Data Paper Zone II Versions EN1 Vol 4 (3) 2019
A dataset of drought indices based on the standardized precipitation evapotranspiration index (SPEI) over Xinjiang, China (1961–2015)
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： 2018 - 07 - 11
： 2018 - 09 - 11
： 2019 - 07 - 19
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Abstract & Keywords
Abstract: The Xinjiang province is located in the Eurasian hinterland of northwestern China. With its complex terrain and fragile ecological environment, Xinjiang is particularly sensitive to global warming and has attracted extensive attention under the background of global warming. Based on daily temperature and precipitation data observed at 55 meteorological stations over Xinjiang during 1961 – 2015, time series of drought indices based on the Standardized Precipitation-Evapotranspiration Index (SPEI) are estimated in this study. The SPEI was calculated for each station at selected time scales of 1, 3, 6, 12, 24 and 48 months. This dataset is useful for research on the trends and spatial-temporal characteristics of drought events over Xinjiang. When used together with regional statistics, it helps reveal the impacts of drought on agricultural production and the eco-hydrological environment.
Keywords: Standardized Precipitation Evapotranspiration Index; Xinjiang; climate change; meteorological stations
Dataset Profile
 Title A dataset of drought indices based on the standardized precipitation evapotranspiration index (SPEI) over Xinjiang, China (1961 – 2015) Corresponding author Yao Junqiang (yaojq1987@126.com) Authors Yao Junqiang, MaoWeiyi, Chen Jing, Fan Yuting Time range 1961–2015 Geographical scope Xinjiang Uygur Autonomous Region, including 55 meteorological stations Spatial resolution Monthly Data volume 2.38 MB Data format *.xlsx Data service system < http://www.sciencedb.cn/dataSet/handle/632> Sources of funding National Key Research and Development Program of China (2018YFC1507101); National Natural Science Foundation of China (41501050, 41605067); Basic Research Operating Expenses of the Central-level Non-profit Research Institutes (IDM201506). Dataset composition This dataset consists of 6 .xlsx files which record drought indices at different timescales, including SPEI 1 mo (1 month timescale), SPEI 3 mo, SPEI 6 mo, SPEI 12 mo, SPEI 24 mo and SPEI 48 mo timescale drought indices. Files of this dataset are named after their index names in English. Each file consists of information on year, month, SPEI index results, and WMO site numbers. Each file has a slightly different data volume depending on the number of sites and the different timescales used e.g. as the drought index on the time scale of 3 months starts from March in 1961, there is no value in January and February for 1961, which is expressed by “NA”..
1.   Introduction
Drought is one of the most severe meteorological disasters, which, due to its high occurrence frequency, long duration, and wide impact scope, has received much attention from both the scientific community and society[1][2][3] . More recently, the continuous intensification of global warming has led the number of extreme weather and climate events to increase. In particular, the occurrence frequency and intensity of droughts have been increasing significantly, which resulted in both great economic loss and ecological problems, such as water resources shortage, exacerbated desertification, and frequent sand storms. Furthermore, droughts are known to have more obvious effects in arid areas[4][5] . As the “Belt and Road” initiative is being implemented, the Xinjiang province, which constitutes the core area of the “Silk Road Economic Belt”, has been receiving much attention. Xinjiang is situated within an arid area of Central Asia and its complex terrain, extremely fragile ecological environment and lower ability to resist disasters make it sensitive to global warming[6][7] . As the global warming has intensified the water cycle process, the climate has changed noticeably in Xinjiang, attracting extensive attention[6]. At the beginning of the 21st century, the climate in arid areas of northwestern China are characterized by “warming and wetting”, especially in the western arid regions (Xinjiang). The climate has continued to change markedly since the very beginning of the century, which manifests as an increasing trend of temperature with rapid transition and fluctuations at a high temperature range for an extended period of time, as well as a slight reduction in the amount of precipitation. This should certainly have an important influence on the changes of the dry and wet climate[6]. Drought occur under multiple temporal-spatial scales, with meteorological drought being the main source, as it plays a role in the other types of drought[8]. The analysis of the changes in meteorological drought characteristics and occurrence requires numerous meteorological observations as basic data.
Currently, the available observational data are comprehensive, and the dry and wet climate is characterized by precipitation, vapour pressure and relative humidity. Yet, not a single element can fully reflect the dry and wet conditions, which is not conducive to the direct analysis of arid climate change. Some indicators which can synthetically reflect the changes in dry and wet climate can be estimated however, and compiled into a joint name-drought index[2]. Variations in precipitation and evaporation are the two major driving factors for the formation of dry and wet climate. The standardized precipitation evapotranspiration index (SPEI) is a multiscale index which takes simultaneously the precipitation and evaporation into account. Thus, it enables the dry and wet climatic changes to be evaluated with a reasonable accuracy at different time scales[9]. The distribution and variation trends of dry and wet can be reflected directly via this index, as well as the drought variations on different scales[10]. It is therefore crucial to provide time series of the SPEI on multiple time scales for furthering the research about wet and dry climate and study the impact of droughts in Xinjiang.
The dataset described in this paper comprises the drought index data on different time scales in Xinjiang. Time series of monthly drought variations on the time scale of 1, 3, 6, 12, 24 and 48 months were established based on monthly air temperature and precipitation data, and the drought index dataset was organized for the period 1961-2015. It provides a reliable data support for further analysis of drought variation trends and their characteristics in a long time series at different time scales in Xinjiang.
2. Data collection and processing
2.1   Distribution of the weather stations
The original observational data used in this paper come from meteorological stations belonging to the China Meteorological Science Data Sharing Service network (http://data.cma.cn/). The meteorological observation stations mainly distribute in the major oasis areas of the Xinjiang Uygur Autonomous Region, while are sparsely distributed in the mountain and desert hinterland. Most meteorological stations have an elevation ranging from 200-1500 m, and span four temperature zones including the plateau temperate zone, warm temperate zone, middle temperate zone and the subfrigid zone. In this paper, data observations from 55 meteorological stations, whose spatial elevation distribution in Xinjiang is shown in Fig. 1, were selected.

