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Hindawi Advances in Meteorology Volume 2019, Article ID 4053718, 10 pages https://doi.org/10.1155/2019/4053718 Research Article Temporal and Spatial Change Monitoring of Drought Grade Based on ERA5 Analysis Data and BFAST Method in the Belt and Road Area during 1989–2017 1 1,2,3 1,4 Changdi Xue , Hua Wu , and Xiaoguang Jiang University of Chinese Academy of Sciences, Beijing 100049, China State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China Key Laboratory of Quantitative Remote Sensing Information Technology, Academy of Opto-Electronics, Chinese Academy of Sciences, Beijing 100094, China Correspondence should be addressed to Hua Wu; wuhua@igsnrr.ac.cn Received 28 February 2019; Accepted 3 October 2019; Published 16 November 2019 Guest Editor: Jayant K. Routray Copyright © 2019 Changdi Xue 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. Drought is a worldwide natural disaster with a wide range of influences and a long duration, which has a huge impact on the agricultural production activities and social economy of local residents. +e Belt and Road Initiative has always received much attention due to its special geographical location and great potential for economic development. At the same time, the Belt and Road region is also deeply affected by drought, especially in some countries and regions, where the agricultural infrastructure is weak and the ecological environment is fragile. How to effectively monitor and evaluate drought has become an urgent problem to be solved. In this study, the ERA5 atmospheric reanalysis data were used, and the self-calibrating Palmer Drought Severity Index was combined with Breaks for Additive Seasonal and Trend (BFAST) to study the temporal and spatial distribution of the 1989–2017 monthly scale of drought in different climate regions of the Belt and Road region. +e results show that the overall change trend of arid area shows a change of “up-down-up-down.” +e winter drought area is larger than the summer drought area, and the drought center gradually moves from the Southeast Asia region in winter to the West–Central Asia region in summer. In the past five years, the drought area decreased gradually at the rate of approximately 0.38 million km per year. billion [6]. Drought-related disasters in the 1980s caused 1. Introduction more than half a million deaths in Africa [7]. Drought is an unusually dry incident over a period, causing Since the beginning of the last century, more and more water shortages for long enough to cause severe hydrological researchers have realized the importance of drought mon- imbalances in the affected areas [1]. +e imbalance of water itoring, but due to technical limitations, most of them use budget is caused by the combined effects of climatic con- weather station data to monitor and forecast drought [8–11]. ditions, underlying surface, and human activities. Compared With the development of remote sensing technology, the use with floods, hurricanes, and other disasters, the process of of satellites to acquire long-term and large-scale meteoro- drought is much slower [2, 3]. It usually takes several months logical data for monitoring environmental disasters has or even several seasons [4]. It is easy to be ignored. Once it gradually become a mainstream method [12–14], such as the becomes a disaster, it has a large scope and lasts for a long TRMM satellite. Its spatial resolution is 0.25 , and the time time [5]. In the United States, drought causes an average of resolution is 1 hour, covering the area between 50 N and $6–8 billion in annual losses, but in 1988, it reached $40 50 S, spanning the period 1998 to 2018. It is a very powerful 2 Advances in Meteorology very heavy losses in the event of an accident [43]. +erefore, source of meteorological data [15–18]. In recent years, re- analysis data have widely been used in climate simulation it is necessary to understand the distribution and variation of drought in the Belt and Road region. Due to the small prediction [19, 20]. Several researchers use ERA-Interim reanalysis dataset from the European Centre for Medium- coverage area of meteorological stations, some stations lack Range Weather Forecasts (ECMWF) and a refined version of data, and the time series is not