Temporal-Spatial Characteristics of Drought in Guizhou Province, China, Based on Multiple Drought Indices and Historical Disaster Records

Qingping Cheng; Lu Gao; Ying Chen; Meibing Liu; Haijun Deng; Xingwei Chen

doi:10.1155/2018/4721269

Temporal-Spatial Characteristics of Drought in Guizhou Province, China, Based on Multiple Drought Indices and Historical Disaster Records

Cheng, Qingping;Gao, Lu;Chen, Ying;Liu, Meibing;Deng, Haijun;Chen, Xingwei 2018-06-14 00:00:00 Hindawi Advances in Meteorology Volume 2018, Article ID 4721269, 22 pages https://doi.org/10.1155/2018/4721269 Research Article Temporal-Spatial Characteristics of Drought in Guizhou Province, China, Based on Multiple Drought Indices and Historical Disaster Records 1,2,3 1,4,5,6 1,4,5,6 1,4,5,6 1,4,5,6 Qingping Cheng, Lu Gao , Ying Chen, Meibing Liu, Haijun Deng, 1,4,5,6 and Xingwei Chen College of Geographical Science, Fujian Normal University, Fuzhou 350007, China Northwest Institute of Eco-Environmental and Resources Research, Chinese Academy of Sciences, Lanzhou 730000, China University of Chinese Academy of Sciences, Beijing 100049, China Institute of Geography, Fujian Normal University, Fuzhou 350007, China Fujian Provincial Engineering Research Center for Monitoring and Assessing Terrestrial Disasters, Fujian Normal University, Fuzhou 350007, China State Key Laboratory of Subtropical Mountain Ecology (Funded by Ministry of Science and Technology and Fujian Province), Fujian Normal University, Fuzhou 350007, China Correspondence should be addressed to Lu Gao; l.gao@foxmail.com Received 11 December 2017; Revised 15 April 2018; Accepted 30 April 2018; Published 14 June 2018 Academic Editor: Stefano Dietrich Copyright © 2018 Qingping Cheng 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. Guizhou Province, China, experienced several severe drought events over the period from 1960 to 2013, causing great economic loss and intractable conﬂicts over water. In this study, the spatial and temporal characteristics of droughts are analyzed with the standard precipitation index (SPI), comprehensive meteorological drought index (CI), and reconnaissance drought index (RDI). Meanwhile, historical drought records are used to test the performance of each index at identifying droughts. All three indices show decreasing annual and autumn trends, with the latter particularly prominent. 29, 30, and 32 drought events were identiﬁed during 1960–2013 by the SPI, CI, and RDI, respectively. Continuous drought is more frequent in winter–spring and summer–autumn. +ere is a signiﬁcant increasing trend in drought event frequency, peak, and strength since the start of the 21st century. Drought duration indicated by CI shows longer durations in the higher-elevation region of central and western Guizhou. +e corresponding drought severity is high in these regions. SPI and RDI indicate longer drought durations in the lower elevation central and eastern regions of Guizhou Province, where the corresponding drought severity is also very strong. SPI shows an increasing trend in drought duration and drought severity across most of the regions of Guizhou. In general, SPI and RDI show an increasing trend in the western Guizhou Province and a decreasing trend in central and eastern Guizhou. Comparing these three drought indices with historical records, the RDI is found to be more objective and reliable than the SPI and CI when identifying the periods of drought in Guizhou. drought, and socioeconomic drought [1]. Meteorological 1. Introduction drought refers to water shortages caused by an imbalance in Drought, a water shortage phenomenon caused by natural precipitation and evaporation. Drought disasters are a product of the coupling of the natural environmental and precipitation anomalies, is one of the most serious natural disasters, causing economic losses globally. +e American socioeconomic systems under speciﬁc time and space con- Meteorological Society classiﬁed droughts into four types: ditions [2]. Among diﬀerent types of natural disasters, drought meteorological drought, agricultural drought, hydrological disasters are among those with the highest frequencies, widest 2 Advances in Meteorology ranges of inﬂuence, longest durations, and greatest losses; every year, including a severe drought covering a large area drought disasters lead not only to food production reduction, every 5–10 years [15]. From 2009 to 2012, the ﬁve provinces of Southwest China (Yunnan Province, Sichuan Province, water shortages, and deterioration of ecosystems and the environment, but also to death and the change of dynasties, Chongqing City, Guizhou Province, and Guangxi Province) given that they are an important factor in restricting suﬀered a severe drought [16, 17]. +is severe drought, which 6 2 sustainable social development [3]. +e factors inﬂuencing aﬀected ∼8.0 ×10 hm of arable land, led not only to a large drought disasters are complex since there are great un- reduction in crop production but also caused drinking water certainties relating to the occurrence and development of shortages for 25 million people and 18 million livestock; drought disasters in both time and space. meanwhile, the drought caused total direct economic losses of Drought is one of the most frequent and widespread more than 40 billion Chinese Yuan [16, 17]. +is drought was natural disasters in China, where the total average area of land the worst in Southwest China since meteorological obser- 7 2 periodically inﬂuenced by droughts is 2.1 × 10 hm (annual vations began [18]. +e increasing frequency of severe 6 2 average value from 1950 to 2013), of which 9.4 ×10 hm droughts in the Southwest demonstrates that droughts are (annual average value from 1950 to 2013) suﬀers drought spreading from northern to southwestern China [19]. disasters in any particular year. Meteorological droughts, as Sun et al. [20] assessed the contributions of decadal described above, can develop into agricultural droughts [4, 5]. potential evapotranspiration (PET) anomalies to drought 6 2 In China, droughts cause an annual average of 2.5 × 10 hm of duration and intensity which could exceed those of pre- no-harvest area (annual average value from 1989 to 2013) and cipitation in Southwest China. Li et al. [21] identiﬁed 87 1.62 ×10 kg of grain loss (annual average value from 1950 to drought events including 9 extreme events using the daily 2013); droughts also cause 2.7 ×10 people (annual average composite drought index (CI) at 101 stations in Southwest value from 1991 to 2013) and 2.0 × 10 livestock (annual av- China. +e droughts are more frequent from November to erage value from 1989 to 2013) to have diﬃculty ﬁnding next April, and the frequency and intensity of drought in- suﬃcient drinking water; together, these factors contribute to creased with a signiﬁcant decrease in precipitation and an annual average direct economic loss of 1.0 × 10 Chinese increase in temperature. Gao et al. [22] found that the Yuan (annual average value from 1950 to 2013, http://www. signiﬁcant soil drying trend happened in autumn, which can mwr.gov.cn/sj/tjgb/zgshzhgb/201612/t20161222_776092.html) be sustained to the next spring. Han et al. [23] showed that the eastern part of southwestern China had an extremely [6]. +e abovementioned information indicates that China as a major agricultural country suﬀered severe meteorological high drought risk, which was greater in the north than south. droughts which caused great economic losses [7]. Drought is Recently, several extreme drought disasters have hit Guiz- the hot spot of research for a long time. Zhai et al. [8] found hou Province, such as that from September 2009 to March that a signiﬁcant dryness trend changes from the southwest to 2010, which caused drinking water shortages for 4.85 million 5 2 the northeast of China. In the early twenty-ﬁrst century, the people, with 7.01 × 10 hm of crops suﬀering from drought, most severe droughts were located in the Southwest of China and direct economic losses of 2.3 billion Chinese Yuan. A covering areas around 0.7 million km . Yu et al. [9] found that subsequent extreme summer drought in 2011 caused the severe and extreme droughts become more serious since drinking water shortages for 5.5 million people and 2.8 6 2 late 1990s for the entire China via examining drought char- million livestock, with 1.763 ×10 hm of crops aﬀected, acteristics such as long-term trend and intensity duration. resulting in an economic loss of 15.76 billion Chinese Yuan. Meanwhile, the drought-prone regions in Northeast China, Only two years later, the extreme summer drought of 2013 Southwest China, south China coastal region, and Northwest caused drinking water shortages for 2.645 million people 6 2 China were investigated by He et al. [7] and Ayantobo et al. and 1.12 million livestock, with 1.763 ×10 hm of crops [10]. Xu et al. [11] indicated that the three drought indices (SPI, aﬀected, causing an economic loss of ∼9.64 billion Chinese RDI, and SPEI) have almost the same performances in the Yuan [24–26]. humid regions. However, SPI and RDI were more appropriate Droughts are typically measured and quantiﬁed using than SPEI in the arid regions. +e Loess Plateau, Sichuan Basin, drought indices; a variety of indices for diﬀerent appli- and Yunnan-Guizhou Plateau have signiﬁcant dry trends, cations have been developed [27, 28]. Based on World which is mainly caused by the signiﬁcant decrease of pre- Meteorological Organization (WMO) statistics, there are cipitation. Liu et al. [12] found that the return periods of 55 commonly used categories of drought indices. Among meteorological drought are longer, with an average of 42.1 these, the comprehensive meteorological drought index years in China. Liu et al. [13] investigated the return period of (CI), standardized precipitation index (SPI), standardized concurrent drought events is 11 years in the water source area precipitation evapotranspiration index (SPEI), and re- and the destination regions of water diversion project. +e connaissance drought index (RDI) are widely used in various probability of concurrent drought events may signiﬁcantly regions [21, 29–36]. At present, case studies of droughts in increase during 2020 to 2050. Shen et al. [14] revealed that the Guizhou Province are rare, with most such studies based on drought probability and intensity are rising and the aﬀected a single drought index [37, 38]. Furthermore, no study has areas of all degrees of drought have an increasing trend during validated these drought indices using historical disaster re- the last 50 years based on the SPEI in Song-Liao River Basin. cords, despite validation of the reliability of these indices +e Southwest is one of the regions of China most being of great importance. +is study aims at building a link frequently aﬀected by drought disasters, with droughts of between drought indices and real drought events in Guizhou diﬀerent degrees of severity occurring in this region almost Province, China. Advances in Meteorology 3 Elevation (m) 105°E 108°E 111°E High: 2794 30°N 30°N 27°N 27°N 0 100 200 (km) 24°N 24°N Low: 229 105°E 108°E 111°E Station River Figure 1: Location of meteorological stations in Guizhou Province. study. It should be noted that some evaporation data (since 2. Study Region and Data Resources 2002) were recorded with E601B equipment. †e E-601B-type ° ° 2.1. Study Region. Guizhou Province (103 36′–109 35′E; evaporator was installed for meteorological stations in ° ° 24 37′–29 13′N), with an area of 176167 km , is located in the China from 1985. †e E-601B-type evaporation evaporator eastern Yunnan-Guizhou Plateau of China (Figure 1). †e is recommended by the World Meteorological Organization elevation of Guizhou Province ranges from 229 to 2794 m, (WMO). †is instrument has the advantages of corrosion- higher in the west than that in the east of province [39]. †e resistant and stable thermal e¦ect, which made the mea- topography is dominated by plateau and mountains: car- surements more close to nature [42]. In order to ensure the bonate rocks in the karst area are widespread and account for continuity, uniformity, and reliability of records, a linear 62% of the total area of Guizhou Province. Guizhou has regression is therefore applied to calibrate evaporation data a humid subtropical monsoon climate with an annual mean collected by E601B (2002–2014) to 20 cm evaporating dish temperature of 15 C and mean annual precipitation of data (1998–2001), according to previous studies [43, 44]. †e 1400 mm. Over 70% of the annual rainfall occurs from May to elevation data (DEM) are from the Shuttle Radar Topography September [39–41]. In general, the ecology and environment Mission (SRTM) with a resolution of 90 m, derived from the of Guizhou Province is extremely fragile, which causes fre- Geospatial Data Cloud of China (http://www.gscloud.cn/). quent land-surface droughts, as illustrated by an old saying †e historical disaster records are derived from China Me- describing drought in Guizhou Province: “a drought every teorological Disaster Yearbook (Guizhou volume) [45–51]. year, a mild drought every three years, a moderate drought †e information such as drought duration, severity, and peaks every ˜ve years, a severe drought every decade.” was extracted from the yearbooks according to the disaster statistics which were originally recorded by the local me- teorological department. Seasons are classi˜ed based on 2.2. Data Resources. †e meteorological data for daily pre- meteorological divisions: spring (March–May), summer cipitation and pan evaporation (from January 1, 1959, to (June–August), autumn (September–November), and winter February 28, 2014) data set are used in this paper from the (December–February), respectively. †e distribution of me- China Meteorological Data Sharing Service Network (http:// teorological stations, together with related information, is data.cma.cn/) V3.0 version. Rigorous quality control had been