The increasing risk of future simultaneous droughts over the Yangtze River basin based on CMIP6 models

doi:10.21203/rs.3.rs-2354327/v1

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Research Article

The increasing risk of future simultaneous droughts over the Yangtze River basin based on CMIP6 models

https://doi.org/10.21203/rs.3.rs-2354327/v1

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published 01 Apr, 2023

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Drought projection is critical for water resource planning and management, as well as disaster prevention and mitigation. As a strategic national water source for China, the Yangtze River Basin (YRB) plays a vital role in the connectivity of rivers and economic development, flowing through 11 provincial administrative regions and is injected into the East China Sea, with a total length of 6,397 kilometers. The watershed covers an area of 1.8 million square kilometers, accounting for about 1/5 of China's total land area. However, frequent droughts have caused water shortages in the YRB in recent years. Based on observed meteorological and hydrological data, the CMIP6 model and SPEI (standardized precipitation evapotranspiration index) drought models were used to elucidate the risk of future simultaneous droughts in the upper and mid-lower reaches of the YRB from 2015 to 2100. The results indicated that, (1) From a historical perspective (1961–2019), the middle reaches of the YRB have seen the highest total basin drought frequency (Frequency of moderate and above drought events) (> 17%), where seasonal drought has mostly been concentrated; (2) Compared with the SSP1-2.6 scenario, the frequency, intensity, and scope of droughts in the YRB will continuously increase, where the frequency of moderate and extreme drought will be stronger under the SSP5-8.5 scenario, from 2020 to 2100; 3) In the near future (2021–2050), the YRB will experience relatively high total drought frequency under the SSP1-2.6 scenario, while in the far future (2071–2100) under the SSP5-8.5 scenario, the SPEI projects a greater intensification of moderate drought; 4) Under all three scenarios, drought is projected to simultaneously occur more frequently basin-wide from 2030–2040. The middle-lower reaches of the YRB are forecast to witness the largest scope and highest intensity of drought under the SSP1-2.6 scenario. While under the SSP5-8.5 scenario, drought is estimated to be distributed basin-wide with a saddle-shaped distribution in the middle reaches, and reaching a maximum in the lower reaches. These trends demonstrate the effects of climate change on spatially variable drought across the entire YRB basin. More attention and improved strategies should be brought to bear to address future simultaneous droughts in the upper and mid-lower YRB.

Drought

Projection

CMIP6

SSPs

Yangtze River

Drought projection plays an essential role in the planning and management of water resource systems Mishra and Desai (2005). However, the projections of future drought events are plagued by many uncertainties and even controversies. Debates on whether future droughts will increase or decrease on a global scale have been taking place for a long time. Drought is anticipated to increase in frequency and severity in the future due to climate change, primarily as a consequence of decreased regional precipitation, but also because of increasing evaporation driven by global warming (Sheffield and Wood 2008, Lavell, Oppenheimer et al. 2012). Climate change is forecast to exacerbate drought frequency and severity over much of the planet (Dai 2011, Dai 2013), while some studies (e.g., (Sheffield, Wood et al. (2012)) have suggested that there has been little change in drought events over the last 60 years, with PDSI using a simplified model of potential evaporation that responds only to changes in temperature, taking into account changes in available energy, humidity and wind speed and thus responds incorrectly to global warming. The main rationale for these controversies is the inadequate simulation capacities of climate models and significant uncertainties in the prediction of future climate. Thus, under these circumstances the accuracies of drought projections still require considerable improvements.

In terms of long-term drought forecasting, various hydrometeorological data outputs of GCMs (Global Climate Models) from different phases of the CMIP (Coupled Multi-model Inter-comparison Project) have been employed (Sheffield and Wood 2008, Zhai, Mondal et al. 2020). Studies using CMIP5 simulations have advanced our understanding of regionally heterogeneous hydroclimatic responses to global warming (Cook, Smerdon et al. 2014). Currently, CMIP6 (Sixth Phase) promises to fill the scientific gaps of the previous CMIP phases because of the common standards, coordination, infrastructure, and documentation that will facilitate the distribution of model outputs and the characterization of the model ensemble (Eyring, Bony et al. 2016). Previous investigations revealed that CMIP6 was more reliable and stable than CMIP5 (Chen et al. 2020; Nie et al. 2020). This is due to CMIP6 adopting more sensible shared socioeconomic pathways (SSPs) than CMIP5 through the exchange of policy assumptions to function in tandem with RCPs (Ritchie et al. 2017).

