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Article

Historical and Future Changes in Meteorological–Hydrological Compound Drought in China

by
Zhuoyuan Li
1,
Er Lu
1,*,
Juqing Tu
2 and
Dian Yuan
1
1
Key Laboratory of Meteorological Disaster, Ministry of Education (KLME), Joint International Research Laboratory of Climate and Environment Change (ILCEC), Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science and Technology, Nanjing 210044, China
2
School of Geographic Information and Tourism, Chuzhou University, Chuzhou 239000, China
*
Author to whom correspondence should be addressed.
Atmosphere 2024, 15(12), 1459; https://doi.org/10.3390/atmos15121459
Submission received: 5 November 2024 / Revised: 29 November 2024 / Accepted: 3 December 2024 / Published: 6 December 2024
(This article belongs to the Special Issue The Water Cycle and Climate Change (3rd Edition))
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Abstract

:
Drought is typically divided into meteorological, agricultural, hydrological, and socioeconomic categories. Generally, the transition from meteorological drought to other types of droughts is known as drought propagation. When drought propagation occurs, different types of droughts may still exist simultaneously or successively. In this study, compound droughts are divided into three categories: hydrological meteorological compound drought (HMD), meteorological hydrological compound drought (MHD), and simultaneous compound drought (SD). ERA5 and CMIP6 data are used for analysis under historical and future scenarios. Different types of compound droughts have emerged in extreme centers in different basins. Our analysis indicates a significant upward trend in the duration of these three compound droughts from 1979 to 2022. Additionally, our projections under SSP5-8.5 and SSP2-4.5 suggest a substantial increase in the occurrence of various compound droughts. HMD, MHD and SD all show a consistent upward trend under the future scenario above the moderate-drought level. MHDs are projected to experience the most significant increase compared to the historical period in the far-future period (2066–2099) under SSP5-8.5.

1. Introduction

Droughts cast a profound impact on human society. The persistent and frequent incidence of drought not only leads to considerable socioeconomic damage—with agriculture suffering the most severe repercussions—but also initiates a series of ecological crises [1,2]. These crises encompass acute water scarcities, intensified desertification processes, and a relative frequency of sandstorms [3,4,5]. In socioeconomic field, the United States incurs an approximate annual loss of USD 6.4 billion, while the European Union faces about EUR 9 billion in losses. In India, severe drought conditions can diminish GDP by an estimated 2% to 5% [6,7,8].
Previous studies categorize droughts into four distinct types: meteorological drought (MD), hydrological drought (HD), agricultural drought, and socio-economic drought [9,10,11]. MD is defined by a deficit in precipitation over a designated period. HD is defined as atypical deficits in water resources, attributable to reduced precipitation and resulting imbalances in surface or groundwater budgets. Indicators such as annual (or monthly) runoff, daily river flow, and water levels serve as critical metrics for assessing this type of drought [11,12]. MD can lead to increased evapotranspiration, reduced soil water content, and decreased runoff and groundwater levels. As these effects accumulate, they may escalate into hydrological droughts. This transition is known as drought propagation [13,14,15,16]. However, the relationship between meteorological and hydrological droughts is not straightforward. When examining time scales, meteorological and hydrological droughts do not exhibit a one-to-one correspondence, meaning that changes in MD do not always immediately result in HD [17]. Due to the time lag effect, drought conditions may be influenced by both MD and HD [18,19]. Therefore, when analyzing the drought conditions in a particular region, it is necessary to consider both types.
Recent researchers have conducted comprehensive assessments of various compound extreme events in China, including diurnal compound hot extremes, compound dry–hot extremes, compound wet–hot extremes, and compound flooding events in coastal regions [20,21]. The IPCC’s Sixth Assessment Report (AR6) [20,22] classified compound events definitions into preconditioned events, where a weather-driven or climate-driven precondition aggravates the impacts of a climatic impact-driver; multivariate events, where multiple drivers and/or climatic impact-drivers lead to an impact; temporally compounding events, where a succession of hazards leads to an impact; and spatially compounding events, where hazards in multiple connected locations cause an aggregated impact. Previous researchers [23,24] introduced the concept of ‘compound drought events’ at a singular reservoir site. Xue et al. [25] defined compound drought as MD, HD, and agricultural droughts that occur at the same time. The effects of drought on population exposure and GDP were studied. Due to the expected rapid growth in GDP, the exposure of GDP to compound drought events in almost all regions of China is projected to increase in the future, especially in eastern China. All of these studies focused on simultaneous compound droughts, but did not take into account the successive compound drought. However, the effects of successive events of multiple droughts are more pronounced than those caused by a single drought. Therefore, it should also be discussed as a type of compound drought. Drawing upon these definitions, our study establishes the conceptualization of meteorological–hydrological compound drought.
The IPCC’s AR6 [22] points out that current emission trajectories are likely to maintain the upward trend in global surface temperatures and strengthen the hydrological cycle. Such an amplification is expected to change the spatial distribution of droughts across China, forcing a general rising trend of aridification. Based on historical observations and simulations for the 21st century, the AR6 emphasizes that increased warming has led to high atmospheric evaporation, intensifying the severity of drought events. Notably, land areas are warming more significantly than oceans, affecting atmospheric circulation and generally reducing near-surface relative humidity, resulting in regional drought phenomena. The AR6, as interpreted by Seneviratne et al. [26], suggests that even with global warming stabilized at a 1.5 to 2.0 °C increase, some regions will still face more frequent and severe agroecological droughts. The most vulnerable regions include central and western Europe, the Mediterranean, eastern and southern parts of Australia, large portions of South America, Central America, North America, and southwestern parts of Africa. In light of ongoing warming trends, the progression of drought conditions continues to be a critical issue to pay attention to.
The remainder of this paper is structured as follows: Section 2 delineates the data and models employed to evaluate historical and prospective conditions. Additionally, this section explains the definition of compound drought. Section 3 presents an analysis of the relative frequency of various drought categories and their anticipated future alterations. Section 4 incorporates the discussion and the conclusions.