Fig. 1   Distribution of meteorological station
2.2   Data production flow
The data production flow is divided into 4 parts: data pre-processing, calculation of potential evapotranspiration, calculation of standardized precipitation and evapotranspiration index (SPEI), and finally organization of the drought index. The overall flow is shown in Fig. 2.

Fig. 2   Production flow of drought index
2.2.1 Data pre-processing
The original meteorological station data originate from the National Meteorological Information Center, and include daily air temperature and precipitation from 76 national primary standard stations in Xinjiang between 1961 and 2015. The daily temperature data were averaged while the successive amounts of precipitation were accumulated to obtain monthly data. In addition, records from the stations with missing data for more than three consecutive months were removed. As a result, out of the original 76 stations data, monthly air temperature and accumulated precipitations from 55 meteorological stations were estimated for the period 1961-2015. The records were then sorted in chronological order, and 55 ASCII text files named as the respective stations were output, so as to satisfy the follow-up calculation of drought index.
2.2.2 Calculation of potential evapotranspiration
Potential evapotranspiration (PEI) is a key variable to calculate the SPEI drought index. Following Thornthwaite[11]method, PET is calculated from the dataset as follows:
$$PET=\left\{\begin{array}{c} 0 T<0\\ 16\left(\frac{N}{12}\right)\left(\frac{NDM}{30}\right){\left(\frac{10T}{I}\right)}^{m} 0\le T<26.5\\ -415.85+32.24T-0.43{T}^{2} T\ge 26.5\end{array}\right\$$ (1)
where T is the monthly average temperature, N is the maximum daily sunshine duration, NDM is the number of days every month, I is the annual heat index, which is obtained by summation of the 12 monthly heat indexes per year. The annual heat index is calculated as below:
$$I=\sum _{i=1}^{12}{\left(\frac{T}{5}\right)}^{1.514}\mathrm{ }\mathrm{ }\mathrm{ }\mathrm{T}>0$$ (2)
m is a coefficient related to I, and can be obtained Equation (3):
m =6.75×10-7I3 -7.71×10-5I2 +1.79×10-2I +0.492 (3)
The estimation of this parameter requires less computed variables for the calculation of PET by the Thornthwaite method, and as the method is simple and easy, it has been widely applied.
2.2.3 Calculation of standardized precipitation evapotranspiration index (SPEI)
The effect of increasing temperature on drought has been increasingly apparent over the past few decades in relation with the global warming, and the SPEI is designed to take into account the impact of temperature variations on drought. Its estimation is made via the CSIC software from Spain (http://digital.csic.es/), and the computational procedure incorporates the following 4 steps[9] :
(1) Calculation of climate level
The climate level Di is the difference between the amount of precipitation Pi and the potential evapotranspiration PETi ,
Di =Pi－PETi (4)
PET was calculated via the Thomthwaite method as delineated in section 1.2.2.
(2) Establishment of an accumulated sequence of climate level on different time scales:
$${D}_{n}^{k}=\sum _{i=0}^{k-1}\left({P}_{n-i}-{PET}_{n-i}\right) , n\ge k$$ (5)
In Eq. (5), k is the time scale(generally monthly), and n is the period over which the calculation is conducted.
(3) Construction of data series by log-logistic probability density function fitting:
$$f\left(x\right)=\frac{\beta }{\alpha }{\left(\frac{\chi -\gamma }{\alpha }\right)}^{\beta -1}{\left[1+{\left(\frac{\chi -\gamma }{\alpha }\right)}^{\beta }\right]}^{-2}$$ (6)
In the formula, $$\alpha$$ is the scale factor, $$\beta$$ is the shape factor, and $$\gamma$$ is the Origin parameter, which can be obtained by estimation of the L-moment parameter. Thus, on a given time scale, the cumulative probability is:
$$F\left(x\right)={\left[1+{\left(\frac{\mathrm{\alpha }}{\mathrm{\chi }-\mathrm{\gamma }}\right)}^{\mathrm{\beta }}\right]}^{-1}$$ (7)
(4) Acquisition of the corresponding time variation sequence of SPEI through the transformation of standard normal distribution by cumulative probability density:
$$SPEI=W-\frac{{C}_{0}+{C}_{1}W+{C}_{2}{W}^{2}}{1+{d}_{1}W+{d}_{2}{W}^{2}+{d}_{3}{W}^{3}},$$ (8)