continuous, while the cov- a previously developed Lagrangian methodology to compile erage area of satellite data is wide, but the time series is short a global climatology of stratosphere-troposphere exchange for drought research. +is paper calculates the self-cali- (STE) from 1979 to 2011 [21]. Several researchers examined brating Palmer Drought Severity Index (scPDSI) using low-frequency variability and trends in temperature from ERA5 atmospheric reanalysis data and analyzes the distri- bution and change of drought in the Belt and Road region 1979 to 2012, and near-surface behaviour of the ERA-In- terim reanalysis is reviewed [22]. Several researchers model from 1989 to 2017 through Breaks for Additive Seasonal and Trend (BFAST) analysis [25, 44, 45]. We hope to contribute the climatic mass balance of the ice cap for the period September 2000 to August 2009 on ERA-Interim reanalysis effectively to drought reduction by providing analysis of the drought situation in the Belt and Road region. [23]. Since the beginning of the 20th century, as the com- plexity of drought formation and the breadth of its effects 2. Materials and Methods have deepened, the researchers constructed a series of drought assessment indicators using easily available ob- 2.1. Area. +e Belt and Road stretches across Asia, Europe, ° ° ° servational elements such as precipitation, temperature, and Africa, from 0 North to 60 North and from 10 West to evaporation, runoff, soil moisture, and remote sensing ° 110 East, and includes 70 countries and cities in the mari- [24–26]. +ese assessment indicators study and depict time regions, including the Western Pacific, Indian Ocean, drought from the perspective of meteorology, hydrology, Mediterranean Sea, Red Sea, and land. In the past research, agriculture, and other disciplines and begin to try to consider most researchers use the state or national boundaries to drought from a multifactor perspective. Representative study the distribution and change of drought in different drought indicators are as follows: Standardized Precipitation regions. It is well known that the same geographical region Index (SPI) [27], the Rainfall Deciles (RD) [28] reflect the usually contains different climatic types, and it may be in- water deficit of atmospheric process, and Computed Soil accurate to compare drought under different climatic types Moisture (CSM) [29] and Soil Moisture Deficit Index in one region. In order to study the variation of drought (SMDI) [30] reflect the water deficit of Soil process, and under different climate regions better, according to the Total Water Deficit (S) [31] reflects the water deficit of the Wladimir Koppen climate zone classification [46], we di- surface process. At the same time, there are a series of vided the whole region into equatorial, arid, warm, snow, standardized comprehensive indexes, such as Normalized and polar. +e climate classification system was proposed by Difference Vegetation Index (NDVI) [32], Standardized a German climatologist Wladimir Koppen. ¨ Climate is Precipitation Evapotranspiration Index (SPEI) [33], Palmer classified according to temperature and precipitation, and Drought Severity Index (PDSI) [34], and Palmer Hydro- according to the distribution of natural vegetation. First, the logical Drought Index (PHDI) [34]. On the one hand, these world’s climate is divided into five climatic zones and indicators reflect the water deficit in the atmosphere, soil, or expressed in majuscule terms: the tropical zone (equatorial), surface, and on the other hand, their regional adaptability the arid zone (arid), the temperate zone (warm), the cold and transplantability are significantly enhanced, which can temperate zone (snow), and the polar climatic zone (polar). be applied to most parts of the world [35]. In view of these In the five climatic zones, except dry climate, all of them are drought indexes, combining with modern climate statistical bounded by isotherms. Precipitation and temperature were diagnosis technology, separating precipitation change trend then used as secondary and tertiary classification indicators from climate series can effectively monitor precipitation to divide the world into 31 climatic regions. For convenience change and then monitor drought [36]. In addition to the and accuracy, this