StudyontheChangeCharacteristicsofandPopulation ...downloads.hindawi.com/journals/amete/2019/7069195.pdf · 2019. 8. 17. · e population of the North China Plain accounts for approximately

Research ArticleStudy on the Change Characteristics of and PopulationExposure to Heatwave Events on the North China Plain

Long-Fei Zhan1 Yanjun Wang2 Hemin Sun3 Jianqing Zhai23 and Mingjin Zhan 12

1Jiangxi Provincial Climate Centre Nanchang 330046 China2Collaborative Innovation Center on Forecast and Evaluation of Meteorological DisastersInstitute for Disaster Risk Management (IDRM) School of Geographical ScienceNanjing University of Information Science amp Technology Nanjing 210044 China3National Climate Center China Meteorological Administration Beijing 100081 China

Correspondence should be addressed to Mingjin Zhan hellorm126com

Received 11 April 2019 Revised 3 July 2019 Accepted 24 July 2019 Published 18 August 2019

Academic Editor Alastair Williams

Copyright copy 2019 Long-Fei Zhan et alis is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

In accordance with the ChinaMeteorological Administration definition this study considered a weather process with a maximumsurface temperature of ge35degC for more than three consecutive days as a heatwave event Based on a dataset of daily maximumtemperatures from meteorological stations on the North China Plain including ordinary and national basicreference surfacestations the intensity-area-duration method was used to analyze the spatiotemporal distribution characteristics of heatwaveevents on the North China Plain (1961ndash2017) Moreover based on demographic data from the Statistical Yearbook andGreenhouse Gas Initiative (GGI) Population Scenario Database of the Austrian Institute for International Applied SystemsAnalysis (IIASA) population exposure to heatwave events was also studied e results showed that the frequency intensity andarea of impact of heatwave events on the North China Plain initially decreased (becoming weaker and less extensive) and thenincreased (becoming stronger and more extensive) Similarly the trend of population exposure to heatwave events initiallydecreased and then increased and the central position of exposure initially moved southward and then returned northwardPopulation exposure in the eastern Taihang Mountains was found significantly higher than in the western Taihang Mountains Inrelation to the change of population exposure to heatwave events on the North China Plain the influence of climatic factors wasfound dominant with an absolute contribution rate of gt75 Except for 2011ndash2017 increase in population also increased theexposure to heatwaves particularly in the first half of the study period Interaction between climatic and population factorsgenerally had less impact on population exposure than either climatic factors or population factors aloneis study demonstrateda method for assessing the impact of heatwave events on population exposure which could form a scientific basis for thedevelopment of government policy regarding adaption to climate change

1 Introduction

Climate change includes not only changes in mean climatebut also variation in weather extremes [1] Over the pastcentury because of the combined effects of human activitiesand natural factors Earthrsquos climate has undergone signifi-cant change in terms of the primary features of warming andthis change has shown an increasing trend in recent years[2 3]e trend has led to a wide range of climate anomaliesespecially an increased frequency of occurrence of regionalextreme high-temperature events [4] e Fifth Assessment

Report issued by the United Nations IntergovernmentalPanel on Climate Change in 2014 showed that the globalsurface temperature increased by 085degC from 1880 to 2012[5] In different regions of the world the annual and dailymaximum temperatures have both shown a significantupward trend since the latter part of the 20th century [6 7]For most of China the trend of increase in air temperaturehas accelerated since the 1980s [8] From a regional per-spective the characteristics of high temperatures are com-plex Overall significant positive trends in the frequency ofoccurrence of extreme maximum temperature events have

HindawiAdvances in MeteorologyVolume 2019 Article ID 7069195 10 pageshttpsdoiorg10115520197069195

been observed in most of China and the North China Plainis one area that has experienced the most significantwarming

Increased temperature will lead to an increase in the riskof heat-related diseases and death in humans [9] e risk ofmorbidity and accidental death among urban vulnerablepopulations and among urban and rural outdoor workersduring a period of heat stress has been identified as one ofthe key risks of climate change in the Fifth AssessmentReport of the Intergovernmental Panel on Climate Change[5] Changes in heatwaves are an important aspect of climatechange and their impact on both natural ecosystems andhuman society might be more severe than the effects of therise in average temperature [10ndash13] e latest HadleyCentre Global Historical Climatology Network Daily ob-servations show that the intensity frequency and durationof global heatwave events are increasing [14] In recent yearsheatwaves have surpassed other natural disasters such ashurricanes lightning tornadoes floods and earthquakes tobecome the primary cause of fatalities in America related tonatural disasters [15ndash17] Moreover heatwaves have killedmore people in Australia than any other natural hazard since1900 [18] It is estimated that around 40000 people incentral and western parts of continental Europe died duringa record heatwave that occurred in the summer of 2003[19 20] In 2013 heatwaves occurred frequently in mostparts of China For example Changsha Chongqing andHangzhou recorded 57 49 and 47 hot days (daily maximumtemperature ge35degC) respectively during which the highestrecorded temperatures reached 382 409 and 405degC re-spectivelyese heatwave incidents led to a direct economicloss of 8356 billion RMB [21] Research on high-temper-ature disasters has attracted widespread attention fromgovernment departments and the scientific community andit has become the focus of climate research under thebackground context of global warming

