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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...
Zhang, Yuqing;Mao, Guangxiong;Chen, Changchun;Shen, Liucheng;Xiao, Binyu
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; firstname.lastname@example.org (G.M.); email@example.com (B.X.) School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China; firstname.lastname@example.org School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200234, China; email@example.com * Correspondence: firstname.lastname@example.org 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 , 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 . Droughts and heatwaves can intensify and expand via land–atmosphere feed- backs . Exploring the possible dependence of drought–heatwave events in different regions is helpful to understand which areas have a high probability of CDHWs . 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 . 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 , such as extreme precipitation , 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 . 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 . 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 , 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 . In order to be consi . 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 . 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 , 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 <