Population Exposure to Compound Droughts and Heatwaves in the Observations and ERA5 Reanalysis Data in the Gan River Basin, China

Yuqing Zhang; Guangxiong Mao; Changchun Chen; Liucheng Shen; Binyu Xiao

doi:10.3390/land10101021

Zhang, Yuqing;Mao, Guangxiong;Chen, Changchun;Shen, Liucheng;Xiao, Binyu

2021-09-28 00:00:00

land Article Population Exposure to Compound Droughts and Heatwaves in the Observations and ERA5 Reanalysis Data in the Gan River Basin, China 1 , 1 2 3 1 Yuqing Zhang * , Guangxiong Mao , Changchun Chen , Liucheng Shen and Binyu Xiao School of Urban and Environmental Sciences, Huaiyin Normal University, Huai’an 223300, China; gxmao123@126.com (G.M.); 51213901029@stu.ecnu.edu.cn (B.X.) School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China; 001309@nuist.edu.cn School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200234, China; slc83328317@126.com * Correspondence: 8201711019@hytc.edu.cn Abstract: The frequency, duration, and magnitude of heatwaves and droughts are expected to increase in a warming climate, which can have profound impacts on the environment, society, and public health, and these may be severely affected speciﬁcally by compound droughts and heatwaves (CDHWs). On the basis of daily maximum temperature data and the one-month standardized precipitation evapotranspiration index (SPEI) from 1961 to 2018, the Gan River Basin (GRB) was taken as a case here to construct CDHW identiﬁcation indicators and quantify the population exposure to CDHWs. We found that ERA5 reanalysis data performed well in overall simulating temperature, precipitation, one-month SPEI, heatwaves, and CDHWs in the GRB from 1961 to 2018. CDHWs during the period from 1997 to 2018 were slightly higher than that in 1961–1997. CDHWs Citation: Zhang, Y.; Mao, G.; Chen, were more likely to occur in the southern parts of the basin due to the relatively high values of C.; Shen, L.; Xiao, B. Population Exposure to Compound Droughts drought–heatwave dependence indices. Atmospheric circulation analysis of the 2003 CDHW in the and Heatwaves in the Observations GRB showed a relatively long-lasting anomalous high pressure and anticyclonic circulation system, and ERA5 Reanalysis Data in the Gan accompanied by the positive convective inhibition and surface net solar radiation anomalies. These River Basin, China. Land 2021, 10, circulating background ﬁelds eventually led to the exceptional 2003 CDHW occurrence in the GRB. 1021. https://doi.org/10.3390/ The population exposure to CDHWs basically increased, especially for the moderate CDHWs in land10101021 ERA5. The change in total exposure was mainly due to climate change. Compared with the period from 1989 to 1998, the contributions of the population change effect in 2009–2018 gradually increased Academic Editor: Giulio Iovine with the increase in the CDHW magnitude both in the observations and ERA5 reanalysis data. Received: 21 August 2021 Keywords: compound droughts and heatwaves; population exposure; ERA5; Gan River Basin; China Accepted: 25 September 2021 Published: 28 September 2021 Publisher’s Note: MDPI stays neutral 1. Introduction with regard to jurisdictional claims in published maps and institutional afﬁl- A combination of climate extremes (e.g., low precipitation and high temperatures) iations. have received much attention due to their disproportionate and ampliﬁed impacts on the ecosystems and societies across the world [1–8]. For example, the 2003 European heat- waves, 2010 Russian heatwaves, 2013 Chinese heatwaves, and 2018 German heatwaves were all accompanied by severe droughts, which caused a large number of casualties, Copyright: © 2021 by the authors. crop failure, wildﬁres, and infrastructural damages [9–12]. The special report by the Licensee MDPI, Basel, Switzerland. Intergovernmental Panel on Climate Change (IPCC) remarked that a combination of mul- This article is an open access article tiple climate events can be termed as a compound event [13], and recommended three distributed under the terms and general deﬁnitions to describe it as such: (a) two or more extreme events occurring si- conditions of the Creative Commons multaneously or successively, (b) a combination of multiple extremes with underlying Attribution (CC BY) license (https:// conditions that amplify the impact of the individual extremes, and (c) a combination of creativecommons.org/licenses/by/ multiple events that are not extremes at their individual level but lead to an extreme 4.0/). Land 2021, 10, 1021. https://doi.org/10.3390/land10101021 https://www.mdpi.com/journal/land Land 2021, 10, 1021 2 of 28 event or impact when they combined. Subsequently, compound events were further di- vided into four categories based on the weather/climate drivers and hazards/risks [2,5]: (a) preconditioned events due to one or more hazards under particular pre-existing con- ditions (e.g., ﬂoods may arise from a combination of extreme precipitation and “precon- ditioned” saturated soils), (b) multivariate events occurring simultaneously in the same region (e.g., concurrent droughts and heatwaves), (c) temporally compounding events, for example, a succession of hazards (the same or different events) that affect a given region (e.g., a ﬂood followed by heatwaves), and (d) spatially compounding events occurring in connected areas that are affected by the same or different hazards within a limited time window (e.g., synchronous crop failures due to heatwaves and/or droughts). According to the complexity of compound events, their eventual impacts in some cases inevitably fall into more than one category due to the soft boundaries (i.e., ﬂexibility of boundaries) within the typology of compound events. Compound droughts and heatwaves (CDHWs) are common natural disaster phenom- ena considered compound events, and they have signiﬁcant impacts on the environment, social economy, and human health. High temperature lasts for a long time and is accom- panied by a shortage of precipitation, which can induce the CDHW occurrence due to the negative correlation between precipitation and temperature during summer in some regions [3]. Droughts and heatwaves can intensify and expand via land–atmosphere feed- backs [14]. Exploring the possible dependence of drought–heatwave events in different regions is helpful to understand which areas have a high probability of CDHWs [3]. Re- cently, the percentile threshold method was used to investigate CDHWs based on the precipitation and temperature data [15–17]. The drought index (e.g., standardized precipi- tation index and standardized precipitation evapotranspiration index) combined with the daily maximum temperature data has been used to further identify CDHWs and for an in-depth understanding of CDHWs [3,18–21]. Although there have been many studies on the indices of droughts or heatwaves [22–26], the CDHW indices are few because the deﬁnitions and dimensions of droughts and heatwaves are different. For the construction of the CDHW index, the drought index and the heatwave index can be normalized separately and then multiplied during the speciﬁc period [19]. Thus, exploring the changes in CDHWs based on the severity (CDHW magnitude index) may prove to be useful for understanding CDHW characteristics. Reanalysis products are important datasets for estimating the hydroclimatic char- acteristics, especially for the areas with sparse observation stations. ERA5 is the new ﬁfth-generation reanalysis dataset released by the European Centre for Medium Range Weather Forecasts (ECMWF), which contains a large number of hydroclimatic variables with a high spatio-temporal resolution. This dataset was established via the 4D-Var assimi- lation method, which combines model data with observations from across the world into a global dataset. As an upgraded version of ERA-Interim, ERA5 has a rigorous physical process foundation and high quality with high spatio-temporal resolution for a long period, and has been widely applied in hydrometeorological investigations and evaluations [27–33]. The ERA5 reanalysis dataset is divided into two parts by time span: 1950–1978 (preliminary back extension) and from 1979 onwards (ﬁnal release plus timely updates). At present, there is a lack of evaluation of ERA5 in CDHWs, especially in humid subtropical basins. Hence, evaluating the ability of ERA5 data for identifying CDHWs can allow scientiﬁc measures to be taken to manage and handle compound events in a timely manner. Heatwaves can cause heat stroke and can affect the elderly, infants, and persons with pre-existing cardiovascular and respiratory conditions, and may further increase morbidity and mortality rates [34–37]. If heatwaves occur during the drought period, then their destructive power is greater than that of individual heatwaves on natural environments, society, economy, and human health. For instance, strong heatwaves coincided with severe droughts in eastern China during the summer of 2013, and caused severe damage to the local environment, society, economy, and human health [11,38]. The damage was particularly severe in both eastern and southern China, which are densely populated areas [11,39]. Land 2021, 10, 1021 3 of 28 Scenario population data (e.g., shared socioeconomic pathways) have been exam- ined to explore the characteristics of population exposure to climate extremes [40], such as extreme precipitation [41], heatwaves [36,42], and droughts [43,44]. Yet, research on the exposure of the population to hydroclimatic extremes using long-term population observation data is still relatively rare, especially for CDHWs. The population exposure to climate extremes depends not only on climate change, but also on changes in the sizes and distributions of human populations [42]. Analysis of the relative contributions of different incorporated parameters (e.g., climate factor, population number, and the nonlinearity of both factors) to changes in overall exposure can provide important information regarding vulnerability to CDHW-related health problems. In this study, we used the Gan River Basin (GRB) as a case to explore the characteristics of population exposure to CDHWs. The primary goals of this study included (1) evaluating the accuracy of ERA5 data in monitoring precipitation, temperature, heatwaves, droughts, and CDHWs; (2) exploring the characteristics of CDHWs and drought–heatwave depen- dence; (3) investigating population exposure to CDHWs, especially compound events in different grades based on CDHW magnitudes; and (4) quantifying the contributions of CDHW (climate change effect), population number (population change effect), and the nonlinearity of the previous two factors (joint change effect) to the overall exposure changes. The evaluation of population exposure to CDHWs was expected to provide a workable basis for mitigating potential losses due to CDHWs in regions that share similar climatic and socio-economic characteristics with the GRB. 2. Materials and Methods 2.1. Study Area The GRB is located within the central and southern parts of the Poyang lake basin (the largest freshwater lake in China), with an area of 80,948 km (approximately the size of South Carolina in the U.S.), and is observed by the Waizhou hydrological station (outlet of the GRB). The GRB represents the largest sub-basin both in area (51%) and runoff (50%) of the Poyang lake basin. Mountains and foothills are most located in the southern parts of the GRB and ﬂat plain areas exist in the northern parts of the GRB. The GRB belongs to a subtropical humid monsoon climate zone and has average annual precipitation of 1600.1 mm and an annual mean temperature of 18.2 C [45]. The GRB mainly covers six prefecture-level cities: Nanchang, Yichun, Xinyu, Pingxiang, Ji’an, and Ganzhou. The total population of the GRB was approximately 27.87 million at the end of 2018. The GRB is often affected by droughts and heatwaves in the summer months, and the number of these two events is likely to increase in the future [46], which may have a great impact on the natural environment, society, and economy. 