1. Introduction
As a hydroclimatic hazard, drought poses a serious threat to society, economy, ecosystem and other sectors [
1,
2]. The droughts have been increasing worldwide [
3,
4]. Droughts occur in any climate zone, and their properties (frequency, duration, and severity) may differ from one to another [
5]. Quantitative assessment of drought features and its development is essential to understanding different drought types at scales from the local to the global [
6]. However, there is currently no general consensus on the definition of drought [
4,
6,
7,
8,
9,
10,
11,
12], which has been a stumbling block in drought monitoring and analysis. The American Meteorological Society [
13] summarized dozens of drought definitions into four categories: meteorological, agricultural, hydrological and socioeconomic droughts. The four categories are associated with different components of hydrologic cycle [
14]. Generally, precipitation is the driving and critical factor in the hydrologic cycle. The absence or reduction of precipitation instigates meteorological drought. Subsequently, short-term dryness in the surface and subsurface layers may result in agricultural drought. Finally, when precipitation deficits stay for a prolonged period, low recharge from soil to water features (lakes, groundwater, and rivers) causes a delayed hydrological drought [
4,
15]. The propagation from meteorological drought to hydrological drought is characterized in terms of pooling, time lag, attenuation, and lengthening [
16,
17,
18]. That is to say, meteorological drought, which is defined as a lack of precipitation over region for a period of time [
4], plays an important role in subsequent drought formation and propagation across different drought types [
19].
In practice, drought assessment for a specific area is often required for disaster prevention from local agencies or communities [
20]. Akhtari et al. [
21] pointed out that tracking the droughts in cities is one of the most objectives of drought mapping. Zhang et al. [
22] assessed drought vulnerability with SPI taking county as a study unit and the assessment was useful for early warning of regional droughts [
23]. The National Temperature and Precipitation Maps of the National Oceanic and Atmospheric Administration’s National Centers for Environmental Information provides the drought products at different scales from national, regional, statewide, and divisional. Hydrologically, the region impacted by drought is not only limited to river network and its vicinity, but also the whole basin [
24]. As an elementary unit of hydrologic processes, basin with a high degree of functional integrity contains abundant hydrological information. Basin-scale analysis is beneficial to reveal the interactions among multiple hydrologic variables [
25]. Thus, it is highly worthwhile to study meteorological drought at a basin scale.
In recent decades, many drought indices have been proposed, such as Z-index Palmer [
8], Palmer Drought Severity Index (PDSI) [
8], Standardized Precipitation Index (SPI) [
26], Effective Drought Index (EDI) [
27], Reconnaissance Drought Index (RDI) [
28], Standardized Precipitation Evapotranspiration Index (SPEI) [
29]. Among these indices, the SPI has been widely applied in many aspects [
3,
27,
30,
31,
32,
33,
34,
35,
36,
37], and recommended as a standard index for tracking meteorological droughts by the World Meteorological Organization [
38]. At present, two processing schemes are frequently adopted for regional drought assessment. In the first case, SPI is first calculated from precipitation for each individual stations and then regional SPI is obtained by averaging the SPI values of all the stations (SPI-mean, hereafter, Case A). In the second case, regional precipitation is at first obtained by averaging the precipitation of all the stations and then SPI is calculated from the average precipitation (precipitation-mean, hereafter, Case B). Livada and Assimakopoulos applied the Case-A processing scheme to analyze temporal trend of droughts in Greece [
31]. Zhai et al. quantitatively analyzed frequencies of dry and wet years and its tendency for 7 basins and 3 regions in China with the time series of averaged annual SPI [
39]. Gao and Zhang used the mean annual SDI and SPI series to disclose a tendency towards wetter condition in the Hexi Corridor, China [
40]. Dash et al. investigated the characteristics of meteorological droughts in Bangladesh using SPI obtained with the Case-B scheme [
41]. The characteristics (frequency, duration, and severity) of historical drought events based on SPI series obtained from areal mean precipitation are often applied to construct and examine the probabilistic models (using the Case B processing scheme) [
42]. For drought assessment of an entire region based on local observations, either of two processing schemes is frequently used. Two processing schemes both involve space average. Hence, the difference between two processing schemes may result from the heterogeneity of precipitation. Furthermore, the mean-precipitation and mean-SPI schemes may produce different SPI values, providing that the local observations are generally not homogeneously distributed [
43]. There are few studies to compare two processing schemes, leaving a gap for users to select a suitable scheme for reliable assessment of regional droughts.