In recent years studies on population exposure tonatural disasters have achieved many results [22ndash27] Re-search on extreme climate events has developed from valueanalysis at a single site [28ndash30] to related investigations ofintensity area of impact and duration [26 31] Based on theintensity-area-duration (IAD) method Zhai et al [31] de-veloped an approach for identification of a regional extremeevent and analysis of its spatial coverage and durationExtreme heatwave events can be more serious than a singlehigh-temperature event [10] However few studies haveconsidered regional persistent extreme heatwave events andthe variation of associated population exposure

e population of the North China Plain accounts forapproximately 30 of the total population of China Fur-thermore the North China Plain represents the center ofChinarsquos political economic cultural transportation edu-cation and tourism activities erefore it is of great sci-entific and practical importance to study the evolution ofheatwave events and associated population exposure on theNorth China Plain is study was based on daily maximumtemperature data (1961ndash2017) from national meteorologicalstations on the North China Plain in combination withdemographic data and Greenhouse Gas Initiative (GGI)

Population Scenario Database of the Austrian Institute forInternational Applied Systems Analysis (IIASA) [32 33]First the frequency intensity area of impact and spatialvariation characteristics of heatwave events in North Chinaare analyzed by grid unit scale Second the temporal andspatial evolution characteristics of population exposure toheatwave events are studied Finally attribution analysis isperformed on the factors affecting population exposure emain objectives of this study were to reveal the contributionsof various factors to population exposure to past heatwaveevents on the North China Plain and to provide a scientificbasis for the development of strategies effective in helpinghumans cope with the adverse effects of such heatwaveevents

2 Data and Methods

21 Study Area e area of the North China Plain con-sidered in this study comprised the region bounded by33degndash42degN 110degndash120degE which includes Beijing Tianjin InnerMongolia Hebei Henan Shanxi Shandong Jiangsu Anhuiand Shaanxi (Figure 1) is region covers an area of ap-proximately 90times105 km2 and it contains approximately30 of the total population of Chinae North China Plainhas a warm temperate semiarid monsoon climate which hasthe characteristics of cold and snow in winter a dry andwindy spring hot rainy summer and cool dry autumn

22 Data Description e climate dataset used in this studywas derived from daily maximum temperature data(1961ndash2017) recorded at national meteorological stations onthe North China Plain which were integrated by the Na-tional Meteorological Information Center of the ChineseMeteorological Administration Following quality controlthe daily maximum temperature data were interpolated to a05deg times 05deg grid using the inverse distance weighting method

e North China Plain population data were obtainedfrom the Chinese Demographic Yearbook Using the GGIPopulation Scenario Database of the IIASA (which hasspatial resolution of 05deg times 05deg and temporal resolution of10a) as the background of population distribution thepopulation data of the North China Plain provinces fromthe Chinese Statistical Yearbook were interpolated to a05deg times 05deg grid using the grid weight calculation method

23 Method

231 Heatwave Event Identification In accordance withthe definition of heatwaves by the China MeteorologicalAdministration this study considered a weather processwith a maximum temperature ge35degC for more than threeconsecutive days as a heatwave event is definition fo-cuses on the hazard of persistently high air temperatureWhen a grid met this definition it was assumed that therewas a heatwave at that grid us we analyzed the fre-quency of heatwave events (ie number of heatwaveevents) intensity of heatwave events (ie maximum dailymaximum temperature of a heatwave event) and areas

2 Advances in Meteorology

affected by heatwave events (ie maximum area covered bythe heatwave)

232 Intensity-Area-Duration (IAD) Method is studyadopted the IAD method which links the three most im-portant features of extreme events [26 31 34 35] ismethod has been applied successfully in analyses of droughtand extreme precipitation events in China [26 31 34] eprinciple of the IAD method is to identify grids thatneighbor the daily maximum temperature ge35degC over agiven timescale and to select continuous areas as an extremeheatwave event e area of impact is determined by thesummation of all grids while the intensity is determined bythe maximum temperature of all grids included in one evente IAD method can be used to study the simultaneouschanges in intensity and area of impact over a given du-ration e steps required in the process are as follows