2. Data and Methods

2.1. Data

This study employed reanalysis data for average monthly temperature, precipitation, and runoff from the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis data (ERA5) (https://cds.climate.copernicus.eu/ (accessed on 2 February 2024)). It covers the global climate from January 1940 to the present and has a high resolution of 0.25° [27]. To compare the precipitation effects of ERA5, we also utilized precipitation data from the Global Precipitation Climatology Centre (GPCC) with a resolution of 1° [28].
The CMIP6 monthly dataset comes from the Global Coupled Model Intercomparison Project (CMIP6, https://pcmdi.llnl.gov/CMIP6/ (accessed on 10 March 2024)), which is part of the World Climate Research Program’s (WCRP) ongoing efforts since 1995. The latest phase, CMIP6, and its branch, the Scenario Model Intercomparison Project (ScenarioMIP), focus on understanding how different future scenarios could affect the physical aspects of the climate and the societal consequences of climate change. ScenarioMIP combines various Shared Socioeconomic Pathways (SSPs) with different levels of radiative forcing to predict climate outcomes [29].
Our study used historical monthly data from 17 climate models (shown in Table 1) in CMIP6, as well as projections for precipitation, temperature, and runoff from 2015 to 2100 under two scenarios: SSP2-4.5 and SSP5-8.5. These 17 models were chosen because their historical and forecasted data include both monthly precipitation, temperature, and runoff. SSP2-4.5, which is a medium emission scenario, expects that by 2100, the amount of energy added to the Earth’s system (radiative forcing) will level off at 4.5 W/m2, leading to a temperature rise of about 2.7 °C. On the other hand, SSP5-8.5 is a high-emission scenario that predicts that CO2 emissions will double by 2050, driven by fast economic growth reliant on fossil fuels and high-energy lifestyles. This scenario suggests that by 2100, the average global temperature could increase by 4.4 °C, with radiative forcing stabilizing at 8.5 W/m2 [22,29,30]. Table 1 presents the full names of the 17 climate models referenced in this study, along with the resolution specifications for each model. Bilinear interpolation was employed to normalize the data to a 1° resolution for subsequent analyses.

2.2. Definition of Drought Index

2.2.1. Meteorological Drought and Hydrological Drought Indexes

In this study, we utilize the Standardized Precipitation Evapotranspiration Index (SPEI) and the Standardized Runoff Index (SRI) to quantify the severity of meteorological and hydrological droughts, respectively. The SPEI is a globally recognized drought index that combines the sensitivity of the Palmer Drought Severity Index (PDSI) to evapotranspiration with the spatial uniformity and temporal adaptability of the Standardized Precipitation Index (SPI) [31,32,33,34]. It quantifies wet/dry anomalies using precipitation and temperature data across multiple time scales (e.g., 1, 3, 12, 24 months). A high SPEI value indicates wet conditions, while a low value indicates dry conditions. This index is particularly suitable for regions with significant temperature fluctuations and is advantageous for longitudinal studies. Similarly, high SRI values denote wet conditions, and low values denote dry conditions. Potential evapotranspiration is computed using the Thornthwaite method [35], a temperature-based approach. The specific calculation method can be found in previous literature [32,33,34,35].
The SPEI is predicated on a straightforward water balance equation, which is essentially the difference between precipitation and potential evapotranspiration. This index is calculated using the following formula:
D i = P i − P E T i
D i represents the water deficit in the i month, P i denotes the precipitation in the i month, and P E T i is the potential evapotranspiration during the same period. The potential evapotranspiration P E T i is computed utilizing the Thornthwaite method [TOBE]. The SRI and SPEI have a similar calculation process, but the SRI takes runoff as the loss condition, and then combines it with the standardization process to obtain the SRI series. Below is an example using the SPEI:
The cumulative probability density is normalized as shown:
P = 1 − F ( x )
When the cumulative probability P ≤ 0.5 ,
W = − 2 ln ⁡ P S P E I = W − c 0 + c 1 W + c 2 W 2 1 − d 1 W + d 2 W 2
When the cumulative probability P > 0.5 ,
W = − 2 ln ⁡ ( 1 − P ) S P E I = W − c 0 + c 1 W + c 2 W 2 1 − d 1 W + d 2 W 2 − W
In this equation, c 0 = 2.5155 ; c 1 = 0.8029 ; d 1 = 1.4328 ; d 2 = 0.1893 .
These calculation programs are available on a website developed with Python, provided by the National Centers for Environmental Information (NCEI) (https://climate-indices.readthedocs.io/en/latest/index.html (accessed on 10 February 2024)).
Classification of wet and dry conditions according to the SPEI and SRI is shown in Table 2. As mentioned before, the lower the number, the more severe the drought. Many different thresholds are applied in different studies [33,36,37,38], and the most common threshold definition is selected for study. In order to facilitate comparison, SPEI1 and SRI1 on the monthly scale were selected for this study.

2.2.2. The Definition of Compound Drought

In our study, we examine compound droughts within three distinct definitions. As shown in Figure 1.
  • Hydrological meteorological compound drought (HMD): The HMD involves an HD preceding an MD.
  • Meteorological hydrological compound drought (MHD): The MHD involves an MD preceding an HD.
  • Simultaneous compound drought (SD): The SD involves meteorological and hydrological droughts that coincide simultaneously.
The run theory [39] is used to extract the characteristics of compound drought. According to the run theory, the start time of compound drought is defined as the onset of the meteorological or hydrological drought, and the end time as the conclusion of the corresponding hydrological or meteorological drought. Furthermore, the negative value of the drought index in each month is the intensity (I). The cumulative I during drought duration is defined as the severity (S). Note that the way to calculate features of MHD (HMD) and SD are slightly different. Referring to previous studies [10,33,34,35,40], the formulas for these indices are as follows:
Duration of HMD/MHD/SD:
Duration = Tstart − Tend
Severity of MHD/HMD:
S M H D ( H M D ) = ∑ H D e n d H D s t a r t S R I i + ∑ M D e n d M D s t a r t S P E I i
where ‘ i ’ represents a certain month in compound drought, ‘ H D s t a r t ’ and ‘ H D e n d ’ mean the start time and the end time of HD; ‘ M D s t a r t ’ and ‘ M D e n d ’ mean the start time and the end time of meteorological drought.
Drought intensity is usually defined as the absolute value of drought indexes. Here, the absolute values of the SPEI/SRI are used to represent the drought intensity. According to the definition by former researchers [24,25], the drought intensity of SD was determined based on the following criteria:
I i 1 = S P E I i o r S R I i S P E I i − S R I i = 0
I i 2 = S R I i S P E I i − S R I i > 0
I i 3 = S P E I i S R I i − S P E I i > 0
Severity of SD:
S S D = ∑ S D e n d S D s t a r t ( I i 1 + I i 2 + I i 3 )
Average intensity of HMD/MHD/SD:
I = S e v e r i t y D u r a t i o n
As outlined in Table 2, compound drought events are categorized into mild, moderate, severe, and extreme levels. In various studies [41,42,43], drought events separated by intervals of 1 to 3 months are still classified as continuous. However, this study exclusively considers compound events that are typical and exhibit an uninterrupted, continuous timeline without any breaks.

