Cr_T, to the wetting trend. If Cr � 0, it means there is no con- o Dp Dt Cr_P, and Cr_O are the Cr values of temperature, precip- tribution. When S_SPEI <0, it means that this grid point itation, and other factors. If Cr> 0, it means that this factor tends to be drier, and the contribution rates are calculated as has a positive contribution to the wetting trend, and the follows: greater the absolute value is, the greater the contribution is. −S SPEI Dp 􏼌 􏼌 􏼌 􏼌 Cr T � 􏼌 􏼌 × 100%, 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 S SPEI + 􏼌S SPEI 􏼌 + S SPEI − SPEI − S SPEI 􏼌 􏼌 􏼌 􏼌 Dp Dt o Dp Dp −S SPEI Dt 􏼌 􏼌 􏼌 􏼌 Cr P � × 100%, 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 (8) 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 S SPEI + 􏼌S SPEI 􏼌 + S SPEI − SPEI − S SPEI 􏼌 Dp􏼌 Dt 􏼌 o Dp Dp􏼌 − 􏼐S SPEI − S SPEI − S SPEI 􏼑 o Dp Dp 􏼌 􏼌 􏼌 􏼌 Cr O � 􏼌 􏼌 × 100%. 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 S SPEI + S SPEI + S SPEI − SPEI − S SPEI 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 Dp Dt o Dp Dp If Cr> 0, it means that this factor has a positive con- large time series. *e SPEI values at all time scales were tribution to the drying trend. If Cr< 0, it means that this mainly negative before 2001 and positive after 2001. *e results of the linear slope and MK test showed that all SPEI factor has a negative contribution to the drying trend. If Cr � 0, it means there is no contribution. It should be noted time series showed an obvious upward trend (p < 0.01), that only the contributions of temperature and precipitation which means that the QTP tended to be wetter as a whole variability to the drought and wetness trends were analyzed during 1979 and 2015. in this study. 3.2. Probability of Drought and Wetness Occurrences. *e 3. Results probabilities of occurrence for each drought and wetness 3.1. Spatiotemporal Variation of SPEI Series in the QTP. category at all CMFD grids in the QTP were calculated. In Based on the temperature and precipitation data recorded at order to analyze the dynamic change of occurrence prob- 50 meteorological stations and 25711 CMFD grid points ability of each drought and wetness category, the whole from 1979 to 2015, the SPEI values at 1-, 3-, 6-, 9-, 12-, and period was divided into two subperiods: the first is from 24-month time scales were calculated. *e variation trend of 1979 to 2000 (Period I) and the second is from 2001 to 2015 (Period II). As shown in Figure 4, the difference between SPEI time series at each station and grid point is given by using the linear slope and MK test (Figure 2). As can be seen probabilities of drought and wetness occurrences based on SPEI at different time scales were small in the whole plateau. from Figures 2(a)–2(f), the variation trend of SPEI time series at different time scales is consistent and the SPEI time During 1979–2000, the probability of mild drought was the series of more than 84% stations showed a downward trend. highest while the probability of slightly wet was the highest Since the meteorological stations are mainly concentrated in during 2001–2015. *e probabilities of extreme drought and the eastern part of the QTP, it is believed that the eastern part moderate drought did not change much between the two of the plateau tended to be drier during 1979 and 2015. subperiods. Compared with Period I, the probability of Figures 2(g)–2(l) show the results obtained based on CMFD extreme drought increased slightly in Period II, and the data. According to statistics, the grid points with a de- probability of moderate drought decreased slightly. How- creasing trend of SPEI are less than 38%, mainly concen- ever, the probabilities of mild drought, slightly wet, mod- erately wet, and extremely wet were different between the trated in the southeast of QTP and Qaidam Basin, which is consistent with the conclusion obtained based on meteo- two subperiods. In Period II, the probability of mild drought decreased, while the probabilities of slightly wet, moderately rological stations. However, in the central and western parts of the plateau, SPEI time series showed upward trend during wet, and extremely wet increased. 