First for a given timescale (here ge3 d) calculate theintensities for the different periods for all grids e gridpoint with the highest intensity is regarded as the ldquocenterwith highest intensityrdquo of a regional extreme daily maximumtemperature event Second among the surrounding eightgrids find the one with the second highest intensity toestablish the ldquocenter with second highest intensityrdquo irdamong the grids surrounding the ldquocenter with secondhighest intensityrdquo find the one with the third highest in-tensity Determine all those grids with daily maximumtemperature greater than the threshold and combine themall as a regional extreme daily maximum temperature eventFourth determine another ldquocenter with highest intensityrdquoand repeat the above steps deducing all the regional extremedaily maximum temperature events over a given timescale

233 Spatialization of Population Population is an im-perative factor in exposure assessment in relation to heat-wave disasterse spatial distribution of the population wasestablished using the IIASA GGI Population Scenario Da-tabase (resolution 05deg times 05deg time 1990 2000 and 2010)based on the total number of provinces in the Chinese

Statistical Yearbook In this process the weight coefficient isinterpolated to each grid point and calculated as follows

POPij 1113944m

j11113944

n

i1

Pij

Pj

Xj (1)

where POPij denotes the population at grid i in provincej Pij is the population of the GGI Population ScenarioDatabase at grid i in province j Pj is the total populationof the GGI Population Scenario Database in province jXj is the total population of province j m is the totalnumber of provinces and n is the total number of grids inprovince j

In this study the population distribution of the NorthChina Plain during 1961ndash1990 1991ndash2000 and 2001ndash2017was based on the GGI Population Scenario Database in 19902000 and 2010 respectively

234 Definition of Population Exposure and Analysis ofContribution Rate e number of people exposed to aheatwave event was defined as the population exposure [36]Population exposure to heatwave events is affected by thefrequency of heatwaves and the number of people Conse-quently population exposure is affected by the combinedeffects of climatic factors (change in frequency of heatwaveevents and constant population size) population factors(constant frequency of heatwave events and change inpopulation size) and climatic-population factors (changesin both frequency of heatwaves events and population size)Changes in population exposure to heatwaves can beexpressed as [37](F + ΔF) times(P + ΔP) minus F times P F times ΔP + P times ΔF + ΔF times ΔP

(2)

For ease of comprehension it is assumed that thecontribution rate of increased population exposure is pos-itive and that the contribution rate of different influencingfactors in the change of population exposure to heatwaves isas follows

Rate of contribution of climatic factorsP times ΔF

|(F + ΔF) times(P + ΔP) minus F times P|times 100 (3)

Rate of contribution of population factorsF times ΔP


Rate of contribution of combined climatic-populationfactors

ΔF times ΔP|(F + ΔF) times(P + ΔP) minus F times P|

times 100 (5)

where F and P are the frequency of heatwave events andpopulation size respectively Ftimes∆P is the influence ofpopulation factors Ptimes∆F is the influence of climate fac-tors and ∆F times∆P is the combined influence of climatic-population factors

120degE110degE 118degE116degE114degE112degE

40degN

42degN

38degN

36degN

34degN

Shanxi

MongoliaMongolia

Taih

ang m

ount

ains

Taihang MountainProvincial boundary

Low 1High 3000

Height (m)

Shaa

nxi

BeijingHebeiTianjinHebei

Shandong

Henan

AnhuiJiangshu

Figure 1 Map of the study area

Advances in Meteorology 3

3 Results

31 Frequency Intensity Area of Impact and Spatial Changesof Heatwave Events e frequency of heatwaves reflects thenumber of local heatwave events where a higher frequencyusually means greater impact Figure 2 shows the frequencyof heatwave events on the North China Plain during1961ndash2017 It can be seen that the decadal mean frequency ofheatwave events on the North China Plain shows a signif-icant initial trend of decrease followed by an increaseDuring the period with a significant downward trend(1961ndash1990) the decadal mean frequency of heatwave eventsduring 1961ndash1970 1971ndash1980 and 1981ndash1990 decreasedfrom 300 to 176 and 107 events respectively During theperiod with an upward trend (1990ndash2017) the decadal meanfrequency of heatwave events during 1991ndash2000 2001ndash2010and 2011ndash2017 increased to 194 231 and 381 eventsrespectively e highest decadal mean frequencies of 60 59and 58 events occurred in 2017 2013 and 2014 respectivelyBased on the daily maximum temperature data we foundthat changes in air temperature caused these changes in thefrequency of occurrence of heatwave events