Under the background of global warming, the East Asian summer monsoon circulation is intensifying, while atmospheric structural instability is also increasing, which provides favorable underlying conditions for increased and extreme precipitation events in China (Chen 2013). Zhai et al. (2020) estimated that future average drought duration and frequency in Northwestern South Asia are expected to increase significantly based on the CMIP6 multi-model product. However, CMIP6 models still disagree on changes to Australian monsoon precipitation in a warmer world. As such, these studies have encouraged the use of direct climate model outputs for drought assessments. Consequently, in-depth studies of the drought frequency, spatial, and temporal distribution in the YRB based on CMIP6 can be further researched. With the use of CMIP6, climate model simulations have become more robust, and likely improved forecasts of South Asian monsoon attributes. Based on CMIP6 simulations, several drought predictions have been conducted at both regional and global scales (Ukkola et al. 2020; Zhai et al. 2020). The latest state GCMs in the CMIP6 archive showed their advanced ability to capture large-scale patterns of climate variables, since CMIP6 has added improvements in multiple aspects, for example, in capturing the spatiotemporal pattern of monsoon over Indian landmass (Gusain et al. 2020). Simulations of downscaled and MME (Multi model ensemble) precipitation and temperature revealed improved performance and higher stability (Zhang et al. 2021). The MME widens the ensemble spread, while the average ensemble-mean error is reduced when forecasting on seasonal time scales (Weigel et al. 2008). It remains complex to employ global models for precise regional drought projection research due to the resolution differences.

GCMs have a limited capacity to resolve local elements that affect regional climate due to their coarse spatial resolution and global scope (Di Virgilio et al. 2022). Regional climate models (RCMs) dynamically downscale global climate model (GCM) outputs to generate higher resolution simulations of present and future climates that can better guide regional climate policies and decision-making. As climate models are expensive to run, with biased calculation accuracies of the changes in climate caused by resolution differences, model evaluations that systematically consider all these factors on a per-GCM basis for the domain in question is requisite toward identifying a subset of CMIP6 GCMs for dynamic downscaling.

The drought index is commonly used for drought projection research. The most prominent index of meteorological drought in the United States is the Palmer Drought Severity Index (PDSI). The PDSI was created with the intent of "measuring the cumulative departure of moisture supply" (Palmer 1965). However, the PDSI has spatial simplicity; thus, it cannot accurately describe characteristic large-scale drought changes (Maity et al. 2016). McKee et al. (1993) proposed the standardized precipitation index (SPI index) in 1993 to reflect the hydrological gain and loss at different time scales, and the SPI index can greatly reduce the spatial and temporal differences in precipitation distribution, making the SPI index effective in responding to meteorological drought conditions in different regions at different times. However, the influence of temperature is ignored in the calculation, so there is a certain error in the description of drought events by the SPI index under the background of global warming. The SPEI considers surface evapotranspiration against the background of global warming, which also considers monthly precipitation and potential evapotranspiration (Gao et al. 2017). SPEI takes into account precipitation, evapotranspiration and temperature factors, and combines the advantages of PDSI index which takes into account the influence of evapotranspiration on drought and multiple time scales of SPI, making SPEI more convenient and scientific in analyzing meteorological drought. SPEI is commonly employed in current research to assess drought characteristics in drought-prone areas at spatiotemporal scales.

The YRB is a strategic water resource in China, and one of the most prosperously developed and densely populated regions in China (Li et al. 2021). As the water source for the South-North Water Transfer, the YRB is related to the security of the national water network. Thus, the occurrence of drought in the YRB has profound impacts on China's economic development, where climatic and geological conditions are extremely complex. The main climate type in the YRB region is subtropical monsoon, where over the last 60 years the drought rate has changed greatly from year to year. The areas with frequent droughts are primarily located in the upper reaches, while high-intensity droughts mostly occur in the middle and lower reaches of the Jinsha River. The multi-year mean of drought intensity in the future projection period is not much different from the historical period (Liu et al., 2020). Since uneven drought conditions exist in the YRB, North Atlantic Oscillation (NAO) and Northern Oscillation Index (NOI) can be utilized as early warning indicators for monitoring drought events (Huang et al. 2018). Wang (2019) used the MMK trend test to analyze the trend of SPEI values in the YRB over a three-month scale. In terms of distribution, the entire YRB showed a trend of weakening drought in the east and west and deepening drought in the middle regions.

Taking all the above into consideration, this study was mostly based on CMIP6 models to evaluate the frequency distribution, and spatial and temporal patterns of drought predictions. These analyses focused on three research questions, (1) Will the frequency, intensity, and duration of drought in the YRB increase under global warming? (2) What will the near-term stress be in the YRB with increasing drought events? (3) Will future drought events occur more prominently and simultaneously in the upper and middle-lower reaches of the YRB under the SSPs scenarios?

2.1 Study area and data

The YRB refers to a vast region through which the mainstream and tributaries of the Yangtze River flow through the hinterland of China, spanning the three major economic zones of Western, Central, and Eastern China. Although the YRB has abundant water resources, the spatial and temporal distribution of precipitation in the basin is very uneven. The average annual precipitation ranges from less than 300 mm in the west to over 2400 mm in the east, where > 60% of the rainfall is concentrated in summer.

This study established 1961–2019 as the historical period, 2020–2100 as the future period. 138 meteorological stations in the YRB were selected. Since the establishments of the meteorological points varied, the meteorological data for 59 years (January 1961 - December 2019) were uniformly selected, and stations with missing meteorological data that accounted for more than 5% of the time series were excluded. Missing values were supplemented by linear interpolation. The Global SPEI Monthly Dataset was at a spatial resolution of 0.5° × 0.5° (http://spei.csic.es/database.html).

