Spatiotemporal Variations of Drought in the Arid Region of Northwestern China during 1950–2012

Wenjun Huang; Jianjun Yang; Yang Liu; Entao Yu

doi:10.1155/2021/6680067

Spatiotemporal Variations of Drought in the Arid Region of Northwestern China during 1950–2012

Huang, Wenjun;Yang, Jianjun;Liu, Yang;Yu, Entao 2021-04-05 00:00:00 Hindawi Advances in Meteorology Volume 2021, Article ID 6680067, 12 pages https://doi.org/10.1155/2021/6680067 Research Article Spatiotemporal Variations of Drought in the Arid Region of Northwestern China during 1950–2012 1,2 1,2 1,2 3,4 Wenjun Huang , Jianjun Yang , Yang Liu, and Entao Yu College of Resources and Environmental Science, Xinjiang University, Urumqi 830046, China Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China Nansen-Zhu International Research Center, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044, China Correspondence should be addressed to Jianjun Yang; yjj@xju.edu.cn Received 9 November 2020; Revised 21 February 2021; Accepted 19 March 2021; Published 5 April 2021 Academic Editor: Budong Qian Copyright © 2021 Wenjun Huang et al. ,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. ,ere are water resource shortages and frequent drought disasters in the arid region of northwestern China (ARNC). ,e purpose of this study is to understand the spatiotemporal variations of the droughts in this region and to further estimate future changes. Multiple drought indexes such as the standardized precipitation index (SPI), the standardized precipitation evapotranspiration index (SPEI), and the self-calibrated Palmer drought severity index (SC-PDSI) are used to investigate the temporal and spatial characteristics of the ARNC drought from 1950 to 2012. Our results indicate the following: (1) ,e drought indexes exhibit signiﬁcant increasing trends, and the highest drought frequency occurred in the 1960s, followed by a decreasing trend during the next few decades. All four seasons exhibit a wet trend, with a higher drought frequency in summer than in the other seasons. (2) ,e changes of the drought indexes in the ARNC also exhibit distinct spatial variations, with a wet trend in the subregions of North Xinjiang (NXJ), the Tianshan Mountains (TS), South Xinjiang (SXJ), and the Qilian Mountains (QL), but with a dry trend in the subregions of the Hexi Corridor (HX) and the western part of Inner Mongolia (WIM). (3) ,ere was a major climate variability in the ARNC that occurred in the 1980s, and there were dry and wet climate oscillation periods of 8a, 17a, and >20a. atmospheric circulation [5, 6]. It is also one of the regions that is 1. Introduction most sensitive to global warming [7, 8]. Water scarcity and Drought is a major natural disaster on a global scale, and severe persistent drought are the main factors limiting local sus- droughts can cause many other environmental problems. In tainable development, and the droughts have also caused the context of global warming, these problems are becoming economic losses in this area. ,erefore, an in-depth analysis of more prominent [1, 2]. ,e frequency of moderate and extreme the spatiotemporal variations of the droughts in the ARNC is of droughts in China has increased since the 1990s, and the area great signiﬁcance to local agricultural, ecological, and socio- experiencing drought is expanding at a rate of 3.72% per decade economic development. [3]. Since 1978, the area aﬀected by droughts in China has been Drought indexes have been widely used to describe the increasing rapidly, and the total area of crop failure caused by degree of drought. In recent years, the application of drought droughts has also been increasing [4]. As the main arid climate indexes has included monitoring droughts, assessing region in China, the arid region in northwestern China droughts, and predicting droughts, and drought indexes can (ARNC) is deeply landlocked, and its precipitation is con- also be used to assess the eﬀects of droughts on meteorology, strained by the combinations of the monsoon and high-latitude agriculture, and hydrology [9–13]. ,e standardized 2 Advances in Meteorology precipitation index (SPI), the standardized precipitation includes the entire Xinjiang Uygur Autonomous Region, the evapotranspiration index (SPEI), and the Parmer drought Hexi Corridor in Gansu Province, the Qilian Mountains, the severity index (PDSI) have been widely used to monitor Alxa Plateau in Inner Mongolia, and a small part of Western drought events. ,e PDSI is calculated using monthly Ningxia Province, accounting for about one-quarter of the temperature and precipitation data and information on the land area of China. ,e ARNC is surrounded by high water-holding capacity of the soils, which makes it a mountains and has an average annual precipitation of less powerful tool for identifying droughts [14]. ,e self-cali- than 230 mm [30]. Moreover, the potential amount of brated Parmer drought severity index (SC-PDSI), which is evapotranspiration is extremely high, making this a classic based on the PDSI, is considered to be more suitable for water-scarce area. ,is region can be divided into six sub- global drought monitoring because it automatically cali- regions based on the topographical features and climate brates itself at any location using dynamically computed features, following the method used in previous studies values instead of empirical constants [15]. ,e SPI only uses [31, 32]: North Xinjiang (NXJ), the Tianshan Mountains precipitation data to estimate drought duration, scale, and (TS), South Xinjiang (SXJ), the Qilian Mountains (QL), the intensity [16, 17]. It is easy to use and calculate for drought Hexi Corridor (HX), and the Western part of Inner Mon- monitoring and assessment in diﬀerent regions and on golia (WIM) (Figure 1). diﬀerent time scales. Vicente-Serrano et al. [18] established the standard precipitation evapotranspiration index (SPEI), 2.2. Drought Indexes. ,e SPI and SPEI can be calculated at which is an index that characterizes the probability of the diﬀerent monthly scales, with typical values of 1, 3, 6, 12, and diﬀerence between the precipitation and evapotranspiration 24 months. In this study, we mainly analyzed the annual and in a certain period of time. It has two advantages, namely, interdecadal variations in drought, so a 12-month time scale multi-scale features and sensitivity to changes in the was selected for the SPI and SPEI, combined with SC-PDSI, evapotranspiration demand [19], and thus, it is widely used to analyze the drought variations using multiple drought worldwide. indexes. ,e drought indexes and the corresponding data- Most previous studies used a single drought index to sets used in this paper are described below: investigate the drought variability in the ARNC [20–22]. However, the use of multiple drought indexes is also one of (1) SPI: ,e SPI from the Global Land Surface the main methods of monitoring drought conditions and of (1949–2012) dataset was obtained from the National guiding early warning assessments [23]. Scholars such as Center for Atmospheric Research (NCAR), and it Tang et al. and Yao et al. [24–26] used the SPI and SPEI to was calculated based on the Climate Research Unit analyze the changes in and eﬀects of droughts in diﬀerent (CRU) TS3.23/TS4.03 monthly precipitation data, provinces of China. Most previous studies concentrated on with a spatial resolution is 1⁰ × 1⁰ [33]. the southwestern and eastern regions of China [27–29] (2) SPEI: ,e SPEI dataset (1901–2015) was obtained where meteorological observation stations are densely dis- from the Spanish National Research Council (CSIC), tributed. Much less research has been conducted on and it was calculated based on the CRU TS3.24.01 northwestern China due to the sparse distribution of the monthly data. ,e amount of potential evapo- meteorological stations in this region. ,erefore, it is nec- transpiration was calculated using the Penman- essary to conduct a comprehensive analysis of the various Monteith formula, and the spatial resolution is drought indexes and compare the results to analyze the 0.5⁰ × 0.5⁰ [34]. spatial and temporal changes in drought in the ARNC in (3) SC-PDSI: ,e global SC-PDSI dataset for 1901–2018 order to improve our understanding of the variations in the was obtained from the CRU, and it was calculated drought in this region. based on the CRU TS4.03 monthly data. ,e po- In this study, we analyzed the drought changes in the tential evapotranspiration was calculated using the ARNC from 1950 to 2012 using the SPI, SPEI, and SC- Penman-Monteith method, with a spatial resolution PDSI in terms of the temporal trend, spatial trend, abrupt of 0.5⁰ × 0.5 [35, 36]. variation, and periodicity. In addition, a comparison of the analysis results of the three selected indexes was According to the national standards of the People’s conducted to reveal the ability of each index to monitor Republic of China, Grades of meteorological drought GB/ the drought changes and characteristics in the ARNC to a T20481-2017 [37], the criteria for drought classiﬁcations for certain extent. ,rough the mutual comparison of the each drought index are listed in Table 1. diﬀerent drought indexes, we can gain a deeper under- standing of the drought variation characteristics in the study area. ,e results of this study provide a scientiﬁc 2.3. Methods. Based on the availability of the dataset basis for drought disaster prevention and mitigation in the products, we chose the time series from 1950 to 2012 for use ARNC. in this study. In order to obtain the three drought indexes at the same resolution, the bilinear interpolation method was used to interpolate the 1⁰ × 1⁰ resolution SPI data to the 2. Study Area and Methods 0.5⁰ × 0.5⁰ resolution of the other two indexes. To study the 2.1. Study Area. ,e ARNC is located in the heart of the changes in the drought indexes in depth, a variety of analysis ° ° Eurasian continent (73–107 E and 35–50 N). ,e study area methods were adopted. Advances in Meteorology 3 the dry/wet transition, with positive wavelets representing I: NXJ II: TS wet conditions and negative wavelets representing dry III: SXJ IV: QL V: HX VI: WIM conditions. ,e wavelet variance reﬂects the distribution of the ﬂuctuation energy of the drought index time series with the time scale (a). It can be used to determine the main oscillation period in the wet and dry evolution process. 3. Results 3.1. Temporal Variations. Figure 2 shows the variations of SPI, SPEI, and SC-PDSI from 1950 to 2012 and the char- DEM acteristics of the drought episodes in the ARNC. ,e changes High 0 300 600 900 km Low in the drought index indicate that the evolutions of each series are similar. ,e trend analysis of the three drought indexes in 80°E 90°E 100°E the ARNC was performed using diﬀerent methods. Table 2 Figure 1: ,e locations of the study area and the six subregions. shows that the drought indexes’ values in the ARNC have increased over the past 63 years. Statistically signiﬁcant up- ward trends were observed for the SPI (0.08/10 a), the SPEI Table 1: Drought classiﬁcations for SPI, SPEI, and SC-PDSI. (0.07/10 a), and the SC-PDSI (0.19/10 a) in the ARNC from Drought level SPI/SPEI SC-PDSI 1950 to 2012. ,e results of the Sen + M-K trend analysis of No drought >−0.5 >−1 the time series of the drought indexes indicate that the Sen Drought ≤−0.5 ≤−1 slopes β of the SPI, SPEI, and SC-PDSI are>0, and |Zc|>1.96. ,erefore, the time series of the drought indexes have sta- tistically signiﬁcant increasing trends. Based on the results of 2.3.1. Sen’s Slope Estimator. ,e ,eil-Sen Median method, these two methods, the overall trend in the ARNC from 1950 also known as Sen’s slope estimation, is a robust non- to 2012 was a dry to wet trend, which was consistent with the parametric statistical trend calculation method [38]. ,is results of previous studies of the surface water resources and method can signiﬁcantly reduce the inﬂuence of outliers and potential evapotranspiration in the ARNC [25, 50]. has a high computational eﬃciency. It is often used for trend analysis of long-term series data. ,e Sen’s slope method is 3.1.1. Interdecadal Variations. ,e interdecadal variation in often used in combination with the Mann-Kendall trend test the drought frequency for each drought index is shown in to determine the signiﬁcance of a sequence trend [39–41]. Figure 3. At the decadal scale, the 1960s was the driest period in the ARNC during 1950–2012, with the drought frequency 2.3.2. Mann-Kendall Test. ,e Mann-Kendall trend test is of the SC-PDSI being as high as 74%, and the average drought another non-parametric method for detecting the signiﬁ- frequencies of the three drought indexes were greater than cance of the trends of climate variables [42, 43]. In this study, 50%. ,e variations in the drought frequencies of the three we used Sen’s slope to judge the increase or decrease in the drought indexes from 1950s to 1980s were consistent, but the drought index and used the M-K trend test to determine drought frequencies of the SPEI and SC-PDSI changed from whether the trend passes the 0.5 signiﬁcance level (here- decreasing to increasing from the 1990s to the beginning of inafter referred to as Sen + M-K trend analysis). When the the 21st century, while the drought frequency of the SPI Sen’s trend degree is β>0, the time series exhibits an upward decreased from the 1980s to the beginning of the 21st century, trend; and when β <0, the time series exhibits a downward showing inconsistency between the SPEI and SC-PDSI. trend. Moreover, when the statistical quantity |Zc| >1.96 in the M-K trend test, the trend passes the 0.05 signiﬁcant level; 3.1.2. Seasonal Variations. In this section, the seasonal otherwise, the trend does not pass the 0.05 signiﬁcance level. changes in the drought indexes were analyzed and the A sequential Mann-Kendall test that estimates progressive frequency of drought occurrence was calculated separately (UF) and backward (UB) series was used to detect abrupt for each season. As can be seen from Figure 4, the trends of variation years in the study series [44, 45]. the drought indexes increased in each season, indicating that the climate became wetter in all of the seasons. ,e seasonal 2.3.3. Other Methods. In addition, we studied the variations variations are consistent with the overall annual trend. In the in the drought indexes using the linear regression method summer, the increasing trends in the SPI, SPEI, and SC- and the non-parametric Pettitt test method [46] in order to PDSI were 0.21/10 a, 0.18/10 a, and 0.45/10 a, respectively determine the abrupt variation year in the drought index (Figures 4(d)–4(f)), which were lower than in the other time series. Morlet wavelet analysis [47–49] was used to seasons and were not signiﬁcant at the 