shown in Figure 1 and Table 1. conducted by China Meteorological Data Sharing Service Network before the data were released. †e software used to 3. Methods detect and adjust shifts in the time series of daily precipitation and pan evaporation is RHtestsV3 and RHtests-dlyPrcp (http:// 3.1. Drought Indices (SPI, CI, and RDI) etccdi.paci˜cclimate.org/software.shtml), respectively. Fi- nally, 19 out of 32 national basic meteorological stations (no 3.1.1. Standard Precipitation Index (SPI). †e SPI was de- gaps exceeding two consecutive weeks) are selected for this veloped by McKee et al. [29]. Within a certain geographic 4 Advances in Meteorology Table 1: Information of meteorological stations and average precipitation and 20 cm pan evaporation in 1960–2013. ° ° Station Longitude ( E) Latitude ( N) Elevation (m) Pan precipitation (mm) Evaporation (mm) P − C E − C V V Anshun 105.9 26.25 1431.1 1310.1 1250.5 0.18 0.09 Bijie 105.27 27.3 1510.6 883.2 986.1 0.15 0.10 Duyun 107.52 26.32 969.1 1419.9 1239.1 0.17 0.08 Dushan 107.55 25.83 1013.3 1307.5 1201.4 0.14 0.09 Guiyang 106.73 26.58 1223.8 1099.6 1377.5 0.16 0.10 Kaili 107.97 26.6 720.3 1142.1 1347.8 0.18 0.09 Luodian 106.77 25.43 440.3 1142.1 1246.5 0.18 0.07 Meitan 107.47 27.77 792.2 1116.5 1052.6 0.15 0.10 Panxian 104.47 25.72 1800 1355 1599.1 0.18 0.12 Rongjiang 108.53 25.97 285.7 1181.6 1174.1 0.17 0.11 Sanhui 108.67 26.97 626.9 1102 1129.7 0.15 0.11 Sinan 108.25 27.95 416.8 1120.1 1148.4 0.19 0.10 Tongzi 106.83 28.13 972 1259.8 1198 0.14 0.09 Tongren 109.17 27.72 353.2 1008.4 1091.5 0.13 0.15 Wangmo 106.08 25.18 566.8 1222.2 1453.8 0.16 0.09 Weining 104.28 26.87 2237.5 889.7 1350.2 0.19 0.08 Xifeng 106.72 27.1 1112.1 1108.4 1282.5 0.15 0.12 Xishui 106.22 28.33 1180.2 1094.3 1002.3 0.14 0.10 Xingren 105.18 25.43 1378.5 1317.3 1517.1 0.15 0.09 Note. P − C , coeﬃcient variation of precipitation; E − C , coeﬃcient variation of pan evaporation. V V (seasonal scale) standardized precipitation indices, com- Table 2: Classiﬁcation of SPI, CI, and RDI. bined with a 30 day relative humidity index, can be used to SPI/CI/RDI value Drought grades calculate a comprehensive meteorological drought index. Value ≤ −2.0 Extreme drought Since the CI can indicate precipitation climate anomalies on −2.0 < value ≤ −1.5 Severe drought both short (months) and long timescales (seasons) [52], this −1.5 < value ≤ −1.0 Moderate drought index is therefore suitable for meteorological drought −1.0 < value ≤ 0 Mild drought monitoring and historical drought assessment. +e ﬁrst step of the calculation is as follows: area, the precipitation usually ﬂuctuates regularly. If the 􏼐P − PET 􏼑 ij ij precipitation is less than the average annual precipitation, MI � , (1) PET a drought may therefore occur in this area. On the contrary, ij precipitation exceeding the annual average may induce where MI is the relative moisture index in the recent 30 days, ﬂooding. +e SPI has many advantages such as being di- P refers to the total amount of precipitation in the recent 30 ij mensionless and standardized, working on multiple scales, days (unit: mm), and PET is the total potential evapo- and being easy to calculate. To calculate the SPI, a frequency ij transpiration in the recent 30 days (mm; here we use distribution function is ﬁrst constructed from a series of evaporation of a 20 cm evaporating dish). +e CI is then long-term precipitation observations. A gamma probability calculated as follows: density function is then ﬁtted to the series, and the cu- mulative probability of an observed precipitation is com- CI � aSPI + bSPI + cM , (2) 30 90 30 puted. +e inverse normal (Gaussian) function, with a mean where SPI and SPI are the standardized precipitation of 0 and a variance of 1, is then applied to transform the 30 90 indexes for 30 d and 90 d periods, respectively. M refers to cumulative distribution to the standard normal distribution. the MI of 30 days. a, b, and c are set as 0.4, 0.4, and 0.8. In Because the SPI is based on the cumulative probability of theory, the weight coeﬃcients a, b, and c are from the average a given timescale, here the total amount of precipitation in values above light drought levels of SPI , SPI , and MI the current month and previous i months (i � 1, 2, 3, . . .) is 30 90 30 divided by the smallest history of SPI , SPI , and MI , used to calculate the SPI on a timescale of i + 1 month. Here, 30 90 30 respectively (GBT 20481-2006 meteorological drought level) SPI (1–12 monthly cumulative precipitation) represents [52]. +e drought classiﬁcation scheme is displayed in Table 2. annual timescales, and SPI (3 monthly cumulative pre- cipitation) represents seasonal timescales. Drought classi- ﬁcation is shown in Table 2. 3.1.3. Reconnaissance Drought Index (RDI). +e drought detection index was proposed by Tsakiris et al. [31, 32] and 3.1.2. Comprehensive Meteorological Drought Index (CI). takes into account the eﬀects of precipitation and evapo- +e comprehensive meteorological drought index (CI) is transpiration on drought. +e RDI has three modes of ex- eﬀective for meteorological drought monitoring and as- pression: the initial value RDI (α ) is presented in an sessment [52]. Both 30 day (month scale) and 90 day aggregated form using a monthly time step and calculated Advances in Meteorology 5 (Figure 2). Drought duration is de˜ned as the number of months from the ˜rst month in which the indicator goes lower than −1 to the last month with a negative value before the indicator returns to positive values. Drought intensity is de˜ned as the number of months in which the drought Severity Duration 0 indicator remains lower than −1. Drought severity is de˜ned as the sum of the monthly absolute values of the index when the index is ≤−1 over the period 1960–2013. Drought peak –1 refers to the month in the “drought event” with the lowest Peak value of the indicator [36]. –2 –3 3.3. Mann–Kendall Test. †e Mann–Kendall (M-K) non- parametric statistical test method, proposed by Mann [56] –4 and Kendall [57] and recommended by the World Meteo- rological Organization (WMO). †e M-K test does not Figure 2: De˜nition of drought characteristics for SPI, CI, and RDI require samples to follow a certain distribution nor is af- based on Run †eory. fected by a few abnormal values. It is widely used in the data of nonnormal distribution of hydrology and meteorology for each month of a hydrological year or a complete year. due to its simplicity. Here, the M-K test is applied to analyze †e second expression is normalized RDI (RDI ), and the the temporal characteristics of SPI, CI, and RDI. For a time third expression is standardized RDI (RDI ). †e initial st series, X x ,x , ... ,x , where n > 10. †e test statistic 1 2 n value α can be calculated with the following formula: Z is calculated as follows: mk ij i i1 S − 1 a ,i 1, ... ,N and j 1, ... , 12, (3)     0 12  ,S > 0    PET   j1 ij Var(S)           where P and PET are precipitation and potential   ij ij Z , 0, S 0 mk evapotranspiration (we use evaporation of the 20 cm         evaporating dish) in jth month of ith hydrological year,         respectively. N is the total number of years. Equation (3) can S + 1      ,S < 0  calculate the RDI for any period of the year. Var(S) †e normalized RDI, RDI , is calculated using the fol- n−1 n lowing equation for each year, in which it is evident that the where,S sgn x − x , parameter a is the arithmetic mean of a values calculated k i 0 0 i1 ki+1 for the N years of data: (i) n(n − 1)(2n + 5) − t t − 1 2t + 5 (i) 0 i i i i1 (4) RDI − 1. Var(S) , (6) †e standard RDI (RDI ) is similar to the standard st precipitation index (SPI) and is calculated as follows: where Var(S) is the variance of the statistic S; x and x are k i (i) the sequential data values; m is the number of tied groups; t y − y (i) k k RDI k , (5) denotes the number of data points in the ith group; n is the st yk length of the data set; and sgn (x − x ) is the sign function, k i determined as (i) (i) where y ln(a ), y is the arithmetic mean of y , and k 0 k k (i) +1,x − x > 0 σ is the standard deviation of y .†e drought classi˜-   k i   yk     cation scheme is shown in Table 2. sgn x − x 0,x − x 0 . (7) k i k i       −1,x − x < 0 k i 3.2. Drought Variables. According to McKee et al. [29] and Spinoni et al. [53], a drought event is de˜ned as being when For the statistic Z value, Z > 0 indicates that the mk mk SPI, CI, and RDI values are lower than −1 (included in this time series has a rising (increasing) trend, while time series month) to positive value (excluding this month), with at with Z < 0 has a falling (decreasing) trend. Absolute values mk least two consecutive such months used to de˜ne drought of Z ≥ 1.65, 1.96, and 2.58 are adopted, respectively, in- mk events from 1960 to 2013 in this study. Drought duration- dicating signi˜cance levels of α 0.1, 0.05, and 0.01. severity-area-intensity/frequency is widely used in drought When the M-K test is further used to test the sequence research [8, 10, 11, 54]. †e derived drought variables mutation, the test statistic is di¦erent from the above Z , by mk [54, 55] based on the Run †eory follow the de˜nitions constructing a rank sequence: SPI/CI/RDI 6 Advances in Meteorology 4. Results 4.1. Temporal Variability. Figure 3 shows the box-plot line and normal distribution curve for CI, SPI, and RDI. All three indices conform to normal distribution, and the distribution of drought indices is also very similar in the box-plot for SPI and RDI. †e normal distribution of the CI is concentrated, and the box-plot re¬ects drought ranks’ relative light. Figure 4 shows annual and seasonal SPI trends and the M-K test in –1 Guizhou Province. Annual and seasonal Z values were, re- spectively, −2.33, −1.99, −0.39, −2.30, and −0.72, and all –2 showed a decreasing trend. Annual, spring, and autumn –3 trends were signi˜cant at the 0.05 signi˜cance level. †e magnitude of the decreasing trend for the annual and autumn –4 trends is larger, at −0.020/10a and −0.023/10a, respectively. As illustrated in Figure 4(a), the annual decreasing trend is SPI CI RDI signi˜cant in 1980–1990 and 2000–2013 at the 0.05 signi˜- SPI 25%~75% cance level. UF and UB intersect in 2006 and break through CI Values within the 1.5 IQR the boundary line in 2012-2013. In spring, UF and UB in- RDI Middle line tersect in 1984 and break the boundary line in 1998–2001, Average value 2007, and 2010–2013. In autumn, UF and UB intersect in 1986 Figure 3: Box-plot line and normal distribution curve for SPI, CI, and break the boundary line in 2003–2013, indicating a sig- and RDI. ni˜cant abrupt decrease in the trend. However, summer and winter mutations are not signi˜cant. According to the drought index, annual severe droughts or extreme droughts are found in 1966, 2009, 1989, and 2013. For seasons, severe or i i−1 extreme droughts in spring are more often in 1979, 1986, S α (k 2, 3, 4, ... ,n), k ij 1988, 1991, and 2011, while in summer they are found in 1972, i1 j 1981, 2011, and 2013. For autumn, the severe or extreme droughts are found in 1969, 1978, 1992, 2002, and 2006. For 1 x > x ,   i j winter, the years with severe or extreme winter droughts are α 1 ≤ j ≤ i, ij 1978, 1985, 2009, and 2012. 0 x < x , i j Z values for annual and seasonal droughts were, re- spectively, −2.26, −0.66, −0.24, −2.69, and −1.51; all showed (8) S − ES k k a decreasing trend, with the trend for annual and autumn UF (k 1, 2, ... ,n), Var S timescales signi˜cant at the 0.05 signi˜cance level (Figure 5). †e rate at which the trend decreases for annual and autumn k(k + 1) timescales is larger, at −0.012/10a and −0.018/10a, respectively. ES , As illustrated in Figure 5(a), the decreasing trend of annual UF is signi˜cant in 1980–1990 and 2000–2013 at the 0.05 sig- k(k − 1)(2k + 5) ni˜cance level. UF and UB intersect in 2006 and break through Var S , 72 the boundary line in 2012-2013. In autumn, UF and UB in- tersect in 1992 and break through the boundary line in where UF is a standard normal distribution and a signi˜- 2005–2013, indicating a signi˜cant abrupt decrease in the cant level α is given. If there is a signi˜cant trend change, the trend. However, the trends in spring, summer, and winter are time series x is arranged in reverse order and then is cal- not signi˜cant. From the drought index, the only year with an culated according to the formula: annual severe drought is 2011. Years with severe or extreme droughts in the spring are 1987, 1988, 2010, and 2011. Years UB −UF k k with severe or extreme droughts in the summer are 1972, 2011, (9) and 2013. Years with severe or extreme droughts in the au- k n + 1 − k (k 1, 2, ... ,n), tumn are 1992 and 2009. Years with severe or extreme where UF is a positive sequence and UB is a reverse se- droughts in the winter are 1962 and 2009. k k quence. If UF exceeds 0, the sequence shows a rising trend, Figure 6 shows annual and seasonal trends in the RDI and a value of <0 indicates a falling trend. †e rising or alongside an M-K test for Guizhou Province. †e annual, falling trend is signi˜cant when these parameters exceed the spring, summer, autumn, and winter Z values were, re- critical line. If the UF and UB curves intersect and the spectively, −1.25, −1.24, −0.12, −2.34, and −0.98, and all k k intersection is between the critical straight lines, the cor- showed a decreasing trend for autumn at the 0.05 signi˜cance responding moment of intersection is de˜ned as the mo- level. †e rate at which the trend decreases on annual and ment when the mutation begins. autumn timescales is larger, at −0.013/10a and −0.022/10a, SPI/CI/RDI Advances in Meteorology 7 4 4 4 4 3 3 3 3 2 2 2 2 1 1 1 1 0 0 0 0 –1 –1 –1 –1 –2 –2 –2 –2 –3 –3 –3 –3 –4 –4 –4 –4 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 Year Year SPI 95% confidence level SPI 95% confidence level 12 3 UF UB UF UB (a) (b) 4 4 4 4 3 3 3 3 2 2 2 2 1 1 1 1 0 0 0 0 –1 –1 –1 –1 –2 –2 –2 –2 –3 –3 –3 –3 –4 –4 –4 –4 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 