The climate model data for this study were derived from CMIP6 (https://esgf-node.llnl.gov/search/cmip6/) (Table 1), which were combined with the MME model (Multi-Model Ensemble) that averaged 10 CMIP6 models. The simulations for SSP1-2.6 (SSP1 + RCP2.6, a green development pathway), SSP2-4.5 (SSP2 + RCP4.5, an intermediate development pathway), and SSP5-8.5 (SSP5 + RCP8.5, a high development pathway) 3 scenarios were selected. (Li et al. 2020).

Table 1

Basic data for the 10 models selected from CMIP6 used in this study.
Code	Model	Abbreviation		Institution/Country
1	BCC-CSM2-MR	BCCC	320×160	BCC/China
2	CESM2-WACCM	CEWA	288×192	NCAR/America
3	CanESM5	CANE	128×64	CCCma/Canada
4	FGOALS-g3	FGOG	180×80	CAS/China
5	GFDL-ESM4	GFES	288×180	NOAA-GFDL/America
6	IPSL-CM6A-LR	IPSL	144×143	IPSL/France
7	MIROC-ES2L	MIRE	128×64	JAMSTE、CAORI、NIES、RCCS/Japan
8	MIROC6	MIRO	256×128	JAMSTEC、AORI、NIESR-CCS/Japan
9	MRI-ESM2-0	MRIE	320×160	MRI/Japan
10	UKESM1	UKES	192×144	NCAS/British

2.2 Methodology

For this study, the SPEI was employed to classify drought grading standards. Grade classification standards refer to "GB/T 20481 − 2017 Meteorological Drought Grade”, as shown in Table 2:

Table 2

SPEI drought grade divisions.
Drought grade	SPEI	Category
0	-0.5 and above	No drought
1	-1.00 to -0.50	mild drought
2	-1.50 to -1.00	moderate drought
3	-2.00 to -1.50	severe drought
4	Below and − 2.00	extreme drought

The SPEI index was calculated based on two different potential evapotranspiration formulas due to different parameter acquisition in the CMIP6 data (Vicente-Serrano et al. 2010). The PET based on the Penman formula (PM) was to calculate the SPEI for the historical period. The PET based on the Thornthwaite formula (Tho) was used to calculate the future SPEI index (Thornthwaite 1948):

Tho:

$$E{T}_{0}=k(10T/I{)}^{\alpha }\times uN/360$$

PM:

$$E{T}_{0}=\frac{0.408\varDelta \left({R}_{n}-G\right)+\gamma \frac{900}{T+273}{U}_{2}({e}_{s}-{e}_{\alpha })}{\varDelta +\gamma (1+0.34{U}_{2})}$$

Record the difference between monthly precipitation and potential evapotranspiration as ${D}_{i}$:

$${D}_{i}={P}_{i}-(E{T}_{0}{)}_{i}$$

Normalize the ${D}_{n}^{k}$ and get the SPEI value:

$${D}_{n}^{k}=\sum _{i=0}^{k-1}[{P}_{n-i}-\left(E{T}_{0}{)}_{n-i}\right](n\ge k)$$

In the above normalization process, Vicente-Serrao et al. compared the Log-logistic distribution, Pearson III distribution, Log normal distribution and Generalized extreme value distribution with the sequence ${D}_{n}^{k}$, and the results showed that the Log-logistic distribution was the best fit to the sequence ${D}_{n}^{k}$ (Thornthwaite 1948).

Drought frequency describes the frequency of drought occurrence at a station or grid point during the study period, and the larger the value, the higher the frequency of drought occurrence, calculated as follows:

$$P=\frac{n}{N}100\text{\%}$$

Where $n$ is the total number of years, that the drought occurred at the site, and $N$ is the total length of the data series of the site, i.e. the total number of time.

The drought station sub-ratio describes the extent of drought impact at a certain point in time when drought occurs in the study area, and the larger the value indicates the greater the extent of drought impact, calculated as follows:

$$K=\frac{m}{M}100\text{\%}$$

The Mann-Kendall (MK) test is often used to evaluate trends of long series of hydrological, meteorological, and other data (Mann 1945). Modified Mann-Kendall (MMK) was proposed by a modified MK test (Hamed et al. 1998) to test the significance of SPEI sequences for different time periods and scenarios. Below are the calculation steps:

for a time series $X=\left\{{x}_{1},{x}_{2},\cdots ,{x}_{n}\right\}$,The period, tatistic $S$ is:

$$S=\sum _{i<j} {a}_{ij}=\sum _{i<j} \text{sgn}\left({x}_{j}-{x}_{i}\right)$$

Where $\text{s}\text{g}\text{n}$ is the sign function, the var ($S$) is calculated as the following formula:

$$\text{v}\text{a}\text{r}\left(S\right)=\eta \times \frac{n(n-1)(2n+5)}{18}$$

$\eta =1+\frac{1}{n(n-1)(n-2)}\times \sum _{i=1}^{n-1} (n-i)(n-i-1)(n-i-2){r}_{i}$ , the statistic $Z$ in the MMK trend test can be obtained, and the calculation formula is as follows:

$$Z=\left\{\begin{array}{cc}\frac{S-1}{\sqrt{\text{v}\text{a}\text{r}\left(S\right)}}& S>0\\ 0& S=0\\ \frac{S+1}{\sqrt{\text{v}\text{a}\text{r}\left(S\right)}}& S<0\end{array}\right.$$