3. Results

3.1. Historical Data Analysis

Figure 2 shows the spatial distribution of annual mean precipitation, annual mean runoff, monthly mean temperature, and annual mean evapotranspiration calculated using ERA5 reanalysis data from 1979 to 2022. Consistent with the findings of prior studies [44], the precipitation spatial distribution shows higher values of precipitation in southern parts of China and the southern parts of the Tibetan Plateau. In the southern parts of the Tibetan Plateau, ERA5 overestimates GPCC by more than 500 mm, which is consistent with the conclusions of previous studies. This overestimation poses a risk of underestimation of drought severity when using the SPEI for historical drought analysis. The runoff distribution, particularly in the Tibetan Plateau as depicted in Figure 2b, is characterized by anomalous values, demanding careful consideration in subsequent calculations. The monthly mean temperature data, presented in Figure 2c, exhibits a distinct latitudinal pattern, corroborating the trends reported in extant literature [4]. Despite certain deviations, ERA5 reanalysis data are characterized by excellent data stability, comprehensive data types, and high resolution [45]. Consequently, to ensure consistency with the runoff data, this study has opted to utilize ERA5 data for subsequent research.
Table 2 clearly illustrates that lower drought index values indicate more severe drought conditions. Consequently, the descending trend observed in Figure 3 shows an increasing trend of drought conditions across the regions throughout the period examined. Corroborating this observation, previous studies [45,46] indicate a marked reduction in the average number of annual precipitation days in China from 1961 to 2020, pointing to a deepening of drought conditions. Specifically, Figure 3a shows a marked move towards MD in most of eastern China, while Figure 3b shows a notable increase in HD in the southern parts of the country.
Figure 4 is a map of river basin distribution in China. Figure 5 illustrates the relative frequency of droughts for SD across China. The relative frequency of SD in most parts of China is about 30%, and the distribution of total SD is relatively even. Notably, mild droughts frequently impact the central regions of northern parts of China and the southeastern coastal areas, as depicted in Figure 5b. These regions experience a higher incidence of droughts compared to the west. However, in terms of the relative frequency of extreme drought (Figure 5e), the relative frequency of extreme drought is higher in western China. In general, the SD formed two arid centers in the Yangtz River basin and the Pearl River basin.
Figure 6 displays the relative frequency of compound drought events where HDs succeed meteorological ones. The relative frequency of HMD surpasses that of SD. Notable drought centers have developed in Huaihe River basin and Haihe River basin, indicating a significant occurrence of HMD (Figure 6a). In the Yangtze River basin, moderate droughts are more frequent, forming a prominent center. Moreover, likely due to SD, the incidence of extreme droughts is more obvious in the central and western regions of China than in the east. It is noteworthy that in the Sichuan basin, there was an epicenter of extreme drought.
Figure 7 depicts the relative frequency of compound droughts where MDs precede hydrological ones. Compared to the other two types of events, these occur less frequently. Within the mild drought category (Figure 7b), southern parts of China experience a higher relative frequency than the northern parts. The main drought centers occur in the southeast coastal zone and the Yangtze River basin. Extreme drought occurrences are notably rare. Consequently, HMD and SD are more common than MHD, leading to more negative effects on society, economy, and agriculture.
Figure 8 illustrates the average duration of different compound drought events spanning from 1979 to 2022. The data reveal a consistent increase in duration across all three types of compound drought events, indicating a growing impact of compound droughts on the climate. Notably, the SD exhibits the most pronounced growth trend, while the MHD presents the highest extreme values, exceeding a value of 2. Despite the total frequency of HMD being higher than that of MHD (refer to Figure 6a), the average duration of HMD remains shorter than that of MHD. In terms of the trend changes for different events, HMD increased by 0.08 months per decade, MHD by 0.14 months per decade, and SD by 0.17 months per decade.
Figure 9 illustrates the distribution of average intensity for various types of compound droughts. The intensity of SD appears relatively uniform, whereas HMD exhibits significant extreme values. It is worth noting that the average drought degree of compound drought is relatively heavy, and the average intensity of the three types of droughts is basically greater than 1. The average intensity of HMD showed significant differences in different areas. The presence of extreme centers greater than 2 in the south of 30° N in East Asia indicates that severe HMD is often encountered near the Yangtze River basin, which may cause serious damage to the production economy. However, the extreme values of HMD have been mitigated in the Pearl River basin further south, and why such a significant difference is caused needs to be further discussed in future studies. This indicates that the overall impact of HD on drought conditions is more pronounced in these areas.

3.2. Future Projection

Figure 10 reveals the spatial distribution of precipitation as simulated by multiple models. Overall, the average simulated data align well with the ERA5 records. The absence of comprehensive historical data results in the depiction of the Tibetan Plateau’s CMIP6 simulation values as excessively high [44]. Figure 11 evaluates the variance between the different model data and the ERA5 data across China. It displays the 25% to 75% quantile range for the differences in historical mean values. The runoff is mostly contained within a −25 to 25 mm range, indicating a tendency for some models to overestimate runoff. Precipitation differences are centered near 0 mm, with most falling within −25 to 25 mm, suggesting an accurate simulation of the historical period. The median temperature differences cluster between 0 and 5 °C, while different distribution is more balanced, spanning from −5 to 5 °C. The latitudinal patterns of temperature, runoff, and potential evapotranspiration, extending from northwest to southeast, are in agreement with the ERA5 data, showcasing consistent climatic trends across the region.
Figure 12 illustrates the projected trends of various drought events under SSP2-4.5 and SSP5-8.5. In our calculations, drought events spanning a continuous timeline are aggregated as a single occurrence. Under both scenarios, the values of MDs show a consistent decrease, indicating that the atmospheric MD will continue to increase in China in the future. The drought change rate under the SSP5-8.5 scenario is −0.089, which is greater than that under the SSP245 scenario, −0.051. Conversely, HDs exhibit a trend towards a more humid future. This aligns with previous findings that CMIP6 simulations tend to overestimate runoff, leading to predictions of increased humidity [47,48]. This opposite trend indicates that different compound droughts will show different trends in the future. When the drought threshold is set at −0.5, only SD shows an increase in the frequency of occurrence in the future. When the drought threshold is set at −1 (Figure 12f–h), the frequency of all three types of compound droughts shows an increasing trend.
In this study, we delineated the future into two distinct periods: the near-future period (2032–2065) and the far-future period (2066–2099). We then calculated the frequency of various droughts and compared the change under different emission scenarios. Since drought disasters are mainly caused by drought conditions with larger magnitudes, this article only gives the change in the drought frequency above moderate droughts. The drought increment is obtained by subtracting the total frequency of droughts in the historical period (1981–2010) from the total frequency of droughts in the near-future (far-future) and then dividing it by the total frequency of droughts in the historical period. Figure 13a presents the increments in drought severity above the moderate level for SPEI1 and SRI1, projected for both the near-future and far-future. Consistent with earlier findings, the MDs, as indicated by SPEI1, are expected to rise significantly during these periods [46,47,48]. In contrast, HDs, denoted by SRI1, are predicted to intensify in southern parts of China. However, northern parts of China are likely to experience a shift towards more humid conditions. Notably, under the SSP5-8.5 emission, the far-future forecast suggests an average decrease in drought severity by −20.62%, signifying an anticipated trend towards a more humid climate.
Figure 14, Figure 15 and Figure 16 illustrate the projected shifts in the frequency of drought events classified as SD, MHD, and HMD. The data suggest a correlation between human-induced carbon emissions and future hydrological conditions: higher emissions are associated with more humid conditions. On the contrary, as the temperature rises, MDs are expected to intensify. This difference results in varying trends of compound events across different regions. Specifically, the frequency of SD with moderate or higher drought levels is on an upward trend, increasing from an average increment of 6.99% to 23.84%. In southern parts of China, there is a clear trend towards increasing drought severity, which worsens over time with rising emissions. Under SSP2-4.5, SD in northern parts of China initially shows a reduction in drought conditions, followed by an increase. Conversely, under SSP5-8.5, there is a consistent trend towards drought reduction, with humidification becoming more pronounced over time. MHD exhibit a steady increase across China, with the average increment rising from 162.55% in Figure 16a to 602.27% in Figure 16d, indicating a future where such events are increasingly common. While the overall trend for HMD is also rising, regional differences are evident: northern parts of China experience an initial increase followed by a decrease, whereas southern parts of China consistently see a rise in such drought frequency.