In Eq. (8), W is a parameter equal to $$\sqrt{-2ln\left(P\right)}。$$. P is the probability to exceed a certain water surplus and deficit. When P≤0.5, P=1−F(x), while as P＞0.5, P=1−P, and the symbol of SPEI is reversed. The other constant terms are C0 =2.515517, C1 =2.515517, C2 =2.515517, d1 =2.515517, d2 =2.515517 and d3 =2.515517, respectively.
To sum up, not only does SPEI possess the feature of multiple timescales, it also considers the effect of temperature sensitivity. This presents obvious advantages in the analysis of dry and wet climate under warming conditions[6][9] .
2.2.4 Selection of time scale and interpretation of results
Drought is a multiscale phenomenon, and differences exist in the effects of the respective time scales on an affected area. Thus, different time scales can reflect different drought conditions. For example, the 3-month time scale shows the meteorological drought, whereas the 6-month scale reflects the agro-ecological drought and the 12-month scale represents the hydrologic drought[6]. Therefore, the time scale of 1, 3, 6, 12, 24, and 48 months are selected in the dataset to calculate the monthly drought indexes on different time scales at each station. Table 1 shows the international classification standard of drought grade based on SPEI. The monthly drought variations at any given station can then be determined following this standard[6].
Table 1   Drought grade based on SPEI
 Extreme drought Moderate drought Light drought Normal Lightly moist Moderately moist Extremely moist SPEI ≤−2.0 −2.0～−1.0 −1.0～−0.5 −0.5～0.5 0.5～1.0 1.0～2.0 ≥2.0
3.   Description of a data sample
The dataset contains the result files for the drought index at 55 stations on the 6 different time scales in Xinjiang. For convenience of computation and application, they are stored in .xlsx format, and named by time scale, such as SPEI-3.xlsx. Each result file includes 4 attribute values: year, month, SPEI and World Meteorological Organization (WMO) identification number of each station (such as 51053). The first, second and third column represent, respectively, the year, month and monthly SPEI drought index. The first line represents the unified WMO number of each meteorological station, and the second line is the drought index of SPEI during the corresponding time at each station, as shown in Fig. 3.

Fig.3   SPEI-1 data sample
4.   Data quality control and evaluation
4.1   Initial data quality control
Data quality control is a necessary step to ensure the accuracy of the estimates of SPEI drought index at each observation station. Abnormal and wrong original observation data can lead to erroneous values of the drought index, thereby affecting the follow-up applied analysis of the dataset. The strict control of the data quality is conducted automatically via software identification and artificial check in the production process of the drought index. The initial quality control is performed on the raw data by the Xinjiang Meteorological Information Center. Firstly, the possible occurrence of missing data in the daily air temperature and amount of precipitation at each station is checked. Data from stations with missing periods exceeding three months are rejected automatically, while for those periods less than 3 months, the missing data are replaced by the average of the adjacent months. Secondly, abnormal values are detected from the daily minimum and maximum for the temperature, and by ensuring that no precipitation less than 0 mm exist. The abnormal values are replaced by the mean value of the adjacent month. Through this data quality control, the daily air temperature and precipitation data from 55 out of 76 national base stations are selected for the period 1961-2015 to calculate the SPEI.
4.2   Evaluation of the drought index product
To further visually display the wet and dry variation trends reflected by the drought index in the dataset, the time variation sequences of drought index on different time scales, and the spatial distribution of changed slope at each station are presented. Fig. 4 shows the interannual variation trend of drought index on different time scales at each station every 10 years while Fig. 5 displays the time variation sequences of the monthly drought index for selected time scales of 1-24 months between 1961 and 2015.