Sen’s slope estimator is a simple nonparametric estimator (Sen 1968). Sen + Mann-Kendall combination can improve the accuracy of judging climate trend changes (Li et al. 2015). The slope between all points ${S}_{i}$ of a given time series is calculated, $X=\left\{{x}_{1},{x}_{2},\cdots ,{x}_{n}\right\}$:

$${S}_{i}=\frac{{x}_{j}-{x}_{k}}{j-k}(i=\text{1,2},\dots ,N)$$

In this study, to increase the applicability of the simulation results in the research area, Bias Corrected Spatial Disaggregation (BCSD) (Li et al. 2010) was used. The data of each mode was resampled to a resolution of 0.5°×0.5°. The Bias Correction was as follows:

$${X}_{m-p,adj}={X}_{m-p}+{F}_{\text{o}-c}^{-1}\left[{F}_{m-p}\left({X}_{m-p}\right)\right]-{F}_{m-c}^{-1}\left[{F}_{m-p}\left({X}_{m-p}\right)\right]$$

The Taylor diagram was adopted to appraise the simulation of all the bias-corrected CMIP6 climate patterns, which provided a concise statistical summary of how well the patterns resembled each other in terms of their centered RMS (root-mean-square) difference, standard deviation, and correlation (Taylor 2001):

Measured data mean square error:

$$S{D}_{obs}={\left[\frac{1}{N}\sum _{n=1}^{N} {\left({O}_{n}-\stackrel{-}{O}\right)}^{2}\right]}^{1/2}$$

Mode data mean square error:

$${SD}_{X}={\left[\frac{1}{N}\sum _{n=1}^{N} {\left({X}_{n}-\stackrel{-}{X}\right)}^{2}\right]}^{1/2}$$

Correlation coefficient:

$$R=\frac{\frac{1}{N}\sum _{n=1}^{N} \left({X}_{n}-\stackrel{-}{X}\right)\left({O}_{n}-\stackrel{-}{O}\right)}{S{D}_{obs}{SD}_{X}}$$

Root mean square error:

$$RMS={\left\{\frac{1}{N}\sum _{n=1}^{N} {\left[\left({X}_{n}-\stackrel{-}{X}\right)\left({O}_{n}-\stackrel{-}{O}\right)\right]}^{2}\right\}}^{1/2}$$

The EOF method (Empirical orthogonal function decomposition), was initially proposed by statistician Pearson in 1902. Lorenz integrated it into atmospheric science research in the mid-1950’s. The basic principle was to decompose the field containing m spatial points over time, and subsequently to extract several major independent and orthogonal spatially distributed and time-varying characteristic patterns from the elemental variable fields.

$$X=\left({x}_{ij}\right)=\left({x}_{1},{x}_{2},\dots ,{x}_{j}\right)=\left[\begin{array}{ccc}{x}_{11}& \cdots & {x}_{1n}\\ ⋮& \ddots & ⋮\\ {x}_{m1}& \cdots & {x}_{mn}\end{array}\right]$$

The EOF decomposition gives:

$$X=VT$$

$$V=\left({v}_{1},{v}_{2},\dots ,{v}_{m}\right)=\left[\begin{array}{ccc}{v}_{11}& \cdots & {v}_{m1}\\ ⋮& \ddots & ⋮\\ {v}_{1n}& \cdots & {v}_{mm}\end{array}\right]$$

$$T= {V}^{T}X=\left[\begin{array}{c}{t}_{1}\\ {t}_{2}\\ ⋮\\ {t}_{m}\end{array}\right]=\left[\begin{array}{ccc}{t}_{11}& \cdots & {t}_{1n}\\ ⋮& \ddots & ⋮\\ {t}_{m1}& \cdots & {t}_{mn}\end{array}\right]$$

V is a function of spatial coordinates only, and is the eigenvector of the covariance array quantity, which is the eigenvalue of the explained variance corresponding to the covariance array; T is the time function, which is uniquely determined by V and X.

3.1 Validation of precipitation and temperature simulation based on CMIP6 model in the YRB

The validity of drought forecasts was verified based on the precipitation and temperature observations from 1961–2019. The spatial distribution of annual precipitation exhibited a decreasing trend from the southeast to northwest. The low precipitation center was situated in the source region of the Yangtze River, where less than 400 mm of precipitation falls annually, on average. Sen's slope estimator's conclusion for the spatial distribution of precipitation showed a "increasing - decreasing - increasing" pattern from west to east, which was in good agreement with the MMK trend test. Moreover, the increased rate in the upper reaches of the YRB was less than that (between 0 and 2) in the lower reaches. At the intersection of Sichuan, Chongqing, and Guizhou, the precipitation had the lowest Sen's slope (-2.98). However, various meteorological stations in the lower and upper reaches of the YRB indicated a large rise in precipitation, while the middle region showed a significant drop through the MMK test (Fig. 2 (a)).