Year Year SPI 95% confidence level SPI 95% confidence level 3 3 UF UB UF UB (c) (d) 4 4 3 3 2 2 1 1 0 0 –1 –1 –2 –2 –3 –3 –4 –4 1960 1970 1980 1990 2000 2010 Year SPI 95% confidence level UF UB (e) Figure 4: M-K trend test of the SPI (if the UF value > 0, the sequence shows a rising trend and indicating wet; UF value < 0 shows a falling trend and indicating drought): (a) annual, (b) spring, (c) summer, (d) autumn, and (e) winter. SPI value SPI value SPI value Z value Z value SPI value SPI value Z value Z value Z value 8 Advances in Meteorology 4 4 4 4 3 3 3 3 2 2 2 2 1 1 1 1 0 0 0 0 –1 –1 –1 –1 –2 –2 –2 –2 –3 –3 –3 –3 –4 –4 –4 –4 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 Year Year CI value 95% confidence level CI value 95% confidence level UB UB UF UF (a) (b) 4 4 4 4 3 3 3 3 2 2 2 2 1 1 1 1 0 0 0 0 –1 –1 –1 –1 –2 –2 –2 –2 –3 –3 –3 –3 –4 –4 –4 –4 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 Year Year CI value 95% confidence level CI value 95% confidence level UB UF UB UF (c) (d) 4 4 3 3 2 2 1 1 0 0 –1 –1 –2 –2 –3 –3 –4 –4 1960 1970 1980 1990 2000 2010 Year CI value 95% confidence level UB UF (e) Figure 5: M-K trend test of the CI: (a) annual, (b) spring, (c) summer, (d) autumn, and (e) winter. CI value CI value CI value Z value Z value CI value CI value Z value Z value Z value Advances in Meteorology 9 4 4 4 4 3 3 3 3 2 2 2 2 1 1 1 1 0 0 0 0 –1 –1 –1 –1 –2 –2 –2 –2 –3 –3 –3 –3 –4 –4 –4 –4 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 Year Year RDI value 95% confidence level RDI value 95% confidence level UF UB UF UB (a) (b) 4 4 4 4 3 3 3 3 2 2 2 2 1 1 1 1 0 0 0 0 –1 –1 –1 –1 –2 –2 –2 –2 –3 –3 –3 –3 –4 –4 –4 –4 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 Year Year RDI value 95% confidence level RDI value 95% confidence level UF UB UF UB (c) (d) 5 5 4 4 3 3 2 2 1 1 0 0 –1 –1 –2 –2 –3 –3 –4 –4 1960 1970 1980 1990 2000 2010 Year RDI value 95% confidence level UF UB (e) Figure 6: M-K trend test of the RDI: (a) annual, (b) spring, (c) summer, (d) autumn, and (e) winter. RDI value RDI value RDI value Z value Z value RDI value RDI value Z value Z value Z value 10 Advances in Meteorology 1960 1966 Table 3: Identiﬁcation of typical drought events by SPI, CI, and RDI in 1960–2013. SPI CI RDI Event Peak Intensity Event Peak Intensity Event Peak Intensity (4-6/8-10) 1960-1/8 3 (1-4/8-12) 1960-1/10 5 (4-6/8-12) 1960-4/8 5 1963 (3-6/8-9) 1963-6/8 5 1963 (3-6/8-9) 1963-6/8 4 1963 (3-6/8-9) 1963-6/8 7 (8-9) 1966-9 3 (1-4/8-9/11-12) 1966-3/9/11 4 (8-9) 1966-9 2 1966/1967 (11-1) 1967-1 2 1969 (2-5) 1969-2 3 1966/1967 (11-1) 1967-1 2 1969 (2-4) 1969-2 1 1978 (2-4/7-9) 1978-2/7 3 1969 (2-5/9-10) 1969-2/9 3 1978 (2-4/7-9) 1978-2/7 2 1979 (1-5) 1979-3 3 1978 (2-4/7-9) 1978-2/7 3 1978/1979 (12-5) 1978-12 3 1979/1980 (11-1) 1979-11 2 1978/1979 (12-4) 1978-12 4 1985/1986 (12-6) 1986-5 4 1985/1986 (12-6) 1986-5 5 1985/1986 (10-6) 1986-5 4 1987 (3-4) 1987-3 2 1987 (3-5) 1987-3 2 1987/1988 (3-5/12-1) 1987-3/12 3 1987/1988 (12-1/3-5) 1987-12,1988-5 4 1988 (1-7/11-12) 1988-3/11 1988/1989 (3-7/11-1) 1988-5/11 4 1988/1989 (11-1/5-8) 1988-11,1989-5 2 1989 (5-12) 1989-11 2 1989 (7-8) 1989-8 1 1992 (8-12) 1992-8 3 1992/1993 (8-2) 1992-8 4 1992 (8-12) 1992-8 4 1993/1994 (3-5/10-2) 1993-2/12 2 1995 (2-5) 1995-2 2 1993/1994 (3-6/12-2) 1993-3/12 2 1998 (9-12) 1998-9 1 1998/1999 (11-3) 1999-3 3 1998 (4-5/9-12) 1998-4/9 2 2002 (9-12) 2002-9 2 2002/2003 (11-3) 2002-11 2 2002 (9-12) 2002-9 2 2003 (8-11) 2003-8 2 2003/2004 (8-1) 2003-8 3 2003 (2-3/8-12) 2003-2/8 3 2005 (9-11) 2005-9 1 2005/2006 (9-1) 2005-9 3 2005 (9-12) 2005-9 1 2009/20 (8-5) 2010-2 7 2009/20 (8-5) 2010-2 2009/20 (8-5) 2010-2 7 20 (2-5/7-8) 2011-4/7 20 (2-5/7-9) 2011-4/7 7 20 (4-5/7-9) 2011-4/7 5 2013-2014 (1-2/7-8/10-2) 2013-1/7/12 5 2012 (1-4/10-12) 2012-1/12 5 2013-2014 (1-2/6-8/10-2) 2013-1/7/12 2013/2014 (1-2/7-2) 2013-1/7 Note. +e bold values mean that they are consistent with the historical records. Advances in Meteorology 11 Drought duration Drought duration 102°E 105°E 108°E 111°E 102°E 105°E 108°E 111°E High: 141 High: 129 27°N 27°N 27°N 27°N 24°N 24°N 24°N 24°N 0 100 200 0 100 200 (km) (km) Low: 82 Low: 75 102°E 105°E 108°E 111°E 102°E 105°E 108°E 111°E Z value Z value –0.01 to 0 –0.63 to 0 0.01 to 1.95 0.01 to 1.95 1.96 to 3.41 1.96 to 2.73 (a) (b) Drought duration 102°E 105°E 108°E 111°E High: 118 27°N 27°N 24°N 24°N 0 100 200 (km) Low: 103 102°E 105°E 108°E 111°E Z value –2.50 to –1.96 –1.95 to 0 0.01 to 1.95 1.96 to 2.73 (c) Figure 7: Trend distribution of drought duration: (a) SPI, (b) CI, and (c) RDI. †e red dots indicate a signi˜cant positive trend (Z > 1.96) and the purple points indicate the insigni˜cant positive trend (0 < Z > 1.96). †e green points indicate the signi˜cant negative trend (Z < −1.95). respectively. Figure 6(d) shows that UF and UB intersect in beginning of the 21st century. †e drought peak also in- creased signi˜cantly since the beginning of the 21st century. 1995 for autumn, breaking the boundary line in 2004–2013. However, the mutation in spring, summer, and winter was Droughts classi˜ed as severe occurred in 1963, 1985-1986, not signi˜cant. From the RDI, years with annual severe 1987-1988, 1992, 2009-2010, 2011, and 2013-2014. In ad- drought or extreme drought are 1966 and 2013, and 2009 and dition, as shown in Table 3, drought events took place in all 2011, respectively. Years with severe or extreme droughts in seasons, especially in winter–spring and summer–autumn. the spring are 1986, 1987, 1988, 1991, and 2007, and 1963, †ere was a persistent drought in summer–autumn–winter– 1991, and 2011, respectively. Years with severe or extreme spring 2009-2010, a persistent drought in spring–summer 2011, droughts in the summer are 1981, and 1972, 2011, and 2013, and a persistent drought in winter–spring–summer–autumn respectively. Years with severe or extreme droughts in the 2013-2014. autumn are 1978, 1992, and 1969, and 2002 and 2011, re- spectively. Years with severe or extreme droughts in the winter are 1968 and 1978, and 2009, respectively. 4.2. Interannual Variability As shown in Table 3, 29, 30, and 32 drought events were identi˜ed from the SPI, CI, and RDI indices, respectively. 4.2.1. Spatial Distribution and Trends of Drought Duration. †e spatial distribution of drought durations and trends for †e performances of the three indices are close with small di¦erences on month scales. Identi˜cation of drought events the three indices is shown in Figure 7. Drought duration is longer in the northwest and relatively short in the southwest in 1963, 1966, 1978-1979, 1985-1986, 1987-1988, 1988-1989, 1992, 2009-2010, 2011, and 2013-2014 is consistent for all of Guizhou Province. In terms of the trend, only one station (Luodian station) shows a decreasing trend (i.e., a tendency three indices. We note that there were more droughts in the 1960s, 1980s, and 2000s, with a particular rise since the to be wet). All other stations showed an increasing trend. 12 Advances in Meteorology Drought severity Drought severity 102°E 105°E 108°E 111°E 102°E 105°E 108°E 111°E High: 200.8 High: 207 27°N 27°N 27°N 27°N 24°N 24°N 24°N 24°N 0 100 200 0 100 200 (km) (km) Low: 136.9 Low: 121.2 102°E 105°E 108°E 111°E 102°E 105°E 108°E 111°E Z value Z value 0.01 to 1.95 –0.51 to 0 1.96 to 3.50 0.01 to 1.95 1.96 to 2.66 (a) (b) Drought severity 102°E 105°E 108°E 111°E High: 176.9 27°N 27°N 24°N 24°N 0 100 200 (km) Low: 152.3 102°E 105°E 108°E 111°E Z value –1.69 to 0 0.01 to 1.96 1.97 to 2.84 (c) Figure 8: Trend distribution of drought severity: (a) SPI, (b) CI, and (c) RDI. †e red dots indicate the signi˜cant positive trend (Z > 1.96) and the purple points indicate the insigni˜cant positive trend (0 < Z > 1.96). †e green points indicate the signi˜cant negative trend (Z < −1.95). Among them, ˜ve stations (Weining, Guiyang, Xifeng, 4.2.2. Spatial Distribution and Trends of Drought Severity. Xishui, and Tongzi stations) showed a signi˜cant in- Figure 8 shows the spatial distribution and trends of drought creasing trend; these are mainly located in the northwest of severity. †e spatial distribution of drought severity is al- Guizhou Province. †e CI shows that droughts lasted for most consistent with that of drought duration. However, more and less time in western and northeast Guizhou more severity droughts are typically found in the northwest Province, respectively. In terms of changes in the trend, of Guizhou Province, where all stations show an increasing four stations (Weining, Bijie, Tongzi, and Xingren stations) trend. Stations with signi˜cant increasing trends are mainly showed signi˜cant increasing trends; three of these stations distributed in the northwest and northeast of Guizhou Province. †e drought severity determined by the CI is also are located in the west. Meanwhile, four stations (Kaili, Duyun, Dushan, and Rongjiang stations) showed non- consistent with drought duration. Drought intensity is of higher magnitude in western Guizhou Province. Among the signi˜cant decreasing trends in the southeast. †e RDI suggests that drought duration is longer in northwest and four stations with signi˜cant increasing trends (Weining, northeast regions and shorter in southern Guizhou Bijie, Panxian, and Tongzi stations), three (Weining, Bijie, Province. Nine stations located in western Guizhou and Panxian stations) are located in the west of the province, Province increased signi˜cantly. Furthermore, ten stations while drought duration showed a decreasing trend in in central and eastern Guizhou Province had a decreasing southeast Guizhou Province. †e drought intensity is also trend. Among these was the one station in southeastern consistent with drought duration based on the RDI. Severe Guizhou (Rongjiang station) with a signi˜cant decreasing droughts are more frequent in eastern Guizhou. However, trend. stations with signi˜cant increasing trends are primarily Advances in Meteorology 13 102°E 105°E 108°E 27°N 27°N Seasonal 14 9 8 6 7 7 6 6 0 100 200 (km) 24°N 105°E 108°E Spring Autumn Summer Winter Figure 9: Statistics of seasonal drought frequency based on historical records in Guizhou Province in 1960–2013. Drought predictions from the RDI are close to the historical located in western Guizhou, while stations with both in- creasing and decreasing trends are located in northeast records in spring and summer. However, this index suggests Guizhou, with stations with decreasing trends located in more droughts in autumn and winter, particularly in winter. central and eastern Guizhou. †e mild and moderate seasonal droughts identi˜ed by the SPI are more frequent than those found in historical records. However, the severe and extreme seasonal droughts 4.3. Validation of †ree Drought Indices Based on Historical are identi˜ed less frequently than the historical records, Disaster Records. Drought frequency in di¦erent seasons especially in spring and summer (Figures 10 and 12). from 1960 to 2013 in Guizhou Province is shown in Figure 9, †e CI identi˜es more frequent mild and moderate based on statistics of Chinese meteorological disasters seasonal droughts than the historical records, while it [45–59]. More drought events are shown to have happened in identi˜es fewer severe and extreme droughts than the his- spring and summer in Guizhou Province. Spring droughts are torical record (Figures 10 and 13). more frequent in the central and west of the Province, where †e RDI identi˜es more mild droughts than historical Anshun City, Bijie City, and Qianxinan City are located. records indicate. However, the moderate, severe, and ex- Summer droughts are more frequent in the central and east of treme droughts identi˜ed by the RDI are close to the his- Guizhou Province, home to Zunyi City, Tongren City, and torical records (Figures 10 and 14). Qiandongnan City. Moderate, severe, and extreme droughts †e drought frequency analysis (Figures 9 and 14) for SPI, are more frequent in spring in western Guizhou and summer CI, and RDI compared to the historical records shows that the in eastern Guizhou Province (Figure 10). Moderate and ex- mild and moderate droughts in winter are more than the treme droughts are more frequent in autumn and winter and historical records. †e historical records describe the severity mainly a¦ect eastern Guizhou Province. of the crop yield reduction. However, the drought indices do Figure 11 shows that droughts are more frequent in not take this into account. †us, the drought statistics by spring, summer, and autumn based on the SPI. However, indices are possible more frequently than the historical re- droughts are less frequent in spring and summer than the cords. Overall, the severe and extreme droughts are less historical records (Figure 9), while droughts in autumn are frequent than the historical records, especially CI. †e RDI is more frequent than the historical records. Winter droughts closer to the historical records compared to the SPI and CI. are highly consistent with historical records based on SPI. Figure 15 shows variation of the three drought indices in †e CI suggests that drought occurrence increased in winter the area historically a¦ected by droughts; among these data and spring. But the historical records show fewer droughts in are the typical drought years shown in Table 4. †e three winter. †e CI is relatively close to historical records in drought indices in the drought-a¦ected area were highest in spring, followed by autumn and summer. Autumn droughts 2011. However, the three drought indices in the a¦ected occurred more frequently than the historical records. Fewer area, particularly CI, are inconsistent with historical records droughts in summer were found in the historical records. in 2010, 1992, 1990, and 1988. Together with Figures 9–14, 14 Advances in Meteorology 102°E 105°E 108°E 102°E 105°E 108°E 2 2 6 22 1 1 5 3 7 2 1 6 9 2 3 27°N 6 27°N 27°N 4 27°N 2 2 8 6 3 2 2 2 5 2 1 11 4 3 4 4 3 1 2 7 2 1 1 8 1 2 7 4 0 100 200 0 100 200 (km) (km) 102°E 105°E 108°E 102°E 105°E 108°E Frequency statistic