4. Conclusions and Discussion

The research landscape has shifted from the study of isolated disaster factors to more holistic analyses of their compound effects. This paper contributes to this evolving field by expanding the traditional definitions of meteorological and hydrological droughts to include compound drought events. We categorized these events into three types: simultaneous compound drought (SD), hydrological meteorological compound drought (HMD), and meteorological hydrological compound drought (MHD). Utilizing a compound index, we analyzed historical and projected changes in China’s compound drought patterns.
Our findings indicate that among the three types of compound drought events, the most prevalent is MHD, with a frequency exceeding 60%, predominantly occurring near the Yangtze River basin. SD ranks second in frequency, while HMD is the least common. The regional impact of these drought events varies significantly. MDs primarily affect northern parts of China more than southern parts of China, and eastern China more than western China. Isolated HDs are widespread across the country, with notable centers near the Huai River basin. For SD, the primary drought centers are located in the Yangtz River Basin and the Pearl River basin. HMDs have drought centers in the Haihe River basin and Huaihe River basin, with extreme values observed in the Sichuan Basin. The main drought centers for MHD are situated in the southeast coastal zone and the Yangtze River basin. The duration of all three types of events shows a significant increasing trend, with SD exhibiting the most pronounced increase, reaching 0.17 months/decade.
Climate change is expected to differentially impact meteorological and hydrological droughts. With global warming, MDs are projected to intensify, whereas HDs are likely to ease. This pattern aligns with previous studies [44,48]. We divided the future into two distinct periods: the near-future period (2032–2065) and the far-future period (2066–2099). Simulations from the CMIP6 multi-model ensemble suggest a general uptick in the frequency of moderate to extreme compound drought events. Although the increase varies by region, all types of compound droughts are expected to rise in southern parts of China. The SSP5-8.5 predicts a higher growth rate in drought occurrences compared to the SSP2-4.5. MHDs under SSP5-8.5 are projected to show the most significant rise. Far-future forecasts for HMD and SD under SSP5-8.5 suggest a trend towards alleviation of compound droughts in the northern parts of China and an intensification in the southern parts of China. Consequently, the increasing frequency of droughts in southern parts of China warrants heightened attention in future climate adaptation strategies.
As climate change intensifies, the frequency and intensity of compound events are projected to increase. Global warming will not only alter individual meteorological events but also modify interactions between different phenomena. For instance, rising temperatures may elevate atmospheric water vapor content, increasing the likelihood of heavy rainfall. These changes are expected to lead to more compound events, with profound impacts on society and the environment. Studies of compound events remain limited. Their occurrence typically involves multiple interrelated factors, and the mechanisms of interaction among these factors are not fully understood. Compound events may vary in characteristics and impacts depending on the region and season, further complicating research efforts.
Adaptation strategies for regions facing more compound drought events will differ significantly when considering unique socio-political contexts and historical resilience patterns. These strategies must be tailored to address the specific challenges and vulnerabilities of each region. This may include adjusting crop varieties, expanding irrigation systems, and adopting coping mechanisms such as splitting herds or migrating to urban areas, in addition to the construction and rehabilitation of water management infrastructures. By integrating these factors, adaptation strategies can be more effective and sustainable, helping regions better manage and reduce the impact of drought on affected populations. Overall, more studies are needed to elucidate these interaction mechanisms and enhance the attribution and prediction of compound events.

Author Contributions

Conceptualization, Z.L.; Methodology, Z.L.; Software, Z.L. and J.T.; Validation, D.Y.; Investigation, J.T. and D.Y.; Writing—original draft, Z.L.; Supervision, E.L.; Funding acquisition, E.L. All authors have read and agreed to the published version of the manuscript.

Funding

This work was jointly sponsored by the National Natural Science Foundation of China (Grant Nos. 42475034 and 41991281). We also acknowledge the support from the Key Natural Science Research Project of Anhui Provincial Department of Education (2023AH051631) and the Scientific Research Project of Chuzhou University (2023qd04).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data that support our research findings are available from the corresponding author upon request. The data are not publicly available due to privacy.