The spatial distribution of the annual mean temperature in the YRB also showed a decreasing trend from southeast to northwest. The highest yearly average temperature was situated in the southern portion of Jiangxi, reaching 20.12°C. The lowest yearly average annual temperature center, located in the source area of the Yangtze River, was < -5°C. These two methods revealed an increasing temperature trend. The growth rate distribution in the YRB was slow in the middle and rapid in the surrounding areas. The Sen’s slope was highest at the source of the Yangtze River, which was > 0.04, and the lowest was 0.009 in Chongqing. Approximately 87% of the meteorological stations in the study area passed the MMK significance test, indicating that the temperature increased significantly.

To enhance the precision and scientific rigor of the CMIP6 model simulation for meteorological drought analysis in the YRB over the coming years, 10 models of CMIP6 and the average MME of that were combined to evaluate the simulation capacity from A to K. The correlation coefficient between each model data in the precipitation Taylor diagram was slightly smaller, ranging between 0.8–0.9. Among the 10 CMIP6 model data, the MIRO mode by dot H showed the highest correlation, smallest standard deviation for precipitation simulation; however, MME data represented by dot K showed the best performance. This meant that compared to other CMIP6 modes, the MME data was the best and was improved by 0.1 to more than 0.9. (Fig. 3). Same applies to temperature Taylor diagram. Therefore, we performed the future meteorological drought simulations based on the MME data (Fig. 3).

Correspondingly, we estimated the precipitation anomalies (PA) and temperature anomalies (TA) for the 10 CMIP6 models in the Shared Socioeconomic Pathways SSP1-2.6, SSP2-4.5, and SSP5-8.5 to perform spatiotemporal drought analyses. Overall, there was reasonable agreement between the observations and CMIP6 estimates from 1961–2014. The PA fluctuated more for all three scenarios, while the TA was continuously increased under the scenarios with higher emissions.

3.2 Characteristics of drought evolution in the YRB over the last 60 years.

The MMK test was performed on the drought occurrence frequency events based on SPEI-12 for the historical period to investigate the spatial distribution of droughts. The overall drought frequency range in the YRB was 16.2–18.8% (Fig. 6(a)). Despite having a frequency range from 9.2–12.3%, moderate drought showed a similar spatial distribution as did total drought. The highest drought frequency occurred at the junction of the upper and middle reaches. In other regions, the frequency of drought was similarly distributed. The severe drought frequency varied from 4.4–6.3% (Fig. 6(c)). In contrast to the remainder of the region, where there was only a differential of ~ 0.6%, we discovered that the junction of Yunnan-Guizhou-Sichuan experienced the highest frequency of extreme drought, at more than 1.8% (Fig. 6(d)).

By further extending the evaluation, we obtained consistent results through Sen’s slope in the variable trends of annual, spring, summer, and autumn drought. It was clear that the annual drought generally presented a ‘mitigate-intensify-mitigate’ spatial distribution trend from west to east. Sen's slope was less than zero for the majority of the YRB, showed a trend of increasing annual drought. The Sen’s slope, which was − 0.001 in the southern portion of Shanxi, was the smallest. Among them, the MMK significance test revealed that interannual drought was noticeably exacerbated at 14 meteorological stations. Both the source and the lower reaches of the YRB exhibited a trend of decreasing drought. Most of the lower reaches of the YRB showed Sen's slopes of > 0.005, and the highest value was 0.009. In Northern part, the Sen’s slope in most areas of the YRB source was > 0.003, the extent of drought mitigation was slightly less than that in the lower reaches of the Yangtze River. The spatial distribution of the spring drought illustrated the trend that, "the western border of the upper YRB became wet, while the east of it became dry" (Fig. 7(b)). Comparable to the spring drought, the broad spatial distribution tendency of the autumn drought was similar. The Shanghai region and Central Jiangxi region showed a slowing trend, which was the most noticeable contrast from the spring drought. Furthermore, with a Sen's slope of less than − 0.02, the intersection of Yunnan, Guizhou, and Sichuan had the strongest intensification. The strongest intensification was observed in the Southern Shanxi region, which showed a reading of -0.27, while the summer drought had the opposite spatial change to the spring drought. Thus, it is likely that severe drought will affect the majority of Western Yuanjiang. The central portion of Yunnan showed the strongest intensification of summer drought (Fig. 7(c)).

3.3 Projections of drought in the YRB

Figure 8 clearly reveals that based on MME data calculation, the ratio of SPEI-12 series over the next 81 years (2020 to 2100) for future drought stations across different seasons and years. The increasing or decreasing trends of the spring drought station ratio under various conditions was the lowest among all types. Further, changes in the drought station ratio for different seasons, and interannually, were found to be synchronous under the same shared socioeconomic pathway scenarios. The ratio of various drought stations was reduced dramatically under SSP1-2.6. Both the ratio of spring and summer drought stations were steady at ~ 25% and 13%, respectively, before and after 2050. The autumn drought station ratio trended downward, whereas a global drought event in 2071 may occur as high as 82.9%. In contrast, the annual drought station ratio displayed a consistent downward trend and was less than 10% toward the end of the 21st century. Under SSP2-4.5, the seasonal and the annual ratio of drought stations did not fluctuate greatly. Under SSP5-8.5, the ratio of drought stations revealed that it will increase significantly, among which all the summer, autumn, and annual ratio of drought stations indicated that there will be continuous regional and global drought events at the end of the 21st century. Moreover, it appeared that as radiative forcing scenarios increased, the proportion of severe and exceptional drought episodes increased dramatically for various seasonal and annual drought events.