study used only Wladimir Koppen’s ¨ traditional methods such as moving average [37], cumula- classification of the world’s five basic climate zones. +e Belt tive anomaly percentage [38], and linear tendency estima- and Road region is divided into five regions: namely, the tion [39], some researchers have also introduced new tropical zone (equatorial), the arid zone (arid), the temperate methods such as spline function to better reflect its real trend zone (warm), the cold temperate zone (snow), and the polar [40]. In addition, the significance test of the change trend is zone (polar), see Figure 1 for classification of search area. also emphasized. More commonly used are the Mann– Kendall detection [41], Yamamoto method [42], and so on. Due to the large population and economic proportion of 2.2. Data. +e atmospheric reanalysis data, which have the the Belt and Road, there are many cities with unbalanced advantages of long time series and high resolution, can be cultural and economic development. Meteorological di- used not only for the diagnostic analysis of weather and sasters, especially droughts, often have serious consequences climate but also for the assimilation of a large number of for agricultural production in different countries. +e region satellite data and routine data such as ground and upper air has a weak national agricultural infrastructure and relatively data. It can also be used in meteorological boundary fields little investment in disaster prevention, mitigation, and for weather and climate models. ERA5 is a new climate relief, as the limitations of economic development lead to reanalysis dataset (5th generation) from ECMWF (the Advances in Meteorology 3 60°N Polar Snow 40°N Warm Arid 20°N Equatorial 0° 10°W 20°E 50°E 80°E 110°E Figure 1: Research area and climate subregion. European Centre for Medium-Range Weather Forecasts) comparability of PDSI, wells and other researchers proposed [21]. e resolution was increased from 79 km to 31 km, the scPDSI that can automatically modify the local climate [26]. time resolution from 3 hours to 1 hour, the number of Firstly, scPDSI has similar variation range under dif- vertical layers from 60 to 137, and the time span from 1950 ferent climatic conditions. is makes it a more suitable to the present (data for some years are still being pro- indicator for comparing the relative availability of water in cessed). Based on the availability of the data, we selected di—erent regions. Secondly, potential evapotranspiration is atmospheric reanalysis data from 1989 to 2017, with the calculated using the physical-based Penman–Monteith pa- main variables being total precipitation and near-surface rameterization, using actual vegetation cover rather than temperature. At the same time, in order to calculate the reference crops. irdly, the seasonal dynamics of snow scPDSI, we used available water-holding capacity (AWC) cover is considered in the water balance model [44, 50]. e from SoilGrids dataset [47–49], which was developed by scPDSI was calculated using a program developed by re- random forest and gradient-enhanced tree algorithms searchers at the University of Nebraska, Lincoln (http:// combined with global soil pro“le data compilation (ap- greenleaf.unl.edu/). proximately 1,30,000 sites) and borehole data (approxi- mately 1.6 million sites). Daily atmospheric reanalysis data in the Belt and Road 3.2. Breaks for Additive Seasonal and Trend. BFAST was originally used to identify vegetation disturbance using area from 1989 to 2017, which were collected from ECMWF, are used in this study. According to the world map of the remote sensing data. Compared with other change detection ¨ methods, such as PCA, Fourier analysis, wavelet analysis, Koppen–Geiger climate classi“cation, we divide the study and transform vector analysis, the advantages are as follows: area into di—erent climate types (available online at https:// people.eng.unimelb.edu.au/mpeel/koppen.html). e soil “rstly, BFAST can analyze all time series data and consider seasonal variation e—ectively avoiding the error caused by data were downloaded from the World Soil Information (available online at https://www.isric.org/explore/soilgrids). seasonal segmentation. Secondly, BFAST iteratively esti- mates the date and number of changes in