0.05 level. ,e three characterize the periodic variations in the ARNC. Wavelet drought indexes exhibited the most consistent trends in the transformation can reﬂect the periodic changes in the spring, with increasing trends passing the 0.05 signiﬁcance drought indexes on diﬀerent time scales and their distri- level. ,e wetting trends described by the SPI and SPEI were bution in the time domain. ,e midpoint of the contours is more consistent in each season, while the increasing trend of 35°N 40°N 45°N 50°N 4 Advances in Meteorology –1 –2 1950 1960 1970 1980 1990 2000 2010 Year (a) –1 –2 1950 1960 1970 1980 1990 2000 2010 Year (b) –2 –4 1950 1960 1970 1980 1990 2000 2010 Year (c) Figure 2: Evolution of the SPI, SPEI, and SC-PDSI in the ARNC during 1950–2012. (a) SPI. (b) SPEI. (c) SC-PDSI. 3.2. Spatial Variations. According to the results of the Table 2: Trend analysis and Sen + M-K trend analysis of the SPI, SPEI, and SC-PDSI. drought index trend analysis for each subregion (Figures 5(a)–5(c)), there were obvious regional diﬀerences Sen + M-K trend in the drought changes in the ARNC. ,e pie charts show the analysis Drought index Propensity rate (/10 a) percentage of the grids with diﬀerent trends. As can be seen, β |Zc| Trend the SPI, SPEI, and SC-PDSI have signiﬁcant increasing ∗ ∗ SPI 0.08 0.001> 0 >1.96 ↑ trends with proportions of 69.9%, 64.1%, and 51.6%, re- ∗ ∗ SPEI 0.07 0.001> 0 >1.96 ↑ spectively, especially in the NXJ, the TS, and most of the SXJ. ∗ ∗ SC-PDSI 0.19 0.002> 0 >1.96 ↑ ,e areas in which the drought indexes decreased were 0.05 signiﬁcance level. mainly concentrated in the HX, the WIM, and the south- western part of the SXJ, and thus, the climate in these areas the SC-PDSI in each season was somewhat greater than became drier. ,e analyses of the SPEI and SPI were similar, those of the SPI and SPEI. and the sum of the percentages of the slight decrease and the ,e drought frequencies in each season indicate that the signiﬁcant decrease was less than 15.0%. However, the SC- SPI had the highest frequency in fall and the lowest fre- PDSI signiﬁcantly decreased the grid percentage by as much quency in spring (Table 3). ,e SPEI had the highest drought as 29.1%, especially for the analysis of dry and wet changes in frequency in winter, followed by fall, and the lowest fre- the QL, which was diﬀerent from the SPI and SPEI. quency in spring. ,e SC-PDSI had the highest frequency in ,erefore, the SC-PDSI was more serious in terms of the summer, followed by fall, winter, and spring. According to drought judgment in the ARNC. the averages of the three drought indexes in each season, the ,e drought indexes in the NXJ, TS, SXJ, and QL ARNC was mainly dominated by summer droughts, with a subregions all had signiﬁcant increasing trends (Table 4). frequency of 28.7%, followed by fall (28.33%) and winter Except for the slightly increasing results of the Sen + M-K (27.78%), and the drought frequency in spring was the trend test of the SC-PDSI in the HX subregion, the HX and lowest (23.15%). WIM subregions mainly exhibited drought index decreasing Advances in Meteorology 5 1950s 1960s 1970s 1980s 1990s 2000s Years SPI SPEI SC-PDSI Figure 3: Interdecadal variations in the drought frequency in the ARNC. SC–PDSI SPI SPEI 2 2 2 ∗ ∗ ∗ 0.24/10a 0.24/10a 0.48/10a 1 1 0 0 –2 –1 –1 –2 –2 –4 Year Year Year Linear trend Linear trend Linear trend No drought line No drought line No drought line (a) (b) (c) SC–PDSI SPI SPEI 2 2 4 0.21/10a 0.18/10a 0.45/10a 1 1 0 0 –1 –1 –2 –2 –2 –3 –3 –4 Year Year Year Linear trend Linear trend Linear trend No drought line No drought line No drought line (d) (e) (f) Figure 4: Continued. Sum. Spr. 1950 1950 1960 1960 1980 1980 1990 1990 Drought frequency (%) 2010 2010 Sum. Spr. 1950 1950 1960 1960 1970 1970 1980 1980 1990 1990 2000 2000 2010 2010 Sum. Spr. 1950 1950 1960 1960 1970 1970 1980 1980 1990 1990 2000 2000 2010 2010 6 Advances in Meteorology SC–PDSI SPI SPEI 2 2 4 ∗ ∗ 0.24/10a 0.21/10a 0.63/10a 1 1 2 0 0 0 –1 –1 –2 –2 –2 –4 Year Year Year Linear trend Linear trend Linear trend No drought line No drought line No drought line (g) (h) (i) SC–PDSI SPI SPEI 2 2 2 ∗ ∗ 0.24/10a 0.21/10a 0.63/10a 1 1 0 0 –2 –1 –1 –2 –2 –4 Year Year Year Linear trend Linear trend Linear trend No drought line No drought line No drought line (j) (k) (l) Figure 4: Seasonal changes in the drought indexes in (a–c) spring, (d–f) summer, (g–i) autumn, and (j–l) winter. 0.05 signiﬁcance level. Table 3: Frequency of droughts in each season (%). Season SPI SPEI SC-PDSI Average drought frequency Spr. 13.89 19.44 36.11 23.15 Sum. 18.89 22.78 44.44 28.70 Fall. 21.11 25.56 38.33 28.33 Win. 18.89 28.33 36.11 27.78 4.1% 2.5% 20.6% 14.7% 2.3% 2.8% 0 300 600 900 0 300 600 900 8.5% 10.6% 69.9% 64.1% km km 80°E 90°E 100°E 80°E 90°E 100°E Significant increase Slight reduction Significant increase Slight reduction Slight increase Significant reduction Slight increase Significant reduction No significant change No significant change (a) (b) Figure 5: Continued. Win. Fal. 35°N 40°N 45°N 50°N 1950 1950 1960 1960 1970 1970 1980 1980 1990 1990 2000 2000 2010 2010 Win. Fal. 1950 1950 1960 1960 1970 1970 35°N 40°N 45°N 50°N 1980 1980 1990 1990 2000 2000 2010 2010 Win. Fal. 1950 1950 1960 1960 1970 1970 1980 1980 1990 1990 2000 2000 2010 2010 Advances in Meteorology 7 2.3% 11.5% 1.9% 29.1% 0 300 600 900 3.6% km 51.6% 80°E 90°E 100°E Significant increase Slight reduction Slight increase Significant reduction No significant change Null value (c) Figure 5: Spatial variations in the drought indexes in the ARNC. (a) SPI. (b) SPEI. (c) SC-PDSI. Table 4: Sen + M-K test for the changes in the SPI, SPEI, and SC-PDSI in each subregion. Subregion Drought index β |Zc| Trend SPI 0.013> 0 >1.96 ↑ I SPEI 0.012> 0 >1.96 ↑ SC-PDSI 0.031> 0 >1.96 ↑ SPI 0.012> 0 >1.96 ↑ II SPEI 0.012> 0 >1.96 ↑ SC-PDSI 0.026> 0 >1.96 ↑ SPI 0.009> 0 >1.96 ↑ III SPEI 0.009> 0 >1.96 ↑ SC-PDSI 0.022> 0 >1.96 ↑ SPI 0.011> 0 >1.96 ↑ SPEI 0.013> 0 >1.96 ↑ IV SC-PDSI 0.033> 0 >1.96 ↑ SPI −0.001< 0 ≤1.96 ↓ SPEI −0.001< 0 ≤1.96 ↓ SC-PDSI 0.009> 0 ≤1.96 ↑ SPI −0.007< 0 ≤1.96 ↓ SPEI −0.003< 0 ≤1.96 ↓ VI SC-PDSI −0.005< 0 ≤1.96 ↓ 0.05 signiﬁcance level. trends. With global climate change, the East Asian monsoon M-K abrupt variation tests showed that the drought in- continues to weaken, making the eastern part of north- dexes exhibited variability in the 1980s, and the abrupt western China aﬀected by the monsoon even drier [51]. ,e variations of the SPI, SPEI, and SC-PDSI occurred in 1987, ARNC experienced a gradually increasing wetting trend 1980, and 1983, respectively. ,e results of Pettitt's abrupt from southeast to northwest. ,e increasing trend in pre- variation test were more consistent, that the year of con- cipitation in the ARNC changes from southeast to northwest version from dry to wet for the SPI, SPEI, and SCPDSI was [52], and the spatial changes in the ARNC’s climate de- 1987. For each subregion, the abrupt variation points in the scribed by the three drought indexes are consistent with the entire Xinjiang region as well as in the HX and WIM spatial changes in precipitation. subregions were concentrated in the 1980s. ,e drought indexes in the NXJ, TS, and SXJ subregions exhibited in- creasing trends after 1980s, while the trends in the drought 3.3. Variation Characteristics indexes in the HX and WIM subregions changed from 3.3.1. Abrupt Dry/Wet Variation Characteristics. Table 5 increasing to decreasing trends. ,e abrupt variation in the presents the results of the analysis of the M-K abrupt QL subregion occurred in the 1970s, with an increasing variation test and the Pettitt abrupt variation test for the trend in the drought indexes compared to the earlier three drought indexes from 1950 to 2012. In the ARNC, the period. 