2.2. Data Meteorological observations included daily precipitation, maximum temperature (T ), and mean temperature (T ) from 1961 to 2018. These observations were pro- max mean vided by the National Meteorological Information Centre of the China Meteorological Administration (http://data.cma.cn/, accessed on 18 September 2021) and were subjected to quality control and homogeneity assessments before release. To ensure the integrity and continuity of the data series, a meteorological station was removed from the study if the proportion of the missing values was more than 0.15% (i.e., 31 days) of the daily values during 1961–2018. The missing value was interpolated using the average value of 10 neighboring stations on the same day. We ultimately selected 47 meteorological stations (Figure 1) across the GRB spanning 1961–2018. Land 2021, 10, x FOR PEER REVIEW 4 of 30 Land 2021, 10, 1021 4 of 28 stations on the same day. We ultimately selected 47 meteorological stations (Figure 1) across the GRB spanning 1961–2018. Figure 1. Location of 47 meteorological stations, one hydrological station (outlet), and 121 ERA5 Figure 1. Location of 47 meteorological stations, one hydrological station (outlet), and 121 ERA5 grids in the GRB. grids in the GRB. The ERA5 reanalysis datasets with 0.25° spatial resolution and hourly temporal res- The ERA5 reanalysis datasets with 0.25 spatial resolution and hourly temporal olution were obtained from the fifth-generation ECMWF atmospheric reanalysis of the resolution were obtained from the ﬁfth-generation ECMWF atmospheric reanalysis of gl the obal global clima climate te [47]. In order to be consi [47]. In order to be s consist tent wi ent th the with yea the r le year ngth of length the observa of the observation tion data, we select data, weed t selected he precip the it pr at ecipitation ion and tem and perat temperatur ure data fr eom data 19fr 6om 1 to 20 1961 18 to in E 2018 RA5 in fo ERA5 r evalfor u- evaluation. The number of ERA5 grids in the GRB is 121 grids (Figure 1). Because the ation. The number of ERA5 grids in the GRB is 121 grids (Figure 1). Because the ERA5 are hourly ERA5 d araeta hourly sets, we datasets, summed t wehsummed e 24 hours the of24 preci h of pit pr at ecipitation ion in a cert inain a certain day asday the d asaily the daily precipitation, and the monthly and annual precipitation could be calculated via a precipitation, and the monthly and annual precipitation could be calculated via a similar similar approach. The maximum value of 24 h for a temperature value in a given day was approach. The maximum value of 24 hours for a temperature value in a given day was regarded as the T of that day, and the mean value of 24 h for a temperature value in a regarded as the Tmax max of that day, and the mean value of 24 hours for a temperature value given day was regarded as the T of that day. in a given day was regarded as the T mean mean of that day. We used ERA5 atmospheric reanalysis data including the 500 hPa geopotential height, We used ERA5 atmospheric reanalysis data including the 500 hPa geopotential water vapor ﬂux, convective inhibition, total cloud cover, and surface net solar radiation height, water vapor flux, convective inhibition, total cloud cover, and surface net solar to explore mechanisms of the 2003 CDHW. These atmospheric reanalysis data in ERA5 radiation to explore mechanisms of the 2003 CDHW. These atmospheric reanalysis data during 1961–2018 were also of 0.25 spatial resolution and hourly temporal resolution. in ERA5 during 1961–2018 were also of 0.25° spatial resolution and hourly temporal res- The GRB resident population data were obtained from China’s economic and social olution. big data research platform (http://data.cnki.net/, accessed on 18 September 2021). The GRB consists of six prefecture-level cities (i.e., Nanchang, Yichun, Xinyu, Pingxiang, Ji’an, and Ganzhou) in Jiangxi province. Because population statistics are generally based on administrative regions, the administrative boundaries of the prefecture-level cities have Land 2021, 10, 1021 5 of 28 been relatively stable in the past three decades, so we used the total population of each prefecture-level city as the statistical unit in the GRB from 1988 to 2018 for analysis. 2.3. Evaluation Metrics To quantitatively compare the ERA5 reanalysis data against ground observations, ﬁve statistical indices including mean bias (Bias), relative bias (RB), the correlation coefﬁcient (r), root mean square error (RMSE), and the distance between the indices of simulation and observation (DISO) [48,49] were employed in this study. These evaluation metrics are expressed as follows: Bias = (S O ) (1) å i i i=1 (S O ) i i i=1 RB = 100% (2) å O i=1 (O O)(S S) i i i=1 r = q q (3) 2 2 n n å O O å S S i i i=1 i=1 R MSE = (S O ) (4) i i i=1 2 2 D I SO = (r 1) + N B + N R MSE (5) where S and O are the simulations (i.e., ERA5) and observations at each time step i i i (e.g., daily and monthly temporal scales), n is the number of time steps, S and O are the mean values of simulations and observations, NB is Bias divided by the O value, and NRMSE is RMSE divided by the O value. The closer Bias, RB, and RMSE are to zero, the closer the simulations are to the observations. DISO is a comprehensive index that combines r, Bias, and RMSE according to the distance between the simulations and observations in a three-dimensional space coordinate system [49]. When the DISO value is equal to zero, the simulated value is equivalent to the observed value. It is worth noting that DISO is invalid when O equals zero [48], and this is because a small difference in O can cause a large difference in DISO when O is very close to zero. 2.4. Drought Deﬁnition In this study, we deﬁned “meteorological drought” as an event that leads to a 1-month SPEI <1 (approximately the 18th percentile of SPEI values in the GRB), which is similar to the meteorological drought criteria deﬁned in previous studies [4,6,21]. The calculation of SPEI is mainly based on monthly precipitation and T , and a detailed calculation of mean the SPEI can be found in previous studies [6,26]. In this study, the Thornthwaite method was used to calculate potential evapotranspiration (PET) because this method is feasible and effective based on relatively few meteorological variables. The Thornthwaite method has already been widely applied in SPEI calculations [6,21,26,50,51]. We deﬁned the drought magnitude as the absolute value of the difference between the drought indicator (DI, i.e., SPEI < –1) and the threshold (SPEI = –1) during a speciﬁc month (denoted as |DDI|). In order to facilitate the construction of the subsequent compound event magnitude index, we respectively normalized the drought and heatwave magnitude indices. The normalization formula is calculated as follows: x x i min R = 0.9 + 0.1 (6) x x max min where R and x are the ith normalized result and input data, and x and x are the i i min max minimum and maximum values of the input data series. The normalization formula (Equation (6)) was used to normalize |DDI| to [0.1 1] (denoted as R|DDI|). R|DDI| was deﬁned as the ﬁnal drought magnitude index (DMI) in Land 2021, 10, 1021 6 of 28 this study. If the minimum R|DDI| is equal to 0, the drought event with a small magnitude may be classiﬁed as 0, which is inappropriately regarded as a drought-free phenomenon [6]. 2.5. Heatwave Deﬁnition A hot event is generally deﬁned as a T in more than the 90th or 95th percentile max during the speciﬁc period [21,22,36]. Considering the climate characteristics of the GRB, a hot event (hot day) here was deﬁned by the daily T > 90th percentile of the local max daily climatology from 1961 to 2018. A heatwave event (one event) was identiﬁed when a hot event lasted for at least three consecutive days at a given station. The intensity of a hot event (day) was deﬁned as the temperature deviations exceeding the 90th percentile thresholds (denoted as |DT|=|T T |). Then, the |DT| was also normalized max max90th with [0.1 1] via Equation (6) (denoted as R|DT|). The heatwave magnitude index (HWMI) can be written as follows: HW M I = RjDTj (7) å i i=1 where RjDTj denotes the magnitude of ith heatwave day and n 3. 2.6. Compound Drought and Heatwave Deﬁnition CDHW was deﬁned in this study as “simultaneous”, meeting the deﬁnition of a meteorological drought and heatwave for the same month at a given station. When one or more heatwaves occurred during a meteorological drought period (i.e., one month), we counted it as a compound event. Heatwaves occurring across monthly boundaries were allocated to the month in which they started [3]. We established the compound drought and heatwave magnitude index (CDHMI), which can be compared across regions and over time by incorporating both the magnitude (intensity per unit time) and the duration of the compound events. For a given station, CDHMI can be calculated as follows: CD H M I = ( R DT ) R DD I (8) j j j j i n i=1 where D is the number of heatwave days for the nth month. RjDTj is the HWMI for n i i=1 the nth month and RjDD I j is the DMI for the nth month. The CDHMI is computed for a single speciﬁc month (e.g., June) and the annual CDHMI reported here is the sum of monthly CDHMI values during the speciﬁc year. 2.7. D-H Dependence Index The drought–heatwave (D-H) dependence index reﬂects the degree of dependence between droughts and heatwaves [3]. This index was constructed as the ratio of monthly heatwave frequency (occurrence number), duration (days), and magnitude for the drought months to the corresponding statistics for all months in the record, respectively denoted as D H , D H , and D H : F D M H F /D M D H = (9) H F/T M H D /D M D H = (10) H D/T M H M /D M D H = (11) H M/T M where H F (H F), H D (H D), and H M (H M) respectively denote the heatwave frequency, D D D duration, and magnitude during drought (all) months in a speciﬁc period (e.g., 1961–2018); D M and T M represent the number of drought months and all months in the record, re- Land 2021, 10, 1021 7 of 28 spectively. The larger the value of the D-H dependence index, the stronger the dependence between droughts and heatwaves. 