Heatwave intensity reflects the strength of heatwaveevents Figure 3 shows the intensity of heatwave events onthe North China Plain It can be seen that the most and leastintense heatwave events on the North China Plain occurredduring 1961ndash1970 and 1971ndash1980 respectively Since the1970s there has been a trend of increase in heatwave in-tensity e average intensity of heatwaves during 1971ndash1980 was approximately 368degC during 1981ndash2000 theaverage intensity of heatwave events remained around369degC and the mean intensity during 2001ndash2010 and2011ndash2017 reached 371 and 373degC respectively During1961ndash2017 the average intensity of heatwaves on the NorthChina Plain always remained gt36degC and there were fouryears with intensity gt38degC

e area of impact of heatwave events on the NorthChina Plain during 1961ndash2017 is shown in Figure 4 echronological trend of the mean area of impact of heatwaveevents shows a decrease during 1961ndash1990 ie it decreasedfrom 065times106 km2 in 1961ndash1970 to 019times106 km2 in1981ndash1990 During 1961ndash1990 the area of impact of heat-wave events on the North China Plain decreased by anannual average of 22times104 km2 During 1981ndash2017 the areaof impact of heatwave events increased significantly Forexample the average annual area of impact in 2011ndash2017was 45 times that during 1981ndash1990 During 1991ndash2017 thearea of impact of heatwave events on the North China Plainincreased by an annual average of approximately 29times104 km2e maximum area of impact of gt16times106 km2 appeared in2014

e spatial distribution of the annual average frequency ofheatwaves on the North China Plain shows that the frequencyof heatwaves decreased during 1961ndash1990 and that the area ofimpact gradually diminished (Figures 5(a)ndash5(c)) e centerof high frequency gradually narrowed from southeast Hebeicentral and eastern Henan western Shandong and northwestAnhui to small areas of southern Hebei central Henan andnorthern Anhui Conversely the frequency and area of

impact of heatwave events on the North China Plain increasedsignificantly during 1981ndash2017 (Figures 5(c)ndash5(f))e centerof high frequency gradually expanded from southeasternHebei and central Henan to southern Hebei southernShandong and northern-central Henan According to thespatial distribution of the annual average frequency during1961ndash2017 (Figure 5(g)) heatwave events have occurred inmost parts on the North China Plain and the center of highfrequency (frequency gt20 times) has been located mainly insoutheastern Hebei eastern Henan and northern Anhui It isevident that topography determines the spatial distribution ofthe frequency of heatwaves ie to the east of the TaihangMountains low terrain means high frequency and to the westof the Taihang Mountains high terrain means low frequency

0

10

20

30

40

50

60

70

1961 1966 1971 1976 1981 1986 1991 1996 2001 2006 2011 2016

Freq

uenc

y (ti

mes

)

Figure 2 Frequency of heatwave events on the North China Plain

36

365

37

375

38

385

1961 1966 1971 1976 1981 1986 1991 1996 2001 2006 2011 2016

Inte

nsity

(degC)

Figure 3 Intensity of heatwave events on the North China Plain

002040608

112141618

1961 1966 1971 1976 1981 1986 1991 1996 2001 2006 2011 2016

Are

a (times1

06 km

2 )

Figure 4 Area affected by heatwave events on the North ChinaPlain


e spatial distribution of the frequency of heatwaves isrelated to population exposure For a population of a certainsize more frequent heatwave events will mean greaterpopulation exposure is means that if the current size ofthe population is maintained an increase in the frequency offuture heatwave events will lead to an increase in populationexposure

32PopulationExposure toHeatwaveEvents e severity ofthe impact of a heatwave event depends not only on theevent itself but also on the levels of exposure and vul-nerability of the population e level of exposure is theextent to which the adverse effects of heatwave eventsaffect the population economy and other factors [39 40]Population exposure is defined as the size of the pop-ulation within an area affected by a heatwave e vari-ation in population exposure to heatwave events on theNorth China Plain shows a downward trend during1961ndash1990 and a significant upward trend during 1981ndash2017 (Figure 6) During 1981ndash1990 population exposuredecreased by approximately 589 (in comparison with1961ndash1970) at an annual average rate of 68 millionDuring 2011ndash2017 population exposure increased byapproximately 3316 (in comparison with 1981ndash1990) atan average annual rate of around 12 million According to

current trends population exposure on the North ChinaPlain will exceed 1 billion in the next decade

e spatial distribution of population exposure toheatwaves on the North China Plain is shown in Figure 7Comparison with Figure 5 reveals that the spatial distri-bution of population exposure is related to the spatio-temporal distribution of heatwave events During1961ndash2017 population exposure to heatwaves occurred inShandong northern Jiangsu northeast and southernHebei Beijing Tianjin Henan southern Shanxi and otherareas e most densely populated area affected by heat-wave events was southeastern Henan (Figure 7) Populationexposure to heatwaves on the North China Plain decreasedduring 1961ndash1990 (Figures 7(a)ndash7(c)) and the extent of thearea of high values gradually retreated from north to southe population affected by heatwave events showed anincreasing trend during 1981ndash2017 (Figures 7(c)ndash7(f )) andthe extent of the area of high values gradually expandedfrom south to north e largest period of populationexposure occurred in 2011ndash2017 (Figure 7(f )) Grid valueswith population exposure of gt40 million occurred in gt20of regions Figure 7(g) shows the spatial distribution ofpopulation exposure on the North China Plain during1961ndash2017 It can be seen that population exposure toheatwaves decreased from south to north and that thecenter of the highest values was located in southeastern

