1. Introduction
Droughts, which last for several days or even years, are complex and recurring natural phenomena [
1]. Their formation is mainly due to a substantial decline in water availability caused by insufficient precipitation [
2,
3]. Global warming and climate change exacerbate droughts [
4,
5]. Severe and persistent droughts can lead to ecological disasters [
6] and pose serious risks to normal human life. A report shows that approximately 800,000 people died from droughts worldwide between 1970 and 2017 [
7]. Effective spatiotemporal assessment of droughts can provide ecological knowledge and help in reducing the impact of drought. It is conducive to regional water resource management and decision-making with regard to ecological water use planning and ultimately alleviates the drought situation and reduces economic losses [
8].
Meteorological station recordings and remote sensing data are two widely used data sources for drought-related research over the past decades [
9,
10,
11,
12,
13]. Remote sensing data present many advantages when used for large areas that lack meteorological stations. However, several uncertainties due to algorithms based on derived products from satellite imageries [
14,
15] and cloud contamination [
16] limit the accuracy of such data. For example, precipitation products from remote sensing mainly rely on passive microwave or infrared technologies [
17,
18], which are likely to be affected by topography and thus induce uncertainties in data quality. In addition, remote sensing precipitation products usually have a coarser spatial resolution; for example, the Tropical Rainfall Measuring Mission (TRMM) and the Climate Precipitation Center’s morphing technique precipitation product have a spatial resolution of 0.25° [
19], which is unsuitable for revealing the details of spatial heterogeneity in a given region. Therefore, using meteorological station recordings to monitor drought status has become a popular method, especially for mountain areas with heavy clouds or rainy weather. Furthermore, meteorological stations usually have long historical records and can establish a long-term drought parameter sequence, which is useful for exploring drought characteristics.
Hydrological and statistical models have been established for drought monitoring and climate response analysis. Wavelet analysis [
8,
20], the soil and water assessment tool (SWAT) , the run theory [
21], and cluster analysis [
2] are commonly used models for monitoring drought. Amongst them, SWAT is currently the most widely used hydrological model. It uses elevation data to divide a watershed into sub-units, which are composed of all areas with similar landscape characteristics [
22]. Combining SWAT with the drought index is found useful in investigating the impact of climate on future and past droughts in multiple regions [
23]. However, the data input format of soil and water assessment tools to be used in these models have the restriction of a specific format, besides the requirement of data in days and the need for real data for the sake of verification, which makes this approach inconvenient to use. Wavelet analysis is a statistical model that explores the changes in drought over time by acquiring spectrograms. For time series with unequal intervals, least-squares wavelet analysis [
24] and least-squares cross wavelet analysis [
8] are applied in drought monitoring based on the drought indices.
Using drought indices derived from the meteorological station observations to monitor drought situations has a long history and is proven to be effective in exploring drought spatiotemporal distribution patterns [
2,
3,
25]. In the past decades, scientists have developed many drought indices by utilizing meteorological station recordings; examples include the precipitation condition index (PCI) [
26], temperature condition index (TCI) [
27], standardized precipitation index (SPI) [
28], and standardized precipitation evapotranspiration index (SPEI) [
29]. Amongst these drought indices, SPEI is the most sophisticated and widely used tool because it is designed to consider both precipitation and potential evapotranspiration (PET) while determining drought, and it can capture multi-temporal characteristics of drought [
3,
12]. When considering a given drought event, scientists usually focus on several quantified characteristics of the drought, such as duration, severity, and intensity [
30]. However, as the SPEI index cannot directly and quantitatively describe the characteristics of drought events, there is a need to use other techniques along with SPEI.