2.8. Contribution Population Exposure to CDHWs The population exposure index is measured by the number of citizens exposed to the relevant indicators (i.e., CDHW frequency and CDHMI) of the compound event, that is, the annual indices of compound events multiplied by the corresponding annual number of people exposed to that outcome [36,42]. Therefore, the measurement units of population exposure to CDHW frequency and CDHMI are person-events and person- CDHMI, respectively. The population data extend from 1988 to 2018, so the GRB population exposure was measured for 1988–2018 as well. We applied the rate of change to analyze the changes in magnitude for exposure: E E j i ERate = 100% (12) where ERate denotes the change rate of the exposure. E and E represent the exposure in i j the period i and j, respectively. According to the relative contribution analysis method [41,42], we assessed the contri- butions of the compound event, population number, and the nonlinearity of the previous two factors (joint change effect) to the overall exposure changes. The decomposition for the exposure change can be expressed as: DE = C P C P j j i i = (C + DC) (P + DP) C P i i i i (13) = C P + DC P + C DP + DC DP C P i i i i i i = DC P + C DP + DC DP i i where DE denotes the change in exposure. C and P represent the compound event index i i (i.e., CDHW frequency or CDHMI) and population number in the period i, respectively. C and P represent the compound event index and population number in the period j, j j respectively. DC and DP denote the changes in compound event index and population number from period i to period j, respectively. The ﬁnal form of Equation (13) consists of three terms that represent the different contributions of these factors to the overall exposure changes. The ﬁrst term, DC P , denotes the contribution from the compound event index to the overall exposure changes under the condition that the population is unchanged (i.e., climate change effect). The second term, C DP, represents the contribution from the population change (i.e., population change effect). The third term, DC DP, is a nonlinear term (i.e., joint change effect or interaction effect) associated with changes in both the compound event index and population number of the exposure. The rate of contribution of each factor can be written as follows: jDC Pj CR = 100% (14) jDC Pj+jC DPj+jDC DPj i i jC DPj CR = 100% (15) jDC Pj+jC DPj+jDC DPj i i jDC DPj CR = 100% (16) jDC Pj+jC DPj+jDC DPj i i where CR , CR , and CR denote the contributions of climate change effect, population C P J change effect, and joint change effect, respectively. Land 2021, 10, 1021 8 of 28 3. Results 3.1. Individual Droughts and Heatwaves Before evaluating ERA5 to simulate CDHWs, we need to evaluate precipitation and temperature in ERA5. On the daily scale, the precipitation in ERA5 can reasonably reﬂect the characteristics of the observed values, with high r (0.767) and low RB (14.18%, repre- senting precipitation overestimation by 14.18%), Bias (0.63 mm), RMSE (5.28 mm), and Land 2021, 10, x FOR PEER REVIEW 9 of 30 DISO (1.223) values (Figure 2a). The simulation effect of ERA5 precipitation on the monthly scale was obviously improved compared to that on the daily scale, with r as high as 0.956 and DISO as low as 0.329 (Figure 2b). Figure 2. Scatter plots of (a,b) precipitation, (c,d) Tmean, and (e,f) Tmax from the ERA5 against the Figure 2. Scatter plots of (a,b) precipitation, (c,d) T , and (e,f) T from the ERA5 against the mean max observations averaged GRB from 1961 to 2018. The left and right columns represent the daily and observations averaged GRB from 1961 to 2018. The left and right columns represent the daily and monthly values, monthly values, respectively respectively. . The daily-scale simulation effect of ERA5 Tmean performed better than that of the pre- cipitation (Figure 2c), with higher r (0.994) and lower RB (0.95%), Bias (0.174 ℃), RMSE (0.92 ℃), and DISO (0.051). The simulation effect of the monthly-scale Tmean was slightly better than that of the daily scale (Figure 2d). The simulation effects of ERA5 Tmax on the daily (Figure 2e) and monthly (Figure 2f) scales were similar to those of the Tmean, but were slightly underestimated. In general, ERA5 performed well in precipitation and tempera- ture in the GRB. The one-month SPEI value fluctuated greatly above and below the zero line (Figure 3a), which reflects the characteristics of the dry/wet conditions at short timescales. Ac- cording to the statistical evaluation results of r (0.927), Bias (0), RB (0.63%), and RMSE (0.333), ERA5 effectively represented the characteristics of the one-month SPEI in the GRB. As shown in Figure 3b, a value of r as high as 0.865 indicates that ERA5 better reflected the annual variations of DMI. Bias and RB were as low as –0.039 and –5.37%, respectively, indicating that ERA5 overall slightly underestimated the DMI. The DMI simulation errors of ERA5 in 1963, 1971, and 1972 were relatively high. Overall, the DMI simulation perfor- mance of ERA5 during 1997–2018 was better than that of 1961–1997. The average value of Land 2021, 10, x FOR PEER REVIEW 9 of 27 Land 2021, 10, 1021 9 of 28 The daily-scale simulation effect of ERA5 Tmean performed better than that of the pre- cipitation (Figure 2c), with higher r (0.994) and lower RB (0.95%), Bias (0.174 ℃), RMSE The daily-scale simulation effect of ERA5 T performed better than that of the mean (0.92 ℃), and DISO (0.051). The simulation effect of the monthly-scale Tmean was slightly precipitation (Figure 2c), with higher r (0.994) and lower RB (0.95%), Bias (0.174 °C), RMSE better than that of the daily scale (Figure 2d). The simulation effects of ERA5 Tmax on the (0.92 °C), and DISO (0.051). The simulation effect of the monthly-scale T was slightly mean daily (Figure 2e) and monthly (Figure 2f) scales were similar to those of the Tmean, but were better than that of the daily scale (Figure 2d). The simulation effects of ERA5 T on the max daily (Figure 2e) and monthly (Figure 2f) scales were similar to those of the T , but were slightly underestimated. In general, ERA5 performed well in precipitation and tempera- mean slightly underestimated. In general, ERA5 performed well in precipitation and temperature ture in the GRB. in the GRB. The one-month SPEI value fluctuated greatly above and below the zero line (Figure The one-month SPEI value ﬂuctuated greatly above and below the zero line (Figure 3a), 3a), which reflects the characteristics of the dry/wet conditions at short timescales. Ac- which reﬂects the characteristics of the dry/wet conditions at short timescales. According cording to the statistical evaluation results of r (0.927), Bias (0), RB (0.63%), and RMSE to the statistical evaluation results of r (0.927), Bias (0), RB (0.63%), and RMSE (0.333), ERA5 (0.333), ERA5 effectively represented the characteristics of the one-month SPEI in the GRB. effectively represented the characteristics of the one-month SPEI in the GRB. As shown As shown in Figure 3b, a value of r as high as 0.865 indicates that ERA5 better reflected in Figure 3b, a value of r as high as 0.865 indicates that ERA5 better reﬂected the annual the annual variations of DMI. Bias and RB were as low as –0.039 and –5.37%, respectively, variations of DMI. Bias and RB were as low as –0.039 and –5.37%, respectively, indicating indic that ating ERA5 that overall ERA5slightly overall s under light estimated ly underest the im DMI. ated t The he DMI. The DMI simulation DMI sim errors ulof ati ERA5 on errors in 1963, 1971, and 1972 were relatively high. Overall, the DMI simulation performance of of ERA5 in 1963, 1971, and 1972 were relatively high. Overall, the DMI simulation perfor- ERA5 during 1997–2018 was better than that of 1961–1997. The average value of the DMI mance of ERA5 during 1997–2018 was better than that of 1961–1997. The average value of in 1997–2018 was slightly higher than that in 1961–1997 both in the observations and ERA5, the DMI in 1997–2018 was slightly higher than that in 1961–1997 both in the observations which indicates that the severity of drought had increased in the GRB. and ERA5, which indicates that the severity of drought had increased in the GRB. Figure 3. Temporal variations of (a) the 1-month SPEI and (b) the DMI per-year averaged GRB from Figure 3. Temporal variations of (a) the 1-month SPEI and (b) the DMI per-year averaged GRB from 1961 to 2018. 1961 to 2018. Except in 1965 and 1966, the annual variations of heatwave frequency in the obser- Except in 1965 and 1966, the annual variations of heatwave frequency in the obser- vations (Figure 4a) were relatively consistent with those in the ERA5 (r of 0.854, RB of vations (Figure 4a) were relatively consistent with those in the ERA5 (r of 0.854, RB of – 0.02%, and DISO of 0.227). The entire basin showed a signiﬁcant upward trend at a rate 0.02%, and DISO of 0.227). The entire basin showed a significant upward trend at a rate of 0.25 (0.27 in ERA5) events/decade from 1961 to 2018. The heatwave frequency showed a of 0.25 (0.27 in ERA5) events/decade from 1961 to 2018. The heatwave frequency showed downward trend from 1961 to 1997 and then a reversal (upward) trend from 1997 to 2018. a downward trend from 1961 to 1997 and then a reversal (upward) trend from 1997 to 2018. The increasing rates of heatwave frequency (1.55 events/decade in the observation and 1.28 events/decade in the ERA5) during 1997–2018 were much greater than the de- Land 2021, 10, 1021 10 of 28 Land 2021, 10, x FOR PEER REVIEW 10 of 27 The increasing rates of heatwave frequency (1.55 events/decade in the observation and 1.28 events/decade in the ERA5) during 1997–2018 were much greater than the decreasing rates before the turning point in 1997. The average value of the heatwave frequency during creasing rates before the turning point in 1997. The average value of the heatwave fre- 1997–2018 (5.214 events/year in the observations and 5.137 events/year in the ERA5) was quency during 1997–2018 (5.214 events/year in the observations and 5.137 events/year in signiﬁcantly higher than during 1961–1997 (4.122 events/year in the observations and 4.182 the ERA5) was significantly higher than during 1961–1997 (4.122 events/year in the obser- events/year in the ERA5). vations and 4.182 events/year in the ERA5). Figure 4. Temporal variations of (a) heatwave frequency, (b) heatwave days, and (c) HWMI per- Figure 4. Temporal variations of (a) heatwave frequency, (b) heatwave days, and (c) HWMI per-year year averaged GRB from 1961 to 2018. averaged GRB from 1961 to 2018. As shown in Figure 4b, the annual variations of heatwave days were similar to those of the frequency from 1961 to 2018. Thus, ERA5 effectively simulated the changes in the annual heatwave days, with an r-value of up to 0.895, an RB of 3.88%, and a DISO of 0.219. There was also a clear turning point around 1997 for heatwave days, i.e., a downward trend (–4.39 days/decade in the observations and –3.12 days/decade in the ERA5) from Land 2021, 10, 1021 11 of 28 As shown in Figure 4b, the annual variations of heatwave days were similar to those of the frequency from 1961 to 2018. Thus, ERA5 effectively simulated the changes in the annual heatwave days, with an r-value of up to 0.895, an RB of 3.88%, and a DISO of 0.219. There was also a clear turning point around 1997 for heatwave days, i.e., a downward trend (–4.39 days/decade in the observations and –3.12 days/decade in the ERA5) from 1961 to 1997, and a signiﬁcant upward trend (9.61 days/decade in the observations and 7.88 days/decade