High-temperature heat wavefrequency (times)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E

01~10

11~20gt20

(a)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


01~10

11~20gt20

(b)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


01~10

11~20gt20

(c)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


01~10

11~20gt20

(d)42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


01~10

11~20gt20

(e)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

EHigh-temperature heat wave

frequency (times)

01~10

11~20gt20

(f )

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


01~10

11~20gt20

(g)

Figure 5 Spatial distribution of the annual average frequency of heatwaves on the North China Plain (a) 1961ndash1970 (b) 1971ndash1980(c) 1981ndash1990 (d) 1991ndash2000 (e) 2001ndash2010 (f ) 2011ndash2017 and (g) 1961ndash2017


Henan Population exposure is greatly affected by eleva-tion ie to the east of the Taihang Mountains populationexposure is large and to the west of the Taihang Mountainpopulation exposure is small Although Beijing and Tianjinhave high population density population exposure is lowin these regions because of the occurrence of fewer heat-wave events

33 Analysis of Factors Influencing Population Exposure toHeatwaves As can be seen from Figure 8 the population ofthe North China Plain is gradually increasing e

population east of the Taihang Mountains is significantlylarger than to the west e high-density population areasare Beijing Tianjin and southeastern parts of the NorthChina Plain

Changes in population exposure to heatwave eventsdepend on changes in the spatial distributions of climate andpopulation Table 1 shows the rate of contribution of eachfactor that influences population exposure where a negativecontribution indicates that the contribution is opposite tothe trend of population exposure and where the absolutevalue indicates the rate of that contribution e change ofpopulation exposure to heatwave events on the North China

0

01

02

03

04

05

06

07

08

1961 1966 1971 1976 1981 1986 1991 1996 2001 2006 2011 2016Po

pula

tion

expo

sure

(bill

ion)

Figure 6 Population exposure to heatwave events on the North China Plain

Population exposure (million)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E

00~1010~20 gt40

20~3030~40

(a)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


00~1010~20 gt40

20~3030~40

(b)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


00~1010~20 gt40

20~3030~40

(c)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


00~1010~20 gt40

20~3030~40

(d)42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


00~1010~20 gt40

20~3030~40

(e)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


00~1010~20 gt40

20~3030~40

(f )

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


00~1010~20 gt40

20~3030~40

(g)

Figure 7 Spatial distribution of population exposure to heatwaves on the North China Plain (a) 1961ndash1970 (b) 1971ndash1980 (c) 1981ndash1990(d) 1991ndash2000 (e) 2001ndash2010 (f ) 2011ndash2017 and (g) 1961ndash2017


Plain has been dominated by climatic factors although therate of contribution decreased gradually and changed from anegative contribution to a positive contribution during1961ndash2010 During 2011ndash2017 in comparison with 2001ndash

2010 the population influenced by climatic factors increasedby up to 002 billion (180) because of the sharp increase inthe frequency of occurrence of heatwaves e most in-fluential period of population factors was during 1971ndash1980

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E

15~20gt20

lt0505~1010~15

Population (million)

(a)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E

15~20gt20

lt0505~1010~15


(b)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E

15~20gt20

lt0505~1010~15


(c)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E

15~20gt20

lt0505~1010~15


(d)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E

15~20gt20

lt0505~1010~15


(e)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E

15~20gt20

lt0505~1010~15


(f )

Figure 8 Spatial distribution of population on the North China Plain (a) 1961ndash1970 (b) 1971ndash1980 (c) 1981ndash1990 (d) 1991ndash2000 (e)2001ndash2010 and (f) 2011ndash2017

Table 1 Analysis of factors influencing population exposure change to heatwave events on the North China Plain

Exposure changes Climatic factors(billion)

Population factors(billion)

Interaction of climate and populationfactors (billion)

Population exposure toheatwave

events (billion)1971ndash1980 comparedto 1961ndash1970 minus 118 (minus 1323) 054 (607) minus 025 (minus 284) minus 089

1981ndash1990 comparedto 1971ndash1980 minus 074 (minus 1210) 023 (385) minus 011 (minus 174) minus 061

1991ndash2000 comparedto 1981ndash1990 096 (790) 013 (106) 013 (103) 122


2011ndash2017 comparedto 2001ndash2010 003 (1800) minus 0005 (minus 244) minus 001 (minus 556) 002


when the population influenced increased by 054 billion(607) in comparison with 1961ndash1970 Subsequently thepopulation influenced by population factors gradually de-creased as the contribution of population factors changedfrom positive to negative In general interaction betweenclimatic and population factors had less impact on pop-ulation exposure than either climatic factors or populationfactors alone

Climatic factors have had the greatest impact on pop-ulation exposure whereas the effect of population factors hashad a decreasing trend which has resulted ultimately in anegative rate of contribution is is because of the sig-nificant increase in heatwaves while some provinces haveexperienced slow or even no population growth in recentyears