Frequency statistic Mild drought Severe drought Mild drought Severe drought Moderate drought Extreme drought Moderate drought Extreme drought (a) (b) 102°E 105°E 108°E 102°E 105°E 108°E 6 7 2 2 1 3 1 1 2 2 7 1 1 2 2 1 1 1 27°N 2 2 27°N 27°N 27°N 1 2 2 2 1 2 2 1 3 1 1 2 4 2 1 22 2 2 3 0 100 200 0 100 200 (km) (km) 102°E 105°E 108°E 102°E 105°E 108°E Frequency statistic Frequency statistic Mild drought Severe drought Mild drought Severe drought Moderate drought Extreme drought Moderate drought Extreme drought (c) (d) Figure 10: Statistics of seasonal drought frequency in di¦erent drought grades based on historical records in Guizhou Province in 1960–2013: (a) spring, (b) summer, (c) autumn, and (d) winter. it is therefore shown that the RDI is more objective and Dunne [8, 11, 60, 61]. However, She²eld et al. [62] dis- reliable at indicating drought than the CI and SPI (the SPI covered that a little change in global drought for the period value is shown here). †erefore, the abovementioned of 1948–2008 based on the Palmer drought severity index. analysis indicated that the relationship between the his- Further, the presented results demonstrated a signi˜cant torical records and drought index still needs to be further drought trend in autumn for the three drought indices, quanti˜ed in the future. which are consistent with Li et al. [21] and Gao et al. [22]. †ese results also show that the drought in spring and summer are dominant from the historical records, which are 5. Discussion and Conclusions increasing [43–51]. †e autumn drought also shows a sig- All three drought indices showed decreasing trends in an- ni˜cant increasing trend in Guizhou Province, which may nual and seasonal in the past 54 years. †e results are have a great impact on autumn crops. Gao et al. [22] found consistent with Zhai et al., Xu et al., Dai, and Milly and that autumn soil moisture anomaly is helpful to further Advances in Meteorology 15 102°E 105°E 108°E 102°E 105°E 108°E N N 11 11 12 13 11 11 7 9 9 8 10 5 8 8 4 5 6 12 11 5 8 27°N 11 27°N 27°N 10 11 27°N 13 12 5 13 8 12 14 10 10 12 5 13 9 13 10 11 0 100 200 0 100 200 (km) (km) 24°N 24°N 105°E 108°E 105°E 108°E Seasonal Seasonal Spring Autumn Spring Autumn Summer Winter Summer Winter (a) (b) 102°E 105°E 108°E 11 11 12 12 8 11 11 9 9 11 11 11 11 27°N 27°N 9 10 9 9 6 8 8 0 100 200 (km) 24°N 105°E 108°E Seasonal Spring Autumn Summer Winter (c) Figure 11: Statistics of seasonal drought frequency based on (a) SPI, (b) CI, and (c) RDI in Guizhou Province in 1960–2013. understand the nature of the drought in Southwest China the SPI only utilizes precipitation information, without and may provide a clue for drought monitoring and risk considering other meteorological variables that may play an management. †e SPI, CI, and RDI identi˜ed 29, 30, and 32 important role for drought. In addition, the weight co- drought events, respectively. Winter–spring and summer– e²cients are relatively arti˜cial and random, which may autumn droughts have become more frequent since the a¦ect the ability of the CI [63–65]. †us, it is possible to be the main reason for the disagreement with the distribution beginning of the 21st century. †e increase in frequency and strengthening trends of drought frequency, duration, peak, of RDI and SPI. For drought severity, the spatial distribu- tions of the three drought indices are also inconsistent. In the and intensity is signi˜cant over the period 1960–2013. †ese results are also consistent with Zhai et al., Yu et al., Xu et al., present study, the drought severity is based on annual Li et al., and Gao et al. [8, 9, 11, 21, 22]. statistics. However, the seasonal statistics show that SPI and In terms of drought duration, the spatial distribution CI account for a large proportion in spring, while RDI of the SPI is close with the RDI during 1960–2013. However, accounts for a large proportion in summer. †erefore, SPI the spatial distribution of the CI is inconsistent with those of and CI show higher drought severity in the western prov- the SPI and RDI. As Section 3.1.2 mentioned that the CI ince. †e RDI shows higher drought severity in the eastern index is composed of SPI and MI; however, some scholars which is consistent with the historical records. Moreover, Xu point out that SPI and MI have certain defects. For instance, et al. [11] also revealed that the spatial distribution of 16 Advances in Meteorology 102°E 105°E 108°E 102°E 105°E 108°E 1 1 1 1 3 3 2 18 4 5 17 19 8 16 9 8 9 19 19 1 27°N 17 2 27°N 8 1 10 27°N 18 27°N 8 13 3 2 3 15 9 2 3 8 13 5 17 16 9 9 1 1 2 4 7 16 9 0 100 200 0 100 200 (km) (km) 102°E 105°E 108°E 102°E 105°E 108°E Frequency statistic Frequency statistic Mild drought Severe drought Mild drought Severe drought Moderate drought Extreme drought Moderate drought Extreme drought (a) (b) 102°E 105°E 108°E 102°E 105°E 108°E 1 1 1 51 3 3 1 17 1 2 20 8 19 16 8 6 9 1 27°N 27°N 19 2 21 27°N 27°N 1 1 17 13 3 2 1 2 18 3 17 5 9 12 3 2 7 17 0 100 200 0 100 200 (km) (km) 102°E 105°E 108°E 102°E 105°E 108°E Frequency statistic Frequency statistic Mild drought Severe drought Mild drought Severe drought Moderate drought Extreme drought Moderate drought Extreme drought (c) (d) Figure 12: Statistics of seasonal drought frequency in di¦erent drought grades based on the SPI in Guizhou Province in 1960–2013: (a) spring, (b) summer, (c) autumn, and (d) winter. drought severity using RDI (3 months reconnaissance revealed that meteorological droughts are the water short- drought index) is almost the same as that using SPI ages caused by an imbalance precipitation and evaporation (3 months standardized precipitation index). However, the [66]. †e most of the drought indices are mainly based on distributions of SPEI (3 months standardized precipitation the precipitation and evaporation calculation. †erefore, they play a vital role in the capture of drought characteristics evapotranspiration index) are quite di¦erent with SPI and RDI as well as the trends. Based on the above analysis and [11, 62]. Evaporation is always the focus of drought research. the historical records (Table 3) of disasters in the drought- However, compared to precipitation, there are still many a¦ected area that consider seasonal drought frequency and uncertainties in evaporation measurement. †erefore, dif- magnitude, the RDI performs more objectively and reliably ferent evaporation models may not get the same results. than SPI and CI. However, the SPI, CI, and RDI all indicate Previous studies applied PDSI, SPI, RDI, and SPEI drought frequencies and durations less or more than those [11, 60–62, 67], which mainly adopted the †ornthwaite and indicated by the historical records. †is may be related to the Penman–Monteith or other regimes to calculate the refer- defects of the SPI, CI, and RDI. Previous studies have ence evapotranspiration (ETo). †erefore, di¦erent drought Advances in Meteorology 17 102°E 105°E 108°E 102°E 105°E 108°E N N 1 1 1 1 1 6 3 4 6 2 2 22 22 4 19 6 27°N 27°N 27°N 11 27°N 1 7 25 9 4 16 9 2 2 6 8 17 19 2 1 6 1 1 1 31 7 9 5 20 13 6 2 7 22 0 100 200 0 100 200 (km) (km) 102°E 105°E 108°E 102°E 105°E 108°E Frequency statistic Frequency statistic Mild drought Severe drought Mild drought Severe drought Moderate drought Extreme drought Moderate drought Extreme drought (a) (b) 102°E 105°E 108°E 102°E 105°E 108°E N N 1 1 10 1 4 5 2 2 6 22 34 20 29 22 30 5 27°N 18 27°N 27°N 27°N 2 20 8 5 51 8 20 11 22 1 1 20 30 7 21 5 3 0 100 200 0 100 200 (km) (km) 102°E 105°E 108°E 102°E 105°E 108°E Frequency statistic Frequency statistic Mild drought Severe drought Mild drought Severe drought Moderate drought Extreme drought Moderate drought Extreme drought (c) (d) Figure 13: Statistics of seasonal drought frequency in di¦erent drought grades based on the CI in Guizhou Province in 1960–2013: (a) spring, (b) summer, (c) autumn, and (d) winter. trends were obtained; for example, Dai [60] demonstrated that drought-prone areas estimated by the SPI is higher than that by the observed global aridity changes are consistent with model the RDI for the period prior to 1998, while it is the converse for predictions up to 2010, which suggest more severe and the period after 1998. Xu et al. [11] indicated that SPEI and RDI widespread droughts in the next 30–90 years caused by de- are sensitive to ETo. †e RDI based on the †ornthwaite creased precipitation or increased evaporation. Meanwhile, equation overestimates the in¬uence of air temperature. †us, Milly and Dunne [61] also found that the historical and future it overestimates the grade of drought. Besides, Vicente-Serrano tendencies are towards continental drying. However, She²eld et al. [68] pointed out that SPI, PDSI, SPDI, and SPEI are et al. [62] indicated that the previous reported increase in sensitive to precipitation and ETo. †e results may be quite global drought is overestimated, and there was little change in di¦erent with respect to di¦erent indices. drought over the period of 1948–2008. In addition, the results All three drought indices indicate that mild droughts based on di¦erent drought indices are also inconsistent. For occurred more frequently than what is shown in the his- example, Zarch et al. [67] showed that the percentage of torical records, across di¦erent seasons and levels of 18 Advances in Meteorology 102°E 105°E 108°E 102°E 105°E 108°E N N 4 2 5 5 2 2 6 5 1 2 7 12 29 5 12 25 24 26 9 8 26 11 28 2 2 7 5 27°N 27°N 27°N 27°N 61 51 25 26 11 11 24 22 11 9 42 42 10 12 26 28 2 2 5 4 52 6 3 6 25 8 25 27 9 27 2 2 5 3 11 25 29 0 100 200 0 100 200 (km) (km) 102°E 105°E 108°E 102°E 105°E 108°E Frequency statistic Frequency statistic Mild drought Severe drought Mild drought Severe drought Moderate drought Extreme drought Moderate drought Extreme drought (a) (b) 102°E 105°E 108°E 102°E 105°E 108°E N N 1 31 2 3 1 2 5 11 29 6 30 11 9 26 25 25 3 32 27°N 27°N 27°N 27°N 11 4 25 6 29 12 9 24 30 2 41 9 26 30 52 52 6 6 3 2 25 5 25 7 26 26 9 10 3 2 4 5 10 27 27 0 100 200 0 100 200 (km) (km) 102°E 105°E 108°E 102°E 105°E 108°E Frequency statistic Frequency statistic Mild drought Severe drought Mild drought Severe drought Moderate drought Extreme drought Moderate drought Extreme drought (c) (d) Figure 14: Statistics of seasonal drought frequency in di¦erent drought grades based on the RDI in Guizhou Province in 1960–2013: (a) spring, (b) summer, (c) autumn, and (d) winter. drought. †is may be related to di¦erent statistical analysis counties in the entire province. †us, a higher density of methods. In this paper, any interval when the indices are weather stations may overcome the index-historical data mismatch. between −1 and 0 is classi˜ed as an occurrence of mild drought. However, it is necessary for a drought to cause Previous studies [69–73] have stated that the occurrence agricultural and socioeconomic damage in order for it to be of droughts in the southwestern region of Guizhou Province noted in historical records. We also point out that the is close to related atmospheric circulation anomalies and drought-a¦ected area was highest in 2011, consistent with special topography [69–75]. In addition, the signi˜cant RDI and CI, but not with SPI. †e density of meteorological decrease in precipitation [11, 21] is an important factor for stations may also play a role. In this study, only data from 19 drought. Meanwhile, the change of potential evaporation is stations are considered. However, the records of drought- also a critical factor [20]. Chen et al. [41] pointed out that the a¦ected areas are based on statistics covering over 88 number of continuous wet days (CWD) was decreasing Advances in Meteorology 19 200 200 1.0 160 160 1 0.5 120 120 0.0 –0.5 80 80 –1 –1.0 40 40 –2 –1.5 –3 0 0 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 Year Year SPI CI Affected area Affected area (a) (b) 3 200 –1 –2 –3 0 1960 1970 1980 1990 2000 2010 Year RDI Affected area (c) Figure 15: Comparison of drought-a¦ected areas based on (a) SPI, (b) CI, and (c) RDI. Table 4: Comparison of historical a¦ected area in typical drought years based on the SPI, CI, and RDI. 4 2 A¦ected area (×10 hm ) 1988 1989 1990 1992 2010 2011 2013 Historical 114.07 104.6 128.93 150.93 163.9 182.25 111.78 SPI historical −0.7 −2.1 −0.5 −0.7 −0.5 −2.6 −1.7 CI historical −0.5 −1.0 −0.5 −0.1 −1.1 −1.5 −0.7 RDI historical −0.7 −1.1 −0.5 −0.7 −0.1 −2.4 −1.9 signi˜cantly while the largest 5 days of rainfall (RX5 day), Guizhou Province. †e rainy season in western Guizhou strong precipitation (R95), and strongest rainy day starts in June; when these rains are late, a spring drought is (R20mm) measures did not have signi˜cant decreasing triggered. Zunyi, Tongren, and Qiannan Cities in eastern Guizhou Province are prone to summer droughts. †is may trends in response to the decreasing trend of the three in- dices (when considering Guizhou Province). In terms of be the result of the rainy season starting early (April) in the drought distribution, all three drought indices indicated area. A precipitation decrease will likely cause a summer more frequent spring droughts in western Guizhou, and drought. Moreover, Milly and Dunne [61] and She²eld et al. more frequent summer droughts in eastern Guizhou. Shen [62] stated that other factors such as runo¦, relative hu- et al. 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Publisher: Hindawi Publishing Corporation
Copyright: Copyright © 2018 Qingping Cheng 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
eISSN: 1687-9317
DOI: 10.1155/2018/4721269
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Abstract

