1. Introduction
As the global climate warms, the water cycle accelerates, and extreme weather events such as droughts and floods have become more frequent and severe [
1]. A drought is a disaster that causes major agricultural losses [
2]. Recently, drought frequency has increased, and China’s annual economic losses caused by droughts are as high as billions of dollars, which has a large impact on its national economy [
3]. Compared to other forms of natural disasters, droughts always cause huge losses. Drought has become an important factor influencing global terrestrial ecosystems. Drought has a significant impact on global carbon and water cycles, which in turn affects human production and life. With the development of drought research, the relationship between the water use efficiency (WUE) and droughts has gradually become the focus of examination. The WUE is an important indicator of the carbon–water cycle coupling and plays a crucial role in ecosystem management [
4,
5]. Therefore, the impact of drought on the ecosystem WUE has become a hot research topic, which can improve the current understanding of the relationship between droughts and the ecosystem’s carbon–water cycle.
An increasing number of scholars have explored the WUE. There are many definitions of the WUE [
6,
7], the more common of which are the net primary productivity (NPP)/evapotranspiration (ET) and gross primary production (GPP)/ET. The time scale of the WUE can also be divided as instantaneous, daily, seasonal, and annual [
8]. Spatially, the variation characteristics of the WUE can be studied using multiple spatial scales, such as latitude (LAT), altitude [
9], ecosystem type [
10], and biome [
11]. Several previous studies have successfully demonstrated the obvious spatial heterogeneity of the WUE [
7,
9,
10,
11]. Some researchers investigated the WUEs of different ecosystems and found that changes in different species are affected by the structure of plants. Under the same external conditions, the WUE increased in areas with closed shrubs and woody sparse trees but decreased in all other grassland types [
12]. Moreover, related research on the WUE in mixed plantations provided a reference for the rational use of water resources in forest ecosystems [
13]. However, cultivated land is different from other natural ecosystems, because it is managed by humans and is closely related to human development. Chinese agriculture uses only 7% of the world’s cultivated land to feed approximately 22% of the global population [
14]. As the main grain-producing area in China, the Huang–Huai–Hai Plain (HHHP) has contributed to 45% of the total grain production in China over the past 30 years. Studies have shown that droughts in the winter and spring in the HHHP impact the yields of winter wheat and summer maize [
15,
16]. Climate change and ecosystems are closely related to drought [
17]. Therefore, to better predict the response of cropland ecosystems to climate change, it is necessary to explore the relationship between the cropland water use efficiency (CWUE) and drought.
Droughts are a traditional topic of research that has gained increasing attention in recent years [
18]. Accurately reflecting drought characteristics and identifying drought events remain difficult tasks. With advances in drought research and improved accuracy in drought-characteristics measurement methods, the use of multi-source data combined with data mining technology to monitor droughts has become a new approach for drought research [
19,
20,
21,
22]. The optimized drought index provides a possibility for an in-depth study of droughts and an ecosystem’s carbon–water cycle. Most previous studies indicate variations in the WUE response to drought on an annual scale [
23] and have shown a significant discrepancy in different regions and ecosystems. Based on climate divisions, there is a negative correlation between the WUE and drought in arid regions, while in humid regions, WUE has both positive and negative correlations with drought [
7]. According to geographic divisions, drought has an enhancement effect on WUE in Southwest China [
23]. The WUE is negatively correlated with drought in Northeast China and Central Inner Mongolia, while it is positively correlated with drought in Central China [
6]. Based on different ecosystems, droughts have lagged effects on the WUEs of shrubs and sparse vegetation, presenting marked differences compared with forest ecosystems [
24]. Current research on the WUE is quite successful at both time and spatial scales. However, to date, few studies in the world have focused on the correlation between CWUE and drought during drought events in the HHHP.
Based on meteorological station data, remote sensing data, and other data, this study used the Cubist algorithm to establish the monthly integrated surface drought index (mISDI) dataset in the HHHP and used the run theory to identify drought events. The correlation analysis between drought events and the CWUE (CWUE = GPP/ET) based on mISDI in the HHHP from 2001 to 2020 will help farmers to establish climate-induced disaster risk awareness, develop advanced vegetation strategies based on empirical knowledge and reduce the damage caused by abrupt environmental changes. The results of this study may assure food production and reduce the impact of drought disasters by analyzing the temporal and spatial trends of droughts and their impact on the CWUE.
4. Discussion
The main contributions of this study are twofold. First, we conducted a targeted study on the CWUE and obtained the relationship between cultivated land and drought in the HHHP from the perspective of the WUE, which has never been reported in previous studies. This study provides a scientific basis for the production and development of food safety in China. Second, this study systematically conducted a spatiotemporal analysis of drought in the HHHP from the two aspects of drought events and drought trends, which provided a reference for drought prevention and disaster prevention in the HHHP.
This study explored the spatial and temporal distributions of droughts in the HHHP and the sensitivity of the CWUE to drought anomalies. Five drought events were identified using the run theory. The results showed that the duration of recent droughts was short, but their frequency increased. Moreover, the period of change from soaked to parched gradually shortened. The recent short-term droughts alternating with wet periods are consistent with an increase in climate variability related to climate change [
50]. Recently, Gou et al. [
51] developed a method to identify small-scale extreme flash drought events. Therefore, for future research, we could combine this method to expand the study of drought events in the HHHP.