4 Conclusions and Discussion

Since the 1990s global warming has had a significant effecton heatwaves in China and this is expected to continueUsing the IAD method based on gridded data whichcombines the three most important elements (intensityarea of impact and duration) of heatwave events this studyinvestigated the spatiotemporal characteristics of regionalheatwaves on the North China Plain is approach whichdiffers from previous analyses based solely on individualstations allows greater understanding of heatwave eventsIn addition gridded temperature data can be more rea-sonably combined with population data which is helpful inassessment of the impact of heatwave events on apopulation

Based on daily maximum temperature data and spa-tiotemporal data of population distribution on the NorthChina Plain during 1961ndash2017 we calculated and analyzedthe spatiotemporal evolution characteristics populationexposure change and factors influencing heatwave events onthe North China Plain over the past 57 years e mainconclusions are as follows

(1) Heatwave events on the North China Plain exhibitedobvious spatiotemporal change From the perspec-tive of the decadal mean the frequency and area ofimpact of heatwave events both declined initially andthen increased with the lowest values during1981ndash1990 e intensity of heatwave events showedan initial downward trend remained reasonablystable during 1981ndash2000 and then increased during2001ndash2017 From the perspective of spatial changethe area of impact and frequency of heatwave eventsboth decreased at first and then increased and thecenter of heatwave events receded southward andthen extended northward From the spatial distri-bution of heatwave events during 1961ndash2017 it wasfound that areas with high incidence of heatwaveevents were located mainly in southeastern Henanand southern Hebei e frequency of occurrence ofheatwaves was found to be affected by elevation ie

heatwaves occurredmore frequently to the east of theTaihang Mountains in comparison with areas to thewestus the findings of this study in relation to thecharacteristics of heatwaves on the North ChinaPlain are similar to other earlier studies [14 41 42]

(2) Under the background of global warming and thegiven spatial distribution of population it wasfound that the trend of population exposure on theNorth China Plain showed significant decreaseduring 1961ndash1990 followed by an increase during1991ndash2017 Spatially during 1961ndash1990 the areaof population exposure gradually narrowed andthe center of high values moved southwardConversely during 1991ndash2017 the center of highvalues moved northward and the number ofpeople affected by heatwaves increased During1961ndash2017 the region with the highest populationexposure was located in southeastern parts ofHenan

(3) Population exposure on the North China Plain wasfound to be influenced by climatic factors pop-ulation factors and combined climatic-populationfactors In the change of population exposure toheatwave events climatic factors played the leadingrole population factors increased the populationexposure to heatwaves in all decades except for amarginal decrease from 2001 to 2010 to 2011ndash2017but there was a decreasing trend in the magnitude ofthis contribution During 1961ndash2010 there was adecreasing trend in the interaction between climaticfactors and population factors e population ex-posure to climatic factors first decreased and thenincreased consistent with the change of frequency ofoccurrence of heatwave events e increase in thepopulation in heatwave event areas has led to anincrease in the number of people exposed to heat-wave events According to the climatic and de-mographic trends it was established that climaticfactors will continue to contribute positively and willremain the most important driver in the futurewhereas population factors will contribute at a low oreven negative rate consistent with the conclusions ofHuang et al [37]

e effect of a heatwave event on population exposure isnot only related to its strength area of impact and durationbut also to the spatiotemporal distribution of the populationLimited by the temporal resolution of the IIASA GGIPopulation Scenario Database the population distributiondata used in this study were the background fields of thespatial distribution of population in 1990 2000 and 2010us we did not consider population distributions withhigher temporal resolution is will be a focus of futureresearch

e mechanism maintaining high temperatures on theNorth China Plain has not yet been fully elucidated Xie et al[43] suggested that the continental subtropical high is the


main synoptic system responsible for high temperatures onthe North China Plain Conversely Wei et al [43] proposedthat the occurrence of summer temperature anomalies onthe North China Plain is determined primarily by east-westanomalies of the western Pacific subtropical high Howeverthe question of how the western Pacific subtropical high andcontinental high pressure might interact to produce hightemperatures on the North China Plain will require furtherin-depth research

In addition to population exposure the disaster riskassociated with heatwaves is related to factors such as hightemperature adaptability of different populations and tothe regional level of defense against the effects of heat-waves Age gender occupation disease status medicalresources and economic level all affect the response of apopulation to the effects of heatwaves erefore it will benecessary to undertake further study on the spatiotemporalevolution of heatwave disaster vulnerability under futurescenarios based on the findings of this study to improvethe level of risk management in relation to heatwaveevents

Our results provide valuable evidence of the suitabilityof the proposed method for assessing the impactof heatwave events on population exposure and theycould provide a scientific basis for the developmentof government policy regarding adaption to climatechange