Acknowledgments

We are very grateful to the anonymous reviewers for their constructive comments and thoughtful suggestions.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Liu, W.; Sun, F.; Lim, W.H.; Zhang, J.; Wang, H.; Shiogama, H.; Zhang, Y. Global drought and severe drought-affected populations in 1.5 and 2 °C warmer worlds. Earth Syst. Dynam. 2018, 9, 267–283. [Google Scholar] [CrossRef]
  2. Schubert, S.D.; Stewart, R.E.; Wang, H.; Barlow, M.; Berbery, E.H.; Cai, W.; Hoerling, M.; Kanikicharla, K.K.; Koster, R.D.; Lyon, B. Global meteorological drought: A synthesis of current understanding with a focus on SST drivers of precipitation deficits. J. Clim. 2016, 29, 3989–4019. [Google Scholar] [CrossRef]
  3. Lesk, C.; Rowhani, P.; Ramankutty, N. Influence of extreme weather disasters on global crop production. Nature 2016, 529, 84–87. [Google Scholar] [CrossRef] [PubMed]
  4. Hermans, K.; McLeman, R. Climate change, drought, land degradation and migration: Exploring the linkages. Curr. Opin. Environ. Sustain. 2021, 50, 236–244. [Google Scholar] [CrossRef]
  5. López-Carr, D.; Pricope, N.G.; Mwenda, K.M.; Daldegan, G.A.; Zvoleff, A. A Conceptual Approach towards Improving Monitoring of Living Conditions for Populations Affected by Desertification, Land Degradation, and Drought. Sustainability 2023, 15, 9400. [Google Scholar] [CrossRef]
  6. Van Dijk, A.I.; Beck, H.E.; Crosbie, R.S.; De Jeu, R.A.; Liu, Y.Y.; Podger, G.M.; Timbal, B.; Viney, N.R. The Millennium Drought in southeast Australia (2001–2009): Natural and human causes and implications for water resources, ecosystems, economy, and society. Water Resour. Res. 2013, 49, 1040–1057. [Google Scholar] [CrossRef]
  7. Su, B.; Huang, J.; Fischer, T.; Wang, Y.; Kundzewicz, Z.W.; Zhai, J.; Sun, H.; Wang, A.; Zeng, X.; Wang, G. Drought losses in China might double between the 1.5 °C and 2.0 °C warming. Proc. Nat. Acad. Sci. USA 2018, 115, 10600–10605. [Google Scholar] [CrossRef] [PubMed]
  8. Ley, D.; Guillén Bolaños, T.; Castaneda, A.; Hidalgo, H.G.; Girot Pignot, P.O.; Fernández, R.; Alfaro, E.J.; Castellanos, E.J. Central America urgently needs to reduce the growing adaptation gap to climate change. Front. Clim. 2023, 5, 1215062. [Google Scholar] [CrossRef]
  9. Wilhite, D.A.; Glantz, M.H. Understanding: The drought phenomenon: The role of definitions. Water Int. 1985, 10, 111–120. [Google Scholar] [CrossRef]
  10. Mishra, A.K.; Singh, V.P. A review of drought concepts. J. Hydrol. 2010, 391, 202–216. [Google Scholar] [CrossRef]
  11. Kchouk, S.; Melsen, L.A.; Walker, D.W.; van Oel, P.R. A review of drought indices: Predominance of drivers over impacts and the importance of local context. Nat. Hazards Earth Syst. Sci. Discuss. 2021, 2021, 1–28. [Google Scholar]
  12. Spinoni, J.; Naumann, G.; Carrao, H.; Barbosa, P.; Vogt, J. World drought frequency, duration, and severity for 1951–2010. Int. J. Climatol. 2014, 34, 2792–2804. [Google Scholar] [CrossRef]
  13. Peters, E.; Bier, G.; Van Lanen, H.A.; Torfs, P. Propagation and spatial distribution of drought in a groundwater catchment. J. Hydrol. 2006, 321, 257–275. [Google Scholar] [CrossRef]
  14. Wu, J.; Chen, X.; Yao, H.; Liu, Z.; Zhang, D. Hydrological drought instantaneous propagation speed based on the variable motion relationship of speed-time process. Water Resour. Res. 2018, 54, 9549–9565. [Google Scholar] [CrossRef]
  15. Bhardwaj, K.; Shah, D.; Aadhar, S.; Mishra, V. Propagation of meteorological to hydrological droughts in India. J. Geophys. Res. Atmos. 2020, 125, e2020JD033455. [Google Scholar] [CrossRef]
  16. Saharwardi, M.S.; Kumar, P.; Sachan, D. Evaluation and projection of drought over India using high-resolution regional coupled model ROM. Clim. Dyn. 2022, 58, 503–521. [Google Scholar] [CrossRef]
  17. Zheng, L.; Liu, Y.; Ren, L.; Zhu, Y.; Yin, H.; Yuan, F.; Zhang, L. Spatio-temporal characteristics and propagation relationship of meteorological drought and hydrological drought in the Yellow River Basin. Water Resour. Prot. 2022, 38, 87–95. (In Chinese) [Google Scholar]
  18. Mtilatila, L.; Bronstert, A.; Bürger, G.; Vormoor, K. Meteorological and hydrological drought assessment in Lake Malawi and Shire River basins (1970–2013). Hydrol. Sci. J. 2020, 65, 2750–2764. [Google Scholar] [CrossRef]
  19. Feng, G.; Chen, Y.; Mansaray, L.R.; Xu, H.; Shi, A.; Chen, Y. Propagation of Meteorological Drought to Agricultural and Hydrological Droughts in the Tropical Lancang–Mekong River Basin. Remote Sens. 2023, 15, 5678. [Google Scholar] [CrossRef]
  20. Zscheischler, J.; Fischer, E.M. The record-breaking compound hot and dry 2018 growing season in Germany. Weather. Clim. Extrem. 2020, 29, 100270. [Google Scholar] [CrossRef]
  21. AghaKouchak, A.; Chiang, F.; Huning, L.S.; Love, C.A.; Mallakpour, I.; Mazdiyasni, O.; Moftakhari, H.; Papalexiou, S.M.; Ragno, E.; Sadegh, M. Climate extremes and compound hazards in a warming world. Annu. Rev. Earth Planet. Sci. 2020, 48, 519–548. [Google Scholar] [CrossRef]
  22. IPCC. Summary for policymakers. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change; Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S.L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M.I., et al., Eds.; Cambridge University Press: Cambridge, UK, 2021. [Google Scholar]
  23. Wu, J.; Chen, X.; Yuan, X.; Yao, H.; Zhao, Y.; AghaKouchak, A. The interactions between hydrological drought evolution and precipitation-streamflow relationship. J. Hydrol. 2021, 597, 126210. [Google Scholar] [CrossRef]
  24. Wu, J.; Yao, H.; Chen, X.; Wang, G.; Bai, X.; Zhang, D. A framework for assessing compound drought events from a drought propagation perspective. J. Hydrol. 2022, 604, 127228. [Google Scholar] [CrossRef]
  25. Xue, R.; Sun, B.; Li, W.; Li, H.; Zhou, B. Future changes in compound drought events and associated population and GDP exposure in China based on CMIP6. Atmos. Oceanic Sci. Lett. 2024, 17, 100461. [Google Scholar] [CrossRef]
  26. Seneviratne, S.I.; Zhang, X.; Adnan, M.; Badi, W.; Dereczynski, C.; Di Luca, A.; Ghosh, S.; Iskandar, I.; Kossin, J.; Lewis, S.; et al. Weather and Climate Extreme Events in a Changing Climate. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change; Mason-Delmotte, V., Ed.; Cambridge University Press: Cambridge, UK, 2021; Volume 11, pp. 1–345. [Google Scholar]