Huang et al. [
7] previously divided the global ecosystem into arid and humid ecosystems and concluded that droughts had a positive or negative impact on the WUEs of both ecosystems. Although the effects of drought on the WUE have been shown to be region- and biome-dependent, at the spatial scale, dividing ecosystems into arid and humid ecosystems is not sufficiently specific. Liu et al. [
6] analyzed the spatial–temporal changes of the annual WUE in different regions and its response to drought depends on the vegetation types and administrative divisions. However, a differential analysis of the response of the same vegetation type to droughts in different regions has not been conducted. Further research [
23] considered the southwest region as the study area and found that the responses of the WUEs of forests, shrublands, and other ecosystems to droughts vary with the drought severity. In summary, the current research on the relationship between droughts and the WUE has led to different conclusions owing to differences in the scales of the study area and the calculation model used for the WUE. Therefore, the present study indicated that the smaller the study area, the more helpful it is to determine the relationship between the WUE and drought. Thus, we suggest that the discussion of the relationship between drought and the WUE should start at a small scale. This study took the HHHP as the research area, selected the cultivated land ecosystem as the research object and explored the spatiotemporal relationship between the CWUE and drought. In addition to the precise spatial scale, the accurate division of time periods was also the focus of the follow-up research on the CWUE in this study. For example, the vegetation growing season can be used as a criterion for the time scale division.
Many studies have examined the time-lag effects of drought on the WUE [
24,
46,
52]. Some researchers believe that the impact of droughts on the WUE has a lag effect of approximately four months, and this effect exists in 70.87% of the global vegetation areas. For shrubs and sparse vegetation, the lag effect of droughts on the WUE is short (1–4 months) [
24]. Owing to the vegetation sparseness of cultivated land, the time-lag effects of drought on the CWUE in the HHHP obtained in this study were in agreement with results obtained previously [
24]. Moreover, this study clearly concluded that there was a lag period of three months in the response of the CWUE to drought. This may be related to the fact that the cultivated land in this area is mainly irrigated. Previous studies pointed out the low WUE in the North China Plain, where droughts are frequent and water shortages are severe, requiring increasing irrigation over time [
47]. Combined with the conclusions of this study, although the HHHP has experienced flash droughts recently, there is a three-month lag period in the response of the CWUE to drought. Therefore, agricultural production activities in the HHHP need to focus on preventing long-term droughts. For example, artificial rainfall, diversion irrigation, and groundwater irrigation could be used to mitigate the effects of drought. In addition, the changing mechanism of the CWUE is affected by various factors, including temperature, vegetation type, and soil properties [
11,
23,
53,
54,
55,
56], which could be the focus of future research on the CWUE.
However, the current study has some limitations. On the one hand, owing to the influence of multi-source data, the mISDI model has a short time range in the time series. On the other hand, as crop rotation can affect the CWUE by changing the GPP, these effects should be considered in future research. This study has a limited scope in that it relates to cultivated land, but such cultivated land is widespread in this important crop-producing area. In follow-up research, the model developed in our study can be extended to other regions and other countries as part of improving our understanding of the WUE during drought. Increasing the efficiency of irrigation will be important aspect of managing the effects of climate change.
5. Conclusions
This study used multi-source data, such as meteorological stations and remote sensing, and the Cubist algorithm to establish the mISDI dataset of the HHHP from 2001 to 2020. The spatial and temporal distributions of drought events and the impact of drought events on CWUE changes were analyzed. The results are summarized as follows.
The mISDI dataset in the HHHP was established, and the June data were taken as an example to establish a drought index model suitable for the HHHP. SPI03, SOSA, VSWI, AWC, DEM, LON, LAT, and YEAR were selected as independent variables, and scPDSI was a dependent variable.
Five drought events were identified using the run theory. The first drought event had the highest intensity (−1.925) and the longest drought duration (18 months) and was considered as the most severe of all the drought events. However, the gradual decline in the mISDI over the 2001–2020 period indicated a trend of worsening drought conditions in most areas of Shandong Province and in the northeastern part of Henan Province. The recent short-term droughts alternating with wet periods was consistent with an increase in climate variability related to climate change. Further studies are needed to determine if the rapid alternation between droughts and wet periods will continue. Drought had an enhanced effect on the CWUE of the HHHP, and the enhancement of the CWUE in the eastern hilly area was more significant.
Using the most severe drought event during the study period as an example, the correlation between drought and the CWUE was studied. The study found that there was a three-month lag period and a significant positive correlation between the response to drought and the CWUE, which was related to the irrigation habits of the HHHP. The areas with significant correlations were dominated by plains with the flattest terrain in the entire area.
In general, the mISDI has high accuracy in the comprehensive drought index model in the HHHP and has been effectively verified in the monitoring of drought events. From the response results of the CWUE and drought, the conclusions can also be verified by previous studies; however, the conclusions are more precise than those of previous studies. The relationship between multiple influencing factors of the WUE requires special attention. In future research, we plan to compare different regions of the country, explore the impact of drought on the WUE in different LATs and LONs, climatic zones, and biomes and try to reveal the underlying mechanism of drought and the WUE.