in the ERA5) from 1997 to 2018. The average number of heatwave days during 1997–2018 was around seven days longer than that during 1961–1997 both in the observations and in ERA5, indicating a robust increase in the heatwave days in the GRB. The changes in the annual HWMI (Figure 4c) were similar to those of the heatwave frequency and heatwave days, with a decreasing trend (–0.13/decade in the observations and –0.08/decade in the ERA5) from 1961 to 1997 and an increasing trend (0.31/decade in the observations and 0.23/decade in the ERA5) from 1997 to 2018. Both in the observations and in ERA5, the average value of the HWMI was signiﬁcantly higher from 1997 to 2018 than that from 1961 to 1997, indicating that the severity of the heatwave had increased in the GRB. 3.2. CDHW Characteristics As shown in Figure 5a, ERA5 accurately reﬂected the annual variability of CDHW in the GRB (r = 0.834, RB = –0.73%, and DISO = 0.527). The annual number of CDHW did not explicitly exhibit signiﬁcant linear trends in the GRB from 1961 to 2018, but presented downward and upward trends during 1961–1997 and 1997–2018 (especially in ERA5), respectively. The annual mean number of CDHWs of the latter period (1997–2018) was larger than that of the earlier period (1961–1997), especially in ERA5, indicating more CDHWs in 1997–2018 relative to 1961–1997. There were more CDHWs both in the observations and ERA5 during the years 1962, 1963, 1964, 1991, 2003, 2007, 2009, and 2018. The variations of the annual CDHMI were similar to those of the CDHW frequencies, with average values of the CDHMI during 1997–2018 higher than those during 1961–1997 (Figure 5b). The highest value of the CDHMI existed in 2003 during 1961–2018. CDHW frequency was 32–36 events in total from 1961 to 2018 (Figure 6a,b). Both in the observations and in ERA5, there were relatively high frequencies throughout the southern parts of the GRB. The mean differences of the CDHW frequency both in the observations and in ERA5 during 1997–2018 and 1961–1997 indicate that the values in the northern and southern regions were relatively higher than in the central regions (Figure 6c,d). This implies that there were increases in CDHWs in the northern and southern parts of the basin. Unlike the mean frequency differences in the observations, the mean frequency differences in ERA5 were basically positive in the entire basin, and approximately a quarter of the basin area showed substantial increases during 1997–2018 relative to 1961–1997. This indicates that the increasing trends of CDHWs in ERA5 were slightly greater than the observed value. Figure 7 shows the spatial characteristics of D-H dependence. The higher the value of the D-H dependence index, the stronger the dependence relationship between droughts and heatwaves. Both in the observations and in ERA5, all the D-H dependence indices (DH , DH , and DH ) showed higher values of the spatial patterns in the southern parts F D M of the basin than those in the northern parts of the basin, indicating that the interaction between the droughts and heatwaves in the southern part of the basin was stronger. The spatial characteristics of the three D-H dependence indices were similar but the overall order of the speciﬁc value was DH < DH < DH , which may have been caused by F D M the frequency (number), duration, and magnitude of the event. The spatial characteristic of each D-H dependence index was roughly similar to the spatial characteristics of the CDHW frequency, indicating that higher values of the D-H dependence indices are prone to producing more compound events. Land 2021, 10, x FOR PEER REVIEW 11 of 27 1961 to 1997, and a significant upward trend (9.61 days/decade in the observations and 7.88 days/decade in the ERA5) from 1997 to 2018. The average number of heatwave days during 1997–2018 was around seven days longer than that during 1961–1997 both in the observations and in ERA5, indicating a robust increase in the heatwave days in the GRB. The changes in the annual HWMI (Figure 4c) were similar to those of the heatwave fre- quency and heatwave days, with a decreasing trend (–0.13/decade in the observations and –0.08/decade in the ERA5) from 1961 to 1997 and an increasing trend (0.31/decade in the observations and 0.23/decade in the ERA5) from 1997 to 2018. Both in the observations and in ERA5, the average value of the HWMI was significantly higher from 1997 to 2018 than that from 1961 to 1997, indicating that the severity of the heatwave had increased in the GRB. 3.2. CDHW Characteristics As shown in Figure 5a, ERA5 accurately reflected the annual variability of CDHW in the GRB (r = 0.834, RB = –0.73%, and DISO = 0.527). The annual number of CDHW did not explicitly exhibit significant linear trends in the GRB from 1961 to 2018, but presented downward and upward trends during 1961–1997 and 1997–2018 (especially in ERA5), re- spectively. The annual mean number of CDHWs of the latter period (1997–2018) was larger than that of the earlier period (1961–1997), especially in ERA5, indicating more CDHWs in 1997–2018 relative to 1961–1997. There were more CDHWs both in the obser- vations and ERA5 during the years 1962, 1963, 1964, 1991, 2003, 2007, 2009, and 2018. The Land 2021, 10, 1021 12 of 28 variations of the annual CDHMI were similar to those of the CDHW frequencies, with average values of the CDHMI during 1997–2018 higher than those during 1961–1997 (Fig- ure 5b). The highest value of the CDHMI existed in 2003 during 1961–2018. Land 2021, 10, x FOR PEER REVIEW 13 of 30 CDHW frequency was 32–36 events in total from 1961 to 2018 (Figure 6a,b). Both in the observations and in ERA5, there were relatively high frequencies throughout the southern parts of the GRB. The mean differences of the CDHW frequency both in the ob- servations and in ERA5 during 1997–2018 and 1961–1997 indicate that the values in the northern and southern regions were relatively higher than in the central regions (Figure 6c, d, respectively). This implies that there were increases in CDHWs in the northern and southern parts of the basin. Unlike the mean frequency differences in the observations, the mean frequency differences in ERA5 were basically positive in the entire basin, and approximately a quarter of the basin area showed substantial increases during 1997–2018 relative to 1961–1997. This indicates that the increasing trends of CDHWs in ERA5 were Figure 5. Annual variations of (a) CDHWs and (b) CDHMI per-year averaged GRB from 1961 to slightly greater than the observed value. Figure 5. Annual variations of (a) CDHWs and (b) CDHMI per-year averaged GRB from 1961 to 2018. Figure 6. Cont. Figure 6. Spatial patterns of CDHW frequency in (a) the observations and (b) ERA5 from 1961 to 2018, and annual mean differences of CDHW frequency in (c) the observation and (d) ERA5 between 1997–2018 and 1961–1997, respectively. The solid black dots indicate passing the 0.05 significance level based on a two-sample t-test for equal means. Land 2021, 10, x FOR PEER REVIEW 13 of 30 CDHW frequency was 32–36 events in total from 1961 to 2018 (Figure 6a,b). Both in the observations and in ERA5, there were relatively high frequencies throughout the southern parts of the GRB. The mean differences of the CDHW frequency both in the ob- servations and in ERA5 during 1997–2018 and 1961–1997 indicate that the values in the northern and southern regions were relatively higher than in the central regions (Figure 6c, d, respectively). This implies that there were increases in CDHWs in the northern and southern parts of the basin. Unlike the mean frequency differences in the observations, the mean frequency differences in ERA5 were basically positive in the entire basin, and approximately a quarter of the basin area showed substantial increases during 1997–2018 relative to 1961–1997. This indicates that the increasing trends of CDHWs in ERA5 were slightly greater than the observed value. Land 2021, 10, 1021 13 of 28 Land 2021, 10, x FOR PEER REVIEW 14 of 30 Figure 7 shows the spatial characteristics of D-H dependence. The higher the value of the D-H dependence index, the stronger the dependence relationship between droughts and heatwaves. Both in the observations and in ERA5, all the D-H dependence indices (DHF, DHD, and DHM) showed higher values of the spatial patterns in the southern parts of the basin than those in the northern parts of the basin, indicating that the interaction between the droughts and heatwaves in the southern part of the basin was stronger. The spatial characteristics of the three D-H dependence indices were similar but the overall order of the specific value was DHF < DHD < DHM, which may have been caused by the Figure 6. Spatial patterns of CDHW frequency in (a) the observations and (b) ERA5 from 1961 to Figure 6. Spatial patterns of CDHW frequency in (a) the observations and (b) ERA5 from 1961 to frequency (number), duration, and magnitude of the event. The spatial characteristic of 2018, and annual mean differences of CDHW frequency in (c) the observation and (d) ERA5 between 2018, and annual mean differences of CDHW frequency in (c) the observation and (d) ERA5 between each D-H dependence index was roughly similar to the spatial characteristics of the 1997–2018 and 1961–1997, respectively. The solid black dots indicate passing the 0.05 significance 1997–2018 and 1961–1997, respectively. The solid black dots indicate passing the 0.05 signiﬁcance CDHW frequency, indicating that higher values of the D-H dependence indices are prone level based on a two-sample t-test for equal means. level based on a two-sample to producing t-test more compound events. for equal means. Figure 7. Spatial patterns of the D-H dependence indices for (a,d) DHF, (b,e) DHD, and (c,f) DHM in the observations and Figure 7. Spatial patterns of the D-H dependence indices for (a,d) DH , (b,e) DH , and (c,f) DH in F D M ERA5 during 1961–2018. the observations and ERA5 during 1961–2018. We extracted the CDHMI values of all CDHWs for all the stations/grids in the GRB from 1961 to 2018 to draw probability density curves via an empirical cumulative density function (CDF). As shown in Figure 8, the CDF curves in the observations and ERA5 ba- sically coincided. The cumulative probabilities corresponding to the CDHMI values of 0.11, 0.22, and 0.4 were 0.5, 0.75, and 0.9, respectively, and represented conditions of half, most, and almost all of the compound events, respectively. The occurrences of moderate, severe, extreme, and exceptional CDHWs correspond to probabilities of 50–100%, 25–50%, 10–25%, and <10%, respectively. Thus, we divided the CDHWs into four magnitude Land 2021, 10, 1021 14 of 28 We extracted the CDHMI values of all CDHWs for all the stations/grids in the GRB from 1961 to 2018 to draw probability density curves via an empirical cumulative density function (CDF). As shown in Figure 8, the CDF curves in the observations and ERA5 basically coincided. The cumulative probabilities corresponding to the CDHMI values of 0.11, 0.22, and 0.4 were 0.5, 0.75, and 0.9, respectively, and represented conditions Land 2021, 10, x FOR PEER REVIEW 15 of 30 of half, most, and almost all of the compound events, respectively. The occurrences of moderate, severe, extreme, and exceptional CDHWs correspond to