1979 and 2015, indicating that these areas tended to be *e spatial differences of drought and wetness proba- wetter. bility variations in the QTP were also analyzed. Considering In order to characterize the drought and wetness trend in that the probabilities calculated by different time scales were the whole QTP, the SPEI series at 1-, 3-, 6-, 9-, 12-, and 24- close, only SPEI calculated at 12-month time scales was used month time scales were averaged over the 25711 CMFD for statistics. Figure 5 shows the probability of each drought pixels, and the temporal evolution of these SPEI series is and wetness category of all CMFD pixels in 2001–2015 displayed in Figure 3. It can be seen from Figure 3 that the minus that in 1979–2000. If the value obtained by sub- traction is positive, it means that the probability in fluctuation of SPEI series calculated by small time scale is relatively large, while the fluctuation is relatively small by 2001–2015 has increased compared with that in 1979–2000; 6 Advances in Meteorology 40°N 40°N 40°N 35°N 35°N 35°N 30°N 30°N 30°N 25°N 25°N 25°N 80°E 90°E 100°E 80°E 90°E 100°E 80°E 90°E 100°E Decreased significantly (p < 0.1) Decreased significantly (p < 0.1) Decreased significantly (p < 0.1) Decreased but not significantly (p > 0.1) Decreased but not significantly (p > 0.1) Decreased but not significantly (p > 0.1) Increased but not significantly (p > 0.1) Increased but not significantly (p > 0.1) Increased but not significantly (p > 0.1) Increased significantly (p < 0.1) Increased significantly (p < 0.1) Increased significantly (p < 0.1) (a) (b) (c) 40°N 40°N 40°N 35°N 35°N 35°N 30°N 30°N 30°N 25°N 25°N 25°N 80°E 90°E 100°E 80°E 90°E 100°E 80°E 90°E 100°E Decreased significantly (p < 0.1) Decreased significantly (p < 0.1) Decreased significantly (p < 0.1) Decreased but not significantly (p > 0.1) Decreased but not significantly (p > 0.1) Decreased but not significantly (p > 0.1) Increased but not significantly (p > 0.1) Increased but not significantly (p > 0.1) Increased but not significantly (p > 0.1) Increased significantly (p < 0.1) Increased significantly (p < 0.1) Increased significantly (p < 0.1) (d) (e) (f) 40°N 40°N 40°N 35°N 35°N 35°N 30°N 30°N 30°N 25°N 25°N 25°N 80°E 90°E 100°E 80°E 90°E 100°E 80°E 90°E 100°E Decreased significantly (p < 0.1) Decreased significantly (p < 0.1) Decreased significantly (p < 0.1) Decreased but not significantly (p > 0.1) Decreased but not significantly (p > 0.1) Decreased but not significantly (p > 0.1) Increased but not significantly (p > 0.1) Increased but not significantly (p > 0.1) Increased but not significantly (p > 0.1) Increased significantly (p < 0.1) Increased significantly (p < 0.1) Increased significantly (p < 0.1) (g) (h) (i) 40°N 40°N 40°N 35°N 35°N 35°N 30°N 30°N 30°N 25°N 25°N 25°N 80°E 90°E 100°E 80°E 90°E 100°E 80°E 90°E 100°E Decreased significantly (p < 0.1) Decreased significantly (p < 0.1) Decreased significantly (p < 0.1) Decreased but not significantly (p > 0.1) Decreased but not significantly (p > 0.1) Decreased but not significantly (p > 0.1) Increased but not significantly (p > 0.1) Increased but not significantly (p > 0.1) Increased but not significantly (p > 0.1) Increased significantly (p < 0.1) Increased significantly (p < 0.1) Increased significantly (p < 0.1) (j) (k) (l) Figure 2: Spatial distributions of the Mann–Kendall trend statistic for SPEI at different time scales for the period 1979–2015. (a)–(f) are the SPEI trends based on observed data, and (g)–(l) are the SPEI trends based on the CMFD data. (a) SPEI-1. (b) SPEI-3. (c) SPEI-6. (d) SPEI-9. (e) SPEI-12. (f) SPEI-24. (g) SPEI-1. (h) SPEI-3. (i) SPEI-6. (j) SPEI-9. (k) SPEI-12. (l) SPEI-24. otherwise, the probability has decreased. As shown in Southern Xinjiang, and the southeast of Tibet, the increased Figure 5(a), the probability of extreme drought in most areas probabilities of extreme drought in other regions were less of the southeastern, southwestern, and northwestern parts of than 10%. In the central and northeastern parts of the the QTP increased during 2001–2015 compared with that plateau, the probability of extreme drought decreased, with a during 1979–2000. However, except for the Qaidam Basin, decrease of less than 10% in almost all regions. From Advances in Meteorology 7 1.5 1.5 1.5 Slope: 0.0016/a Slope: 0.0018/a Slope: 0.0015/a 1.0 M-K test: p<0.01 1.0 1.0 M-K test: p<0.01 M-K test: p<0.01 0.5 0.5 0.5 0.0 0.0 0.0 -0.5 -0.5 -0.5 -1.0 -1.0 -1.0 -1.5 -1.5 -1.5 1979 1986 1993 2000 2007 2014 1979 1986 1993 2000 2007 