the seasonal and e ERA5 reanalysis data and soil data used to support the “ndings of this study can be downloaded from the trend components and characterizes the changes by extracting the magnitude and direction of the changes. corresponding website on the Internet (https://climate. copernicus.eu/). e scPDSI data used to support the irdly, BFAST is suitable for all types of remote sensing data and can be applied to other time series data without the “nding of this study are available from the corresponding author upon request. need to select reference cycles, set thresholds, or de“ne change trajectories [45, 51, 52]. e general pattern is as follows: 3. Method Y T + S + e . (1) t t t t 3.1. scPDSI. is study uses the scPDSI [26], which is a drought index based on the relationship between water It is widely used and can be used in meteorology, hy- supply and demand proposed by Wayne Palmer in 1965. drology, economics, and other “elds. By decomposing the PDSI can take into account not only the current water supply data over a period of time into long-term trend components, and demand situation but also the e—ect of dry and wet periodic components, and the remaining components, in situation and its duration on the current drought situation which the trend components are “tted using a linear model, [34]. In order to improve the transplantability and spatial the model is (2), and the periodic components are “tted 4 Advances in Meteorology using a periodic model. +e model takes the form of ERA5 reanalysis data formula (3): Data preprocessing T � α + β t, (2) t i Climate classification 2πkt S � α sin + δ . (3) t j,k j,k scPDSI k�1 Get change point Another key point is the identification of mutation BFAST points in time series. +e BFASTalgorithm uses the ordinary Yes Analyze the cause least squares (OLS) residual-based MOving SUM (MOSUM) test to determine whether there are mutation points and uses Abrupt climate change point Conclusions Bayesian information theory to determine the optimal No number of mutation points. +e position of the mutation End point in the time series is estimated by the least squares. As shown in Figure 2, the data preprocessing and scPDSI Figure 2: Graphical framework of methodology. were firstly calculated based on the climate zone classifi- cation. +e BFAST algorithm was then used to detect the presence of abrupt climate change points in the time series. Table 1: Drought category based on the scPDSI value. +e causes of abrupt climate change were finally analyzed. In Rank Category scPDSI this study, the BFAST algorithm is implemented in the R 1 No drought >− 1.0 language BFAST algorithm package (http://bfast.r-forge.r- 2 Slight drought − 2.0 : − 1.0 project.org/). 3 Medium drought − 3.0 : − 2.0 4 Severe drought − 4.0 : − 3.0 4. Results 5 Extreme drought <− 4.0 +e scPDSI from 1989 to 2017 was calculated according to BFAST algorithm decomposition. Among them, from 1989 the calculation method of scPDSI mentioned in 3.1, and then to 1998, the drought area percentage gradually increased the drought grades of 29 years were classified by using the from 20% to 70%, which is the largest change in the past 30 drought index classification method in Table 1. Figure 3 years. From 1999 to the present, the trend of drought change shows the distribution of the average scPDSI drought index basically accords with the change every five years. It can be in 1998, with extreme droughts in central Africa, southern seen from the periodic component that the drought area in and Central Asia, and southern Europe, in contrast to in- the whole region of Belt and Road reaches the maximum in creased precipitation in northern Europe and Central Asia. winter and spring and is lower in summer and autumn. +e +e rest are slightly and moderately dry. Figure 4 shows the trend component T indicates that there are four arid climate distribution of drought grades from January to December t change points from 1989 to 2017 and five periods of different 1998. For Europe, there were more severe droughts in periods of drought. +e linear fitting of the monthly area February, April, and November, mainly in southern coun- change line of the five-stage drought period was obtained, tries and regions, including Italy, Greece, Albania, Serbia, and the slopes of the five-stage trend