probabilities of 50–100%, 25–50%, 10–25%, and <10%, respectively. Thus, we divided the CDHWs into four grades: moderate (0 < CDHMI ≤ 0.11), severe (0.11 < CDHMI ≤ 0.22), extreme (0.22 < magnitude grades: moderate (0 < CDHMI 0.11), severe (0.11 < CDHMI 0.22), extreme CDHMI ≤ 0.4), and exceptional (CDHMI > 0.4). (0.22 < CDHMI 0.4), and exceptional (CDHMI > 0.4). Figure 8. Empirical CDF of the CDHMI for all the CDHWs at all the stations/grids in the observa- Figure 8. Empirical CDF of the CDHMI for all the CDHWs at all the stations/grids in the observa- tion/ERA5 in the GRB from 1961 to 2018. tion/ERA5 in the GRB from 1961 to 2018. In order to further explore the relationship between droughts and heatwaves, we an- In order to further explore the relationship between droughts and heatwaves, we analyzed alyzed thei their r chcharacteristics aracteristics under under di dif ffere fern ent t gr grades ades ((magnitudes) magnitudes) of of CDHWs ( CDHWs (Figur Figure e 9 9) ).. In In the case of moderate CDHWs, the SPEI value was around –1.3 (Figure 9a) and the number the case of moderate CDHWs, the SPEI value was around –1.3 (Figure 9a) and the number of of he heatwave atwave days days w was as ar aro ound und 8 8 d days ays ( (Figur Figure e 9 9b b)) in in t the he ob observations servations and and ER ERA5. A5. When When t the he SPEI SPEI va value lue wa was s as as l low ow as as –1 –1.5 .5 ( (Figur Figure e 9 9a), a), th the e n number umber of of he heatwave atwave days days inc incr reased eased to abo to about ut 16 16 days days (F (Figur igure e 9b 9b)) under under sev sever ere e CDHWs. CDHWs. Under Under e extr xtreme CDH eme CDHW W cond conditions, itions, the the SPEI SPEI value was further decreased to about –1.7 (Figure 9a) and the heatwave days increased to value was further decreased to about –1.7 (Figure 9a) and the heatwave days increased to about 21 days (Figure 9b). about 21 days (Figure 9b). Under moderate, severe, and extreme CDHWs, the SPEI values and the heatwave days (box range and median) in the observations were basically the same as in ERA5. Under exceptional CDHW conditions, the SPEI values (Figure 9a) in ERA5 (the box range is –2 to –2.3) were lower than the observed values (the box range is –1.8 to –2.1), whereas the heatwave days (Figure 9b) in ERA5 were only two days higher than in the observations (the median value was around 30 days). As the CDHMI increased, the SPEI value gradually decreased and the number of heatwave days gradually increased, indicating that the large CDHMI could be attributed to the enhanced interaction between droughts (lower SPEI) and heatwaves (longer duration) during the speciﬁc periods. In general, the drought severities and the number of the heatwave days reﬂected by ERA5 were slightly greater than those in the observations. 3.3. Population Exposure to CDHWs The population of the six prefecture-level cities in the GRB showed signiﬁcant in- creasing trends (Figure 10), with a growth rate of 0.614 million/decade in Nanchang, 0.328 million/decade in Yichun, 0.074 million/decade in Xinyu, 0.107 million/decade in Pingxiang, 0.214 million/decade in Ji’an, and 0.547 million/decade in Ganzhou. The population of Ganzhou, Nanchang, and Yichun cities all exceeded 5 million at the end of Land 2021, 10, x FOR PEER REVIEW 14 of 27 Figure 8. Empirical CDF of the CDHMI for all the CDHWs at all the stations/grids in the observa- tion/ERA5 in the GRB from 1961 to 2018. In order to further explore the relationship between droughts and heatwaves, we an- Land 2021, 10, 1021 15 of 28 alyzed their characteristics under different grades (magnitudes) of CDHWs (Figure 9). In the case of moderate CDHWs, the SPEI value was around –1.3 (Figure 9a) and the number of heatwave days was around 8 days (Figure 9b) in the observations and ERA5. When the SPEI value was as low as –1.5 (Figure 9a), the number of heatwave days increased to about 2018, especially Ganzhou city (with a population of 8.68 million). From the perspective of 16 days (Figure 9b) under severe CDHWs. Under extreme CDHW conditions, the SPEI population and administrative area, Nanchang city had the highest population density (as value was further decreased to about –1.7 (Figure 9a) and the heatwave days increased to well as the highest growth rate) among these cities in the GRB. about 21 days (Figure 9b). Land 2021, 10, x FOR PEER REVIEW 17 of 30 Figure 9. (a) SPEI values and (b) heatwave days under CDHW conditions at moderate, severe, ex- Figure 9. (a) SPEI values and (b) heatwave days under CDHW conditions at moderate, severe, treme, and exceptional grades, relatively in the GRB during 1961–2018. extreme, and exceptional grades, relatively in the GRB during 1961–2018. Figure 10. Annual population variations from 1988 to 2018 at the prefecture-level city scale in the GRB. Figure 10. Annual population variations from 1988 to 2018 at the prefecture-level city scale in the GRB. We investigated the annual variability of the overall population exposure to the CDHW frequency and CDHMI in the GRB. Although the CDHW frequency exposure in ERA5 was slightly overestimated, the annual changes of frequency exposure in ERA5 were basically similar to the observations (Figure 11a). The annual CDHW frequency ex- posure showed an increasing trend both in the observations and in ERA5 in the GRB from 1988 to 2018. For example, the CDHW frequency exposure in the observations (ERA5) increased by about 1.67-fold (2.2-fold) from 18 (18) million person-days in 1988 to 30 (40) million person-days in 2018, indicating a robust increase in the CDHW frequency expo- sure in the GRB during 1988–2018. The increasing rate of CDHW frequency exposure in ERA5 (7.03 million person-events/decade) was approximately double than that in the ob- servations (3.18 million person-events/decade). Annual variation of the CDHMI exposure in ERA5 was consistent with the observations (Figure 11b), both showing slight upward trends. The CDHMI exposures in 2003 and 2007 were much higher than in other years, indicating that a large number of people were exposed to severe compound events in these two years. In general, annual population exposure to the CDHW frequency and CDHMI all showed increasing trends, with higher values in the years 1991, 2003, 2007, and 2013. Land 2021, 10, 1021 16 of 28 We investigated the annual variability of the overall population exposure to the CDHW frequency and CDHMI in the GRB. Although the CDHW frequency exposure in ERA5 was slightly overestimated, the annual changes of frequency exposure in ERA5 were basically Land 2021, 10, x FOR PEER REVIEW 16 of 27 similar to the observations (Figure 11a). The annual CDHW frequency exposure showed an increasing trend both in the observations and in ERA5 in the GRB from 1988 to 2018. For example, the CDHW frequency exposure in the observations (ERA5) increased by about 1.67-fold (2.2-fold) from 18 (18) million person-days in 1988 to 30 (40) million person-days million person-days in 2018, indicating a robust increase in the CDHW frequency expo- in 2018, indicating a robust increase in the CDHW frequency exposure in the GRB during sure in the GRB during 1988–2018. The increasing rate of CDHW frequency exposure in 1988–2018. The increasing rate of CDHW frequency exposure in ERA5 (7.03 million person- ERA5 (7.03 million person-events/decade) was approximately double than that in the ob- events/decade) was approximately double than that in the observations (3.18 million servations (3.18 million person-events/decade). Annual variation of the CDHMI exposure person-events/decade). Annual variation of the CDHMI exposure in ERA5 was consistent in ERA5 was consistent with the observations (Figure 11b), both showing slight upward with the observations (Figure 11b), both showing slight upward trends. The CDHMI trends. The CDHMI exposures in 2003 and 2007 were much higher than in other years, exposures in 2003 and 2007 were much higher than in other years, indicating that a large indicating that a large number of people were exposed to severe compound events in number of people were exposed to severe compound events in these two years. In general, these two years. In general, annual population exposure to the CDHW frequency and annual population exposure to the CDHW frequency and CDHMI all showed increasing CDHMI all showed increasing trends, with higher values in the years 1991, 2003, 2007, trends, with higher values in the years 1991, 2003, 2007, and 2013. and 2013. Figure 11. Variations of annual population exposure to (a) CDHW frequency and (b) CDHMI from Figure 11. Variations of annual population exposure to (a) CDHW frequency and (b) CDHMI from 1988 to 2018 in the GRB. 1988 to 2018 in the GRB. ERA5 reasonably reflected the characteristics of population exposure to the CDHW ERA5 reasonably reﬂected the characteristics of population exposure to the CDHW frequencies at different grades (Figure 12). The ERA5 simulation effect (r = 0.85 and DISO frequencies at different grades (Figure 12). The ERA5 simulation effect (r = 0.85 and = 0.779) of population exposure to moderate CDHW events (Figure 12a) was better than DISO = 0.779) of population exposure to moderate CDHW events (Figure 12a) was better that of than the other three that of the other grade level three grade s. Modera levels. te CDHW exposu Moderate CDHWre showed exposure showed a significa a signiﬁcant nt in- creas incr ingeasing trend in trend observ in observations ations and ER and A5, ERA5, and the and upward the upwar trend d intr ER end A5 in w ERA5 as about was 1.about 5- fold the obse 1.5-fold the rvation. The observation. increasing tre The increasing nd of sever trend e of CDHW exposure severe CDHW exposur in ERA e5in was ERA5 1.76 was million person-events/decade, which was obviously higher than the observed value of 0.383 million person-events/decade (Figure 12b). There was no obvious upward or down- ward trend in the exposure of extreme and exceptional CDHWs, but the inter-annual fluc- tuations were relatively large (Figure 12c,d). In general, ERA5 had a greater upward trend than the observed value when it reflected the characteristics of population exposure to the CDHW frequencies at different grades. Land 2021, 10, 1021 17 of 28 1.76 million person-events/decade, which was obviously higher than the observed value of 0.383 million person-events/decade (Figure 12b). There was no obvious upward or downward trend in the exposure of extreme and exceptional CDHWs, but the inter-annual ﬂuctuations were relatively large (Figure 12c,d). In general, ERA5 had a greater upward Land 2021, 10, x FOR PEER REVIEW 17 of 27 trend than the observed value when it reﬂected the characteristics of population exposure to the CDHW frequencies at different grades. Figure 12. Variations of annual population exposure to (a) moderate, (b) severe, (c) extreme, and (d) exceptional CDHW Figure 12. Variations of annual population exposure to (a) moderate, (b) severe, (c) extreme, and (d) exceptional CDHW frequencies from 1988 to 2018 in the GRB. frequencies from 1988 to 2018 in the GRB. In order to facilitate comparative analysis, we divided the exposure study period into In order to facilitate comparative analysis, we divided the exposure study period into three decades, namely, 