2014 1979 1986 1993 2000 2007 2014 Year Year Year (a) (b) (c) 1.5 1.5 Slope: 0.0014/a Slope: 0.0016/a 1.0 Slope: 0.0014/a 1.0 1.0 M-K test: p<0.01 M-K test: p<0.01 0.5 M-K test: p<0.01 0.5 0.5 0.0 0.0 0.0 -0.5 -0.5 -0.5 -1.0 -1.0 -1.0 -1.5 -1.5 -1.5 1979 1986 1993 2000 2007 2014 1979 1986 1993 2000 2007 2014 1979 1986 1993 2000 2007 2014 Year Year Year (d) (e) (f) Figure 3: Temporal variability of the SPEI values at 1-, 3-, 6-, 9-, 12-, and 24-month time scales for the period 1979–2015 in the QTP. 18 16 18 14 16 12 12 6 6 3 2 0 0 Before 2001 Before 2001 Before 2001 After 2001 After 2001 After 2001 (a) (b) (c) 18 18 20 16 16 2 2 0 0 Before 2001 Before 2001 Before 2001 After 2001 After 2001 After 2001 (d) (e) (f) Figure 4: Changes of drought and wetness probability before and after 2001. (a) SPEI-1. (b) SPEI-3. (c) SPEI-6. (d) SPEI-9. (e) SPEI-12. (f) SPEI-24. SPEI-9 SPEI-1 Probability (%) Probability (%) Extreme drought Extreme drought Moderate drought Moderate drought Mild drought Mild drought Slightly wet Slightly wet Moderately wet Moderately wet Extremely wet Extremely wet SPEI-12 SPEI-3 Probability (%) Probability (%) Extreme drought Extreme drought Moderate drought Moderate drought Mild drought Mild drought Slightly wet Slightly wet Moderately wet Moderately wet Extremely wet Extremely wet Probability (%) Probability (%) SPEI-24 SPEI-6 Extreme drought Extreme drought Moderate drought Moderate drought Mild drought Mild drought Slightly wet Slightly wet Moderately wet Moderately wet Extremely wet Extremely wet 8 Advances in Meteorology 40°N 40°N 40°N 30°N 30°N 30°N 80°E 90°E 100°E 80°E 90°E 100°E 80°E 90°E 100°E <-20 [0, 10) <-20 [0, 10) <-20 [0, 10) [-20, -10) [10, 20) [-20, -10) [10, 20) [-20, -10) [10, 20) [10, 0) >20 [10, 0) >20 [10, 0) >20 (a) (b) (c) 40°N 40°N 40°N 30°N 30°N 30°N 80°E 90°E 100°E 80°E 90°E 100°E 80°E 90°E 100°E <-20 [0, 10) <-20 [0, 10) <-20 [0, 10) [-20, -10) [10, 20) [-20, -10) [10, 20) [-20, -10) [10, 20) [10, 0) >20 [10, 0) >20 [10, 0) >20 (d) (e) (f) Figure 5: Drought and wetness probability variation before and after 2001 in the QTP (after 2001 values minus before 2001). *e red area represents that the probability in 2001–2015 has increased compared with that in 1979–2000, while the blue area represents that the probability has decreased. (a) Extreme drought. (b) Moderate drought. (c) Mild drought. (d) Slightly wet. (e) Moderately wet. (f) Extremely wet. Figures 5(b) and 5(c), it can be seen that the probabilities of rates for the SPEI trend, respectively, in the drought-prone moderate drought and mild drought in the Qaidam Basin regions. However, in the regions that tend to be wetter, the and the southeast of plateau increased obviously during increase of the temperature and precipitation results in 2001–2015, while the probability mainly decreased in other negative contribution and positive contribution rate for the SPEI trend, respectively. Figures 6(a) and 6(b) show the Cr regions, but the decreased value of the probability of mild drought was higher than that of moderate drought. During values of the precipitation and temperature variability in 2001–2015, the probabilities of slightly wet and moderately drought-prone regions. It can be seen that the contribution wet showed the opposite trend. *e probabilities of slightly rates of temperature in these regions range from −33% to wet and moderately wet decreased in the Qaidam Basin and 100%, with an average value of 60%. *e contribution rates the southeast of the plateau but increased in other regions of precipitation are between −100% and 50%, and the av- (Figures 5(d) and 5(e)). It can be seen from Figure 5(f) that erage is −11%. *e increase of temperature was the main the probability of extremely wet increased in most other reason that the Qaidam Basin and southeast and southwest regions except the Qaidam Basin. According to this result, of Qinghai-Tibetan Plateau tended to be drier during the probability of drought had increased and the probability 1979–2015. Spatially, the Cr values of temperature to SPEI of wetness had decreased obviously in the regions that trend in the Qaidam Basin were positive, while the con- tributions of precipitation were negative. However, in some tended to be drier. However, the results were opposite in the regions that tended to be wetter. areas of the southeast and southwest of the QTP, the con- tribution rates of temperature and precipitation to the drought trend were both positive. Figures 6(c) and 6(d) show 3.3. 