were 0.000191, 0.0053, Austria, Hungary, and the West Coast of Spain; for Asia, − 0.00513, 0.0034, and − 0.00439. Based on the ERA5 reso- West Asia is the main arid region, and the drought has also lution, the total area of the Belt and Road is estimated to be affected Central Asia in some months, with Saudi Arabia, approximately 86.80 million km . So, the drought area Yemen, Iraq, Pakistan, and Iran being the main affected decreased gradually at the rate of approximately 0.38 million countries. Central Asia, on the other hand, is dominated by km per year from 2012 to 2017. central Russia and Kazakhstan; for Africa, it’s a perennial drought, with frequent droughts in Ethiopia, Djibouti, and northern Africa, mainly in winter and spring. It has a great 5. Discussion impact on the daily life of the local people. As a region with abundant rainfall, Southeast Asia is prone to drought, which 5.1. Regional Variation Patterns of Drought Levels. +e occurs mainly in autumn and winter, especially from Sep- tropics have plenty of rainfall, with an annual average tember to December. +e intensity of the drought gradually temperature of 800 mm or more, mainly in central Africa, increased. Figure 5 illustrates the intensity change trend of much of India, and Southeast Asia; the arid regions include PDSI from 1989 to 2017 in different climate regions, and northern Africa, West Central Asia, and Northwest China. Figure 5 illustrates the change trend of drought area +e annual precipitation is low, and the underlying surface is (scPDSI< − 1) from 1989 to 2017 in different climate regions, mainly desert and mountainous; the temperate zone mainly in order to better reflect the drought change. includes most of Europe and southwest China, which is Figure 5(e) shows the percentage change of drought area strongly influenced by the monsoon, and the precipitation is from 1989 to 2017. It is obvious that the drought area shows seasonal. +e cold temperate zone mainly includes northern a trend of “up-down-up-down,” which becomes clearer after Asia, eastern Europe, and the edge of the Qinghai–Tibet Advances in Meteorology 5 60°N 5 40°N –1 –2 20°N –3 –4 0° –5 10°W 20°E 50°E 80°E 110°E Figure 3: Monthly average scPDSI in 1998. 60°N 60°N Polar Polar Snow Snow 40°N 40°N Warm Warm Arid Arid 20°N 20°N Equatorial Equatorial 0°N 0°N 10°W 20°E 50°E 80°E 110°E 10°W 20°E 50°E 80°E 110°E (a) (b) 60°N 60°N Polar Polar Snow Snow 40°N 40°N Warm Warm Arid Arid 20°N 20°N Equatorial Equatorial 0°N 0°N 10°W 20°E 50°E 80°E 110°E 10°W 20°E 50°E 80°E 110°E (c) (d) 60°N 60°N Polar Polar Snow Snow 40°N 40°N Warm Warm Arid Arid 20°N 20°N Equatorial Equatorial 0°N 0°N 10°W 20°E 50°E 80°E 110°E 10°W 20°E 50°E 80°E 110°E (e) (f) 60°N 60°N Polar Polar Snow Snow 40°N 40°N Warm Warm Arid Arid 20°N 20°N Equatorial Equatorial 0°N 0°N 10°W 20°E 50°E 80°E 110°E 10°W 20°E 50°E 80°E 110°E (g) (h) Figure 4: Continued. 6 Advances in Meteorology 60°N 60°N Polar Polar Snow Snow 40°N 40°N Warm Warm Arid Arid 20°N 20°N Equatorial Equatorial 0°N 0°N 10°W 20°E 50°E 80°E 110°E 10°W 20°E 50°E 80°E 110°E (i) (j) 60°N 60°N Polar Polar Snow Snow 40°N 40°N Warm Warm Arid Arid 20°N 20°N Equatorial Equatorial 0°N 0°N 10°W 20°E 50°E 80°E 110°E 10°W 20°E 50°E 80°E 110°E (k) (l) Figure 4: Spatiotemporal distribution of drought in Belt and Road area. (a) Jan. 1998. (b) Feb. 1998. (c) Mar. 1998. (d) Apr. 1998. (e) May. 1998. (f) Jun. 1998. (g) Jul. 1998. (h) Aug. 1998. (i) Sept. 1998. (j) Oct. 1998. (k) Nov. 1998. (l) Dec. 1998. 1.0 Y 0 −0.5 −1 −2 −2.0 0.02 0.04 −0.04 −0.02 1.0 T 0.0 −1 −1.0 −2 −2.0 1.0 1.0 e 0.0 e 0.0 t t −1.0 −1.0 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 Time Time (a) (b) 0.5 0.5 −0.5 t −0.5 −1.5 0.01 0.005 −0.03 −0.010 1.0 0.5 0.5 T T 0.0 t t −0.5 −0.5 −1.5 0.2 e 0.0 −0.4 −1.0 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 Time Time (c) (d) t 0 −2 0.02 −0.04 t 0 −1 −2 1.5 t 0.0 −1.5 1990 1995 2000 2005 2010 2015 Time (e) Figure 5: Trends of monthly scPDSI for the subregions. (a) Equatorial. (b) Arid. (c) Warm. (d) Snow. (e) Polar. Advances in Meteorology 7 0.5 0.5 Y Y t t 0.3 0.3 0.1 0.1 0.010 0.01 S S t t −0.005 −0.02 0.5 0.5 T T t t 0.3 0.3 0.1 0.1 0.3 e 0.1 0.1 e −0.1 −0.2 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 Time Time (a) (b) 0.5 0.3 Y Y 0.3 t t 0.1 0.1 0.010 0.010 S S t t −0.005 −0.010 0.4 0.5 0.4 0.3 T T t 0.3 t 0.2 0.2 0.1 0.1 0.05 0.1 e e t t −0.1 −0.10 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 Time Time (c) (d) 0.6 0.7 0.4 Y 0.5 0.2 0.3 0.0 0.1 0.005 S 0.005 t S −0.010 −0.010 0.6 0.7 0.4 0.5 0.2 0.3 0.0 0.1 0.1 e 0.1 −0.2 −0.1 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 Time Time (e) (f) Figure 6: Trends of monthly average area percentage of the subregions in the Belt and Road area. (a) Equatorial. (b) Arid. (c) Warm. (d) Snow. (e) Polar. (f) Entire study area. Plateau. e annual average temperature is relatively low, (Figure 5(c)), the drought is serious in winter, and most of and ice and snow are the basic forms of precipitation in the areas are mild and moderately drought. From the trend winter. For the tropical region (Figure 5(a)), it can be seen component, the slope of the “ve-stage period was 0.00742, from the periodic component that the drought mainly oc- − 0.022, 0.0232, − 0.00774, and 0.0198. e degree of drought curs in autumn, and judging from the scPDSI of the monthly increased between 1995 and 2000. For the cold temperate scale, it is a mild drought because of the mean value. So the zone (Figure 5(d)), the summer drought was severe, and drought is less severe than it actually is. According to the most of the areas were slightly arid. From the trend com- trend component, the slope of drought grade change in “ve ponent, the slope of the four-stage period was 0.00168, periods was 0.0195, − 0.0292, 0.0343, − 0.017, and 0.0372, − 0.00743, − 0.00162, and 0.00008, and the degree of drought which showed obvious cycle change. For arid climate areas tends to be stable between 2000 and 2015. For the polar (Figure 5(b)), summer and autumn are frequent periods of climate region (Figure 5(e)), the drought is serious in spring drought, and most areas are moderate or extreme drought; and spring, and most areas are slightly dry. From the trend from the perspective of the trend component, the slope of component, the slopes of the four periods were − 0.00747, drought grade change in “ve periods was 0.0124, − 0.0297, 0.0319, 0.0228, and − 0.0168. e drought degree was 0.0311, − 0.0194, and 0.0277, and the drought degree in- gradually serious between 1990 and 1998 and then gradually creased between 1995 and 2000. For the temperate zone improved from 1999 to 2005 and from 2006 to 2011, but it 8 Advances in Meteorology can be seen that the annual average scPDSI shows a drought risk under different climatic influences can also be downward trend after 2012. obtained. +e development trend of drought in different cli- matic zones would be clarified. Based on the ERA5 data, the spatial distribution and temporal and spatial variation of 5.2. Regional Variation Patterns of Drought Areas. drought in the Belt and Road region from 1989 to 2017 were Comparing the changes of the arid area of the five climate studied by using the adaptive Palmer drought model. Overall, type areas with the whole study area, it is found that the the results of our research and analysis of the distribution of entire area shows a trend of “up-down-up-down.” Of these, drought in the Belt and Road are as follows. the percentage change in the arid area of the polar climate In terms of spatial distribution, the area of winter region was the largest, increasing from 11.33% in October drought is larger than that of summer drought, and winter 1995 to 67% in April 1998. For the polar climate region drought mainly occurs in central Africa and West Asia, as (Figure 5(e)), the trend of the percentage change of arid area well as parts of Southeast Asia summer droughts occur is divided into four stages, which are the increasing trend of mainly in Central and Western Asia, much of Southeast drought in different degrees, but when the arid area reaches a Asia, and northern Africa. Taking into account the year-to- certain degree, there will be a sharp decline. +is indicates year distribution, the arid regional center moves eastward that the duration of drought in polar climate region is short, from West–Central Asia as the seasons change from winter and the slope of the four segments was 0.00601, 0.003, to summer. In April 1999, 72.9 percent of the Belt and Road 0.00151, and 0.000541, respectively. +is indicates that the area was dry, the highest