1989–1998 (T ), 1999–2008 (T ), and 2009–2018 (T ). T represents 1 2 3 2-1 three decades, namely, 1989–1998 (T1), 1999–2008 (T2), and 2009–2018 (T3). T2-1 represents the change in the T period relative to the T period. T and T follow the same rule. As 2 1 3-2 3-1 the change in the T2 period relative to the T1 period. T3-2 and T3-1 follow the same rule. As shown in Table 1, for the moderate CDHW frequency exposure, the exposure increased shown in Table 1, for the moderate CDHW frequency exposure, the exposure increased by 26% (62% in ERA5) from T to T , and increased by 79% (102% in ERA5) from T to 1 2 2 by 26% (62% in ERA5) from T1 to T2, and increased by 79% (102% in ERA5) from T2 to T3. T . The moderate CDHW frequency exposure increased by 6.39 million person-events The moderat (10.67 millioneperson-events CDHW frequency expo in ERA5) from sure incre T to T a , with sed by 6. a rate 39 of mi 127% llion person-eve (227% in ERA5). nts (10.67 1 3 Severe CDHW frequency exposure also presented an upward trend both in the observations million person-events in ERA5) from T1 to T3, with a rate of 127% (227% in ERA5). Severe and in ERA5 during the past three decades, but its upward rate was less than the moderate CDHW frequency exposure also presented an upward trend both in the observations and CDHW frequency exposure. The extreme and exceptional frequency CDHW exposures in in ERA5 during the past three decades, but its upward rate was less than the moderate the observations had smaller changes of –0.05 million person-events (–3%) and 0.07 million CDHW frequency exposure. The extreme and exceptional frequency CDHW exposures in person-events (9%) from T to T . However, the extreme and exceptional frequency CDHW 1 3 the observations had smaller changes of –0.05 million person-events (–3%) and 0.07 mil- exposures in ERA5 showed a larger increase in T relative to T , especially for extreme 3 1 lion person-events (9%) from T1 to T3. However, the extreme and exceptional frequency grade, with a change of 2.2 million person-events (169%). The total exposure increased by CDHW exposures in ERA5 showed a larger increase in T3 relative to T1, especially for 40% (89%) from T to T , and by 25% (60%) from T to T . The total exposure showed a 1 2 2 3 extreme grad substantial incr e, with ease from a change of T to T , with 2.2 million an increment person-e of 8.09 vents (169%). The total exp (17.23) million person-events osure in- 1 3 and a rate of 76% (201%) in observation (ERA5). creased by 40% (89%) from T1 to T2, and by 25% (60%) from T2 to T3. The total exposure showed a substantial increase from T1 to T3, with an increment of 8.09 (17.23) million per- son-events and a rate of 76% (201%) in observation (ERA5). Table 1. Population exposure to CDHW frequency and the change rates. CDHW Frequency Exposure Change Rate (%) (Million Person-Events) T1 T2 T3 T2-1 T3-2 T3-1 Obs (ERA5) Obs (ERA5) Obs (ERA5) Obs (ERA5) Obs (ERA5) Obs (ERA5) Moderate 5.05 (4.7) 6.38 (7.61) 11.44 (15.37) 26 (62) 79 (102) 127 (227) Severe 3 (2.04) 3.52 (3.17) 4.68 (6.33) 17 (55) 33 (100) 56 (210) Extreme 1.9 (1.3) 2.22 (1.16) 1.85 (3.5) 17 (−11) −17 (202) −3 (169) Exceptional 0.8 (0.54) 2.98 (4.24) 0.87 (0.61) 273 (685) −71 (−86) 9 (13) All 10.75 (8.58) 15.1 (16.18) 18.84 (25.81) 40 (89) 25 (60) 76 (201) Land 2021, 10, 1021 18 of 28 Table 1. Population exposure to CDHW frequency and the change rates. CDHW Frequency Exposure Change Rate (%) (Million Person-Events) T T T T T T 1 2 3 2-1 3-2 3-1 Obs (ERA5) Obs (ERA5) Obs (ERA5) Obs (ERA5) Obs (ERA5) Obs (ERA5) Land 2021, 10, x FOR PEER REVIEW 20 of 30 Moderate 5.05 (4.7) 6.38 (7.61) 11.44 (15.37) 26 (62) 79 (102) 127 (227) Severe 3 (2.04) 3.52 (3.17) 4.68 (6.33) 17 (55) 33 (100) 56 (210) Extreme 1.9 (1.3) 2.22 (1.16) 1.85 (3.5) 17 (11) 17 (202) 3 (169) Exceptional 0.8 (0.54) 2.98 (4.24) 0.87 (0.61) 273 (685) 71 (86) 9 (13) All 10.75 (8.58) 15.1 (16.18) 18.84 (25.81) 40 (89) 25 (60) 76 (201) All 10.75 (8.58) 15.1 (16.18) 18.84 (25.81) 40 (89) 25 (60) 76 (201) We decomposed the exposure change to evaluate the relative contributions of cli- We decomposed the exposure change to evaluate the relative contributions of climate, mate, population, and joint change effects. As shown in Figure 13, we found that the con- population, and joint change effects. As shown in Figure 13, we found that the contribution tribution of the climate change effect was the largest (over 68%), even reaching 73.87% in of the climate change effect was the largest (over 68%), even reaching 73.87% in T , 2-1 T2-1, whereas the contribution of the joint change effect was the lowest (below 11%) in the whereas the contribution of the joint change effect was the lowest (below 11%) in the observed CDHW frequency exposure. The CDHW frequency contributions of ERA5 were observed CDHW frequency exposure. The CDHW frequency contributions of ERA5 were similar to those in the observations, but the contributions of the climate change effect were similar to those in the observations, but the contributions of the climate change effect larger (reaching about 80%) than in the observations, and the contributions of the popu- were larger (reaching about 80%) than in the observations, and the contributions of the lation change effect were about 10% lower than in the observations. For the CDHMI ex- population change effect were about 10% lower than in the observations. For the CDHMI posure, the contributions of climate change both in the observations and ERA5 showed exposure, the contributions of climate change both in the observations and ERA5 showed straight downward trends during T2-1, T3-2, and T3-1. We found that population growth was straight downward trends during T , T , and T . We found that population growth 2-1 3-2 3-1 responsible for 52.88% of the CDHMI exposure in T3-1, which showed a large difference in was responsible for 52.88% of the CDHMI exposure in T , which showed a large difference 3-1 ERA5 (population change effect of 10.02%). in ERA5 (population change effect of 10.02%). Figure 13. Decomposition of the change in population exposure to (a,b) CDHW and (c,d) CDHMI in the GRB. T , T , 2-1 3-2 Figure 13. Decomposition of the change in population exposure to (a,b) CDHW and (c,d) CDHMI in the GRB. T2-1, T3-2, and T represent the contributions of changes in 1999–2008, 2009–2018, and 2009–2018 relative to 1989–1998, 1999–2008, and T3-1 3-1 represent the contributions of changes in 1999–2008, 2009–2018, and 2009–2018 relative to 1989–1998, 1999–2008, and and 1989–1998, respect 1989–1998, respectively ively.. We further analyzed the contributions of CDHW frequency exposures at different grades in different periods (Figure 14). In general, the climate change effect was the main contributor, followed by the population change effect, and finally the joint change effect. In moderate CDHW frequency exposures, the contributions of climate change were more than 60%, and even exceeded 80% in ERA5. In T3-1, the contribution of climate change for moderate CDHW frequency exposure was 76% (81% in ERA5), and the population and joint change effects were of similar magnitude (about 10%). In the observations of severe CDHW frequency exposures, the contributions of climate change effect were still high, but their magnitude was about 10% lower than that of moderate events. Compared to the Land 2021, 10, 1021 19 of 28 We further analyzed the contributions of CDHW frequency exposures at different grades in different periods (Figure 14). In general, the climate change effect was the main contributor, followed by the population change effect, and ﬁnally the joint change effect. In moderate CDHW frequency exposures, the contributions of climate change were more than 60%, and even exceeded 80% in ERA5. In T , the contribution of climate change for 3-1 moderate CDHW frequency exposure was 76% (81% in ERA5), and the population and joint change effects were of similar magnitude (about 10%). In the observations of severe CDHW frequency exposures, the contributions of climate change effect were still high, but their magnitude was about 10% lower than that of moderate events. Compared to the severe CDHW frequency exposures, the contributions of climate change and population change effects for extreme CDHW frequency exposures were basically the same magnitude (about 45%) in T , but ERA5 did not capture those effects well. In the exceptional CDHW 3-1 frequency exposures, ERA5 and observations showed similar characteristics. In T and 2-1 T , the contributions of climate change effects both in the observations and ERA5 were 3-2 much higher than the contributions of the population and joint change effects. In T , 3-1 the population contributions (68% in the observations and 79% in ERA5) were much higher than the contributions of the climate and joint change effects. Both the observations and ERA5 in T showed that the contributions of population change effects gradually 3-1 increased with the increase in CDHMI, whereas the contributions of climate change and joint change effects gradually decreased with the increase in CDHMI. 3.4. Case of 2003 CDHWs The CDHMI exposure value in 2003 was much higher than in other years, so we isolated a case analysis of the 2003 CDHWs. The heatwave days during the summer of 2003 in the GRB were the longest (44.5 days) in the observations (the summer heatwave days in ERA5 ranked second, up to 44.2 days) since 1961 (Figure 15a,b). In the summer of 2003, precipitation in the GRB was only 342.5 mm in the observations (386.1 mm in ERA5), which was about 60% of its multi-year average summer precipitation (Figure 15c,d). The average one-month SPEI values in the summer of 2003 were –0.96 in the observations and –1.11 in ERA5 (Figure 15e,f). The July SPEI values in 2003 were as low as –1.97 in the observations and –2.37 in ERA5 (Figure 15g,h), and these values indicate that the GRB experienced the worst extreme droughts in the summer of 2003. The relatively long-term high temperatures and the extreme precipitation deﬁcits (extreme droughts) in the summer of 2003 led to exceptional CDHWs, which caused economic losses directly tied to agricultural production by several billion CNY at least [52,53], and also caused signiﬁcant stress on the ecosystem and human health. We conducted the composite of the atmospheric circulation anomalies (Figure 16) for July of 2003 due to the most serious CDHWs in the GRB during July of 2003. High positive values in 500 hPa geopotential height anomalies driven by the strong subtropical high (5 880 gpm) during July of 2003 over the GRB (Figure 16a) caused the air to sink and increase temperature, which means that heatwaves were prone to occur. As shown in Figure 16b, the anticyclone anomaly center near the GRB suppressed precipitation due to the relatively large positive values of water vapor flux divergence anomalies, which likely made the basin prone to droughts. It was extremely difficult to produce precipitation under the specific conditions of large positive anomalies of convective inhibition (Figure 16c, negative anomalies of total cloud cover over about half of the GRB) and surface net solar radiation (Figure 16d), which eventually led to the exceptional CDHW in the summer of 2003. Land 2021, 10, 1021 20 of 28 Land Land 2021 2021, , 10 10, x FO , x FOR PE R PEER R ER RE EVIE VIEW W 22 of 22 of 30 30 Figure 14. The same as Figure 13, but for CDHW frequency exposure at (a,b) moderate, (c,d) severe, (e,f) extreme, and Figure 14. Figure 14. The The s sa ame as Fi me as Figure gure 13, 13, but for but for CDHW frequenc CDHW frequency y expos exposu ure at ( re at (a a,,b b) moderate, ( ) moderate, (c c,,d d) s ) se ev ve ere re, , (g,h) exceptional grades. ((e e,,f f) extreme, a ) extreme, an nd ( d (g g,,h h)) ex except ceptional g ional gr rades ades.. 3. 