35°N 40°N 45°N 50°N 8 Advances in Meteorology Table 5: Analysis of the catastrophe points of the SPI, SPEI, and SC-PDSI. SPI SPEI SC-PDSI Study area M-K Pettitt M-K Pettitt M-K Pettitt I 1987 −/+ 1987 −/+ 1984 −/+ 1984 −/+ 1987 −/+ 1987 −/+ II 1993 −/+ 1987 −/+ 1987 −/+ 1987 −/+ 1987 −/+ 1987 −/+ III 1981 −/+ 1987 −/+ 1980 −/+ 1987 −/+ 1977 −/+ 1987 −/+ IV 1978 −/+ 1972 −/+ 1976 −/+ 1972 −/+ 1978 −/+ 2002 −/+ V 1985 +/− 1985 +/− 1985 +/− 1985 +/− 1990 +/− 1967 +/− VI 1982 +/− 1982 +/− 1981 +/− 1982 +/− 1972 +/− 1980 +/− ARNC 1987 −/+ 1987 −/+ 1980 −/+ 1987 −/+ 1983 −/+ 1987 −/+ 40 40 3.5 3.5 2.5 2.5 30 30 1.5 1.5 0.5 0.5 –0.5 –0.5 –1.5 –1.5 –2.5 –2.5 –3.5 –3.5 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 Time (year) Time (year) (a) (b) 40 50 3 40 0 20 –1 10 –2 –3 –4 1950 1960 1970 1980 1990 2000 2010 0 5 10 15 20 25 30 35 40 Time (year) Year (c) (d) 50 60 0 5 10 15 20 25 30 35 40 0 5 10 15 20 25 30 35 40 Year Year (e) (f) Figure 6: Continued. Period (year) Period (year) Morlet variance Period (year) Morlet variance Morlet variance Advances in Meteorology 9 4 4 2 2 –2 –2 –4 –4 Year Year SPI SPI SPEI SPEI SC-PDSI (g) (h) –2 –4 Year SPI SPEI SC-PDSI (i) Figure 6: (a–c) Wavelet transformation and (d–f) wavelet variance of the SPI, SPEI, and SC-PDSI, and (g–i) wavelet coeﬃcient during the 8a, 17a, and >20 a oscillation period in the ARNC from 1950 to 2012. ,is conﬁrms that there was a variation in the climate PDSI and is the main period for the SPI and SPEI. ,e SPI, from dry to wet in the ARNC in the 1980s. Taking into SPEI, and SC-PDSI exhibited ﬁve dry-wet cycles during the account the atmospheric circulation factors, the South Asian 17a oscillation period, including ﬁve wet periods and ﬁve dry monsoon has increased and the westerly winds have periods (Figure 6(h)). For the periodic variations of this time weakened since the 1980s, resulting in more moisture being scale, the three indexes were very consistent. All three in- transported from the Indian Ocean to the arid zone [53]. dexes exhibited oscillation periods of >20 a, i.e., 28 a (SPI), However, the changing trends for the subregions after the 27 a (SPEI), and 26 a (SC-PDSI) (Figure 6(i)), and the transition period were not the same. drought indexes experienced three consistent alternating dry-wet cycles during the >20 a oscillation period. Based on the analysis results for the three drought indexes, there were 3.3.2. Periodic Variation Characteristics. In order to identify oscillation periods of 8a, 17a, and >20a in the dry-wet phase the periodicity of the drought variations in the ARNC from changes in the ARNC during the 63 years (1950 to 2012) 1950 to 2012, the wavelet transformation and wavelet var- study period, but larger scale periods may also occur. iance based on the drought indexes are shown in Figure 6. In the ARNC, a phase of dry-wet transformation was observed 4. Discussion at diﬀerent time scales during the 63 years study period. ,ere were obvious short (5–20a) and long (>20a) cycles in As drought is a complex climatic phenomenon that is af- the wet and dry variations (Figures 6(a)–6(c)). Based on the fected by many meteorological factors, multiple drought wavelet variance analysis results (Figures 6(d)–6(f)), the SPI index analysis using the widely accepted SPI, SPEI, and SC- had oscillatory periods of 8a, 17a, and 28a, but there may be PDSI is important for drought monitoring in the ARNC. longer oscillatory periods that were not observed due to the ,e frequency of drought occurrence in the SPI is length of the drought index time series data. ,e 8a oscil- consistent with the changes in precipitation in the ARNC latory period was also observed for the SPEI, and the 8a [54, 55], with a trend of increasing precipitation after the oscillatory period occurred from 1951 to 2012. ,e SPI and 1980s. A more obvious trend of increasing humidity SPEI experienced ten very consistent alternating dry-wet throughout the region occurred in the 1990s, and a trend of cycles during the 8a oscillation period (Figure 6(g)). ,e 17a increasing precipitation has occurred since 2000; however, oscillation period is the most obvious period for the SC- the rate of the increase in precipitation has decreased. Based Morlet wavelet coefficient Morlet wavelet coefficient Morlet wavelet coefficient 2010 10 Advances in Meteorology on the increasing trends in temperature and evapotrans- to 2012, with diﬀerent oscillation periods of 8 a, 17 a, piration, and the decreasing trend in relative humidity and >20 a. [31, 55, 56], the changes in all of these climatic factors may have had a negative impact on the drought mitigation in the Data Availability ARNC. ,erefore, the results of the interdecadal drought frequency analysis based on the SPI, which only considered ,e SPI for Global Land Surface (1949–2012) dataset is from the variability in precipitation, revealed that the application the National Center for Atmospheric Research (NCAR) of the SPI in the ARNC is limited. repository (https://rda.ucar.edu/datasets/ds298.0/). ,e Guo et al. [57] found that the drought changes in Central SPEI base v.2.5 dataset is from the Spanish National Re- Asia are closely related to the El Niño-Southern Oscillation search Council (CSIC) repository (http://hdl.handle.net/ (ENSO). ,e ENSO can inﬂuence every element of the East 10261/153475). ,e SC-PDSI for global land dataset is Asian monsoon system through telecorrelation, and drought from the Climate Research Unit (CRU) repository (https:// is often correlated with the teleconnection index [58]. Wang crudata.uea.ac.uk/cru/data/drought/). et al. [59] proposed that the drought evolution in the ARNC may be inﬂuenced by the North Atlantic Oscillation, the Conflicts of Interest Arctic Oscillation, and the Northern Hemisphere Polar Vortex by studying the relationship between droughts and ,e authors declare no conﬂicts of interest. the teleconnection indices. Many factors lead to variations in drought, including natural and unnatural factors. ,e analysis of the reasons for abrupt variations in dry and wet Authors’ Contributions climate is diﬃcult because the drought indexes are calculated W. H., Y. L., J. Y., and E. Y. conceptualized the study; W. H., based on diﬀerent principles and they consider diﬀerent Y. L., and J. Y. contributed to data curation; W. H. wrote the meteorological factors and the natural and unnatural factors original draft; W. H., Y. L., J. Y., and E. Y reviewed and aﬀect the diﬀerent meteorological factors. edited the article. All authors have read and agreed on the Chen et al. [60, 61] found that the ARNC, which is published version of the manuscript. particularly sensitive to global climate change, actually ex- perienced a major climate variability in the mid-1980s. However, the major factors responsible for this variation Acknowledgments have not been identiﬁed. 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Publisher: Hindawi Publishing Corporation
Copyright: Copyright © 2021 Wenjun Huang et al. 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.