  27. Hersbach, H.; Bell, B.; Berrisford, P.; Hirahara, S.; Horányi, A.; Muñoz-Sabater, J.; Nicolas, J.; Peubey, C.; Radu, R.; Schepers, D.; et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 2020, 146, 1999–2049. [Google Scholar] [CrossRef]
  28. Becker; Finger, P.; Meyer-Christoffer, A.; Rudolf, B.; Schamm, K.; Schneider, U.; Ziese, M. A description of the global land-surface precipitation data products of the Global Precipitation Climatology Centre with sample applications including centennial (trend) analysis from 1901–present. Earth Syst. Sci. Data 2013, 5, 71–99. [Google Scholar] [CrossRef]
  29. O’Neill, B.C.; Tebaldi, C.; Van Vuuren, D.P.; Eyring, V.; Friedlingstein, P.; Hurtt, G.; Knutti, P.; Kriegler, E.; Lamarque, J.-F.; Lowe, J.; et al. The scenario model intercomparison project (ScenarioMIP) for CMIP6. Geosci. Model Dev. 2016, 9, 3461–3482. [Google Scholar] [CrossRef]
  30. Riahi, K.; Van Vuuren, D.P.; Kriegler, E.; Edmonds, J.; O’neill, B.C.; Fujimori, S.; Bauer, N.; Calvin, K.; Dellink, R.; Fricko, O. The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Global Environ. Chang. 2017, 42, 153–168. [Google Scholar] [CrossRef]
  31. Mckee, T.B.; Doesken, N.J.; Kleist, J.R. The relationship of drought frequency and duration to time scales. In Proceedings of the 8th Conference on Applied Climatology, Anaheim, CA, USA, 17–22 January 1993; Volume 17, pp. 179–183. [Google Scholar]
  32. Shukla, S.; Wood, A.W. Use of a standardized runoff index for characterizing hydrologic drought. Geophys. Res. Lett. 2008, 35, L02405. [Google Scholar] [CrossRef]
  33. Vicente-Serrano, S.M.; Beguería, S.; López-Moreno, J.I. A Multiscalar Drought Index Sensitive to Global Warming: The Standardized Precipitation Evapotranspiration Index. J. Clim. 2010, 23, 1696–1718. [Google Scholar] [CrossRef]
  34. Vicente-Serrano, S.M.; Beguería, S.; Lorenzo-Lacruz, J.; Camarero, J.J.; López-Moreno, J.I.; Azorin-Molina, C.; Revuelto, J.; Morán-Tejeda, E.; Sanchez-Lorenzo, A. Performance of drought indices for ecological, agricultural, and hydrological applications. Earth Interact. 2012, 16, 1–27. [Google Scholar] [CrossRef]
  35. Thornthwaite, C.W. An approach toward a rational classification of climate. Geogr. Rev. 1948, 38, 55–94. [Google Scholar] [CrossRef]
  36. Loukas, A.; Vasiliades, L.; Vasilief, N. Drought management in the eastern Mediterranean: A comparative analysis of drought indices. Water Resour. Manag. 2014, 28, 1155–1174. [Google Scholar]
  37. Morid, S.; Smakhtin, V.; Moghaddasi, M. Comparison of seven meteorological indices for drought monitoring in Iran. Int. J. Climatol. 2006, 26, 971–985. [Google Scholar] [CrossRef]
  38. Beguería, S.; Vicente-Serrano, S.M. SPEI: A drought index based on the Standardized Precipitation Evapotranspiration Index. Remote Sens. 2013, 5, 860–880. [Google Scholar]
  39. Yevjevich, V.M. An Objective Approach to Definitions and Investigations of Continental Hydrologic Droughts; Colorado State University: Fort Collins, CO, USA, 1967. [Google Scholar]
  40. Xu, K.; Yang, D.; Yang, H.; Li, Z.; Qin, Y.; Shen, Y. Spatio-temporal variation of drought in China during 1961–2012: A climatic perspective. J. Hydrol. 2015, 526, 253–264. [Google Scholar] [CrossRef]
  41. Aksoy, H.; Cetin, M.; Eris, E.; Burgan, H.I.; Cavus, Y.; Yildirim, I.; Sivapalan, M. Critical drought intensity-duration-frequency curves based on total probability theorem-coupled frequency analysis. Hydrol. Sci. J. 2021, 66, 1337–1358. [Google Scholar] [CrossRef]
  42. Su, L.; Cao, Q.; Xiao, M.; Mocko, D.M.; Barlage, M.; Li, D.; Peters-Lidard, C.D.; Lettenmaier, D.P. Drought Variability over the Conterminous United States for the Past Century. J. Hydrometeorol. 2021, 22, 1153–1168. [Google Scholar] [CrossRef]
  43. Zhang, Y.; You, Q.; Chen, C.; Wang, H.; Ullah, S.; Shen, L. Characteristics of flash droughts and their association with compound meteorological extremes in China: Observations and model simulations. Sci. Total Environ. 2024, 916, 170133. [Google Scholar] [CrossRef]
  44. Jiao, D.; Xu, N.; Yang, F.; Xu, K. Evaluation of spatial-temporal variation performance of ERA5 precipitation data in China. Sci. Rep. 2021, 11, 17956. [Google Scholar] [CrossRef]
  45. Vitart, F.; Balsamo, G.; Bidlot, J.-R.; Lang, S.; Tsonevsky, I.; Richardson, D.; Balmaseda, M. Use of ERA5 reanalysis to initialise re-forecasts proves beneficial. ECMWF Newsl. 2019, 161, 26–31. [Google Scholar]
  46. Wang, C.; Huang, M.; Zhai, P.; Yu, R. Change of summer drought over China during 1961–2020 based on standardized precipitation evapotranspiration index. Theor. Appl. Climatol. 2023, 153, 297–309. [Google Scholar] [CrossRef]
  47. Zhang, Z.; Zhang, W.; Yang, B.; Xie, W.; Tao, C.; Hong, Z.; Xie, Y.; Li, J.; Li, L.; Meng, L. Long-term spatiotemporal characteristics of meteorological drought in China from a three-dimensional (longitude, latitude, time) perspective. Int. J. Appl. Earth Obs. Geoinf. 2024, 126, 103633. [Google Scholar] [CrossRef]
  48. Zhou, J.; Lu, H.; Yang, K.; Jiang, R.; Yang, Y.; Wang, W.; Zhang, X. Projection of China’s future runoff based on the CMIP6 mid-high warming scenarios. Sci. China Earth Sci. 2023, 66, 528–546. [Google Scholar] [CrossRef]
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Figure 1. Three definitions of compound drought. The red columns represent SPEI, and the blue columns represent SRI. Only values less than 0 are displayed here. The three colored rectangles from left to right represent HMD, MHD, and SD, respectively. Only values that reach the drought threshold are counted in the compound drought events. In this diagram, the threshold used is −0.5, represented by the gray dotted line.
Figure 1. Three definitions of compound drought. The red columns represent SPEI, and the blue columns represent SRI. Only values less than 0 are displayed here. The three colored rectangles from left to right represent HMD, MHD, and SD, respectively. Only values that reach the drought threshold are counted in the compound drought events. In this diagram, the threshold used is −0.5, represented by the gray dotted line.