3.4. C 4. Ca ase se of of 20 2003 03 C CD DH HW Ws s The CDHMI The CDHMI exposure exposure val valu ue e in in 2003 w 2003 wa as mu s much h ch hiighe gher r than than in in othe other ye r year ars, s, so so we we i is so- o- lla atte ed d a a c ca as se e a an na ally ys siis s o off t th he e 2 20 00 03 3 C CD DH HW Ws s.. T Th he e h he ea attw wa av ve e d da ay ys s d du ur riin ng g t th he e s su um mm me er r o off 2 20 00 03 3 iin n the G the GR RB were the B were the llo ongest (4 ngest (44.5 da 4.5 days) ys) iin n the ob the observ serva attiio ons ns (the summer heatw (the summer heatwa ave days ve days iin n E ER RA A5 5 r ra an nk ke ed d s se ec co on nd d,, u up p t to o 4 44 4.2 .2 days) si days) since 19 nce 1961 61 ( (F Figure 15 igure 15a,b) a,b). . In In the the summer summer of of 200 2003, 3, precipit precipitat ation ion in t in th hee G GR RB B was was onl only y 3 34 42 2..5 5 mm mm in t in th hee observ observat ations ions ( (3 386 86.1 .1 mm mm iin n E ER RA A5 5), ), which w which wa as s about about 6 60 0% % of of it its s mu mult lti-y i-ye ear ar a av verage erage s su umm mmer prec er precipit ipitat atiio on (F n (Fig igure ure 1 15 5c c,d). ,d). The The aver average one age one- -month SPEI values month SPEI values in the summer of in the summer of 2 2003 003 were were –0 –0.9 .96 6 in the observati in the observatio ons a ns an nd d Land 2021, 10, x FOR PEER REVIEW 23 of 30 Land 2021, 10, x FOR PEER REVIEW 23 of 30 –1.11 in ERA5 (Figure15e,f). The July SPEI values in 2003 were as low as –1.97 in the ob- –1.11 in ERA5 (Figure15e,f). The July SPEI values in 2003 were as low as –1.97 in the ob- servations and –2.37 in ERA5 (Figure 15g,h), and these values indicate that the GRB expe- servations and –2.37 in ERA5 (Figure 15g,h), and these values indicate that the GRB expe- rienced the worst extreme droughts in the summer of 2003. The relatively long-term high rienced the worst extreme droughts in the summer of 2003. The relatively long-term high temperatures and the extreme precipitation deficits (extreme droughts) in the summer of temperatures and the extreme precipitation deficits (extreme droughts) in the summer of 2003 led to exceptional CDHWs, which caused economic losses directly tied to agricul- 2003 led to exceptional CDHWs, which caused economic losses directly tied to agricul- Land 2021, 10, 1021 21 of 28 tural production by several billion CNY at least [52,53], and also caused significant stress tural production by several billion CNY at least [52,53], and also caused significant stress on the ecosystem and human health. on the ecosystem and human health. Figure 15. Rank-average during June–August of (a,b) heatwave days, (c,d) precipitation, (e,f) SPEI values, and (g,h) rank Figure 15. Rank-average during June–August of (a,b) heatwave days, (c,d) precipitation, (e,f) SPEI values, and (g,h) rank Figure 15. Rank-average during June–August of (a,b) heatwave days, (c,d) precipitation, (e,f) SPEI values, and (g,h) rank for the July SPEI values in the GRB from 1961 to 2018. for the July SPEI values in the GRB from 1961 to 2018. for the July SPEI values in the GRB from 1961 to 2018. We conducted the composite of the atmospheric circulation anomalies (Figure 16) for We conducted the composite of the atmospheric circulation anomalies (Figure 16) for July of 2003 due to the most serious CDHWs in the GRB during July of 2003. High positive July of 2003 due to the most serious CDHWs in the GRB during July of 2003. High positive values in 500 hPa geopotential height anomalies driven by the strong subtropical high (5 values in 500 hPa geopotential height anomalies driven by the strong subtropical high (5 880 gpm) during July of 2003 over the GRB (Figure 16a) caused the air to sink and increase 880 gpm) during July of 2003 over the GRB (Figure 16a) caused the air to sink and increase temperature, which means that heatwaves were prone to occur. As shown in Figure 16b, temperature, which means that heatwaves were prone to occur. As shown in Figure 16b, Land 2021, 10, x FOR PEER REVIEW 24 of 30 Land 2021, 10, x FOR PEER REVIEW 24 of 30 the anticyclone anomaly center near the GRB suppressed precipitation due to the rela- the anticyclone anomaly center near the GRB suppressed precipitation due to the rela- tively large positive values of water vapor flux divergence anomalies, which likely made tively large positive values of water vapor flux divergence anomalies, which likely made the basin prone to droughts. It was extremely difficult to produce precipitation under the the basin prone to droughts. It was extremely difficult to produce precipitation under the specific conditions of large positive anomalies of convective inhibition (Figure 16c, nega- specific conditions of large positive anomalies of convective inhibition (Figure 16c, nega- Land 2021, 10, 1021 22 of 28 tive anomalies of total cloud cover over about half of the GRB) and surface net solar radi- tive anomalies of total cloud cover over about half of the GRB) and surface net solar radi- ation (Figure 16d), which eventually led to the exceptional CDHW in the summer of 2003. ation (Figure 16d), which eventually led to the exceptional CDHW in the summer of 2003. Figure 16. Spatial patterns of (a) the 500 hPa geopotential height (contour, gpm) and its anomalies (shaded, gpm), (b) Figure 16. Spatial patterns of (a) the 500 hPa geopotential height (contour, gpm) and its anomalies (shaded, gpm), (b) Figure 16. Spatial patterns of (a) the 500 hPa geopotential height (contour, gpm) and its anomalies (shaded, gpm), −1 −1 −5 −2 −1 −1 −1 −5 −2 −1 integrated layer water vapor flux anomalies (vector, kg·m ·s ) and divergence anomalies (shaded, 10 kg·m ·s ), (c) integrated layer water vapor flux anomalies (vector, kg·m ·s 1 ) and diver 1 gence anomalies (shaded, 10 kg· 5 m ·s2 ), (c 1) (b) integrated layer water vapor ﬂux anomalies (vector, kgm s ) and divergence anomalies (shaded, 10 kgm s ), −1 −1 convective inhibition (shaded, J·kg ) and total cloud cover anomalies (contour, 0–10 tenths), and (d) surface net solar convective inhibition (shaded, J·kg ) and total cloud cover anomalies (contour, 0–10 tenths), and (d) surface net solar (c) convective inhibition (shaded, Jkg ) and total cloud cover anomalies (contour, 0–10 tenths), and (d) surface net solar 2 2 2 2 radiation (contour, MJ/m ) and its anomalies (shaded, MJ/m ) for July 2003. The anomaly values for July 2003 are relative radiation (contour, MJ/m ) and its anomalies (shaded, MJ/m ) for July 2003. The anomaly values for July 2003 are relative 2 2 radiation (contour, MJ/m ) and its anomalies (shaded, MJ/m ) for July 2003. The anomaly values for July 2003 are relative to July 1961–2018. to July 1961–2018. to July 1961–2018. 4. Discussion 4. Disc 4. Discussion ussion CDHWs can amplify the damage of the individual droughts and heatwaves, ulti- CDHWs ca CDHWs can n aam m ppl lifif yy the the da da mmage of the i age of the indin vdi idv uiadu l dal ro drought ughts and s and heat w he av at ewav s, ule tis m , ult ateliy - mately wreaking a catastrophic effect on the environment, society, economy, and human mat wree aly ki wre ng a a ca ktia ng stro ap cat hic ae st ff ro ecphic e t on thffect e env on iro t nh m e environ ent, sociem ty,ent, society, economy, an economy, and human heald hum th [44,5 a4 n ]. healt The sh u [ m4 m 4,e 54 r C ]. The sum DHWs in m Eu err C ope DiHWs n 2003in anEurop d Chin ea iin n 200 2013 3 a ca nu d Chi sed an ha i igh n 20 inc13 rea ca seused a in mor hig tality h, health [44,54]. The summer CDHWs in Europe in 2003 and China in 2013 caused a high incre espec a ise all y in im n o th rte alit eld y,e espec rly an ia dlly in in fan tt he p el op derly ulatio and inf ns as w ant el lpopu as in lat pie orn ss as ons wel with l as p i re n- e person xisting s increase in mortality, especially in the elderly and infant populations as well as in persons cardiovascular and respiratory conditions [9,11,34,36,55]. In this study, we used the GRB, a subtropical region in southern China (heatwaves and droughts are prone to occur in summer in this region), as an example to investigate characteristics of population exposure to CDHWs based on observation and ERA5 data. ERA5 had a slight overestimation for the precipitation in the GRB, which is similar to the ﬁndings of previous studies [30,32]. This may be because ERA5 tends to overestimate Land 2021, 10, 1021 23 of 28 the light precipitation events, which makes the proportion of light precipitation events in the total precipitation events relatively high [56]. In most parts of China, light precipitation days accounted for more than 60% of all precipitation days [56,57]. ERA5 slightly over- estimated the T , and slightly underestimated the T in the GRB, which is similar mean max to the results of a previous study in Southeast Asia [58]. The ability of ERA5 to simulate the T was slightly better than its ability to simulate the T . This may have been due mean max to the fact that we derived the daily T that came from hourly data, which ignored the max information of the sub-hourly extreme temperatures. In short, ERA5 can better reﬂect the characteristics of temperature and precipitation in the GRB. ERA5 was more effective at simulating the one-month SPEI values and droughts in the GRB. The average magnitude of the droughts during 1997–2018 was slightly higher than that in 1961–1997. For the frequency, duration, and magnitude of heatwaves, ERA5 also had good applicability in the GRB. Heatwaves showed signiﬁcant increasing trends from 1997 to 2018 in the observations and ERA5, which may have been caused by the greater warming after the 1990s than before the 1990s [22,36]. For the frequency and the magnitude of CDHWs, ERA5 performed well in simulating their annual variation characteristics, with r-values above 0.8 and an RB within 3%. The average frequency and magnitude of CDHWs during 1997–2018 were relatively larger than during 1961–1997, especially in ERA5, which may have been caused by the increase in the number of individual droughts and heatwaves in the GRB. The East Asian summer monsoon (EASM) is characterized by the northward move- ment of the subtropical high frontal zone and the associated rain belt during the early summer months through the late summertime in China, and it includes two abrupt jumps associated with the Meiyu season [59]. The location and duration of the rain belt have a signiﬁcant impact on regional ﬂoods and droughts. If the EASM weakens, causing its northward movement to be insufﬁcient, southern China becomes prone to ﬂood disasters due to the rain belt remaining in this region for a long period. In contrast, if the rain belt jumps to the north too early (i.e., a