Data Availability

e daily maximum temperature data (1961ndash2017) used tosupport the findings of this study were supplied by httpdatacmacnunder account and so cannot be made freelyavailable e North China Plain population data used tosupport the findings of this study have been deposited inthe Chinese Demographic Yearbook (httpwwwstatsgovcntjsjndsj) GGI Population Scenario Database of theIIASA used to support the findings of this study weresupplied by httpwwwiiasaacatweb-appsggiGgiDbdsdActionspatial under account and can be freelyavailable All other data are available from the authors uponreasonable request

Conflicts of Interest

e authors declare that they have no conflicts of interest

Acknowledgments

is study was supported by the CDM Fund ldquoJiangxi Cli-mate Change Adaptation Programrdquo (2014102) and bilateralcooperation project between the Natural Science Founda-tion of China and the Pakistan Science Foundation(41661144027) e CMA Climate Change Science Fund(CCSF (201722 201810)) provides a policy-oriented trainingcourse for PhD students e authors are thankful for thesupport of the High-level Talent Recruitment Program of the

Nanjing University of Information Science and Technology(NUIST)

References

[1] E M Fischer and R Knutti ldquoAnthropogenic contribution toglobal occurrence of heavy-precipitation and high-tempera-ture extremesrdquo Nature Climate Change vol 5 no 6pp 560ndash564 2015

[2] C W Team R K Pachauri and L A Meyer ldquoIPCC climatechange 2014 synthesis report summary for policymakersrdquo inContribution of Working Groups I II and III to the FifthAssessment Report of the Intergovernmental Panel on ClimateChange Cambridge University Press Cambridge UK 2014

[3] S Sodoudi P Shahmohamadi K Vollack U Cubasch andA I Che-Ani ldquoMitigating the urban heat island effect inmegacity Tehranrdquo Advances in Meteorology vol 2014 ArticleID 547974 19 pages 2014

[4] Y Ma X-M Zeng Y Zhang et al ldquoImpact of the choice ofland surface scheme on a simulated heatwave event the caseof Sichuan-Chongqing area ChinardquoAdvances inMeteorologyvol 2017 Article ID 9545896 17 pages 2017

[5] R K Pachauri M R Allen V R Barros et al ldquoClimatechange 2014 synthesis reportrdquo in Contribution of WorkingGroups I II and III to the Fifth Assessment Report of theIntergovernmental Panel on Climate Change p 151 Cam-bridge University Press Cambridge UK 2014

[6] M R Kousari H Ahani and R Hendi-Zadeh ldquoTemporal andspatial trend detection of maximum air temperature in Iranduring 1960ndash2005rdquo Global and Planetary Change vol 111pp 97ndash110 2013

[7] A C Kruger and S S Sekele ldquoTrends in extreme temperatureindices in South Africa 1962ndash2009rdquo International Journal OfClimatology vol 33 no 3 pp 661ndash676 2013

[8] Y Shi Y Shen E Kang et al ldquoRecent and future climatechange in Northwest Chinardquo Climatic Change vol 80 no 3-4 pp 379ndash393 2007

[9] L Bai G Ding S Gu et al ldquoe effects of summer tem-perature and heat waves on heat-related illness in a coastal cityof China 2011ndash2013rdquo Environmental Research vol 132pp 212ndash219 2014

[10] C R Upperman J Parker C Jiang X He R Murtuguddeand A Sapkota ldquoFrequency of extreme heat event as a sur-rogate exposure metric for examining the human health ef-fects of climate changerdquo PLoS One vol 10 no 12 Article IDe144202 2015

[11] H Shen H Ma J Wang L Wang and J Li ldquoVariationcharacteristics of extreme air temperature events in QinghaiProvincerdquo Journal of Glaciology and Geocryology vol 34pp 1371ndash1382 2012

[12] T Fischer M Gemmer L Liu and B Su ldquoChange-points inclimate extremes in the Zhujiang River Basin South China1961ndash2007rdquo Climatic Change vol 110 no 3-4 pp 783ndash7992012

[13] T Fischer B Su Y Luo and T Scholten ldquoProbability dis-tribution of precipitation extremes for weather index-basedinsurance in the Zhujiang river basin South Chinardquo Journalof Hydrometeorology vol 13 no 3 pp 1023ndash1037 2012

[14] S E Perkins L V Alexander and J R Nairn ldquoIncreasingfrequency intensity and duration of observed global heat-waves and warm spellsrdquo Geophysical Research Letters vol 39no 20 p 20714 2012

[15] National Center for Health Statistics National Vital StatisticsSystem Mortality Data 2007


[16] National Weather Service Weather Fatalities 2018[17] S Greene L S Kalkstein D M Mills and J Samenow ldquoAn

examination of climate change on extreme heat events andclimate-mortality relationships in large US CitiesrdquoWeatherClimate and Society vol 3 no 4 pp 281ndash292 2011