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Figure 2. Spatial distribution of ERA5 for the period 1972–2022: (a) annual precipitation (unit: mm); (b) annual runoff (unit: mm); (c) monthly temperature (unit: °C); (d) annual potential evapotranspiration (unit: mm) calculated by the Thornthwaite method; (e) anomalies of annual average GPCC precipitation (unit: mm) minus ERA5 precipitation from 1981 to 2010.
Figure 2. Spatial distribution of ERA5 for the period 1972–2022: (a) annual precipitation (unit: mm); (b) annual runoff (unit: mm); (c) monthly temperature (unit: °C); (d) annual potential evapotranspiration (unit: mm) calculated by the Thornthwaite method; (e) anomalies of annual average GPCC precipitation (unit: mm) minus ERA5 precipitation from 1981 to 2010.
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Figure 3. Spatial distribution of trend values of (a) SPEI1 and (b) SRI1 on the monthly scale from 1979 to 2022. Dotted areas pass the 95% confidence test according to the ERA5 dataset.
Figure 3. Spatial distribution of trend values of (a) SPEI1 and (b) SRI1 on the monthly scale from 1979 to 2022. Dotted areas pass the 95% confidence test according to the ERA5 dataset.
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Figure 4. Elevation distribution map of river basins in China.
Figure 4. Elevation distribution map of river basins in China.
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Figure 5. The (a) total, (b) mild, (c) moderate, (d) severe and (e) extreme drought relative frequency (unit: %) of SD during 1979–2022 in East Asia. The drought thresholds are shown in Table 2. Results are calculated using the ERA5 dataset. Note that the total drought here refers to the sum of all kinds of drought.
Figure 5. The (a) total, (b) mild, (c) moderate, (d) severe and (e) extreme drought relative frequency (unit: %) of SD during 1979–2022 in East Asia. The drought thresholds are shown in Table 2. Results are calculated using the ERA5 dataset. Note that the total drought here refers to the sum of all kinds of drought.
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Figure 6. The (a) total, (b) mild, (c) moderate, (d) severe and (e) extreme drought relative frequency (unit: %) of HMD during 1979–2022 in East Asia. The drought thresholds are shown in Table 2. Results are calculated by ERA5 dataset. Note that the total drought here refers to the sum of all kinds of drought.
Figure 6. The (a) total, (b) mild, (c) moderate, (d) severe and (e) extreme drought relative frequency (unit: %) of HMD during 1979–2022 in East Asia. The drought thresholds are shown in Table 2. Results are calculated by ERA5 dataset. Note that the total drought here refers to the sum of all kinds of drought.
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Figure 7. The (a) total, (b) mild, (c) moderate, (d) severe and (e) extreme drought relative frequency (unit: %) of MHD during 1979–2022 in East Asia. The drought thresholds are shown in Table 2. Results are calculated by ERA5 dataset. Note that the total drought here refers to the sum of all kinds of drought.
Figure 7. The (a) total, (b) mild, (c) moderate, (d) severe and (e) extreme drought relative frequency (unit: %) of MHD during 1979–2022 in East Asia. The drought thresholds are shown in Table 2. Results are calculated by ERA5 dataset. Note that the total drought here refers to the sum of all kinds of drought.
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Figure 8. Regional average duration (unit: month) of different levels in 1979–2022: (a) SD (b) MHD (c) HMD China. Black dot lines are linear variations of total compound drought events.
Figure 8. Regional average duration (unit: month) of different levels in 1979–2022: (a) SD (b) MHD (c) HMD China. Black dot lines are linear variations of total compound drought events.
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Figure 9. Regional average intensity of (a) SD, (b) MHD and (c) HMD in China during 1979–2022.
Figure 9. Regional average intensity of (a) SD, (b) MHD and (c) HMD in China during 1979–2022.
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Figure 10. Multi-model ensemble distributions of annual means based on CMIP6 historical simulations from 1979 to 2014: (a) annual precipitation (mm); (b) annual runoff (mm); (c) monthly temperature (°C); (d) annual evapotranspiration (mm).
Figure 10. Multi-model ensemble distributions of annual means based on CMIP6 historical simulations from 1979 to 2014: (a) annual precipitation (mm); (b) annual runoff (mm); (c) monthly temperature (°C); (d) annual evapotranspiration (mm).
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Figure 11. Violin plot distribution of climatic differences in China: (a) precipitation (unit: mm), (b) runoff (unit: mm) and (c) temperature (unit: °C). These differences are historical datasets assessed in comparison to the ERA5 reanalysis data spanning from 1979 to 2014. The red line indicates that the differences are 0. Box lines delineate the quartile range, marking the spread of the middle 25% of the data. The white points within the graph signify the mean values of each model. For more details of the models, refer to Table 1.
Figure 11. Violin plot distribution of climatic differences in China: (a) precipitation (unit: mm), (b) runoff (unit: mm) and (c) temperature (unit: °C). These differences are historical datasets assessed in comparison to the ERA5 reanalysis data spanning from 1979 to 2014. The red line indicates that the differences are 0. Box lines delineate the quartile range, marking the spread of the middle 25% of the data. The white points within the graph signify the mean values of each model. For more details of the models, refer to Table 1.
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Figure 12. Estimated value change trends from 1979 to 2100 of (a) MD and (b) HD are expressed by SPEI1 and SRI1. Estimated change trends from 1979 to 2100 in the frequency of (c) SD, (d) HMD and (e) MHD when the drought threshold is −0.5 and (f) SD, (g) HMD and (h) MHD when the drought threshold is −1 under SSP2-4.5 and SSP5-8.5. The shaded areas represent 25% to 75% standard deviations of the 17 models. The red dashed and blue dashed lines are linear variations. The historical data of the period 1979–2014 are simulated using CMIP6 models.