Hindawi Advances in Meteorology Volume 2018, Article ID 4721269, 22 pages https://doi.org/10.1155/2018/4721269 Research Article Temporal-Spatial Characteristics of Drought in Guizhou Province, China, Based on Multiple Drought Indices and Historical Disaster Records 1,2,3 1,4,5,6 1,4,5,6 1,4,5,6 1,4,5,6 Qingping Cheng, Lu Gao , Ying Chen, Meibing Liu, Haijun Deng, 1,4,5,6 and Xingwei Chen College of Geographical Science, Fujian Normal University, Fuzhou 350007, China Northwest Institute of Eco-Environmental and Resources Research, Chinese Academy of Sciences, Lanzhou 730000, China University of Chinese Academy of Sciences, Beijing 100049, China Institute of Geography, Fujian Normal University, Fuzhou 350007, China Fujian Provincial Engineering Research Center for Monitoring and Assessing Terrestrial Disasters, Fujian Normal University, Fuzhou 350007, China State Key Laboratory of Subtropical Mountain Ecology (Funded by Ministry of Science and Technology and Fujian Province), Fujian Normal University, Fuzhou 350007, China Correspondence should be addressed to Lu Gao; l.gao@foxmail.com Received 11 December 2017; Revised 15 April 2018; Accepted 30 April 2018; Published 14 June 2018 Academic Editor: Stefano Dietrich Copyright © 2018 Qingping Cheng 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. Guizhou Province, China, experienced several severe drought events over the period from 1960 to 2013, causing great economic loss and intractable conﬂicts over water. In this study, the spatial and temporal characteristics of droughts are analyzed with the standard precipitation index (SPI), comprehensive meteorological drought index (CI), and reconnaissance drought index (RDI). Meanwhile, historical drought records are used to test the performance of each index at identifying droughts. All three indices show decreasing annual and autumn trends, with the latter particularly prominent. 29, 30, and 32 drought events were identiﬁed during 1960–2013 by the SPI, CI, and RDI, respectively. Continuous drought is more frequent in winter–spring and summer–autumn. +ere is a signiﬁcant increasing trend in drought event frequency, peak, and strength since the start of the 21st century. Drought duration indicated by CI shows longer durations in the higher-elevation region of central and western Guizhou. +e corresponding drought severity is high in these regions. SPI and RDI indicate longer drought durations in the lower elevation central and eastern regions of Guizhou Province, where the corresponding drought severity is also very strong. SPI shows an increasing trend in drought duration and drought severity across most of the regions of Guizhou. In general, SPI and RDI show an increasing trend in the western Guizhou Province and a decreasing trend in central and eastern Guizhou. Comparing these three drought indices with historical records, the RDI is found to be more objective and reliable than the SPI and CI when identifying the periods of drought in Guizhou. drought, and socioeconomic drought [1]. Meteorological 1. Introduction drought refers to water shortages caused by an imbalance in Drought, a water shortage phenomenon caused by natural precipitation and evaporation. Drought disasters are a product of the coupling of the natural environmental and precipitation anomalies, is one of the most serious natural disasters, causing economic losses globally. +e American socioeconomic systems under speciﬁc time and space con- Meteorological Society classiﬁed droughts into four types: ditions [2]. Among diﬀerent types of natural disasters, drought meteorological drought, agricultural drought, hydrological disasters are among those with the highest frequencies, widest 2 Advances in Meteorology ranges of inﬂuence, longest durations, and greatest losses; every year, including a severe drought covering a large area drought disasters lead not only to food production reduction, every 5–10 years [15]. From 2009 to 2012, the ﬁve provinces of Southwest China (Yunnan Province, Sichuan Province, water shortages, and deterioration of ecosystems and the environment, but also to death and the change of dynasties, Chongqing City, Guizhou Province, and Guangxi Province) given that they are an important factor in restricting suﬀered a severe drought [16, 17]. +is severe drought, which 6 2 sustainable social development [3]. +e factors inﬂuencing aﬀected ∼8.0 ×10 hm of arable land, led not only to a large drought disasters are complex since there are great un- reduction in crop production but also caused drinking water certainties relating to the occurrence and development of shortages for 25 million people and 18 million livestock; drought disasters in both time and space. meanwhile, the drought caused total direct economic losses of Drought is one of the most frequent and widespread more than 40 billion Chinese Yuan [16, 17]. +is drought was natural disasters in China, where the total average area of land the worst in Southwest China since meteorological obser- 7 2 periodically inﬂuenced by droughts is 2.1 × 10 hm (annual vations began [18]. +e increasing frequency of severe 6 2 average value from 1950 to 2013), of which 9.4 ×10 hm droughts in the Southwest demonstrates that droughts are (annual average value from 1950 to 2013) suﬀers drought spreading from northern to southwestern China [19]. disasters in any particular year. Meteorological droughts, as Sun et al. [20] assessed the contributions of decadal described above, can develop into agricultural droughts [4, 5]. potential evapotranspiration (PET) anomalies to drought 6 2 In China, droughts cause an annual average of 2.5 × 10 hm of duration and intensity which could exceed those of pre- no-harvest area (annual average value from 1989 to 2013) and cipitation in Southwest China. Li et al. [21] identiﬁed 87 1.62 ×10 kg of grain loss (annual average value from 1950 to drought events including 9 extreme events using the daily 2013); droughts also cause 2.7 ×10 people (annual average composite drought index (CI) at 101 stations in Southwest value from 1991 to 2013) and 2.0 × 10 livestock (annual av- China. +e droughts are more frequent from November to erage value from 1989 to 2013) to have diﬃculty ﬁnding next April, and the frequency and intensity of drought in- suﬃcient drinking water; together, these factors contribute to creased with a signiﬁcant decrease in precipitation and an annual average direct economic loss of 1.0 × 10 Chinese increase in temperature. Gao et al. [22] found that the Yuan (annual average value from 1950 to 2013, http://www. signiﬁcant soil drying trend happened in autumn, which can mwr.gov.cn/sj/tjgb/zgshzhgb/201612/t20161222_776092.html) be sustained to the next spring. Han et al. [23] showed that the eastern part of southwestern China had an extremely [6]. +e abovementioned information indicates that China as a major agricultural country suﬀered severe meteorological high drought risk, which was greater in the north than south. droughts which caused great economic losses [7]. Drought is Recently, several extreme drought disasters have hit Guiz- the hot spot of research for a long time. Zhai et al. [8] found hou Province, such as that from September 2009 to March that a signiﬁcant dryness trend changes from the southwest to 2010, which caused drinking water shortages for 4.85 million 5 2 the northeast of China. In the early twenty-ﬁrst century, the people, with 7.01 × 10 hm of crops suﬀering from drought, most severe droughts were located in the Southwest of China and direct economic losses of 2.3 billion Chinese Yuan. A covering areas around 0.7 million km . Yu et al. [9] found that subsequent extreme summer drought in 2011 caused the severe and extreme droughts become more serious since drinking water shortages for 5.5 million people and 2.8 6 2 late 1990s for the entire China via examining drought char- million livestock, with 1.763 ×10 hm of crops aﬀected, acteristics such as long-term trend and intensity duration. resulting in an economic loss of 15.76 billion Chinese Yuan. Meanwhile, the drought-prone regions in Northeast China, Only two years later, the extreme summer drought of 2013 Southwest China, south China coastal region, and Northwest caused drinking water shortages for 2.645 million people 6 2 China were investigated by He et al. [7] and Ayantobo et al. and 1.12 million livestock, with 1.763 ×10 hm of crops [10]. Xu et al. [11] indicated that the three drought indices (SPI, aﬀected, causing an economic loss of ∼9.64 billion Chinese RDI, and SPEI) have almost the same performances in the Yuan [24–26]. humid regions. However, SPI and RDI were more appropriate Droughts are typically measured and quantiﬁed using than SPEI in the arid regions. +e Loess Plateau, Sichuan Basin, drought indices; a variety of indices for diﬀerent appli- and Yunnan-Guizhou Plateau have signiﬁcant dry trends, cations have been developed [27, 28]. Based on World which is mainly caused by the signiﬁcant decrease of pre- Meteorological Organization (WMO) statistics, there are cipitation. Liu et al. [12] found that the return periods of 55 commonly used categories of drought indices. Among meteorological drought are longer, with an average of 42.1 these, the comprehensive meteorological drought index years in China. Liu et al. [13] investigated the return period of (CI), standardized precipitation index (SPI), standardized concurrent drought events is 11 years in the water source area precipitation evapotranspiration index (SPEI), and re- and the destination regions of water diversion project. +e connaissance drought index (RDI) are widely used in various probability of concurrent drought events may signiﬁcantly regions [21, 29–36]. At present, case studies of droughts in increase during 2020 to 2050. Shen et al. [14] revealed that the Guizhou Province are rare, with most such studies based on drought probability and intensity are rising and the aﬀected a single drought index [37, 38]. Furthermore, no study has areas of all degrees of drought have an increasing trend during validated these drought indices using historical disaster re- the last 50 years based on the SPEI in Song-Liao River Basin. cords, despite validation of the reliability of these indices +e Southwest is one of the regions of China most being of great importance. +is study aims at building a link frequently aﬀected by drought disasters, with droughts of between drought indices and real drought events in Guizhou diﬀerent degrees of severity occurring in this region almost Province, China. Advances in Meteorology 3 Elevation (m) 105°E 108°E 111°E High: 2794 30°N 30°N 27°N 27°N 0 100 200 (km) 24°N 24°N Low: 229 105°E 108°E 111°E Station River Figure 1: Location of meteorological stations in Guizhou Province. study. It should be noted that some evaporation data (since 2. Study Region and Data Resources 2002) were recorded with E601B equipment. †e E-601B-type ° ° 2.1. Study Region. Guizhou Province (103 36′–109 35′E; evaporator was installed for meteorological stations in ° ° 24 37′–29 13′N), with an area of 176167 km , is located in the China from 1985. †e E-601B-type evaporation evaporator eastern Yunnan-Guizhou Plateau of China (Figure 1). †e is recommended by the World Meteorological Organization elevation of Guizhou Province ranges from 229 to 2794 m, (WMO). †is instrument has the advantages of corrosion- higher in the west than that in the east of province [39]. †e resistant and stable thermal e¦ect, which made the mea- topography is dominated by plateau and mountains: car- surements more close to nature [42]. In order to ensure the bonate rocks in the karst area are widespread and account for continuity, uniformity, and reliability of records, a linear 62% of the total area of Guizhou Province. Guizhou has regression is therefore applied to calibrate evaporation data a humid subtropical monsoon climate with an annual mean collected by E601B (2002–2014) to 20 cm evaporating dish temperature of 15 C and mean annual precipitation of data (1998–2001), according to previous studies [43, 44]. †e 1400 mm. Over 70% of the annual rainfall occurs from May to elevation data (DEM) are from the Shuttle Radar Topography September [39–41]. In general, the ecology and environment Mission (SRTM) with a resolution of 90 m, derived from the of Guizhou Province is extremely fragile, which causes fre- Geospatial Data Cloud of China (http://www.gscloud.cn/). quent land-surface droughts, as illustrated by an old saying †e historical disaster records are derived from China Me- describing drought in Guizhou Province: “a drought every teorological Disaster Yearbook (Guizhou volume) [45–51]. year, a mild drought every three years, a moderate drought †e information such as drought duration, severity, and peaks every ˜ve years, a severe drought every decade.” was extracted from the yearbooks according to the disaster statistics which were originally recorded by the local me- teorological department. Seasons are classi˜ed based on 2.2. Data Resources. †e meteorological data for daily pre- meteorological divisions: spring (March–May), summer cipitation and pan evaporation (from January 1, 1959, to (June–August), autumn (September–November), and winter February 28, 2014) data set are used in this paper from the (December–February), respectively. †e distribution of me- China Meteorological Data Sharing Service Network (http:// teorological stations, together with related information, is data.cma.cn/) V3.0 version. Rigorous quality control had been shown in Figure 1 and Table 1. conducted by China Meteorological Data Sharing Service Network before the data were released. †e software used to 3. Methods detect and adjust shifts in the time series of daily precipitation and pan evaporation is RHtestsV3 and RHtests-dlyPrcp (http:// 3.1. Drought Indices (SPI, CI, and RDI) etccdi.paci˜cclimate.org/software.shtml), respectively. Fi- nally, 19 out of 32 national basic meteorological stations (no 3.1.1. Standard Precipitation Index (SPI). †e SPI was de- gaps exceeding two consecutive weeks) are selected for this veloped by McKee et al. [29]. Within a certain geographic 4 Advances in Meteorology Table 1: Information of meteorological stations and average precipitation and 20 cm pan evaporation in 1960–2013. ° ° Station Longitude ( E) Latitude ( N) Elevation (m) Pan precipitation (mm) Evaporation (mm) P − C E − C V V Anshun 105.9 26.25 1431.1 1310.1 1250.5 