The areal SPI values are the crucial basis and input variables for drought evolution, regional drought vulnerability assessment, and drought quantitative models. Therefore, the reasonable areal SPI series are of great importance. However, the different processing schemes might result in the dissimilar estimation results. Thus, this study uses both processing schemes to compare their pros and cons, and attempts to provide evidence on selection of an advisable scheme for drought analysis. First, in combination with the Thiessen polygon weighting approach, it testifies the frequency distribution of available data of monthly precipitation. Then, the characteristics of drought events obtained from two processing schemes is analyzed and compared. The Poyang Lake Basin in China is taken to be a case study area for its geographic and ecological importance. Moreover, the basin contains Poyang Lake wetlands which is in the first batch of The Ramsar Convention List of Wetlands of International Importance [
44]. However, during the decade, the Poyang Lake wetlands have been under constant threat from anthropogenic activities and droughts [
45,
46]. The study could provide implications for regional drought analysis. Meanwhile, researches in this region could provide some clues to monitor drought in other similar areas.
4. Discussion
This study investigated the effects of two different processing schemes on meteorological drought assessment with long-term precipitation data in the Poyang Lake Basin for 1957–2014. The results provide the important evidences on selection of a suitable scheme for reliable assessment of regional droughts.
In a view of drought identification, both processing schemes revealed the similar long-term trend, number of drought events, and drought duration as well. Hence, the two schemes were often used to analyze the characteristics and tendency of regional drought [
31,
56]. However, the drought severity was generally alleviated in the case of the SPI-mean scheme compared to that of the precipitation-mean scheme. In the study of Dash et al. [
41], we noticed that the SPI series from selected individual stations had barely extreme SPI values (SPI > 3.0 or SPI < −3.0), whereas the extreme values (even SPI > 4.0 or SPI < −4.0) were found frequently in SPI series obtained from the regional average of observed data in the research. The details exhibited by Dash et al. are consistent with the results of the research. Additionally, the relationship between drought characteristics, especially drought duration and drought severity, is often used for constructing drought probabilistic models [
42,
56]. However, due to the difference in drought duration and severity obtained from two processing schemes (
Figure 4), it might affect the drought quantitative approaches related to them.
From a perspective of regional drought monitoring, ΔSPI between two SPI series had a significantly positive correlation with the number of stations (
p < 0.005). The number of stations recording less precipitation at the same period could be applied to represent the severity of dry condition (i.e., precipitation deficit). Therefore, the more the number of stations (i.e., precipitation deficit) were, the larger the difference obtained from two processing schemes was (
Table 5). When one drought occurs over the region, the quantitative assessment should be taken seriously. In addition, the study is relevant in satellite remote sensing of precipitation and its application to monitor regional drought [
57]. Satellite precipitation data generally cover large areas (e.g., 25 km for TRMM). In essence, these data can be considered as spatially averaged. Use of these data may generate different results from those of ground observations. Therefore, the research can provide implications for accurate drought monitoring using satellite precipitation data.
A quantified comparison with two SPI series was carried out addressing a significantly positive correlation between Case A and Case B for every month. The annual tends of the slope values of the linear regression at first increased from January and then decreased after June. The slope values were larger than 1.20 in March-September, suggesting the greater difference between precipitation-mean scheme and SPI-mean scheme. From a perspective of the transformation from precipitation density to cumulative probability to SPI calculation, the study revealed the causes of the difference between two processing schemes. The precipitation-mean processing scheme averaged and weakened the extreme precipitation situations and made the precipitation more clustered in some certain (
Table 3 and
Figure 2). However, when less precipitation was recorded by most or all of the meteorological stations over the basin, less precipitation would be deviated from the new precipitation-mean series more seriously (
Figure 8). Smaller density was found in the precipitation-density curve. Comparison with the cumulative probabilities for other meteorological stations at the less precipitation, the cumulative probability for Case B was less. Therefore, the less SPI value was calculated through the inverse of cumulative standard normal distribution function (
Figure 8). Based on calculation principles, the mean-precipitation processing scheme is first linear and then nonlinear transformation, and the mean-SPI scheme just the opposite. It is just in the homogeneous areas that the two processing schemes may produce the same results.