We simulated the spatial distribution of various drought frequency levels under different future scenarios following validation (Fig. 9). Here, two future periods were extracted, i.e., near future (NF, 2021–2050) and far future (FF, 2070–2100). We focused primarily on the evolution and spatial distribution of extreme and total drought frequency. Figure 9 shows that under SSP1-2.6 (2021–2050), the frequency of extreme droughts will range from 0.8–4.4%. The occurrence of extreme drought frequencies in the upper and middle reaches of the Hanjiang River basin were highest, exceeding 4%. The second highest frequency was 3%, which was concentrated in the Dongting Lake area. Central Jiangsu and Southern Jiangxi were the lowest at 1%. The frequency of intense droughts ranged from 0.04–3.61% from 2021 to 2050 under SSP2-4.5. The frequency spatial distribution was observed to steadily decrease from west to east. The frequency of extreme drought was greater than 2% west of the Qinghai-Tibet Plateau's southeastern edge, which might exceed 3% in some areas. The boundary between Chongqing and Guizhou had the second highest frequency. However, except for the west of the southeastern margin of the Qinghai-Tibet Plateau and the boundary between Chongqing and Guizhou, the frequency of extreme drought increases ranged from 0.14–2.9% from 2071 to 2100. The highest extreme in the research area was at the confluence upper and middle reaches where it was ~ 2.5%. Correspondingly, the northern region at the upper reaches had the highest frequency of intense droughts from 2021 to 2050 under SSP5-8.5. However, from 2071 to 2100, the frequency of drought grew considerably in other locations, ranging from 0.4–6.6%, with the exception of a small decrease in the upper reaches.

Internal variability (e.g., natural variability independent of external forcing), presents a third major source of uncertainty for climate change projections (Deser et al. 2012), which must be accounted for when assessing changes in drought events (Trenberth et al. 2014). We analyzed the total drought frequency under SSP1-2.6 from 2021–2050, when the spatial distribution gradually decreased from the center of the upper reaches to the surrounding areas. Under SSP2-4.5, the drought frequency occurring from 2071–2100 increased and decreased across the entire basin, typically occurring between from 5.5% − 30.2%. Compared with 2021–2050 (at 15%) it remained stable at ~ 10%, while the total drought occurring in the southeast edge of the Qinghai-Tibet Plateau increased by ~ 5%. Under SSP5-8.5, non-climate change policy interventions, population expansion, the slow development of scientific and technological development, and the exacerbation of fossil energy consumption caused greenhouse gas emissions to increase. Drought at the junction of upper and middle reaches increased to varying degrees, while the frequency of total drought was from between 3.6% − 45.96%. At the southeastern edge of the Tibet Plateau the frequency of total drought evidently increased even more than 45%.

We also achieved the prediction of spatial distribution trends under different future scenarios using Sen’s slope. Here, we listed only future annual drought distribution variation trends, but they had the following features. The drought grid number expanded the most with greater radiative forcing, which meant that annual droughts were more susceptible to radiative shifts than seasonal droughts. The middle reaches of the YRB had propensities to become extremely moist under various radiative situations and seasonal conditions. During the annual drought changes, the western area of the upper reaches of the YRB suffered significant drought intensification.

The temporal future drought distribution is illustrated in Table 3. Generally, the probability of the occurrence of drought increased and deepened as the emission scenario increased, with correspondingly intensification.

Table 3

Drought categories based on initial classification of SPEI values.
Drought grade		Probability of occurrence at SPEI-12 (%)
		SSP1-2.6			SSP2-4.5		SSP5-8.5
		2021–2050		2071–2100	2021–2050	2071–2100	2021–2050	2071–2100
0	No drought	67.95		68.44	68.44	63.35	61.24	56.98
1	mild drought	15.27		15.07	15.07	16.39	17.08	17.55
2	moderate drought	10.11		10.21	10.21	12.09	13.42	15.08
3	severe drought	5.10	4.83		4.83	6.28	6.75	8.46
4	extreme drought	1.57	1.44		1.44	1.89	1.50	1.93

In Table 3, under SSP1-2.6, the probability of extreme drought in the NF was higher than in the FF. The probability of severe drought increased by 1.65% and 3.63% under SSP1-2.6 and SSP5-8.5 in the NF and FF, respectively, which were second to the moderate drought increases (from 10.11–10.21%) under SSP1-2.6, to 13.42% and 15.08% under SSP5-8.5 in the NF and FF, respectively. The probability of mild drought outweighed by 17%. Results under SSP2-4.5 were compatible with those under SSP5-8.5.