0.18 0.09 Bijie 105.27 27.3 1510.6 883.2 986.1 0.15 0.10 Duyun 107.52 26.32 969.1 1419.9 1239.1 0.17 0.08 Dushan 107.55 25.83 1013.3 1307.5 1201.4 0.14 0.09 Guiyang 106.73 26.58 1223.8 1099.6 1377.5 0.16 0.10 Kaili 107.97 26.6 720.3 1142.1 1347.8 0.18 0.09 Luodian 106.77 25.43 440.3 1142.1 1246.5 0.18 0.07 Meitan 107.47 27.77 792.2 1116.5 1052.6 0.15 0.10 Panxian 104.47 25.72 1800 1355 1599.1 0.18 0.12 Rongjiang 108.53 25.97 285.7 1181.6 1174.1 0.17 0.11 Sanhui 108.67 26.97 626.9 1102 1129.7 0.15 0.11 Sinan 108.25 27.95 416.8 1120.1 1148.4 0.19 0.10 Tongzi 106.83 28.13 972 1259.8 1198 0.14 0.09 Tongren 109.17 27.72 353.2 1008.4 1091.5 0.13 0.15 Wangmo 106.08 25.18 566.8 1222.2 1453.8 0.16 0.09 Weining 104.28 26.87 2237.5 889.7 1350.2 0.19 0.08 Xifeng 106.72 27.1 1112.1 1108.4 1282.5 0.15 0.12 Xishui 106.22 28.33 1180.2 1094.3 1002.3 0.14 0.10 Xingren 105.18 25.43 1378.5 1317.3 1517.1 0.15 0.09 Note. P − C , coeﬃcient variation of precipitation; E − C , coeﬃcient variation of pan evaporation. V V (seasonal scale) standardized precipitation indices, com- Table 2: Classiﬁcation of SPI, CI, and RDI. bined with a 30 day relative humidity index, can be used to SPI/CI/RDI value Drought grades calculate a comprehensive meteorological drought index. Value ≤ −2.0 Extreme drought Since the CI can indicate precipitation climate anomalies on −2.0 < value ≤ −1.5 Severe drought both short (months) and long timescales (seasons) [52], this −1.5 < value ≤ −1.0 Moderate drought index is therefore suitable for meteorological drought −1.0 < value ≤ 0 Mild drought monitoring and historical drought assessment. +e ﬁrst step of the calculation is as follows: area, the precipitation usually ﬂuctuates regularly. If the 􏼐P − PET 􏼑 ij ij precipitation is less than the average annual precipitation, MI � , (1) PET a drought may therefore occur in this area. On the contrary, ij precipitation exceeding the annual average may induce where MI is the relative moisture index in the recent 30 days, ﬂooding. +e SPI has many advantages such as being di- P refers to the total amount of precipitation in the recent 30 ij mensionless and standardized, working on multiple scales, days (unit: mm), and PET is the total potential evapo- and being easy to calculate. To calculate the SPI, a frequency ij transpiration in the recent 30 days (mm; here we use distribution function is ﬁrst constructed from a series of evaporation of a 20 cm evaporating dish). +e CI is then long-term precipitation observations. A gamma probability calculated as follows: density function is then ﬁtted to the series, and the cu- mulative probability of an observed precipitation is com- CI � aSPI + bSPI + cM , (2) 30 90 30 puted. +e inverse normal (Gaussian) function, with a mean where SPI and SPI are the standardized precipitation of 0 and a variance of 1, is then applied to transform the 30 90 indexes for 30 d and 90 d periods, respectively. M refers to cumulative distribution to the standard normal distribution. the MI of 30 days. a, b, and c are set as 0.4, 0.4, and 0.8. In Because the SPI is based on the cumulative probability of theory, the weight coeﬃcients a, b, and c are from the average a given timescale, here the total amount of precipitation in values above light drought levels of SPI , SPI , and MI the current month and previous i months (i � 1, 2, 3, . . .) is 30 90 30 divided by the smallest history of SPI , SPI , and MI , used to calculate the SPI on a timescale of i + 1 month. Here, 30 90 30 respectively (GBT 20481-2006 meteorological drought level) SPI (1–12 monthly cumulative precipitation) represents [52]. +e drought classiﬁcation scheme is displayed in Table 2. annual timescales, and SPI (3 monthly cumulative pre- cipitation) represents seasonal timescales. Drought classi- ﬁcation is shown in Table 2. 3.1.3. Reconnaissance Drought Index (RDI). +e drought detection index was proposed by Tsakiris et al. [31, 32] and 3.1.2. Comprehensive Meteorological Drought Index (CI). takes into account the eﬀects of precipitation and evapo- +e comprehensive meteorological drought index (CI) is transpiration on drought. +e RDI has three modes of ex- eﬀective for meteorological drought monitoring and as- pression: the initial value RDI (α ) is presented in an sessment [52]. Both 30 day (month scale) and 90 day aggregated form using a monthly time step and calculated Advances in Meteorology 5 (Figure 2). Drought duration is de˜ned as the number of months from the ˜rst month in which the indicator goes lower than −1 to the last month with a negative value before the indicator returns to positive values. Drought intensity is de˜ned as the number of months in which the drought Severity Duration 0 indicator remains lower than −1. Drought severity is de˜ned as the sum of the monthly absolute values of the index when the index is ≤−1 over the period 1960–2013. Drought peak –1 refers to the month in the “drought event” with the lowest Peak value of the indicator [36]. –2 –3 3.3. Mann–Kendall Test. †e Mann–Kendall (M-K) non- parametric statistical test method, proposed by Mann [56] –4 and Kendall [57] and recommended by the World Meteo- rological Organization (WMO). †e M-K test does not Figure 2: De˜nition of drought characteristics for SPI, CI, and RDI require samples to follow a certain distribution nor is af- based on Run †eory. fected by a few abnormal values. It is widely used in the data of nonnormal distribution of hydrology and meteorology for each month of a hydrological year or a complete year. due to its simplicity. Here, the M-K test is applied to analyze †e second expression is normalized RDI (RDI ), and the the temporal characteristics of SPI, CI, and RDI. For a time third expression is standardized RDI (RDI ). †e initial st series, X x ,x , ... ,x , where n > 10. †e test statistic 1 2 n value α can be calculated with the following formula: Z is calculated as follows: mk ij i i1 S − 1 a ,i 1, ... ,N and j 1, ... , 12, (3)     0 12  ,S > 0    PET   j1 ij Var(S)           where P and PET are precipitation and potential   ij ij Z , 0, S 0 mk evapotranspiration (we use evaporation of the 20 cm         evaporating dish) in jth month of ith hydrological year,         respectively. N is the total number of years. Equation (3) can S + 1      ,S < 0  calculate the RDI for any period of the year. Var(S) †e normalized RDI, RDI , is calculated using the fol- n−1 n lowing equation for each year, in which it is evident that the where,S sgn x − x , parameter a is the arithmetic mean of a values calculated k i 0 0 i1 ki+1 for the N years of data: (i) n(n − 1)(2n + 5) − t t − 1 2t + 5 (i) 0 i i i i1 (4) RDI − 1. Var(S) , (6) †e standard RDI (RDI ) is similar to the standard st precipitation index (SPI) and is calculated as follows: where Var(S) is the variance of the statistic S; x and x are k i (i) the sequential data values; m is the number of tied groups; t y − y (i) k k RDI k , (5) denotes the number of data points in the ith group; n is the st yk length of the data set; and sgn (x − x ) is the sign function, k i determined as (i) (i) where y ln(a ), y is the arithmetic mean of y , and k 0 k k (i) +1,x − x > 0 σ is the standard deviation of y .†e drought classi˜-   k i   yk     cation scheme is shown in Table 2. sgn x − x 0,x − x 0 . (7) k i k i       −1,x − x < 0 k i 3.2. Drought Variables. According to McKee et al. [29] and Spinoni et al. [53], a drought event is de˜ned as being when For the statistic Z value, Z > 0 indicates that the mk mk SPI, CI, and RDI values are lower than −1 (included in this time series has a rising (increasing) trend, while time series month) to positive value (excluding this month), with at with Z < 0 has a falling (decreasing) trend. Absolute values mk least two consecutive such months used to de˜ne drought of Z ≥ 1.65, 1.96, and 2.58 are adopted, respectively, in- mk events from 1960 to 2013 in this study. Drought duration- dicating signi˜cance levels of α 0.1, 0.05, and 0.01. severity-area-intensity/frequency is widely used in drought When the M-K test is further used to test the sequence research [8, 10, 11, 54]. †e derived drought variables mutation, the test statistic is di¦erent from the above Z , by mk [54, 55] based on the Run †eory follow the de˜nitions constructing a rank sequence: SPI/CI/RDI 6 Advances in Meteorology 4. Results 4.1. Temporal Variability. Figure 3 shows the box-plot line and normal distribution curve for CI, SPI, and RDI. All three indices conform to normal distribution, and the distribution of drought indices is also very similar in the box-plot for SPI and RDI. †e normal distribution of the CI is concentrated, and the box-plot re¬ects drought ranks’ relative light. Figure 4 shows annual and seasonal SPI trends and the M-K test in –1 Guizhou Province. Annual and seasonal Z values were, re- spectively, −2.33, −1.99, −0.39, −2.30, and −0.72, and all –2 showed a decreasing trend. Annual, spring, and autumn –3 trends were signi˜cant at the 0.05 signi˜cance level. †e magnitude of the decreasing trend for the annual and autumn –4 trends is larger, at −0.020/10a and −0.023/10a, respectively. As illustrated in Figure 4(a), the annual decreasing trend is SPI CI RDI signi˜cant in 1980–1990 and 2000–2013 at the 0.05 signi˜- SPI 25%~75% cance level. UF and UB intersect in 2006 and break through CI Values within the 1.5 IQR the boundary line in 2012-2013. In spring, UF and UB in- RDI Middle line tersect in 1984 and break the boundary line in 1998–2001, Average value 2007, and 2010–2013. In autumn, UF and UB intersect in 1986 Figure 3: Box-plot line and normal distribution curve for SPI, CI, and break the boundary line in 2003–2013, indicating a sig- and RDI. ni˜cant abrupt decrease in the trend. However, summer and winter mutations are not signi˜cant. According to the drought index, annual severe droughts or extreme droughts are found in 1966, 2009, 1989, and 2013. For seasons, severe or i i−1 extreme droughts in spring are more often in 1979, 1986, S α (k 2, 3, 4, ... ,n), k ij 1988, 1991, and 2011, while in summer they are found in 1972, i1 j 1981, 2011, and 2013. For autumn, the severe or extreme droughts are found in 1969, 1978, 1992, 2002, and 2006. For 1 x > x ,   i j winter, the years with severe or extreme winter droughts are α 1 ≤ j ≤ i, ij 1978, 1985, 2009, and 2012. 0 x < x , i j Z values for annual and seasonal droughts were, re- spectively, −2.26, −0.66, −0.24, −2.69, and −1.51; all showed (8) S − ES k k a decreasing trend, with the trend for annual and autumn UF (k 1, 2, ... ,n), Var S timescales signi˜cant at the 0.05 signi˜cance level (Figure 5). †e rate at which the trend decreases for annual and autumn k(k + 1) timescales is larger, at −0.012/10a and −0.018/10a, respectively. ES , As illustrated in Figure 5(a), the decreasing trend of annual UF is signi˜cant in 1980–1990 and 2000–2013 at the 0.05 sig- k(k − 1)(2k + 5) ni˜cance level. UF and UB intersect in 2006 and break through Var S , 72 the boundary line in 2012-2013. In autumn, UF and UB in- tersect in 1992 and break through the boundary line in where UF is a standard normal distribution and a signi˜- 2005–2013, indicating a signi˜cant abrupt decrease in the cant level α is given. If there is a signi˜cant trend change, the trend. However, the trends in spring, summer, and winter are time series x is arranged in reverse order and then is cal- not signi˜cant. From the drought index, the only year with an culated according to the formula: annual severe drought is 2011. Years with severe or extreme droughts in the spring are 1987, 1988, 2010, and 2011. Years UB −UF k k with severe or extreme droughts in the summer are 1972, 2011, (9) and 2013. Years with severe or extreme droughts in the au- k n + 1 − k (k 1, 2, ... ,n), tumn are 1992 and 2009. Years with severe or extreme where UF is a positive sequence and UB is a reverse se- droughts in the winter are 1962 and 2009. k k quence. If UF exceeds 0, the sequence shows a rising trend, Figure 6 shows annual and seasonal trends in the RDI and a value of <0 indicates a falling trend. †e rising or alongside an M-K test for Guizhou Province. †e annual, falling trend is signi˜cant when these parameters exceed the spring, summer, autumn, and winter Z values were, re- critical line. If the UF and UB curves intersect and the spectively, −1.25, −1.24, −0.12, −2.34, and −0.98, and all k k intersection is between the critical straight lines, the cor- showed a decreasing trend for autumn at the 0.05 signi˜cance responding moment of intersection is de˜ned as the mo- level. †e rate at which the trend decreases on annual and ment when the mutation begins. autumn timescales is larger, at −0.013/10a and −0.022/10a, SPI/CI/RDI Advances in Meteorology 7 4 4 4 4 3 3 3 3 2 2 2 2 1 1 1 1 0 0 0 0 –1 –1 –1 –1 –2 –2 –2 –2 –3 –3 –3 –3 –4 –4 –4 –4 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 Year Year SPI 95% confidence level SPI 95% confidence level 12 3 UF UB UF UB (a) (b) 4 4 4 4 3 3 3 3 2 2 2 2 1 1 1 1 0 0 0 0 –1 –1 –1 –1 –2 –2 –2 –2 –3 –3 –3 –3 –4 –4 –4 –4 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 Year Year SPI 95% confidence level SPI 95% confidence level 3 3 UF UB UF UB (c) (d) 4 4 3 3 2 2 1 1 0 0 –1 –1 –2 –2 –3 –3 –4 –4 1960 1970 1980 1990 2000 2010 Year SPI 95% confidence level UF UB (e) Figure 4: M-K trend test of the SPI (if the UF value > 0, the sequence shows a rising trend and indicating wet; UF value < 0 shows a falling trend and indicating drought): (a) annual, (b) spring, (c) summer, (d) autumn, and (e) winter. SPI value SPI value SPI value Z value Z value SPI value SPI value Z value Z value Z value 8 Advances in Meteorology 4 4 4 4 3 3 3 3 2 2 2 2 1 1 1 1 0 0 0 0 –1 –1 –1 –1 –2 –2 –2 –2 –3 –3 –3 –3 –4 –4 –4 –4 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 Year Year CI value 95% confidence level CI value 95% confidence level UB UB UF UF (a) (b) 4 4 4 4 3 3 3 3 2 2 2 2 1 1 1 1 0 0 0 0 –1 –1 –1 –1 –2 –2 –2 –2 –3 –3 –3 –3 –4 –4 –4 –4 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 