Fig. 4   Variation trends of drought index of SPEI on different time scales

Fig. 5   Time variations of drought index of SPEI on time scales ranging from 1 to 24 months
The spatial distribution of the drought index trend (Fig. 4) demonstrates that wetting mainly appears in the northwestern part of Xinjiang, while drying is dominant in the southeastern part. This is consistent with results from previous studies of warming and wetting in the westernmost region of northwestern China (west of Xinjiang)[12][13] . The time variations in Fig. 5 show an obvious period of warming and wetting (positive index values) from the mid-1980s to the mid-1990s in Xinjiang. However, as the temperature has been increasing since the 21st century, the evaporation has been intensified, while the increasing trend in the amount of precipitation has slowed down or even reduced slightly. This results in a remarkable warming and drying trend. The aridification focuses in the southern and eastern parts of the Tianshan Mountain range of Xinjiang, while the humidification dominates in the northwestern and southwestern regions of Xinjiang (Pamirs Plateau). This coincides with the results obtained by the multi-source data including the precipitation data, self-calibrated Palmer drought severity index (sc-PDSI) and land water storage retrieved from Gravity Recovery and Climate Experiment (GRACE) satellite reported by Ma et al[14], who proposed that northern and northwestern China have been under the influence of a drying trend in recent years.
The aridification is also having an important influence on agriculture disasters and disturbance of animal husbandry. The disaster-damaged crop areas in Xinjiang are used to further verify the reliability of the dataset[15]. From the comparison of disaster-damaged crop areas and drought indexes on the 12-month time scale displayed in Fig.6, the relationship between the two variables is found to be good, with a correlation coefficient greater than 0.60. This indicates that the 12-month time scale drought index can reflect the effects of drought well on the agriculture-related disasters (Figs. 6 and 7).

Fig. 6   Comparison between 12-month SPEI and yearly disaster-damaged crop area

Fig. 7   Scatter diagram of 12-month SPEI and disaster-damaged crop area
5.   Data value and use suggestion
The analysis and evaluation of regional climate change and ecological environment is directly impacted by the occurrence frequency, intensity and variation trend of drought events. The dataset described in this paper may be used together with soil moisture, vegetation cover and hydrological runoff data, in order to thoroughly explore the characteristics of drought under different scales in Xinjiang. This dataset may also be used for association analysis of statistical data such as the analysis of local disaster-damaged crop area and disaster losses, as well as the evaluation of the effect of drought events on agricultural and animal husbandry production.
The dataset includes the results of drought indexes at 55 meteorological stations on different time scales from 1961-2015 in Xinjiang. The data files are stored in accordance with time scale in the .xlsx format, which is convenient for further processing and applications. The users may download data selectively based on their actual need.
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Data citation
Yao J, Mao W, Hu W, Chen J & Fan Y.A dataset of drought indices based on the standardized precipitation evapotranspiration index (SPEI) over Xinjiang, China (1961–2015). Science Data Bank, DOI: 10.11922/sciencedb.632(2019).
Article and author information
Yao J, Mao W, Hu W, Chen J & Fan Y.A dataset of drought indices based on the standardized precipitation evapotranspiration index (SPEI) over Xinjiang, China (1961–2015). China Scientific Data 4(2019). DOI: 10.11922/csdata.2018.0048.zh
Yao Junqiang
Main task: data product design and paper writing.
yaojq1987@126.com
associate research fellow. Major research filed is climate change and water cycle in arid areas.
Mao Weiyi
Main task: paper review and revision.
research fellow. Major research field is short-term climate prediction.
Hu Wenfeng
Main task: data arrangement and paper revision.
lecturer. Major research field is land-atmosphere interaction.
Chen Jing
Main task: basic data processing and result analysis.
master, assistant research fellow. Major research field is extreme weather and climate event and variations.
Fan Yuting
Main task: basic data management and application.
doctor, associate research fellow. Major research field is reconstruction of tree-ring hydrologic climate and hydrologic study of isotopes.
National Key Research and Development Program of China (2018YFC1507101); National Natural Science Foundation of China (41501050, 41605067); Basic Research Operating Expenses of the Central-level Non-profit Research Institutes (IDM201506).
Publication records
Published: July 19, 2019 （ VersionsEN1
Updated: July 19, 2019 （ VersionsEN2
Released: Sept. 11, 2018 （ VersionsZH3
Published: July 19, 2019 （ VersionsZH4
References

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