3.4 Risk of future drought events in upper and mid-lower reaches of the YRB

We initially analyzed the variations of SPEI-12 in the upper and middle-lower reaches of the YRB during the historical period. Upstream flows were primarily responsible for the water entering the downstream, where the occurrence of drought affected the inflow. From a historical perspective, upstream droughts exhibited stable fluctuations.. From 1992 to the end of 2010, the upstream drought index fluctuated significantly and the SPEI index in the midstream was ~-1; however, the downstream SPEI index was mostly close to -1.5 during this period. In the future, under the SSP1-2.6 scenario (2028–2035), most of SPEI indices showed negative values close between − 1 and 0, while downstream, the SPEI indices were lower than − 1.5, and even close to -2, with 2080 to 2090 being very harsh. Under the higher SSP2-4.5, when the negative upstream SPEI values were large, a period of more intense drought occurred downstream. Specifically, the absolute values of the SPEI became larger, particularly downstream, where the majority of the values were lower than − 1 and even to -2, which represented severe drought shown in Table 2. Moreover, under higher SSP5-8.5 emissions, when the upstream SPEI fluctuated in the range of -1, the downstream SPEI not only decreased to -2, but it also occurred over many years and more frequently. From 2055 to 2060, the upstream and downstream SPEI changes were inconsistent, although they were more consistent under the three different scenarios until 2030.

Basin-wide droughts were further analyzed by EOF, where the contribution margins of the first spatial mode (EOF1) and second spatial mode (EOF2) were 33.86% and 17.66% under SSP1-2.6, 30.32% and 19.24% under SSP2-4.5, and 40.00% and 17.54% under SSP5-8.5, respectively. Spatial values showed a negative to positive trend from upstream to downstream in EOF1 under SSP1-2.6 (Fig. 13(a)). The basin presented an overall negative spatial distribution to a greater degree in the upper and middle reaches (EOF1<-3), followed by the lower reaches under SSP1-2.6, which were consistent in reflecting basin-wide drought. The distribution of the SPEI across the EOF2 region showed a consistent pattern, with positive values (> 0.1) that were mostly concentrated in Yunnan Province. Conversely, under SSP2-4.5, the SPEI showed a regressive distribution from upstream to downstream. A saddle-shaped distribution was shown in the middle-lower parts, and the low-value centre was located in Hubei Province. Under SSP5-8.5, the spatial distribution patterns were similar to that of the SSP1-2.6, with a higher spatial density consistency.

Multplied with the spatial mode (EOF), time coefficients (PCA) were selected from 2028 to 2040 under both SSP1-2.6 and SSP2-4.5 (2030–2045) under SSP5-8.8 (Fig. 12) to present the years and duration of simultaneous basin-wide drought for the Yangtze River. The duration of PCA1 increased under the high scenario and the intensity was enhanced. EOF1 showed a larger area of positive values (Fig. 13(e)), combined PCA1 positive values (Fig. 14(e)), with the duration of simultaneous drought being extended. The results of SSP5-8.5 were often more noticeable than those of SSP2-4.5 (Fig. 13(c), Fig, 14(c)). The results for the three SSP scenarios showed bounded distinctions.

We demonstrated that CMIP6 could predict the future spatial distribution of drought and extreme drought events in the YRB with reasonable accuracy. According to Xu et al. (2022), both the CMIP5 and CMIP6 model MME could sensibly duplicate the climate means, and the biases were generally lower in the CMIP6 models than the CMIP5 models. The CMIP6 models exhibited proficiency in simulating the extreme precipitation and temperature indices over the YRB. However, the capacity of CMIP6 in the upper and middle reaches of the YRB was still insufficiently captured. Additionally, the magnitude of drought frequency responses in the CMIP6 projections depended on the region and season, which was consistent with earlier studies (Cook et al. 2020) that emphasized drought metrics. Scenario uncertainty outweighed model uncertainty from mid-century onwards (Hawkins et al., 2011), which would experience more droughts occurring in the Southeast Asian region under SSP1-2.6, SSP2-4.5, and SSP5-8.5 (Li et al. 2021; Zeng et al. 2022). Overall, our results aligned with those previous studies based on CMIP6 model projections. Our analysis focused further on drought from seasonable characteristics.

The MME technique has been considered as a beneficial strategy for enhancing weather and climate forecasting to reduce uncertainties (Feng et al. 2011; Sun et al. 2015). The Taylor plots study of prediction and temperature precision indicated that the dependability of MME was higher than that of other methods. The northwest-southeast fluctuation can be adequately reproduced using the CMIP6 MME model. While considerably better than any one model, the data trend that emerged via MME projections still had sizable deviations, which were not thorough explainable. We used these data to estimate the frequency and spatial distribution of drought. Jiang et al. (2022) suggested that quantitative future climate change records with high credibility generated by robust GCMs merged datasets from CMIP6 were scarce. This indicated that more possibilities needed to be added to the model projections for future droughts, and the impact factors on regional downscaling need to be further investigated.