ISSN: 1687-9309
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DOI: 10.1155/2021/6680067
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Abstract

Hindawi Advances in Meteorology Volume 2021, Article ID 6680067, 12 pages https://doi.org/10.1155/2021/6680067 Research Article Spatiotemporal Variations of Drought in the Arid Region of Northwestern China during 1950–2012 1,2 1,2 1,2 3,4 Wenjun Huang , Jianjun Yang , Yang Liu, and Entao Yu College of Resources and Environmental Science, Xinjiang University, Urumqi 830046, China Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China Nansen-Zhu International Research Center, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044, China Correspondence should be addressed to Jianjun Yang; yjj@xju.edu.cn Received 9 November 2020; Revised 21 February 2021; Accepted 19 March 2021; Published 5 April 2021 Academic Editor: Budong Qian Copyright © 2021 Wenjun Huang et al. ,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. ,ere are water resource shortages and frequent drought disasters in the arid region of northwestern China (ARNC). ,e purpose of this study is to understand the spatiotemporal variations of the droughts in this region and to further estimate future changes. Multiple drought indexes such as the standardized precipitation index (SPI), the standardized precipitation evapotranspiration index (SPEI), and the self-calibrated Palmer drought severity index (SC-PDSI) are used to investigate the temporal and spatial characteristics of the ARNC drought from 1950 to 2012. Our results indicate the following: (1) ,e drought indexes exhibit signiﬁcant increasing trends, and the highest drought frequency occurred in the 1960s, followed by a decreasing trend during the next few decades. All four seasons exhibit a wet trend, with a higher drought frequency in summer than in the other seasons. (2) ,e changes of the drought indexes in the ARNC also exhibit distinct spatial variations, with a wet trend in the subregions of North Xinjiang (NXJ), the Tianshan Mountains (TS), South Xinjiang (SXJ), and the Qilian Mountains (QL), but with a dry trend in the subregions of the Hexi Corridor (HX) and the western part of Inner Mongolia (WIM). (3) ,ere was a major climate variability in the ARNC that occurred in the 1980s, and there were dry and wet climate oscillation periods of 8a, 17a, and >20a. atmospheric circulation [5, 6]. It is also one of the regions that is 1. Introduction most sensitive to global warming [7, 8]. Water scarcity and Drought is a major natural disaster on a global scale, and severe persistent drought are the main factors limiting local sus- droughts can cause many other environmental problems. In tainable development, and the droughts have also caused the context of global warming, these problems are becoming economic losses in this area. ,erefore, an in-depth analysis of more prominent [1, 2]. ,e frequency of moderate and extreme the spatiotemporal variations of the droughts in the ARNC is of droughts in China has increased since the 1990s, and the area great signiﬁcance to local agricultural, ecological, and socio- experiencing drought is expanding at a rate of 3.72% per decade economic development. [3]. Since 1978, the area aﬀected by droughts in China has been Drought indexes have been widely used to describe the increasing rapidly, and the total area of crop failure caused by degree of drought. In recent years, the application of drought droughts has also been increasing [4]. As the main arid climate indexes has included monitoring droughts, assessing region in China, the arid region in northwestern China droughts, and predicting droughts, and drought indexes can (ARNC) is deeply landlocked, and its precipitation is con- also be used to assess the eﬀects of droughts on meteorology, strained by the combinations of the monsoon and high-latitude agriculture, and hydrology [9–13]. ,e standardized 2 Advances in Meteorology precipitation index (SPI), the standardized precipitation includes the entire Xinjiang Uygur Autonomous Region, the evapotranspiration index (SPEI), and the Parmer drought Hexi Corridor in Gansu Province, the Qilian Mountains, the severity index (PDSI) have been widely used to monitor Alxa Plateau in Inner Mongolia, and a small part of Western drought events. ,e PDSI is calculated using monthly Ningxia Province, accounting for about one-quarter of the temperature and precipitation data and information on the land area of China. ,e ARNC is surrounded by high water-holding capacity of the soils, which makes it a mountains and has an average annual precipitation of less powerful tool for identifying droughts [14]. ,e self-cali- than 230 mm [30]. Moreover, the potential amount of brated Parmer drought severity index (SC-PDSI), which is evapotranspiration is extremely high, making this a classic based on the PDSI, is considered to be more suitable for water-scarce area. ,is region can be divided into six sub- global drought monitoring because it automatically cali- regions based on the topographical features and climate brates itself at any location using dynamically computed features, following the method used in previous studies values instead of empirical constants [15]. ,e SPI only uses [31, 32]: North Xinjiang (NXJ), the Tianshan Mountains precipitation data to estimate drought duration, scale, and (TS), South Xinjiang (SXJ), the Qilian Mountains (QL), the intensity [16, 17]. It is easy to use and calculate for drought Hexi Corridor (HX), and the Western part of Inner Mon- monitoring and assessment in diﬀerent regions and on golia (WIM) (Figure 1). diﬀerent time scales. Vicente-Serrano et al. [18] established the standard precipitation evapotranspiration index (SPEI), 2.2. Drought Indexes. ,e SPI and SPEI can be calculated at which is an index that characterizes the probability of the diﬀerent monthly scales, with typical values of 1, 3, 6, 12, and diﬀerence between the precipitation and evapotranspiration 24 months. In this study, we mainly analyzed the annual and in a certain period of time. It has two advantages, namely, interdecadal variations in drought, so a 12-month time scale multi-scale features and sensitivity to changes in the was selected for the SPI and SPEI, combined with SC-PDSI, evapotranspiration demand [19], and thus, it is widely used to analyze the drought variations using multiple drought worldwide. indexes. ,e drought indexes and the corresponding data- Most previous studies used a single drought index to sets used in this paper are described below: investigate the drought variability in the ARNC [20–22]. However, the use of multiple drought indexes is also one of (1) SPI: ,e SPI from the Global Land Surface the main methods of monitoring drought conditions and of (1949–2012) dataset was obtained from the National guiding early warning assessments [23]. Scholars such as Center for Atmospheric Research (NCAR), and it Tang et al. and Yao et al. [24–26] used the SPI and SPEI to was calculated based on the Climate Research Unit analyze the changes in and eﬀects of droughts in diﬀerent (CRU) TS3.23/TS4.03 monthly precipitation data, provinces of China. Most previous studies concentrated on with a spatial resolution is 1⁰ × 1⁰ [33]. the southwestern and eastern regions of China [27–29] (2) SPEI: ,e SPEI dataset (1901–2015) was obtained where meteorological observation stations are densely dis- from the Spanish National Research Council (CSIC), tributed. Much less research has