percentage in nearly three de- rate of change in the extent of drought has gradually de- cades. According to the classification results of different creased over time, especially since 2008, the change in the climatic regions, the change trend of the percentage of arid area of drought has been moderate, and the tropical, arid, area in arid, tropical, and temperate climate regions is “up- and temperate climatic regions basically conform to the down-up-down.” For the cold temperate zone and the polar overall trend of the change in the area of drought climate zone, the trend of change increases slowly, and it is (Figures 6(a)–6(c)). But the variation range of adjacent not clear whether the area of drought is likely to reduce the months in tropical climate region is larger than that in arid trend. In the past five years, the drought area decreased climate region and temperate climate region. +e proportion gradually at the rate of approximately 0.38 million km per of arid areas in the cold temperate zone is relatively small year. (Figure 6(d)), ranging from 10 to 30 percent. On the one +is study used ERA5 atmospheric reanalysis data and hand, because the cold temperate mainly in Russia, deep into self-calibrating Palmer Drought Severity Index to describe the hinterland of the Asian continent, less precipitation. On the occurrence and development trends of droughts in the the other hand, the cold temperate zone area is vast, but the Belt and Road from 1989 to 2017. It has a certain reference change rate of drought area will be stable. For the cold value for revealing the spatial and temporal distribution temperate regions (Figure 6(e)), from 1989 to 2017, there characteristics of drought in the Belt and Road area. From were two major abrupt climate change points, the first in the view of data, extending the time span to 50 years may be 2001 and the second in 2012. +e slopes of the three aridity more conducive to the estimation and analysis of drought area curves were 0.00189, 0.000423, and 0.000353. +e rate of trends. From the point of view of drought assessment, increase in arid areas is levelling off. further research can be carried out using multiple drought indices for comprehensive assessment. For agricultural production, the next study will take into account agri- 6. Conclusions cultural factors such as crop drought tolerance and agri- Most previous studies were confined to smaller research areas, cultural production data, and further study will take into in which case, climate differences between different regions account the direct impact of drought on agricultural need not to be taken into account. Although the BFAST time production. series analysis method may get the trend and distribution characteristics of the drought index in different periods, the Data Availability selected research area in this study is large, where it is not a good way to use the BFAST method alone. In addition, con- +e ERA5 reanalysis data and soil data used to support the sidering the long study period, the scPDSI may be more findings of this study can be downloaded from the corre- suitable for the reason that it can be self-calibrated according to sponding website on the Internet. +e scPDSI data used to the local climate and comprehensively and accurately describe support the finding of this study are available from the the drought situation. Consequently, the authors tried to in- corresponding author upon request. troduce the Ko¨ppen–Geiger climate zone into the change monitoring of the temporal and spatial distribution of the Conflicts of Interest 1989–2017 monthly scale of drought with the scPDSI and the BFAST method. +e classification by climate type avoids the +e authors declare that they have no conflicts of interest. selective error caused by subjective judgment. +e change trend of drought and the abrupt change point of climate in the whole Authors’ Contributions climate region can be well captured. In addition, by comparing the drought situation in different climatic regions, the degree of +e authors contributed equally to this paper. Advances in Meteorology 9 region of India,” Physics and Chemistry of the Earth, Parts A/ Acknowledgments B/C, vol. 83-84, pp. 14–27, 2015. [16] S. Park, J. Im, J. Eunna, and R. 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