Year Year CI value 95% confidence level CI value 95% confidence level UB UF UB UF (c) (d) 4 4 3 3 2 2 1 1 0 0 –1 –1 –2 –2 –3 –3 –4 –4 1960 1970 1980 1990 2000 2010 Year CI value 95% confidence level UB UF (e) Figure 5: M-K trend test of the CI: (a) annual, (b) spring, (c) summer, (d) autumn, and (e) winter. CI value CI value CI value Z value Z value CI value CI value Z value Z value Z value Advances in Meteorology 9 4 4 4 4 3 3 3 3 2 2 2 2 1 1 1 1 0 0 0 0 –1 –1 –1 –1 –2 –2 –2 –2 –3 –3 –3 –3 –4 –4 –4 –4 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 Year Year RDI value 95% confidence level RDI value 95% confidence level UF UB UF UB (a) (b) 4 4 4 4 3 3 3 3 2 2 2 2 1 1 1 1 0 0 0 0 –1 –1 –1 –1 –2 –2 –2 –2 –3 –3 –3 –3 –4 –4 –4 –4 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 Year Year RDI value 95% confidence level RDI value 95% confidence level UF UB UF UB (c) (d) 5 5 4 4 3 3 2 2 1 1 0 0 –1 –1 –2 –2 –3 –3 –4 –4 1960 1970 1980 1990 2000 2010 Year RDI value 95% confidence level UF UB (e) Figure 6: M-K trend test of the RDI: (a) annual, (b) spring, (c) summer, (d) autumn, and (e) winter. RDI value RDI value RDI value Z value Z value RDI value RDI value Z value Z value Z value 10 Advances in Meteorology 1960 1966 Table 3: Identiﬁcation of typical drought events by SPI, CI, and RDI in 1960–2013. SPI CI RDI Event Peak Intensity Event Peak Intensity Event Peak Intensity (4-6/8-10) 1960-1/8 3 (1-4/8-12) 1960-1/10 5 (4-6/8-12) 1960-4/8 5 1963 (3-6/8-9) 1963-6/8 5 1963 (3-6/8-9) 1963-6/8 4 1963 (3-6/8-9) 1963-6/8 7 (8-9) 1966-9 3 (1-4/8-9/11-12) 1966-3/9/11 4 (8-9) 1966-9 2 1966/1967 (11-1) 1967-1 2 1969 (2-5) 1969-2 3 1966/1967 (11-1) 1967-1 2 1969 (2-4) 1969-2 1 1978 (2-4/7-9) 1978-2/7 3 1969 (2-5/9-10) 1969-2/9 3 1978 (2-4/7-9) 1978-2/7 2 1979 (1-5) 1979-3 3 1978 (2-4/7-9) 1978-2/7 3 1978/1979 (12-5) 1978-12 3 1979/1980 (11-1) 1979-11 2 1978/1979 (12-4) 1978-12 4 1985/1986 (12-6) 1986-5 4 1985/1986 (12-6) 1986-5 5 1985/1986 (10-6) 1986-5 4 1987 (3-4) 1987-3 2 1987 (3-5) 1987-3 2 1987/1988 (3-5/12-1) 1987-3/12 3 1987/1988 (12-1/3-5) 1987-12,1988-5 4 1988 (1-7/11-12) 1988-3/11 1988/1989 (3-7/11-1) 1988-5/11 4 1988/1989 (11-1/5-8) 1988-11,1989-5 2 1989 (5-12) 1989-11 2 1989 (7-8) 1989-8 1 1992 (8-12) 1992-8 3 1992/1993 (8-2) 1992-8 4 1992 (8-12) 1992-8 4 1993/1994 (3-5/10-2) 1993-2/12 2 1995 (2-5) 1995-2 2 1993/1994 (3-6/12-2) 1993-3/12 2 1998 (9-12) 1998-9 1 1998/1999 (11-3) 1999-3 3 1998 (4-5/9-12) 1998-4/9 2 2002 (9-12) 2002-9 2 2002/2003 (11-3) 2002-11 2 2002 (9-12) 2002-9 2 2003 (8-11) 2003-8 2 2003/2004 (8-1) 2003-8 3 2003 (2-3/8-12) 2003-2/8 3 2005 (9-11) 2005-9 1 2005/2006 (9-1) 2005-9 3 2005 (9-12) 2005-9 1 2009/20 (8-5) 2010-2 7 2009/20 (8-5) 2010-2 2009/20 (8-5) 2010-2 7 20 (2-5/7-8) 2011-4/7 20 (2-5/7-9) 2011-4/7 7 20 (4-5/7-9) 2011-4/7 5 2013-2014 (1-2/7-8/10-2) 2013-1/7/12 5 2012 (1-4/10-12) 2012-1/12 5 2013-2014 (1-2/6-8/10-2) 2013-1/7/12 2013/2014 (1-2/7-2) 2013-1/7 Note. +e bold values mean that they are consistent with the historical records. Advances in Meteorology 11 Drought duration Drought duration 102°E 105°E 108°E 111°E 102°E 105°E 108°E 111°E High: 141 High: 129 27°N 27°N 27°N 27°N 24°N 24°N 24°N 24°N 0 100 200 0 100 200 (km) (km) Low: 82 Low: 75 102°E 105°E 108°E 111°E 102°E 105°E 108°E 111°E Z value Z value –0.01 to 0 –0.63 to 0 0.01 to 1.95 0.01 to 1.95 1.96 to 3.41 1.96 to 2.73 (a) (b) Drought duration 102°E 105°E 108°E 111°E High: 118 27°N 27°N 24°N 24°N 0 100 200 (km) Low: 103 102°E 105°E 108°E 111°E Z value –2.50 to –1.96 –1.95 to 0 0.01 to 1.95 1.96 to 2.73 (c) Figure 7: Trend distribution of drought duration: (a) SPI, (b) CI, and (c) RDI. †e red dots indicate a signi˜cant positive trend (Z > 1.96) and the purple points indicate the insigni˜cant positive trend (0 < Z > 1.96). †e green points indicate the signi˜cant negative trend (Z < −1.95). respectively. Figure 6(d) shows that UF and UB intersect in beginning of the 21st century. †e drought peak also in- creased signi˜cantly since the beginning of the 21st century. 1995 for autumn, breaking the boundary line in 2004–2013. However, the mutation in spring, summer, and winter was Droughts classi˜ed as severe occurred in 1963, 1985-1986, not signi˜cant. From the RDI, years with annual severe 1987-1988, 1992, 2009-2010, 2011, and 2013-2014. In ad- drought or extreme drought are 1966 and 2013, and 2009 and dition, as shown in Table 3, drought events took place in all 2011, respectively. Years with severe or extreme droughts in seasons, especially in winter–spring and summer–autumn. the spring are 1986, 1987, 1988, 1991, and 2007, and 1963, †ere was a persistent drought in summer–autumn–winter– 1991, and 2011, respectively. Years with severe or extreme spring 2009-2010, a persistent drought in spring–summer 2011, droughts in the summer are 1981, and 1972, 2011, and 2013, and a persistent drought in winter–spring–summer–autumn respectively. Years with severe or extreme droughts in the 2013-2014. autumn are 1978, 1992, and 1969, and 2002 and 2011, re- spectively. Years with severe or extreme droughts in the winter are 1968 and 1978, and 2009, respectively. 4.2. Interannual Variability As shown in Table 3, 29, 30, and 32 drought events were identi˜ed from the SPI, CI, and RDI indices, respectively. 4.2.1. Spatial Distribution and Trends of Drought Duration. †e spatial distribution of drought durations and trends for †e performances of the three indices are close with small di¦erences on month scales. Identi˜cation of drought events the three indices is shown in Figure 7. Drought duration is longer in the northwest and relatively short in the southwest in 1963, 1966, 1978-1979, 1985-1986, 1987-1988, 1988-1989, 1992, 2009-2010, 2011, and 2013-2014 is consistent for all of Guizhou Province. In terms of the trend, only one station (Luodian station) shows a decreasing trend (i.e., a tendency three indices. We note that there were more droughts in the 1960s, 1980s, and 2000s, with a particular rise since the to be wet). All other stations showed an increasing trend. 12 Advances in Meteorology Drought severity Drought severity 102°E 105°E 108°E 111°E 102°E 105°E 108°E 111°E High: 200.8 High: 207 27°N 27°N 27°N 27°N 24°N 24°N 24°N 24°N 0 100 200 0 100 200 (km) (km) Low: 136.9 Low: 121.2 102°E 105°E 108°E 111°E 102°E 105°E 108°E 111°E Z value Z value 0.01 to 1.95 –0.51 to 0 1.96 to 3.50 0.01 to 1.95 1.96 to 2.66 (a) (b) Drought severity 102°E 105°E 108°E 111°E High: 176.9 27°N 27°N 24°N 24°N 0 100 200 (km) Low: 152.3 102°E 105°E 108°E 111°E Z value –1.69 to 0 0.01 to 1.96 1.97 to 2.84 (c) Figure 8: Trend distribution of drought severity: (a) SPI, (b) CI, and (c) RDI. †e red dots indicate the signi˜cant positive trend (Z > 1.96) and the purple points indicate the insigni˜cant positive trend (0 < Z > 1.96). †e green points indicate the signi˜cant negative trend (Z < −1.95). Among them, ˜ve stations (Weining, Guiyang, Xifeng, 4.2.2. Spatial Distribution and Trends of Drought Severity. Xishui, and Tongzi stations) showed a signi˜cant in- Figure 8 shows the spatial distribution and trends of drought creasing trend; these are mainly located in the northwest of severity. †e spatial distribution of drought severity is al- Guizhou Province. †e CI shows that droughts lasted for most consistent with that of drought duration. However, more and less time in western and northeast Guizhou more severity droughts are typically found in the northwest Province, respectively. In terms of changes in the trend, of Guizhou Province, where all stations show an increasing four stations (Weining, Bijie, Tongzi, and Xingren stations) trend. Stations with signi˜cant increasing trends are mainly showed signi˜cant increasing trends; three of these stations distributed in the northwest and northeast of Guizhou Province. †e drought severity determined by the CI is also are located in the west. Meanwhile, four stations (Kaili, Duyun, Dushan, and Rongjiang stations) showed non- consistent with drought duration. Drought intensity is of higher magnitude in western Guizhou Province. Among the signi˜cant decreasing trends in the southeast. †e RDI suggests that drought duration is longer in northwest and four stations with signi˜cant increasing trends (Weining, northeast regions and shorter in southern Guizhou Bijie, Panxian, and Tongzi stations), three (Weining, Bijie, Province. Nine stations located in western Guizhou and Panxian stations) are located in the west of the province, Province increased signi˜cantly. Furthermore, ten stations while drought duration showed a decreasing trend in in central and eastern Guizhou Province had a decreasing southeast Guizhou Province. †e drought intensity is also trend. Among these was the one station in southeastern consistent with drought duration based on the RDI. Severe Guizhou (Rongjiang station) with a signi˜cant decreasing droughts are more frequent in eastern Guizhou. However, trend. stations with signi˜cant increasing trends are primarily Advances in Meteorology 13 102°E 105°E 108°E 27°N 27°N Seasonal 14 9 8 6 7 7 6 6 0 100 200 (km) 24°N 105°E 108°E Spring Autumn Summer Winter Figure 9: Statistics of seasonal drought frequency based on historical records in Guizhou Province in 1960–2013. Drought predictions from the RDI are close to the historical located in western Guizhou, while stations with both in- creasing and decreasing trends are located in northeast records in spring and summer. However, this index suggests Guizhou, with stations with decreasing trends located in more droughts in autumn and winter, particularly in winter. central and eastern Guizhou. †e mild and moderate seasonal droughts identi˜ed by the SPI are more frequent than those found in historical records. However, the severe and extreme seasonal droughts 4.3. Validation of †ree Drought Indices Based on Historical are identi˜ed less frequently than the historical records, Disaster Records. Drought frequency in di¦erent seasons especially in spring and summer (Figures 10 and 12). from 1960 to 2013 in Guizhou Province is shown in Figure 9, †e CI identi˜es more frequent mild and moderate based on statistics of Chinese meteorological disasters seasonal droughts than the historical records, while it [45–59]. More drought events are shown to have happened in identi˜es fewer severe and extreme droughts than the his- spring and summer in Guizhou Province. Spring droughts are torical record (Figures 10 and 13). more frequent in the central and west of the Province, where †e RDI identi˜es more mild droughts than historical Anshun City, Bijie City, and Qianxinan City are located. records indicate. However, the moderate, severe, and ex- Summer droughts are more frequent in the central and east of treme droughts identi˜ed by the RDI are close to the his- Guizhou Province, home to Zunyi City, Tongren City, and torical records (Figures 10 and 14). Qiandongnan City. Moderate, severe, and extreme droughts †e drought frequency analysis (Figures 9 and 14) for SPI, are more frequent in spring in western Guizhou and summer CI, and RDI compared to the historical records shows that the in eastern Guizhou Province (Figure 10). Moderate and ex- mild and moderate droughts in winter are more than the treme droughts are more frequent in autumn and winter and historical records. †e historical records describe the severity mainly a¦ect eastern Guizhou Province. of the crop yield reduction. However, the drought indices do Figure 11 shows that droughts are more frequent in not take this into account. †us, the drought statistics by spring, summer, and autumn based on the SPI. However, indices are possible more frequently than the historical re- droughts are less frequent in spring and summer than the cords. Overall, the severe and extreme droughts are less historical records (Figure 9), while droughts in autumn are frequent than the historical records, especially CI. †e RDI is more frequent than the historical records. Winter droughts closer to the historical records compared to the SPI and CI. are highly consistent with historical records based on SPI. Figure 15 shows variation of the three drought indices in †e CI suggests that drought occurrence increased in winter the area historically a¦ected by droughts; among these data and spring. But the historical records show fewer droughts in are the typical drought years shown in Table 4. †e three winter. †e CI is relatively close to historical records in drought indices in the drought-a¦ected area were highest in spring, followed by autumn and summer. Autumn droughts 2011. However, the three drought indices in the a¦ected occurred more frequently than the historical records. Fewer area, particularly CI, are inconsistent with historical records droughts in summer were found in the historical records. in 2010, 1992, 1990, and 1988. Together with Figures 9–14, 14 Advances in Meteorology 102°E 105°E 108°E 102°E 105°E 108°E 2 2 6 22 1 1 5 3 7 2 1 6 9 2 3 27°N 6 27°N 27°N 4 27°N 2 2 8 6 3 2 2 2 5 2 1 11 4 3 4 4 3 1 2 7 2 1 1 8 1 2 7 4 0 100 200 0 100 200 (km) (km) 102°E 105°E 108°E 102°E 105°E 108°E Frequency statistic Frequency statistic Mild drought Severe drought Mild drought Severe drought Moderate drought Extreme drought Moderate drought Extreme drought (a) (b) 102°E 105°E 108°E 102°E 105°E 108°E 6 7 2 2 1 3 1 1 2 2 7 1 1 2 2 1 1 1 27°N 2 2 27°N 27°N 27°N 1 2 2 2 1 2 2 1 3 1 1 2 4 2 1 22 2 2 3 0 100 200 0 100 200 (km) (km) 102°E 105°E 108°E 102°E 105°E 108°E Frequency statistic Frequency statistic Mild drought Severe drought Mild drought Severe drought Moderate drought Extreme drought Moderate drought Extreme drought (c) (d) Figure 10: Statistics of seasonal drought frequency in di¦erent drought grades based on historical records in Guizhou Province in 1960–2013: (a) spring, (b) summer, (c) autumn, and (d) winter. it is therefore shown that the RDI is more objective and Dunne [8, 11, 60, 61]. However, She²eld et al. [62] dis- reliable at indicating drought than the CI and SPI (the SPI covered that a little