By defining multiple thresholds, the run-theory [
21] method can be used as a powerful tool to distinguish drought and non-drought statuses and extract drought characteristics from drought indices [
31]. For example, Sun et al. [
32] proposed a method for urban drought disaster risk analysis and assessment by integrating the run theory, the copula function, the crop growth model, and natural disaster risk assessment technology. Malik et al. [
33] classified droughts using the run theory and reported that the probability of occurrence of moderate drought events is relatively higher than that of severe and extreme drought events in Uttarakhand (India). He et al. [
31] used the run theory to identify the frequency, duration, severity, and intensity of all droughts in China on the basis of the monthly percentage of the precipitation anomaly rate index and found that China has five obvious meteorological drought-prone areas. These studies have shown that the run theory method can effectively identify and detect the temporal characteristics of drought [
34,
35]. Hence, determining whether the run theory can effectively and efficiently detect temporal drought features from SPEI sequences is an interesting topic.
With regard to the spatial distribution of drought characteristics, drought cluster analysis methods are widely used to identify homogeneous regions with similar drought characteristics. These methods include fuzzy c-means [
5], quantile regression [
2], and K-means. Drought indices, such as PCI, TCI, SPI, and SPEI, are normally set as input data for these cluster analysis methods to detect the spatial distribution of drought characteristics [
2,
5,
25,
36,
37,
38]. Shiau et al. [
2] utilized the hierarchical agglomerative clustering algorithm to analyze the different quantile slopes of SPI-3 at 12 stations in Taiwan, and the results showed that stations along the east coast were likely to be severely affected. Xie et al. [
37] used k-means clustering to divide Xinjiang’s drought spatial distribution patterns into three clusters on the basis of SPI. By using the standardized precipitation temperature index as a feature, Ali et al. [
38] partitioned the 52 meteorological stations in Pakistan into nine clusters. However, despite reflecting the statuses of drought and wetness, these drought index values were used directly in cluster analysis methods, which might be inappropriate for effectively distinguishing the two statuses. Introducing quantitative characteristics that indicate the degree and severity of drought may be a better option for describing drought in spatial and temporal aspects.
In this study, we developed a method to identify the spatiotemporal distribution of drought characteristics on the basis of meteorological station records in Hunan Province, China, which is a major grain-producing area and is often affected by floods and droughts [
39]. This method consists of two steps. Firstly, it is designed to derive drought characteristics, including drought duration, severity, and intensity, from the SPEI index, by integrating the run-theory method. Secondly, drought spatial distribution patterns are derived by clustering drought characteristics via the K-means cluster method. Hunan Province is located in a subtropical region with relatively abundant rainfall, but it is still susceptible to seasonal drought events. According to historical statistics, the average drought in Hunan Province is about 701,000 hm
2/year, of which the catastrophic drought area accounts for approximately 324,000 hm
2 [
40]. Therefore, studying the drought characteristics of Hunan Province from spatial and temporal aspects is meaningful for ecological protection, regional water supply, and agricultural planning.
The rest of the paper is organized as follows.
Section 2 presents an overview of the study area and the dataset used.
Section 3 describes the research method in detail.
Section 4 demonstrates the spatiotemporal distribution and drought characteristics of homogeneous regional stations in Hunan Province.
Section 5 and
Section 6 provide the discussion and conclusions, respectively.
5. Discussion
The overall distribution of drought events in Hunan Province was determined by analyzing the SPEI-12 time series values of all stations (
Figure 4). From 1965 to 2015, the province experienced eight drought events that lasted for more than 10 months (
Figure 4). The interval between drought events from 1965 to 2000 was seven years (1970, 1977, and 1984), and the drought period decreased by two years from 2000 to 2015 (2004, 2006, 2008, 2010, and 2012). Zhang et al. [
54] analyzed the drought in Hunan Province using the rotated empirical orthogonal function and discovered three main drought cycles, namely, 2, 7, and 18 years. The drought time distribution is mainly affected by the atmospheric circulation, in which two- to three-year periodic oscillation is related to the quasi-two-year oscillation (QBO) of the tropospheric atmospheric circulation, and the six-year cycle is related to the seven-year quasi-period of the five-year related ENSO event. On average, the drought at each station lasts for about two months, and summer and winter droughts are prone to occur. Zhang et al. [
40] analyzed the annual and seasonal distributions of droughts in Hunan Province between 1989 and 2008. During this period, nine summer droughts, six winter droughts, and five spring and autumn droughts occurred, a result that is consistent with the conclusion of this study. This study proved this conclusion on a longer time scale. A possible reason Hunan Province is prone to summer drought is that it is affected by the Pacific subtropical high-pressure air mass. When the airflow does not go from south to north, a strong summer drought emerges, and this phenomenon occurs in the Yangtze River Basin. This condition once again proves the strong influence of atmospheric circulation on regional drought.