been conducted on and it was calculated based on the CRU TS3.24.01 northwestern China due to the sparse distribution of the monthly data. ,e amount of potential evapo- meteorological stations in this region. ,erefore, it is nec- transpiration was calculated using the Penman- essary to conduct a comprehensive analysis of the various Monteith formula, and the spatial resolution is drought indexes and compare the results to analyze the 0.5⁰ × 0.5⁰ [34]. spatial and temporal changes in drought in the ARNC in (3) SC-PDSI: ,e global SC-PDSI dataset for 1901–2018 order to improve our understanding of the variations in the was obtained from the CRU, and it was calculated drought in this region. based on the CRU TS4.03 monthly data. ,e po- In this study, we analyzed the drought changes in the tential evapotranspiration was calculated using the ARNC from 1950 to 2012 using the SPI, SPEI, and SC- Penman-Monteith method, with a spatial resolution PDSI in terms of the temporal trend, spatial trend, abrupt of 0.5⁰ × 0.5 [35, 36]. variation, and periodicity. In addition, a comparison of the analysis results of the three selected indexes was According to the national standards of the People’s conducted to reveal the ability of each index to monitor Republic of China, Grades of meteorological drought GB/ the drought changes and characteristics in the ARNC to a T20481-2017 [37], the criteria for drought classiﬁcations for certain extent. ,rough the mutual comparison of the each drought index are listed in Table 1. diﬀerent drought indexes, we can gain a deeper under- standing of the drought variation characteristics in the study area. ,e results of this study provide a scientiﬁc 2.3. Methods. Based on the availability of the dataset basis for drought disaster prevention and mitigation in the products, we chose the time series from 1950 to 2012 for use ARNC. in this study. In order to obtain the three drought indexes at the same resolution, the bilinear interpolation method was used to interpolate the 1⁰ × 1⁰ resolution SPI data to the 2. Study Area and Methods 0.5⁰ × 0.5⁰ resolution of the other two indexes. To study the 2.1. Study Area. ,e ARNC is located in the heart of the changes in the drought indexes in depth, a variety of analysis ° ° Eurasian continent (73–107 E and 35–50 N). ,e study area methods were adopted. Advances in Meteorology 3 the dry/wet transition, with positive wavelets representing I: NXJ II: TS wet conditions and negative wavelets representing dry III: SXJ IV: QL V: HX VI: WIM conditions. ,e wavelet variance reﬂects the distribution of the ﬂuctuation energy of the drought index time series with the time scale (a). It can be used to determine the main oscillation period in the wet and dry evolution process. 3. Results 3.1. Temporal Variations. Figure 2 shows the variations of SPI, SPEI, and SC-PDSI from 1950 to 2012 and the char- DEM acteristics of the drought episodes in the ARNC. ,e changes High 0 300 600 900 km Low in the drought index indicate that the evolutions of each series are similar. ,e trend analysis of the three drought indexes in 80°E 90°E 100°E the ARNC was performed using diﬀerent methods. Table 2 Figure 1: ,e locations of the study area and the six subregions. shows that the drought indexes’ values in the ARNC have increased over the past 63 years. Statistically signiﬁcant up- ward trends were observed for the SPI (0.08/10 a), the SPEI Table 1: Drought classiﬁcations for SPI, SPEI, and SC-PDSI. (0.07/10 a), and the SC-PDSI (0.19/10 a) in the ARNC from Drought level SPI/SPEI SC-PDSI 1950 to 2012. ,e results of the Sen + M-K trend analysis of No drought >−0.5 >−1 the time series of the drought indexes indicate that the Sen Drought ≤−0.5 ≤−1 slopes β of the SPI, SPEI, and SC-PDSI are>0, and |Zc|>1.96. ,erefore, the time series of the drought indexes have sta- tistically signiﬁcant increasing trends. Based on the results of 2.3.1. Sen’s Slope Estimator. ,e ,eil-Sen Median method, these two methods, the overall trend in the ARNC from 1950 also known as Sen’s slope estimation, is a robust non- to 2012 was a dry to wet trend, which was consistent with the parametric statistical trend calculation method [38]. ,is results of previous studies of the surface water resources and method can signiﬁcantly reduce the inﬂuence of outliers and potential evapotranspiration in the ARNC [25, 50]. has a high computational eﬃciency. It is often used for trend analysis of long-term series data. ,e Sen’s slope method is 3.1.1. Interdecadal Variations. ,e interdecadal variation in often used in combination with the Mann-Kendall trend test the drought frequency for each drought index is shown in to determine the signiﬁcance of a sequence trend [39–41]. Figure 3. At the decadal scale, the 1960s was the driest period in the ARNC during 1950–2012, with the drought frequency 2.3.2. Mann-Kendall Test. ,e Mann-Kendall trend test is of the SC-PDSI being as high as 74%, and the average drought another non-parametric method for detecting the signiﬁ- frequencies of the three drought indexes were greater than cance of the trends of climate variables [42, 43]. In this study, 50%. ,e variations in the drought frequencies of the three we used Sen’s slope to judge the increase or decrease in the drought indexes from 1950s to 1980s were consistent, but the drought index and used the M-K trend test to determine drought frequencies of the SPEI and SC-PDSI changed from whether the trend passes the 0.5 signiﬁcance level (here- decreasing to increasing from the 1990s to the beginning of inafter referred to as Sen + M-K trend analysis). When the the 21st century, while the drought frequency of the SPI Sen’s trend degree is β>0, the time series exhibits an upward decreased from the 1980s to the beginning of the 21st century, trend; and when β <0, the time series exhibits a downward showing inconsistency between the SPEI and SC-PDSI. trend. Moreover, when the statistical quantity |Zc| >1.96 in the M-K trend test, the trend passes the 0.05 signiﬁcant level; 3.1.2. Seasonal Variations. In this section, the seasonal otherwise, the trend does not pass the 0.05 signiﬁcance level. changes in the drought indexes were analyzed and the A sequential Mann-Kendall test that estimates progressive frequency of drought occurrence was calculated separately (UF) and backward (UB) series was used to detect abrupt for each season. As can be seen from Figure 4, the trends of variation years in the study series [44, 45]. the drought indexes increased in each season, indicating that the climate became wetter in all of the seasons. ,e seasonal 2.3.3. Other Methods. In addition, we studied the variations variations are consistent with the overall annual trend. In the in the drought indexes using the linear regression method summer, the increasing trends in the SPI, SPEI, and SC- and the non-parametric Pettitt test method [46] in order to PDSI were 0.21/10 a, 0.18/10 a, and 0.45/10 a, respectively determine the abrupt variation year in the drought index (Figures 4(d)–4(f)), which were lower than in the other time series. Morlet wavelet analysis [47–49] was used to seasons and were not signiﬁcant at the 0.05 level. ,e three characterize the periodic variations in the ARNC. Wavelet drought indexes exhibited the most consistent trends in the transformation can reﬂect the periodic changes in the spring, with increasing trends passing the 0.05 signiﬁcance drought indexes on diﬀerent time scales and their distri- level. ,e wetting trends