6e Respective Contributions of Temperature and Pre- theCr values of the precipitation and temperature variability cipitation Variability to the Drought and Wetness Trend. in the regions that tend to be wetter. *e Cr values of Using the method introduced in Section 2.3.2., the Cr values temperature in these regions ranged from −50% to 74%, with of the temperature and precipitation variability to the an average value of −9%. *e contribution rates of pre- drought and wetness trends for 25711 grids in the QTP were cipitation were between −46% and 100%, and the average is calculated. Generally, the increase of the temperature and 61%. According to this result, the variation of precipitation precipitation results in positive and negative contribution was the main reason that the northeast, central, and western Advances in Meteorology 9 N N 40°N 40°N 30°N 30°N 20°N 20°N 80°E 90°E 100°E 80°E 90°E 100°E -0.33 ~ 0 0.33 ~ 0.66 -1 ~ -0.66 0 ~ 0.33 0 ~ 0.33 0.66 ~ 1 -0.66 ~ -0.33 0.33 ~ 0.50 -0.33 ~ 0 (a) (b) N N 40°N 40°N 30°N 30°N 20°N 20°N 80°E 90°E 100°E 80°E 90°E 100°E -0.5 ~ -0.33 0.33 ~ 0.66 -0.46 ~ -0.33 0.33 ~ 0.66 -0.33 ~ 0 0.66 ~ 0.74 -0.33 ~ 0 0.66 ~ 1 0 ~ 0.33 0 ~ 0.33 (c) (d) Figure 6: *e respective contribution rate of the precipitation and temperature variability to the SPEI trend in the QTP. (a) and (b) are the contribution rates of the precipitation and temperature variability in the regions tending to be drier, and (c) and (d) are the contribution rates in the regions tending to be wetter. (a) Cr_temperature. (b) Cr_precipitation. (c) Cr_temperature. (d) Cr_precipitation. parts of the QTP tended to be wetter, which was different found that Tibet was showing a wetting trend in the period of from the regions that tend to be drier. Spatially, theCr values 1979–2015. It should be noted that Li et al. [64] assessed the drought condition over Tibet only based on meteorological of temperature and precipitation are both positive in the northeast and western parts of the QTP. However, in most of data from 38 stations, and these stations are mainly con- the central parts, Cr values of precipitation are positive, but centrated in the south of Tibet. In our study, both stations Cr values of temperature are negative. and CMFD pixels showed that drought conditions were aggravating in the same regions. However, in the north and central Tibet, most areas showed obvious wetting trends, 4. Discussion which means that only the observed data cannot truly reflect the overall drought and wetness trend in Tibet. 4.1. Comparison with Previous Studies. In previous studies, Li et al. [63] applied SPEI to characterize the drought conditions in the southeast part of the QTP during 1982–2012, and the results explicitly showed a drying trend. 4.2. Drought and Wetness Variability in Different Climate Regions. Previous studies have shown that most wet regions Chen et al. [41] calculated six drought indexes with mete- orological grid data at 0.5 degree resolution, analyzed the were becoming wetter and dry regions were becoming drier change trend of drought and wetness in different climatic under global warming [9, 65], and the trend of drought and regions of China, and concluded that the QTP had an ob- wetness in different climatic zones of the QTP is not clear. vious trend of wetting. *e above conclusions are highly *e China Meteorological Administrations takes the cu- consistent with ours, indicating that the conclusions ob- mulative temperature with the daily average temperature of tained in this study are credible. In the western part, Li et al. no less than 10 C and the multi-year average value of the [64] concluded that dryness conditions were aggravating temperature in the coldest month as the heat index and the during the period 1971–2014 across Tibet. However, we dryness as the moisture index and divides China into 32 10 Advances in Meteorology WE 40°N 35°N 30°N 25°N 0 500 km 75°E 80°E 85°E 90°E 95°E 100°E 105°E IIID1 VA3 HB1 HD2 HB2 HD1 HC3 HA1 II C2 HC2 HVA1 II D1 IVA2 HC1 VA5 Figure 7: *e climate regionalization in the QTP. secondary climate regions [66], including 15 climate regions IIC2 HC1 in the QTP (Figure 7). *ese 15 climate regions are Southern HC2 Xinjiang (IIID1), Northern Tibet (HD2), Southern Tibet HC3 (HC3), Central Tibet (HC2), Qinba (IVA2), Sichuan (VA3), HB1 Changdu (HB2), Bomi and Western Sichuan (HA1), HB2 Dawang and Zayu (HVA1), Qilian and Qinghai Lake (HC1), VA5 Southern Qinghai (HB1), Qaidam (HD1), Central Inner HVA1 HA1 Mongolia (IIC2), Inner Mongolia and Gansu (IID1), and VA3 North Yunnan (VA5). Among these climatic zones, Qinba, IVA2 Sichuan, Bomi and Western Sichuan, Dawang and Zayu, and IID1 North Yunnan belong to the humid regions, Changdu and HD1 Southern Qinghai belong to the semihumid regions, HD2 Southern Xinjiang, Northern Tibet, Qaidam, and Inner IIID1 Mongolia and Gansu belong to the arid regions, and 0 20406080 100 Southern Tibet, Central Tibet, Qilian and Qinghai Lake, and Percentage (%) Central Inner Mongolia belong to the semiarid regions. Figure 8: Percentages of areas tending to be drier (red bar) and Previous studies had shown that the SPEI at 12-month wetter (blue bar) significantly (p< 0.1) in different climate regions time scale (SPEI-12) is suitable for describing the long-term based on the trend of SPEI-12 in the QTP during 1979–2015. drought and wetness trend [67]. In this study, we also se- lected SPEI-12 to analyze the characteristics of drought and Zayu, Qilian and Qinghai Lake, Southern Qinghai, and wetness changes in different climatic regions of the QTP. Central Inner Mongolia. According to this result, most of Following the variation trend of SPEI-12 from each CMFD the arid and humid regions in the QTP tended to be drier, grid, the percentages of areas tending to be drier and wetter while the semiarid regions tended to be wetter. significantly (p < 0.1) in different climate zone were also calculated (Figure 8). As shown in Figure 8, climate regions tending to be drier significantly include Southern Xinjiang, 5. Conclusion Qinba, Sichuan, Changdu, Bomi and Western Sichuan, Qaidam, Inner Mongolia and Gansu, and North Yunnan. *is study investigated the drought and wetness trend and Climate regions tending significantly to be wetter include the respective contributions of temperature and precipita- Northern Tibet, Southern Tibet, Central Tibet, Dawang and tion variability by using SPEI calculated based on the Climate regions Advances in Meteorology 11 observed and CMFD dataset in the QTP during 1979–2015. downloaded from the Resource and Environment Data *e main results are summarized as follows: Science Cloud Platform and Data Center (https://www. resdc.cn). *e observed data were provided by the Na- (1) *e SPEI series based on the observed and CMFD tional Meteorological Information Center (https://data.cma. data showed that the southeast of the QTP and the cn/). Qaidam Basin in the northeast tended to be drier during 1979–2015, but most areas in the middle and Conflicts of Interest west of the plateau tended to be wetter. *e SPEI sequence obtained by averaging all CMFD grid *e authors declare that they have no conflicts of interest. points revealed that the whole QTP tends to be wetter during the study period. Acknowledgments (2) On the whole, the probability of drought was de- *is research was supported by the National Natural Science creasing and the probability of wetness was in- Foundation of China (42171467, 42001060, and 41705139) creasing in the QTP during 1979–2015. In regions and the Basic Research Project of Qinghai Province (2021- that tended to be drier, the probabilities of mild ZJ-947Q). *e authors also thank the National Tibetan drought, moderate drought, and extreme drought Plateau Data Center, Resource and Environment Science were also increasing, while in areas that tend to be and Data Center in Beijing, and China Meteorological wetter, the probabilities of all l grades of drought Administration for providing the meteorological data for were decreasing. this study. (3) In the drier regions, temperature is the dominant factor controlling the change trend of SPEI, the References average contribution rate of temperature is 60%, and the contribution rate of precipitation is only −11%. [1] D. J. Lorenz, J. A. Otkin, M. Svoboda, C. R. Hain, In the wetter regions, the contribution rate of pre- M. C. Anderson, and Y. 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Advances in MeteorologyHindawi Publishing Corporation

Published: Sep 29, 2021

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