This study proposed an effective framework for dividing meteorological stations through an SPEI time series by using the K-means clustering method to analyze the spatial distribution characteristics of partial drought. The results showed that Hunan Province can be divided into three clusters. Cluster 1 stations are mainly distributed in the high-altitude areas of Hunan Province, and Cluster 2 stations are mainly distributed in southern Hunan. Cluster 3 stations are mainly distributed in basins and hilly areas and prone to severe drought. An interesting phenomenon is that Nanling Mountain serves as the dividing line between Clusters 1 and 2, which further proves the influence of topographical factors on drought [
14]. Yang et al. [
55] reported that in plateaus and mountainous areas with complex terrain, the order of drought spread varies with altitude. Agricultural droughts in high-altitude areas evolve into hydrological droughts. The opposite is true for low-altitude areas. Altitude plays a key role in the spread of drought in plateau areas. Wang et al. [
56] studied the spread of drought in 16 sub-basins of a river basin from 1980 to 2014, and the results showed that the topographic index and hydrological drought have a significant correlation. These experimental results show that topographical factors are important content in drought research whether in plateau or watershed areas. The impact of Hunan Province on drought due to its special topography is also obvious. The reason is manifested in two aspects: (1) The high altitude affects the distribution of airflow, which in turn affects precipitation. (2) The second aspect is directly affected by photosynthesis. Topography also indirectly influences vegetation types, and the drought-resistance capabilities of various vegetation types still differ [
57]. All three clusters can be identified as severe droughts. However, for several drought events in similar specific periods, the dry/wet transition of each cluster is different (
Table 5), which also proves the regional differences in the spatial distribution of drought in Hunan Province.
The effective zoning of drought in Hunan Province can allow the government to make powerful decisions on allocating water resources and preventing droughts. The north-western region of Hunan, Cluster 1, is prone to slow, low-intensity drought. This area is mainly the woodland area. Therefore, woodland species must be diversified to create a good ecological environment. The southern part of Hunan in cluster 2 is prone to rapid high-intensity drought, and preventive measures are difficult. Water conservancy project construction should be implemented properly. Dongting Lake and its four main rivers (Xiangjiang, Zishui, Yuanshui, and Lishui) should be introduced into the area to effectively solve the water supply problem. Cluster 3 has diversified vegetation and is prone to severe drought. Given the large area of arable land in this cluster, the planting structure needs to be rationally improved, and food security issues must be reduced.
We selected several drought characteristics extracted from SPEI through the operational theory for clustering. Results showed that Hunan Province is divided into three arid regions, each of which has similar characteristics. However, this study only performed clustering of drought characteristics. The regionalization of aridity is affected by airflow, topography, vegetation types, and human activities. These factors should be considered in the future.
The framework proposed in this study is flexible. For different regions, we can add other drought features. For example, wavelet analysis can be used to extract sites with similar frequency spectra for clustering. Considering the impact of terrain on drought, we can compare SWAT’s DEM division results with drought feature division results via overlay analysis. Moreover, the main factors that affect drought can be extracted through principal component analysis then clustered. For the clustered results, we can use cross-wavelet analysis to compare the correlations between subregional drought and hydrology, vegetation, climate, and other variables to ensure the results are convincing.