described by the SPI and SPEI were bution in the time domain. ,e midpoint of the contours is more consistent in each season, while the increasing trend of 35°N 40°N 45°N 50°N 4 Advances in Meteorology –1 –2 1950 1960 1970 1980 1990 2000 2010 Year (a) –1 –2 1950 1960 1970 1980 1990 2000 2010 Year (b) –2 –4 1950 1960 1970 1980 1990 2000 2010 Year (c) Figure 2: Evolution of the SPI, SPEI, and SC-PDSI in the ARNC during 1950–2012. (a) SPI. (b) SPEI. (c) SC-PDSI. 3.2. Spatial Variations. According to the results of the Table 2: Trend analysis and Sen + M-K trend analysis of the SPI, SPEI, and SC-PDSI. drought index trend analysis for each subregion (Figures 5(a)–5(c)), there were obvious regional diﬀerences Sen + M-K trend in the drought changes in the ARNC. ,e pie charts show the analysis Drought index Propensity rate (/10 a) percentage of the grids with diﬀerent trends. As can be seen, β |Zc| Trend the SPI, SPEI, and SC-PDSI have signiﬁcant increasing ∗ ∗ SPI 0.08 0.001> 0 >1.96 ↑ trends with proportions of 69.9%, 64.1%, and 51.6%, re- ∗ ∗ SPEI 0.07 0.001> 0 >1.96 ↑ spectively, especially in the NXJ, the TS, and most of the SXJ. ∗ ∗ SC-PDSI 0.19 0.002> 0 >1.96 ↑ ,e areas in which the drought indexes decreased were 0.05 signiﬁcance level. mainly concentrated in the HX, the WIM, and the south- western part of the SXJ, and thus, the climate in these areas the SC-PDSI in each season was somewhat greater than became drier. ,e analyses of the SPEI and SPI were similar, those of the SPI and SPEI. and the sum of the percentages of the slight decrease and the ,e drought frequencies in each season indicate that the signiﬁcant decrease was less than 15.0%. However, the SC- SPI had the highest frequency in fall and the lowest fre- PDSI signiﬁcantly decreased the grid percentage by as much quency in spring (Table 3). ,e SPEI had the highest drought as 29.1%, especially for the analysis of dry and wet changes in frequency in winter, followed by fall, and the lowest fre- the QL, which was diﬀerent from the SPI and SPEI. quency in spring. ,e SC-PDSI had the highest frequency in ,erefore, the SC-PDSI was more serious in terms of the summer, followed by fall, winter, and spring. According to drought judgment in the ARNC. the averages of the three drought indexes in each season, the ,e drought indexes in the NXJ, TS, SXJ, and QL ARNC was mainly dominated by summer droughts, with a subregions all had signiﬁcant increasing trends (Table 4). frequency of 28.7%, followed by fall (28.33%) and winter Except for the slightly increasing results of the Sen + M-K (27.78%), and the drought frequency in spring was the trend test of the SC-PDSI in the HX subregion, the HX and lowest (23.15%). WIM subregions mainly exhibited drought index decreasing Advances in Meteorology 5 1950s 1960s 1970s 1980s 1990s 2000s Years SPI SPEI SC-PDSI Figure 3: Interdecadal variations in the drought frequency in the ARNC. SC–PDSI SPI SPEI 2 2 2 ∗ ∗ ∗ 0.24/10a 0.24/10a 0.48/10a 1 1 0 0 –2 –1 –1 –2 –2 –4 Year Year Year Linear trend Linear trend Linear trend No drought line No drought line No drought line (a) (b) (c) SC–PDSI SPI SPEI 2 2 4 0.21/10a 0.18/10a 0.45/10a 1 1 0 0 –1 –1 –2 –2 –2 –3 –3 –4 Year Year Year Linear trend Linear trend Linear trend No drought line No drought line No drought line (d) (e) (f) Figure 4: Continued. Sum. Spr. 1950 1950 1960 1960 1980 1980 1990 1990 Drought frequency (%) 2010 2010 Sum. Spr. 1950 1950 1960 1960 1970 1970 1980 1980 1990 1990 2000 2000 2010 2010 Sum. Spr. 1950 1950 1960 1960 1970 1970 1980 1980 1990 1990 2000 2000 2010 2010 6 Advances in Meteorology SC–PDSI SPI SPEI 2 2 4 ∗ ∗ 0.24/10a 0.21/10a 0.63/10a 1 1 2 0 0 0 –1 –1 –2 –2 –2 –4 Year Year Year Linear trend Linear trend Linear trend No drought line No drought line No drought line (g) (h) (i) SC–PDSI SPI SPEI 2 2 2 ∗ ∗ 0.24/10a 0.21/10a 0.63/10a 1 1 0 0 –2 –1 –1 –2 –2 –4 Year Year Year Linear trend Linear trend Linear trend No drought line No drought line No drought line (j) (k) (l) Figure 4: Seasonal changes in the drought indexes in (a–c) spring, (d–f) summer, (g–i) autumn, and (j–l) winter. 0.05 signiﬁcance level. Table 3: Frequency of droughts in each season (%). Season SPI SPEI SC-PDSI Average drought frequency Spr. 13.89 19.44 36.11 23.15 Sum. 18.89 22.78 44.44 28.70 Fall. 21.11 25.56 38.33 28.33 Win. 18.89 28.33 36.11 27.78 4.1% 2.5% 20.6% 14.7% 2.3% 2.8% 0 300 600 900 0 300 600 900 8.5% 10.6% 69.9% 64.1% km km 80°E 90°E 100°E 80°E 90°E 100°E Significant increase Slight reduction Significant increase Slight reduction Slight increase Significant reduction Slight increase Significant reduction No significant change No significant change (a) (b) Figure 5: Continued. Win. Fal. 35°N 40°N 45°N 50°N 1950 1950 1960 1960 1970 1970 1980 1980 1990 1990 2000 2000 2010 2010 Win. Fal. 1950 1950 1960 1960 1970 1970 35°N 40°N 45°N 50°N 1980 1980 1990 1990 2000 2000 2010 2010 Win. Fal. 1950 1950 1960 1960 1970 1970 1980 1980 1990 1990 2000 2000 2010 2010 Advances in Meteorology 7 2.3% 11.5% 1.9% 29.1% 0 300 600 900 3.6% km 51.6% 80°E 90°E 100°E Significant increase Slight reduction Slight increase Significant reduction No significant change Null value (c) Figure 5: Spatial variations in the drought indexes in the ARNC. (a) SPI. (b) SPEI. (c) SC-PDSI. Table 4: Sen + M-K test for the changes in the SPI, SPEI, and SC-PDSI in each subregion. Subregion Drought index β |Zc| Trend SPI 0.013> 0 >1.96 ↑ I SPEI 0.012> 0 >1.96 ↑ SC-PDSI 0.031> 0 >1.96 ↑ SPI 0.012> 0 >1.96 ↑ II SPEI 0.012> 0 >1.96 ↑ SC-PDSI 0.026> 0 >1.96 ↑ SPI 0.009> 0 >1.96 ↑ III SPEI 0.009> 0 >1.96 ↑ SC-PDSI 0.022> 0 >1.96 ↑ SPI 0.011> 0 >1.96 ↑ SPEI 0.013> 0 >1.96 ↑ IV SC-PDSI 0.033> 0 >1.96 ↑ SPI −0.001< 0 ≤1.96 ↓ SPEI −0.001< 0 ≤1.96 ↓ SC-PDSI 0.009> 0 ≤1.96 ↑ SPI −0.007< 0 ≤1.96 ↓ SPEI −0.003< 0 ≤1.96 ↓ VI SC-PDSI −0.005< 0 ≤1.96 ↓ 0.05 signiﬁcance level. trends. With global climate change, the East Asian monsoon M-K abrupt variation tests showed that the drought in- continues to weaken, making the eastern part of north- dexes exhibited variability in the 1980s, and the abrupt western China aﬀected by the monsoon even drier [51]. ,e variations of the SPI, SPEI, and SC-PDSI occurred in 1987, ARNC experienced a gradually increasing wetting trend 1980, and 1983, respectively. ,e results of Pettitt's abrupt from southeast to northwest. ,e increasing trend in pre- variation test were more consistent, that the year of con- cipitation in the ARNC changes from southeast to northwest version from dry to wet for the SPI, SPEI, and SCPDSI was [52], and the spatial changes in the ARNC’s climate de- 1987. For each subregion, the abrupt variation points in the scribed by the three drought indexes are consistent with the entire Xinjiang region as well as in the HX and WIM spatial changes in precipitation. subregions were concentrated in the 1980s. ,e drought indexes in the NXJ, TS, and SXJ subregions exhibited in- creasing trends after 1980s, while the trends in the drought 3.3. Variation Characteristics indexes in the HX and WIM subregions changed from 3.3.1. Abrupt Dry/Wet Variation Characteristics. Table 5 increasing to decreasing trends. ,e abrupt variation in the presents the results of the analysis of the M-K abrupt QL subregion occurred in the 1970s, with an increasing variation test and the Pettitt abrupt variation test for the trend in the drought indexes compared to the earlier three drought indexes from 1950 to 2012. In the ARNC, the period. 