Drought is considered one the most devastating disasters and can affect a host of economic and social activities [1
]. Drought adversely impacts both surface and groundwater resources, with effects including reduction of water supply, water quality deterioration, crop failure, vegetation productivity loss, hydropower generation decrease, and riparian habitat destruction [2
]. Furthermore, some developing regions disproportionately affected by droughts have poor ability to cope with drought, which increases wealth inequality between the rich and the poor [7
]. Due to the severe consequences of drought, it is critical to assess the variability of drought and determine the most influential contributing factors.
To monitor and analyze drought, many indices have been developed [8
]; these include the Standardized Precipitation Index (SPI) [10
], Standardized Antecedent Precipitation Evapotranspiration Index (SAPEI) [11
], and Standardized Precipitation Evapotranspiration Index (SPEI) [12
]. SPI has a wide range of application due to its simple calculation and the easily accessible input data—that is, precipitation. However, this simplicity has advantages and disadvantages. The application of SPI in semi-arid and arid regions is often limited by the fact that it only considers precipitation and neglects other factors such as temperature and evapotranspiration that play an important role in drought monitoring [13
]. The SPEI is regarded as one of the most reliable to identify drought as it comprises both precipitation and evapotranspiration, parameters that significantly impact drought [16
]. In addition, one crucial advantage of the SPEI over other widely used drought indices that consider PET is that its multi-scalar characteristics enable it to identify different drought types and their effects in the context of global warming [15
]. Therefore, compared with SPI, the SPEI is the most suitable for the study of drought in the Chinese mainland, which exhibits a variety of climate types. The SPEI is more sensitive to changes in potential evapotranspiration (PET) than other parameters, especially in arid regions such as the Sahara and Middle East [20
]. PET is the most important variable to consider when using the SPEI for drought analyses. The SPEI can return significant conflicting results regarding changes in drought due to differences between calculations of PET; these different calculations include the simple Thornthwaite (TH) equation, the Hargreaves (HS) equation, and the Penman–Monteith (PM) equation [12
Recent studies have compared the effects of different PET equations on drought indices. For example, Van der Schrier et al. [24
] and Dai [25
] compared the effects of using the TH and PM equations for PET to obtain the Palmer Drought Severity Index (PDSI) on a global scale and reported no difference in the resulting PDSI trends. However, Sheffield et al. [26
] reviewed these studies and argued that errors in the forcing data or the calibration period were sufficient to explain why no differences were found and concluded that there were in fact large differences in the PDSI when using either the TH or PM equations for PET. In fact, they saw that the two approaches disagreed in the sign of the trends across America, Eastern sub-Saharan Africa, Western Russia, Southeast Asia, and Australia. In regions that are water-limited, the actual evaporation is small, and therefore the impact of PET on the PDSI is also small. However, in energy-limited regions, the differences in PET can translate into large differences, even changing the sign of the PDSI trends. Begueria et al. [17
] compared the effect of using the TH, HS, and PM equations for PET when calculating the SPEI (SPEI_th, SPEI_hs, and SPEI_pm) at the global scale. They postulated that the magnitude of the differences between the SPEI series based on different PET equations might be related to the weight that the different equations give to precipitation relative to the climatic water balance at each site; at the global scale, all three PET equations returned downward trends, but the trends from SPEI_th and SPEI_pm were stronger than that from SPEI_hs. In general, these differences were more pronounced in semi-arid to mesic areas and less pronounced in humid regions. Clearly, the equations used to calculate PET can greatly influence the SPEI [17
The PM equation has been recommended as the sole standard method for the computation of PET by Food and Agriculture Organization (FAO) [29
]. It is physically based and explicitly incorporates both physiological and meteorological parameters. In the context of global climate change, other climate variables can also produce changes in drought and the temperature-based TH and HS are not as accurate as PM when data are abundant. Therefore, because the PM equation considers more meteorological factors and physical mechanisms, it is preferred when calculating SPEI. Where data quality is questionable, or where historical data are missing, HS is commonly recommended because it is surprisingly comparable to PM over a wide range of climates [30
]. Due to data limitations, the TH equation is most widely used because it only considers temperature, and this makes TH the most primitive equation for calculating PET. It has been widely used because its calculation principle is simple and requires less data, and it still effectively reflects changes in evapotranspiration, especially in arid and semi-arid areas [17
]. However, due to the large number of input variables (e.g., wind speed, relative humidity, and radiation), there are some uncertainties in the calculation of the PM equation [17
]. Thus, the effect of using different PET equations for calculation of drought indices remains an open question.
As China has suffered several prolonged and severe droughts in recent decades, such as the 2009–2010 drought in Southwest China, Eastern China drought during 2013, and North China drought in 2014 [32
], the study of drought has received increasing attention. Yu et al. [18
] investigated the dryness/wetness cycles in China using the SPEI calculated with the TH equation and found that a significant trend toward drier conditions was present in most of China. However, Wang et al. [34
] used the self-calibrating Palmer Drought Severity Index (scPDSI), obtained with the PM equation, to show that China experienced an increasing wetness trend at both the annual and seasonal scales. The differences in drought trends may be closely related to the indices used and use of different equations to calculate PET [18
]. China has a diverse climate and the most suitable PET equation may be different for different regions [35
]. Therefore, it is essential to use the appropriate PET methods for drought analyses in different climatic regions [20
]. However, few studies have attempted to identify the most suitable PET equation for calculating the SPEI for different agricultural regions in China.
In addition, the availability of water resources in China is strongly influenced by climatic conditions. Fluctuations in water resource availability are often linked to anomalous atmospheric conditions, as they can increase or decrease rainfall and affect key parameters (e.g., temperature, soil moisture, and evaporation) of the hydrological cycle which significantly influence the occurrence of droughts [1
]. For instance, El Niño-Southern Oscillation (ENSO) is the strongest inter-annual signal of the coupled air–sea system in the tropical Pacific. It not only impacts the oceanic parameters within the Pacific but also influences the climate of areas bordering the Pacific, as well as the global climate [40
]. Wang et al. [41
] revealed that ENSO events had a strong influence on regional precipitation in the Yellow River Basin of China, resulting in a 51% decrease in runoff to the sea. Zhang et al. [42
] reported an (in-phase or antiphase) interconnection between ENSO and the annual maximum streamflow from the upper to the lower Yangtze River Basin. These studies demonstrated that large-scale atmospheric circulation factors have a strong influence on the climate of China. However, the dominant atmospheric circulation indices that affect drought in each agricultural region of China remain unclear. Furthermore, relatively few studies have analyzed the relationships between drought and atmospheric circulation factors for different agricultural regions in China.
Therefore, the objectives of this study were to (1) select the most suitable PET formula to calculate SPEI, (2) to determine the drought trends and periodic features in different agricultural regions of China, and (3) to deduce the relationships between atmospheric circulation factors and drought. This study deepened our understanding of historical drought events in different agricultural regions of China and provided a scientific basis for the management of future agriculture water resources.