strong EASM) and remains in northern China for a long period, ﬂoods are likely to occur in northern China and droughts are likely to occur in southern China (e.g., the GRB). Global warming, multi-decade/multi-annual variability (e.g., Paciﬁc Decadal Oscillations or El Niño–Southern Oscillations), and intraseasonal variability can also affect the location and duration of the rain belt [20,60–62], thereby affecting the CDHW characteristics in the GRB. Analysis of the 2003 CDHWs in the GRB revealed that signiﬁcantly positive 500 hPa geopotential height anomalies and anticyclonic patterns over the GRB can reduce cloud cover, thereby increasing the amount of incoming radiation and the resulting evapotran- spiration. Soil moisture deﬁcits reduce evapotranspiration (i.e., high evaporative capacity with low evapotranspiration due to limited water), and a larger proportion of the incoming radiation is employed to warm up the environment, which leads to an accumulation of sensible heat in the surface atmosphere that can develop into a heatwave or exaggerate a heatwave’s magnitude [14]. A very dry antecedent surface condition driven by chronic precipitation deﬁcits can create a positive feedback loop between atmospheric heating and further drying of the soil [3,36,63], thereby providing conditions conducive to CDHWs. In fact, the D-H dependence reﬂects the characteristics of the coupling between the land and the atmosphere [3]. In general, the soil moisture deﬁcit is inﬂuenced by enhanced land–atmosphere coupling during summer, which appears to intensify surface warming and anticyclonic circulation anomalies, eventually leading to CDHWs [64]. ERA5 could better capture the spatial characteristics of the D-H dependence in the GRB, and a relatively stronger D-H dependence in the southern parts of the basin produced a higher probability of CDHWs. Previous studies also demonstrated that a high risk of CDHWs existed in southern China [3,65]. On the basis that ERA5 can better reﬂect the characteristics of the CDHMI cumulative probability density in the GRB, we found that higher the CDHMI value, the greater the severity of drought and the duration of heatwaves. Land 2021, 10, 1021 24 of 28 The total population of the GRB increased signiﬁcantly from 1988 to 2018. An increase in population can increase the risk of more population being exposed to CDHWs. ERA5 better reﬂected the annual variations of the exposure values for CDHW frequency and CDHMI, with r-values above 0.8 and RB values within 15%. Regardless of the grades (magnitude) of CDHWs, the increasing trend of the ERA5-simulated exposure of CDHW frequency was more apparent than the observed frequency, which was related to the annual frequency variations of CDHWs in the ERA5 and observations. CDHWs also presented an upward trend on the global scale [54]. The moderate CDHW frequency exposure showed a signiﬁcant upward trend both in the observations and in ERA5. However, the population exposure to severe, extreme, and exceptional CDHWs did not show clear upward trends. As the CDHMI increased, the characteristics of the multi-annual variability also became more apparent. From T to T , the population exposure to CDHWs basically increased in the ob- 1 3 servations and ERA5. This increase was particularly high for moderate CDHWs due to increased population and hot extremes. The contributions of the climate change effect to CDHW exposures were much greater than those of population and joint change effects. This was different from the results of exposure characteristics related to extreme climate events reported in previous studies [41,42,44], because the previous studies were based on future climate and population outputs by the models. Under the characteristics of different CDHW grades, the contribution rates of climate change effect were basically the main cause for the change in the CDHW exposures. In T , with the increased in the CDHMI, 3-1 the contributions of population change effects had gradually increased, especially under the exceptional CDHW exposures. According to the results of this study, the population exposed to CDHWs has in- creased in the GRB (Figure 17). A comprehensive understanding of the mechanisms of CDHWs plays a vital role in predicting CDHWs and thus providing adaptive responses for different populations. The spatio-temporal characteristics of CDHWs were associated with local and teleconnected land–atmosphere feedbacks [14,66], which may promote the intensiﬁcation and propagation (expansion and concatenation) of CDHWs. Elucidating the relative contributions from local (i.e., self-intensiﬁcation) and teleconnected (i.e., self- propagation, such as the inﬂow of heat to other regions) land–atmosphere feedbacks in a more comprehensive manner can advance our understanding of CDHW mechanisms, and will be conducted in future work. Extreme heatwaves such as the 2010 Russian heatwave can be intensiﬁed by the weakened soil moisture constraints [10]. Will the future equivalent of the 2003 CDHW in the GRB increase in its severity due to the weakening soil moisture constraints? This question will be investigated in future work based on the sixth phase of the Coupled Model Intercomparison Project (CMIP6). Different age-related physiological and thermoregulatory properties allow the body to adapt to CDHWs. People 14 and 65 years of age (i.e., younger and older individuals) are more vulnerable to hot extremes and experience greater heat-related health impacts than others [67–70]. Future work will explore the exposure of children and elderly people to CDHWs and investigate the extent to which different groups or occupations are affected by CDHWs. Land 2021, 10, 1021 25 of 28 Land 2021, 10, x FOR PEER REVIEW 27 of 30 Figure 17. Schematic diagram of population exposure to compound droughts and heatwaves over the GRB. Figure 17. Schematic diagram of population exposure to compound droughts and heatwaves over the GRB. 5. Conclusions 5. Conclusions CDHWs are more harmful to human health than individual extremes, especially in CDHWs are more harmful to human health than individual extremes, especially in areas with high population density. This study used the GRB as a case to explore CDHW characteristics and quantify the population exposed to them both in the observations and areas with high population density. This study used the GRB as a case to explore CDHW in ERA5. Our conclusions can be summarized as follows: characteristics and quantify the population exposed to them both in the observations and (1) ERA5 has high simulation accuracy for the temperature, precipitation, one-month in ERA5. Our conclusions can be summarized as follows: SPEI, heatwaves, and CDHWs in the GRB from 1961 to 2018. The CDHWs during 1997–2018 (1) ERA5 has high simulation accuracy for the temperature, precipitation, one-month were slightly higher than during 1961–1997, especially due to significant increases in heatwaves SPEI, heatwaves, and CDHWs in the GRB from 1961 to 2018. The CDHWs during 1997– from 1997 to 2018. Relatively high values of D-H dependence indices existed in the southern 2018 were slightly higher than during 1961–1997, especially due to significant increases in parts of the basin, indicating that CDHWs were more likely to occur in this region. heatwaves from 1997 to 2018. Relatively high values of D-H dependence indices existed (2) The large CDHMI values were attributed to the enhanced interaction between in the southern parts of the basin, indicating that CDHWs were more likely to occur in droughts and heatwaves during the speciﬁc periods. According to the case of the 2003 summer CDHW in the GRB, strongly positive 500 hPa geopotential height, anticyclonic cir- this region. culation (positive values of water vapor ﬂux divergence anomalies), convective inhibition, (2) The large CDHMI values were attributed to the enhanced interaction between and surface net solar radiation anomalies over the GRB could have easily caused CDHWs. droughts and heatwaves during the specific periods. According to the case of the 2003 (3) The population of the GRB increased signiﬁcantly from 1988 to 2018, which in- summer CDHW in the GRB, strongly positive 500 hPa geopotential height, anticyclonic creased the risks of CDHW exposure, especially for moderate CDHWs. Population expo- circulation (positive values of water vapor flux divergence anomalies), convective inhibi- sure to CDHWs basically increased from T to T , especially in ERA5. The contributions of 1 3 tion, and surface net solar radiation anomalies over the GRB could have easily caused the climate change effect to CDHW exposure were much greater than the contributions CDHWs. of the population and joint change effects. The contributions of population change effects (3) The population of the GRB increased significantly from 1988 to 2018, which in- gradually increased with increasing CDHMI during T in the observations and ERA5. 3-1 creased the risks of CDHW exposure, especially for moderate CDHWs. Population expo- sure to CDHWs basically increased from T1 to T3, especially in ERA5. The contributions of the climate change effect to CDHW exposure were much greater than the contributions of the population and joint change effects. The contributions of population change effects gradually increased with increasing CDHMI during T3-1 in the observations and ERA5. Land 2021, 10, 1021 26 of 28 Author Contributions: Conceptualization, Y.Z. and G.M.; methodology, Y.Z.; software, Y.Z. and L.S.; formal analysis, Y.Z. and C.C.; data curation, Y.Z. and L.S.; writing—original draft preparation, Y.Z.; writing—review and editing, Y.Z. and C.C.; visualization, Y.Z. and B.X.; supervision, C.C. and G.M.; funding acquisition, Y.Z. and G.M. All authors have read and agreed to the published version of the manuscript. Funding: This study was jointly supported by the Humanities and Social Sciences Foundation of the Chinese Ministry of Education (19YJCZH259; 18YJA790061), the National Natural Science Foundation of China (41907384), the Social Science Foundation of Jiangsu Province (18EYB008), and the Huaishang Talent Foundation (42ZYQ00). 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 on request. 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Population Exposure to Compound Droughts and Heatwaves in the Observations and ERA5 Reanalysis Data in the Gan River Basin, China

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Land – Multidisciplinary Digital Publishing Institute

Published: Sep 28, 2021

Keywords: compound droughts and heatwaves; population exposure; ERA5; Gan River Basin; China

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Population Exposure to Compound Droughts and Heatwaves in the Observations and ERA5 Reanalysis Data in the Gan River Basin, China

Population Exposure to Compound Droughts and Heatwaves in the Observations and ERA5 Reanalysis Data in the Gan River Basin, China

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Population Exposure to Compound Droughts and Heatwaves in the Observations and ERA5 Reanalysis Data in the Gan River Basin, China

Population Exposure to Compound Droughts and Heatwaves in the Observations and ERA5 Reanalysis Data in the Gan River Basin, China

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