35°N 40°N 45°N 50°N 8 Advances in Meteorology Table 5: Analysis of the catastrophe points of the SPI, SPEI, and SC-PDSI. SPI SPEI SC-PDSI Study area M-K Pettitt M-K Pettitt M-K Pettitt I 1987 −/+ 1987 −/+ 1984 −/+ 1984 −/+ 1987 −/+ 1987 −/+ II 1993 −/+ 1987 −/+ 1987 −/+ 1987 −/+ 1987 −/+ 1987 −/+ III 1981 −/+ 1987 −/+ 1980 −/+ 1987 −/+ 1977 −/+ 1987 −/+ IV 1978 −/+ 1972 −/+ 1976 −/+ 1972 −/+ 1978 −/+ 2002 −/+ V 1985 +/− 1985 +/− 1985 +/− 1985 +/− 1990 +/− 1967 +/− VI 1982 +/− 1982 +/− 1981 +/− 1982 +/− 1972 +/− 1980 +/− ARNC 1987 −/+ 1987 −/+ 1980 −/+ 1987 −/+ 1983 −/+ 1987 −/+ 40 40 3.5 3.5 2.5 2.5 30 30 1.5 1.5 0.5 0.5 –0.5 –0.5 –1.5 –1.5 –2.5 –2.5 –3.5 –3.5 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 Time (year) Time (year) (a) (b) 40 50 3 40 0 20 –1 10 –2 –3 –4 1950 1960 1970 1980 1990 2000 2010 0 5 10 15 20 25 30 35 40 Time (year) Year (c) (d) 50 60 0 5 10 15 20 25 30 35 40 0 5 10 15 20 25 30 35 40 Year Year (e) (f) Figure 6: Continued. Period (year) Period (year) Morlet variance Period (year) Morlet variance Morlet variance Advances in Meteorology 9 4 4 2 2 –2 –2 –4 –4 Year Year SPI SPI SPEI SPEI SC-PDSI (g) (h) –2 –4 Year SPI SPEI SC-PDSI (i) Figure 6: (a–c) Wavelet transformation and (d–f) wavelet variance of the SPI, SPEI, and SC-PDSI, and (g–i) wavelet coeﬃcient during the 8a, 17a, and >20 a oscillation period in the ARNC from 1950 to 2012. ,is conﬁrms that there was a variation in the climate PDSI and is the main period for the SPI and SPEI. ,e SPI, from dry to wet in the ARNC in the 1980s. Taking into SPEI, and SC-PDSI exhibited ﬁve dry-wet cycles during the account the atmospheric circulation factors, the South Asian 17a oscillation period, including ﬁve wet periods and ﬁve dry monsoon has increased and the westerly winds have periods (Figure 6(h)). For the periodic variations of this time weakened since the 1980s, resulting in more moisture being scale, the three indexes were very consistent. All three in- transported from the Indian Ocean to the arid zone [53]. dexes exhibited oscillation periods of >20 a, i.e., 28 a (SPI), However, the changing trends for the subregions after the 27 a (SPEI), and 26 a (SC-PDSI) (Figure 6(i)), and the transition period were not the same. drought indexes experienced three consistent alternating dry-wet cycles during the >20 a oscillation period. Based on the analysis results for the three drought indexes, there were 3.3.2. Periodic Variation Characteristics. In order to identify oscillation periods of 8a, 17a, and >20a in the dry-wet phase the periodicity of the drought variations in the ARNC from changes in the ARNC during the 63 years (1950 to 2012) 1950 to 2012, the wavelet transformation and wavelet var- study period, but larger scale periods may also occur. iance based on the drought indexes are shown in Figure 6. In the ARNC, a phase of dry-wet transformation was observed 4. Discussion at diﬀerent time scales during the 63 years study period. ,ere were obvious short (5–20a) and long (>20a) cycles in As drought is a complex climatic phenomenon that is af- the wet and dry variations (Figures 6(a)–6(c)). Based on the fected by many meteorological factors, multiple drought wavelet variance analysis results (Figures 6(d)–6(f)), the SPI index analysis using the widely accepted SPI, SPEI, and SC- had oscillatory periods of 8a, 17a, and 28a, but there may be PDSI is important for drought monitoring in the ARNC. longer oscillatory periods that were not observed due to the ,e frequency of drought occurrence in the SPI is length of the drought index time series data. ,e 8a oscil- consistent with the changes in precipitation in the ARNC latory period was also observed for the SPEI, and the 8a [54, 55], with a trend of increasing precipitation after the oscillatory period occurred from 1951 to 2012. ,e SPI and 1980s. A more obvious trend of increasing humidity SPEI experienced ten very consistent alternating dry-wet throughout the region occurred in the 1990s, and a trend of cycles during the 8a oscillation period (Figure 6(g)). ,e 17a increasing precipitation has occurred since 2000; however, oscillation period is the most obvious period for the SC- the rate of the increase in precipitation has decreased. Based Morlet wavelet coefficient Morlet wavelet coefficient Morlet wavelet coefficient 2010 10 Advances in Meteorology on the increasing trends in temperature and evapotrans- to 2012, with diﬀerent oscillation periods of 8 a, 17 a, piration, and the decreasing trend in relative humidity and >20 a. [31, 55, 56], the changes in all of these climatic factors may have had a negative impact on the drought mitigation in the Data Availability ARNC. ,erefore, the results of the interdecadal drought frequency analysis based on the SPI, which only considered ,e SPI for Global Land Surface (1949–2012) dataset is from the variability in precipitation, revealed that the application the National Center for Atmospheric Research (NCAR) of the SPI in the ARNC is limited. repository (https://rda.ucar.edu/datasets/ds298.0/). ,e Guo et al. [57] found that the drought changes in Central SPEI base v.2.5 dataset is from the Spanish National Re- Asia are closely related to the El Niño-Southern Oscillation search Council (CSIC) repository (http://hdl.handle.net/ (ENSO). ,e ENSO can inﬂuence every element of the East 10261/153475). ,e SC-PDSI for global land dataset is Asian monsoon system through telecorrelation, and drought from the Climate Research Unit (CRU) repository (https:// is often correlated with the teleconnection index [58]. Wang crudata.uea.ac.uk/cru/data/drought/). et al. [59] proposed that the drought evolution in the ARNC may be inﬂuenced by the North Atlantic Oscillation, the Conflicts of Interest Arctic Oscillation, and the Northern Hemisphere Polar Vortex by studying the relationship between droughts and ,e authors declare no conﬂicts of interest. the teleconnection indices. Many factors lead to variations in drought, including natural and unnatural factors. ,e analysis of the reasons for abrupt variations in dry and wet Authors’ Contributions climate is diﬃcult because the drought indexes are calculated W. H., Y. L., J. Y., and E. Y. conceptualized the study; W. H., based on diﬀerent principles and they consider diﬀerent Y. L., and J. Y. contributed to data curation; W. H. wrote the meteorological factors and the natural and unnatural factors original draft; W. H., Y. L., J. Y., and E. Y reviewed and aﬀect the diﬀerent meteorological factors. edited the article. All authors have read and agreed on the Chen et al. [60, 61] found that the ARNC, which is published version of the manuscript. particularly sensitive to global climate change, actually ex- perienced a major climate variability in the mid-1980s. However, the major factors responsible for this variation Acknowledgments have not been identiﬁed. 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Journal

Advances in Meteorology – Hindawi Publishing Corporation

Published: Apr 5, 2021

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Spatiotemporal Variations of Drought in the Arid Region of Northwestern China during 1950–2012

Spatiotemporal Variations of Drought in the Arid Region of Northwestern China during 1950–2012

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Spatiotemporal Variations of Drought in the Arid Region of Northwestern China during 1950–2012

Spatiotemporal Variations of Drought in the Arid Region of Northwestern China during 1950–2012

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