SPEI-12 was the main assessment method for drought variations, as SPEI values over different timescales reflect complex and long-term variations in drought conditions. The drought intensity, distribution trend, and duration were also well demonstrated in this study, particularly for the prediction of the simultaneous occurrences of YRB drought events under increased emissions scenarios. Other impacts on SPEI-12 were not addressed. Li et al. (2021) calculated and analyzed the correlation coefficients between the SPEI-12 and ENSO indices of each station. According to SPEI, the middle reaches of the LMRB are forecast to have the greatest increases in drought duration and intensity in the dry season, while the upper Lancang and middle Mekong rivers are projected to experience longer drought durations and greater drought intensity in the wet season (Dong et al. 2022). We simulated the ratio of drought stations for different future periods under the different scenarios and found that the duration of consecutive droughts increased. Distinguishing wet and dry seasons will provide more reflections for thought for subsequent studies.

Additionally, this study showed that the lower reaches of the YRB in Hubei and Jiangsu are predicted to experience continuous prolonged droughts from 2030–2040. Emission scenario increments will intensify simultaneous droughts across the entire YRB. However, the impacts of human activities were not considered separately. The construction of the Three Gorges Dam induced more humidity to the climate of the lower reaches of the basin (Li et al. 2021), which was contrary to our findings that the intensity of future drought events in the lower YRB will be enhanced. This also provides ideas for subsequent investigations. The impacts of anthropogenic activities on hydrological processes continue to change, which results in disordered stochastic changes in streamflows and droughts (Zhang et al., 2018). Considering the unbalanced distribution of hydraulic engineering facilities and spatially heterogeneous changes in future drought events, additional studies will be required to explore how influencing factors precisely impact regional variations in droughts for the YRB region under future climate changes.

Uncertainties exist in climate simulations and future climate change projections, which have often been a limiting factor, particularly at local and regional scales (Knutti et al. 2013). Consequently, future climate change projections should be interpreted with caution. Ensemble methods that assign weights based on the performance of each model should be employed to provide more reliable climate projections.

For this study, we explored future drought projections using SPEI for the YRB, based on the MME derived from the average of 10 CMIP6 models and historical observations data under three climate change scenarios. We emphasized spatiotemporal patterns and comparative variations of drought events in the upper, and middle-lower reaches of the YRB, resulting in the following conclusions:

(1) The average of 10 CMIP6 models showed a good verification of historical precipitation and temperature for drought predictions. The MME model accurately replicated historical drought spatiotemporal changes. The MMK and Sen’s slope demonstrated consistency for historical droughts. From a historical perspective, the middle reaches of the YRB experienced intensifying drought frequency.

(2) In the future (2020–2100), under elevated emission scenario increments, the frequency, intensity, and scope of droughts for the YRB will sustainably increase. Compared with the SSP1-2.6 scenario, under SSP5-8.5 scenario, the probability of the occurrence of moderate drought increased from 10.11–15.08%, and extreme drought events varied from 5.10–8.46%. In the NF, the total drought frequency was relatively high under the SSP1-2.6 scenario, remaining in the range of from 15%-25%, while under the SSP5-8.5 scenario, the total drought frequency was higher in the FF, with a frequency of over 35% in the Sichuan basin. The frequency of extreme droughts will remain at ~ 4%-5% under the SSP5-8.5 scenario. These trends demonstrated the effects of anthropogenic climate change on the spatial variability of drought across the entire YRB.

(3) Under all three scenarios, droughts were forecast to occur concurrently more frequently in the upper and middle-lower reaches of the YRB over the 2030–2040 period based on SPEI. During this period, the middle-lower reaches of the YRB are likely to experience the largest scope and highest intensity of drought under the SSP1-2.6 scenario. Under the SSP5-8.5 scenario, there was a basin-wide distribution drought and a trend of increasing drought from the upper to the lower reaches. The middle reaches indicated a saddle-shaped distribution, which attained a maximum in the lower reaches, while Sichuan Basin was the sub-peak.

This innovative work provides evidence for drought projection for the YRB that spans multiple regions in the NF, which is significant for the further elucidation of the effects of climate change along the Yangtze River.

Funding: This work was supported by National Natural Science Foundation of China [grant number 41971025]; Research Funding of China Three Gorges Corporation [grant number 202003251]; and Open Research Foundation of Hubei Key Laboratory of Intelligent YRB and Hydropower Science [grant number ZH2002000113].

Conflicts of Interest: The authors declare no conflict of interest.

Availability of data and material: Not applicable.

Code availability: Not applicable.

Authors’ Contributions: Ying Zhang: Writing- Original draft preparation, Software, Data Curation; Zengxin Zhang: Conceptualization, Writing - Review & Editing; Rui Kong and Zhenhua Peng: Writing - Review & Editing; Qi Zhang and Xi Chen: Resources, Data curation. All authors read and approved the final manuscript.

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The increasing risk of future simultaneous droughts over the Yangtze River basin based on CMIP6 models

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Abstract

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1. Introduction

2. Data And Methodology

2.1 Study area and data

2.2 Methodology

3. Results

3.1 Validation of precipitation and temperature simulation based on CMIP6 model in the YRB

3.2 Characteristics of drought evolution in the YRB over the last 60 years.

3.3 Projections of drought in the YRB

3.4 Risk of future drought events in upper and mid-lower reaches of the YRB

4. Discussion

5. Conclusions

Declarations

References

Additional Declarations

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