change in global drought for the period value is shown here). †erefore, the abovementioned of 1948–2008 based on the Palmer drought severity index. analysis indicated that the relationship between the his- Further, the presented results demonstrated a signi˜cant torical records and drought index still needs to be further drought trend in autumn for the three drought indices, quanti˜ed in the future. which are consistent with Li et al. [21] and Gao et al. [22]. †ese results also show that the drought in spring and summer are dominant from the historical records, which are 5. Discussion and Conclusions increasing [43–51]. †e autumn drought also shows a sig- All three drought indices showed decreasing trends in an- ni˜cant increasing trend in Guizhou Province, which may nual and seasonal in the past 54 years. †e results are have a great impact on autumn crops. Gao et al. [22] found consistent with Zhai et al., Xu et al., Dai, and Milly and that autumn soil moisture anomaly is helpful to further Advances in Meteorology 15 102°E 105°E 108°E 102°E 105°E 108°E N N 11 11 12 13 11 11 7 9 9 8 10 5 8 8 4 5 6 12 11 5 8 27°N 11 27°N 27°N 10 11 27°N 13 12 5 13 8 12 14 10 10 12 5 13 9 13 10 11 0 100 200 0 100 200 (km) (km) 24°N 24°N 105°E 108°E 105°E 108°E Seasonal Seasonal Spring Autumn Spring Autumn Summer Winter Summer Winter (a) (b) 102°E 105°E 108°E 11 11 12 12 8 11 11 9 9 11 11 11 11 27°N 27°N 9 10 9 9 6 8 8 0 100 200 (km) 24°N 105°E 108°E Seasonal Spring Autumn Summer Winter (c) Figure 11: Statistics of seasonal drought frequency based on (a) SPI, (b) CI, and (c) RDI in Guizhou Province in 1960–2013. understand the nature of the drought in Southwest China the SPI only utilizes precipitation information, without and may provide a clue for drought monitoring and risk considering other meteorological variables that may play an management. †e SPI, CI, and RDI identi˜ed 29, 30, and 32 important role for drought. In addition, the weight co- drought events, respectively. Winter–spring and summer– e²cients are relatively arti˜cial and random, which may autumn droughts have become more frequent since the a¦ect the ability of the CI [63–65]. †us, it is possible to be the main reason for the disagreement with the distribution beginning of the 21st century. †e increase in frequency and strengthening trends of drought frequency, duration, peak, of RDI and SPI. For drought severity, the spatial distribu- tions of the three drought indices are also inconsistent. In the and intensity is signi˜cant over the period 1960–2013. †ese results are also consistent with Zhai et al., Yu et al., Xu et al., present study, the drought severity is based on annual Li et al., and Gao et al. [8, 9, 11, 21, 22]. statistics. However, the seasonal statistics show that SPI and In terms of drought duration, the spatial distribution CI account for a large proportion in spring, while RDI of the SPI is close with the RDI during 1960–2013. However, accounts for a large proportion in summer. †erefore, SPI the spatial distribution of the CI is inconsistent with those of and CI show higher drought severity in the western prov- the SPI and RDI. As Section 3.1.2 mentioned that the CI ince. †e RDI shows higher drought severity in the eastern index is composed of SPI and MI; however, some scholars which is consistent with the historical records. Moreover, Xu point out that SPI and MI have certain defects. For instance, et al. [11] also revealed that the spatial distribution of 16 Advances in Meteorology 102°E 105°E 108°E 102°E 105°E 108°E 1 1 1 1 3 3 2 18 4 5 17 19 8 16 9 8 9 19 19 1 27°N 17 2 27°N 8 1 10 27°N 18 27°N 8 13 3 2 3 15 9 2 3 8 13 5 17 16 9 9 1 1 2 4 7 16 9 0 100 200 0 100 200 (km) (km) 102°E 105°E 108°E 102°E 105°E 108°E Frequency statistic Frequency statistic Mild drought Severe drought Mild drought Severe drought Moderate drought Extreme drought Moderate drought Extreme drought (a) (b) 102°E 105°E 108°E 102°E 105°E 108°E 1 1 1 51 3 3 1 17 1 2 20 8 19 16 8 6 9 1 27°N 27°N 19 2 21 27°N 27°N 1 1 17 13 3 2 1 2 18 3 17 5 9 12 3 2 7 17 0 100 200 0 100 200 (km) (km) 102°E 105°E 108°E 102°E 105°E 108°E Frequency statistic Frequency statistic Mild drought Severe drought Mild drought Severe drought Moderate drought Extreme drought Moderate drought Extreme drought (c) (d) Figure 12: Statistics of seasonal drought frequency in di¦erent drought grades based on the SPI in Guizhou Province in 1960–2013: (a) spring, (b) summer, (c) autumn, and (d) winter. drought severity using RDI (3 months reconnaissance revealed that meteorological droughts are the water short- drought index) is almost the same as that using SPI ages caused by an imbalance precipitation and evaporation (3 months standardized precipitation index). However, the [66]. †e most of the drought indices are mainly based on distributions of SPEI (3 months standardized precipitation the precipitation and evaporation calculation. †erefore, they play a vital role in the capture of drought characteristics evapotranspiration index) are quite di¦erent with SPI and RDI as well as the trends. Based on the above analysis and [11, 62]. Evaporation is always the focus of drought research. the historical records (Table 3) of disasters in the drought- However, compared to precipitation, there are still many a¦ected area that consider seasonal drought frequency and uncertainties in evaporation measurement. †erefore, dif- magnitude, the RDI performs more objectively and reliably ferent evaporation models may not get the same results. than SPI and CI. However, the SPI, CI, and RDI all indicate Previous studies applied PDSI, SPI, RDI, and SPEI drought frequencies and durations less or more than those [11, 60–62, 67], which mainly adopted the †ornthwaite and indicated by the historical records. †is may be related to the Penman–Monteith or other regimes to calculate the refer- defects of the SPI, CI, and RDI. Previous studies have ence evapotranspiration (ETo). †erefore, di¦erent drought Advances in Meteorology 17 102°E 105°E 108°E 102°E 105°E 108°E N N 1 1 1 1 1 6 3 4 6 2 2 22 22 4 19 6 27°N 27°N 27°N 11 27°N 1 7 25 9 4 16 9 2 2 6 8 17 19 2 1 6 1 1 1 31 7 9 5 20 13 6 2 7 22 0 100 200 0 100 200 (km) (km) 102°E 105°E 108°E 102°E 105°E 108°E Frequency statistic Frequency statistic Mild drought Severe drought Mild drought Severe drought Moderate drought Extreme drought Moderate drought Extreme drought (a) (b) 102°E 105°E 108°E 102°E 105°E 108°E N N 1 1 10 1 4 5 2 2 6 22 34 20 29 22 30 5 27°N 18 27°N 27°N 27°N 2 20 8 5 51 8 20 11 22 1 1 20 30 7 21 5 3 0 100 200 0 100 200 (km) (km) 102°E 105°E 108°E 102°E 105°E 108°E Frequency statistic Frequency statistic Mild drought Severe drought Mild drought Severe drought Moderate drought Extreme drought Moderate drought Extreme drought (c) (d) Figure 13: Statistics of seasonal drought frequency in di¦erent drought grades based on the CI in Guizhou Province in 1960–2013: (a) spring, (b) summer, (c) autumn, and (d) winter. trends were obtained; for example, Dai [60] demonstrated that drought-prone areas estimated by the SPI is higher than that by the observed global aridity changes are consistent with model the RDI for the period prior to 1998, while it is the converse for predictions up to 2010, which suggest more severe and the period after 1998. Xu et al. [11] indicated that SPEI and RDI widespread droughts in the next 30–90 years caused by de- are sensitive to ETo. †e RDI based on the †ornthwaite creased precipitation or increased evaporation. Meanwhile, equation overestimates the in¬uence of air temperature. †us, Milly and Dunne [61] also found that the historical and future it overestimates the grade of drought. Besides, Vicente-Serrano tendencies are towards continental drying. However, She²eld et al. [68] pointed out that SPI, PDSI, SPDI, and SPEI are et al. [62] indicated that the previous reported increase in sensitive to precipitation and ETo. †e results may be quite global drought is overestimated, and there was little change in di¦erent with respect to di¦erent indices. drought over the period of 1948–2008. In addition, the results All three drought indices indicate that mild droughts based on di¦erent drought indices are also inconsistent. For occurred more frequently than what is shown in the his- example, Zarch et al. [67] showed that the percentage of torical records, across di¦erent seasons and levels of 18 Advances in Meteorology 102°E 105°E 108°E 102°E 105°E 108°E N N 4 2 5 5 2 2 6 5 1 2 7 12 29 5 12 25 24 26 9 8 26 11 28 2 2 7 5 27°N 27°N 27°N 27°N 61 51 25 26 11 11 24 22 11 9 42 42 10 12 26 28 2 2 5 4 52 6 3 6 25 8 25 27 9 27 2 2 5 3 11 25 29 0 100 200 0 100 200 (km) (km) 102°E 105°E 108°E 102°E 105°E 108°E Frequency statistic Frequency statistic Mild drought Severe drought Mild drought Severe drought Moderate drought Extreme drought Moderate drought Extreme drought (a) (b) 102°E 105°E 108°E 102°E 105°E 108°E N N 1 31 2 3 1 2 5 11 29 6 30 11 9 26 25 25 3 32 27°N 27°N 27°N 27°N 11 4 25 6 29 12 9 24 30 2 41 9 26 30 52 52 6 6 3 2 25 5 25 7 26 26 9 10 3 2 4 5 10 27 27 0 100 200 0 100 200 (km) (km) 102°E 105°E 108°E 102°E 105°E 108°E Frequency statistic Frequency statistic Mild drought Severe drought Mild drought Severe drought Moderate drought Extreme drought Moderate drought Extreme drought (c) (d) Figure 14: Statistics of seasonal drought frequency in di¦erent drought grades based on the RDI in Guizhou Province in 1960–2013: (a) spring, (b) summer, (c) autumn, and (d) winter. drought. †is may be related to di¦erent statistical analysis counties in the entire province. †us, a higher density of methods. In this paper, any interval when the indices are weather stations may overcome the index-historical data mismatch. between −1 and 0 is classi˜ed as an occurrence of mild drought. However, it is necessary for a drought to cause Previous studies [69–73] have stated that the occurrence agricultural and socioeconomic damage in order for it to be of droughts in the southwestern region of Guizhou Province noted in historical records. We also point out that the is close to related atmospheric circulation anomalies and drought-a¦ected area was highest in 2011, consistent with special topography [69–75]. In addition, the signi˜cant RDI and CI, but not with SPI. †e density of meteorological decrease in precipitation [11, 21] is an important factor for stations may also play a role. In this study, only data from 19 drought. Meanwhile, the change of potential evaporation is stations are considered. However, the records of drought- also a critical factor [20]. Chen et al. [41] pointed out that the a¦ected areas are based on statistics covering over 88 number of continuous wet days (CWD) was decreasing Advances in Meteorology 19 200 200 1.0 160 160 1 0.5 120 120 0.0 –0.5 80 80 –1 –1.0 40 40 –2 –1.5 –3 0 0 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 Year Year SPI CI Affected area Affected area (a) (b) 3 200 –1 –2 –3 0 1960 1970 1980 1990 2000 2010 Year RDI Affected area (c) Figure 15: Comparison of drought-a¦ected areas based on (a) SPI, (b) CI, and (c) RDI. Table 4: Comparison of historical a¦ected area in typical drought years based on the SPI, CI, and RDI. 4 2 A¦ected area (×10 hm ) 1988 1989 1990 1992 2010 2011 2013 Historical 114.07 104.6 128.93 150.93 163.9 182.25 111.78 SPI historical −0.7 −2.1 −0.5 −0.7 −0.5 −2.6 −1.7 CI historical −0.5 −1.0 −0.5 −0.1 −1.1 −1.5 −0.7 RDI historical −0.7 −1.1 −0.5 −0.7 −0.1 −2.4 −1.9 signi˜cantly while the largest 5 days of rainfall (RX5 day), Guizhou Province. †e rainy season in western Guizhou strong precipitation (R95), and strongest rainy day starts in June; when these rains are late, a spring drought is (R20mm) measures did not have signi˜cant decreasing triggered. Zunyi, Tongren, and Qiannan Cities in eastern Guizhou Province are prone to summer droughts. †is may trends in response to the decreasing trend of the three in- dices (when considering Guizhou Province). In terms of be the result of the rainy season starting early (April) in the drought distribution, all three drought indices indicated area. A precipitation decrease will likely cause a summer more frequent spring droughts in western Guizhou, and drought. Moreover, Milly and Dunne [61] and She²eld et al. more frequent summer droughts in eastern Guizhou. Shen [62] stated that other factors such as runo¦, relative hu- et al. [75] pointed out that drought characteristics are mainly midity, wind speed, and other physical mechanisms should the result of uneven spatiotemporal distribution of water also be taken into account. †e relationship between global resources in Guizhou Province. †e spring drought is the drought and climate change can be assessed more accurately most severe in Bijie City and Liupanshui City in western by combining physical hydrological models and large SPI RDI Affected area CI Affected area Affected area 20 Advances in Meteorology quantities of measured and satellite remote-sensing data. References Furthermore, the inﬂuence of human activities is also an [1] A. K. Mishra and V. P. Singh, “Review of drought concepts,” important factor that cannot be ignored. Journal of Hydrology, vol. 91, no. 1-2, pp. 202–216, 2010. +e karst landform is also an important factor for the [2] T. Stocker, D. G. Qin, G. Plattner, M. Tignor, S. 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Temporal-Spatial Characteristics of Drought in Guizhou Province, China, Based on Multiple Drought Indices and Historical Disaster Records

Temporal-Spatial Characteristics of Drought in Guizhou Province, China, Based on Multiple Drought Indices and Historical Disaster Records

Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Temporal-Spatial Characteristics of Drought in Guizhou Province, China, Based on Multiple Drought Indices and Historical Disaster Records

Temporal-Spatial Characteristics of Drought in Guizhou Province, China, Based on Multiple Drought Indices and Historical Disaster Records

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