Figure 12. Estimated value change trends from 1979 to 2100 of (a) MD and (b) HD are expressed by SPEI1 and SRI1. Estimated change trends from 1979 to 2100 in the frequency of (c) SD, (d) HMD and (e) MHD when the drought threshold is −0.5 and (f) SD, (g) HMD and (h) MHD when the drought threshold is −1 under SSP2-4.5 and SSP5-8.5. The shaded areas represent 25% to 75% standard deviations of the 17 models. The red dashed and blue dashed lines are linear variations. The historical data of the period 1979–2014 are simulated using CMIP6 models.
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Figure 13. Projected frequency changes (unit: %) in the MD under SSP2-4.5 in (a) 2032–2065 and (b) 2066–2099 and the SSP5-8.5 in (c) 2032–2065 and (d) 2066–2099. Projected changes (unit: %) in the HD under SSP2-4.5 in (e) 2032–2065 and (f) 2066–2099 and under SSP5-8.5 in (g) 2032–2065 and (h) 2066–2099 (reference period 1981–2014 according to ERA5 dataset [27]). The drought threshold is −0.5.
Figure 13. Projected frequency changes (unit: %) in the MD under SSP2-4.5 in (a) 2032–2065 and (b) 2066–2099 and the SSP5-8.5 in (c) 2032–2065 and (d) 2066–2099. Projected changes (unit: %) in the HD under SSP2-4.5 in (e) 2032–2065 and (f) 2066–2099 and under SSP5-8.5 in (g) 2032–2065 and (h) 2066–2099 (reference period 1981–2014 according to ERA5 dataset [27]). The drought threshold is −0.5.
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Figure 14. Projected frequency changes (unit: %) in the SD below −1 (moderate) under SSP2-4.5 in (a) 2032–2065 and (b) 2066–2099 and the SSP5-8.5 in (c) 2032–2065 and (d) 2066–2099 (reference period 1981–2014 according to ERA5 dataset [27]).
Figure 14. Projected frequency changes (unit: %) in the SD below −1 (moderate) under SSP2-4.5 in (a) 2032–2065 and (b) 2066–2099 and the SSP5-8.5 in (c) 2032–2065 and (d) 2066–2099 (reference period 1981–2014 according to ERA5 dataset [27]).
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Figure 15. Projected frequency changes (unit: %) in the MHD below −1 (moderate) the SSP2-4.5 in (a) 2032–2065 and (b) 2066–2099 and the SSP5-8.5 in (c) 2032–2065 and (d) 2066–2099 (reference period 1981–2014 according to ERA5 dataset [27]).
Figure 15. Projected frequency changes (unit: %) in the MHD below −1 (moderate) the SSP2-4.5 in (a) 2032–2065 and (b) 2066–2099 and the SSP5-8.5 in (c) 2032–2065 and (d) 2066–2099 (reference period 1981–2014 according to ERA5 dataset [27]).
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Figure 16. Projected frequency changes (unit: %) in the HMD below −1 (moderate) under SSP2-4.5 in (a) 2032–2065 and (b) 2066–2099 and the SSP5-8.5 in (c) 2032–2065 and (d) 2066–2099 (reference period 1981–2014 according to ERA5 dataset [27]).
Figure 16. Projected frequency changes (unit: %) in the HMD below −1 (moderate) under SSP2-4.5 in (a) 2032–2065 and (b) 2066–2099 and the SSP5-8.5 in (c) 2032–2065 and (d) 2066–2099 (reference period 1981–2014 according to ERA5 dataset [27]).
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Table 1. Information regarding the 17 CMIP6 models selected.
Table 1. Information regarding the 17 CMIP6 models selected.
Mode AbbreviationMode Full NameMode Resolution
ACCESS-CM2 (r1i1p1f1)Australian Community Climate and Earth System Simulator—Coupled Model 2192 × 144
ACCESS-ESM1-5 (r1i1p1f1)Australian Community Climate and Earth System Simulator—Earth System Model 1.5192 × 145
BCC-CSM2-MR (r1i1p1f1)Beijing Climate Center Climate System Model version 2—Medium Resolution320 × 160
CAS-ESM2-0 (r1i1p1f1)Chinese Academy of Sciences Earth System Model version 2—Medium Resolution256 × 128
CESM2-WACCM (r1i1p1f1)Community Earth System Model version 2—Whole Atmosphere Community Climate Model144 × 96
CMCC-CM2-SR5 (r1i1p1f1)Centro Euro-Mediterraneo sui Cambiamenti Climatici—Coupled Model 2—Standard Resolution 5288 × 192
CMCC-ESM2 (r1i1p1f1)Centro Euro-Mediterraneo sui Cambiamenti Climatici—Earth System Model version 2288 × 192
CanESM5 (r1i1p1f1)Community Earth System Model version 2—Whole Atmosphere Community Climate Model128 × 64
CanESM5-1 (r1i1p1f1)Canadian Earth System Model version 5.1128 × 64
FGOALS-f3-L (r1i1p1f1)Flexible Global Ocean-Atmosphere-Land System model version f3-L288 × 180
FIO-ESM-2-0 (r1i1p1f1)First Institute of Oceanography-Earth System Model version 2.0288 × 192
GFDL-ESM4 (r1i1p1f1)Geophysical Fluid Dynamics Laboratory Earth System Model version 4.1288 × 180
IPSL-CM6A-LR (r1i1p1f1)Institut Pierre Simon Laplace Climate Model version 6A-Low Resolution144 × 143
INM-CM4-8 (r1i1p1f1)Institute for Numerical Mathematics Climate Model version 4.8180 × 120
KIOST-ESM (r1i1p1f1)Korea Institute of Ocean Science and Technology Earth System Model192 × 96
MRI-ESM2-0 (r1i1p1f1)Meteorological Research Institute Earth System Model version 2.0320 × 160
TaiESM1 (r1i1p1f1)Taiwan Earth System Model version 1288 × 192
Table 2. Drought and wet categories according to SPEI or SRI values.
Table 2. Drought and wet categories according to SPEI or SRI values.
CategorySPEI/SRI
Extreme wet[2, +∞)
Severe wet[1.5, 2)
Moderate wet[1, 1.5)
Slight wet[0.5, 1)
Normal(−0.5, 0.5)
Mild drought(−1, −0.5]
Moderate drought(−1.5, −1]
Severe drought(−2, −1.5]
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Li, Z.; Lu, E.; Tu, J.; Yuan, D. Historical and Future Changes in Meteorological–Hydrological Compound Drought in China. Atmosphere 2024, 15, 1459. https://doi.org/10.3390/atmos15121459

AMA Style

Li Z, Lu E, Tu J, Yuan D. Historical and Future Changes in Meteorological–Hydrological Compound Drought in China. Atmosphere. 2024; 15(12):1459. https://doi.org/10.3390/atmos15121459

Chicago/Turabian Style

Li, Zhuoyuan, Er Lu, Juqing Tu, and Dian Yuan. 2024. "Historical and Future Changes in Meteorological–Hydrological Compound Drought in China" Atmosphere 15, no. 12: 1459. https://doi.org/10.3390/atmos15121459

APA Style

Li, Z., Lu, E., Tu, J., & Yuan, D. (2024). Historical and Future Changes in Meteorological–Hydrological Compound Drought in China. Atmosphere, 15(12), 1459. https://doi.org/10.3390/atmos15121459

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