[18] L Coates K Haynes J OrsquoBrien J McAneney andF D de Oliveira ldquoExploring 167 years of vulnerability anexamination of extreme heat events in Australia 1844ndash2010rdquoEnvironmental Science amp Policy vol 42 pp 33ndash44 2014

[19] A-J Valleron and A Boumendil ldquoEpidemiologie et caniculesanalyses de la vague de chaleur 2003 en Francerdquo ComptesRendus Biologies vol 327 no 12 pp 1125ndash1141 2004

[20] P A Stott D A Stone and M R Allen ldquoHuman contri-bution to the European heatwave of 2003rdquo Nature vol 432no 7017 pp 610ndash614 2004

[21] China Meteorological Administration gte Top Ten Weatherand Climate Extreme Events in China in 2013 China Mete-orological Administration Beijing China 2013

[22] C L Gray and V Mueller ldquoNatural disasters and populationmobility in Bangladeshrdquo Proceedings of the National Academyof Sciences vol 109 no 16 pp 6000ndash6005 2012

[23] S Re Mind the Risk A Global Ranking of Cities under gtreatfrom Natural Disasters 2013

[24] Y Hirabayashi R Mahendran S Koirala et al ldquoGlobal floodrisk under climate changerdquo Nature Climate Change vol 3no 9 pp 816ndash821 2013

[25] B Jongman P J Ward and J C J H Aerts ldquoGlobal exposureto river and coastal flooding long term trends and changesrdquoGlobal Environmental Change vol 22 no 4 pp 823ndash8352012

[26] C Jing T Jiang Y J Wang et al ldquoA study on regionalextreme precipitation events and the exposure of populationand economy in Chinardquo Acta Meteorologica Sinica vol 74no 4 pp 572ndash582 2016

[27] M Zhan X Li H Sun J Zhai T Jiang and Y WangldquoChanges in extreme maximum temperature events andpopulation exposure in China under global warming sce-narios of 15 and 20degC analysis using the regional climatemodel COSMO-CLMrdquo Journal of Meteorological Researchvol 32 no 1 pp 99ndash112 2018

[28] P X Wang and J H Yang ldquoExtreme high temperature eventsand response to regional warming in recent 45 years inNorthwest Chinardquo Journal of Desert Research vol 27 no 4pp 649ndash654 2007

[29] F Zhang H Gao and X Cui ldquoFrequency of extreme hightemperature days in China 1961ndash2003rdquo Weather vol 63no 2 pp 46ndash49 2008

[30] B Su M Zhan J Zhai Y Wang and T Fischer ldquoSpatio-temporal variation of haze days and atmospheric circulationpattern in China (19612013)rdquo Quaternary Internationalvol 380-381 pp 14ndash21 2015

[31] J Zhai J Huang B Su et al ldquoIntensityndashareandashdurationanalysis of droughts in China 1960ndash2013rdquo Climate Dynamicsvol 48 no 1-2 pp 151ndash168 2017

[32] S Rao K Riahi E Stehfest et al IMAGE and MESSAGEScenarios Limiting GHG Concentration to Low Levels IIASALaxenburg Austria 2008

[33] International Institute for Applied System Analysis (IIASA)GGI Scenario Database Ver 20 International Institute forApplied System Analysis (IIASA) Laxenburg Austria 2009

[34] J Huang J Zhai T Jiang et al ldquoAnalysis of future droughtcharacteristics in China using the regional climate modelCCLMrdquo Climate Dynamics vol 50 no 1-2 pp 507ndash5252018

[35] H Sun Y Wang J Chen et al ldquoExposure of population todroughts in the Haihe River Basin under global warming of15 and 20degC scenariosrdquo Quaternary International vol 453pp 74ndash84 2017

[36] A Fouillet G Rey F Laurent et al ldquoExcess mortality relatedto the August 2003 heat wave in Francerdquo International Ar-chives of Occupational and Environmental Health vol 80no 1 pp 16ndash24 2006

[37] D Huang L Zhang and G Gao ldquoChanges in populationexposure to high temperature under a future scenario inChina and its influencing factorsrdquo Journal of GeographicalSciences vol 10 no 2 2018

[38] C B Field V Barros T F Stocker et al ldquoIPCC 2012managing the risks of extreme events and disasters to advanceclimate change adaptationrdquo A Special Report of WorkingGroups I and II of the Intergovernmental Panel on ClimateChange Cambridge University Press vol 30 no 11pp 7575ndash7613 Cambridge UK 2012

[39] C W Team R K Pachauri and L A Meyer ldquoIPCC 2014climate change 2014 synthesis reportrdquo Contribution ofWorking Groups I II and III to the Fifth Assessment Report ofthe intergovernmental panel on Climate Change Vol 151IPCC Geneva Switzerland 2014

[40] G A Meehl and C Tebaldi ldquoMore intense more frequentand longer lasting heat waves in the 21st centuryrdquo Sciencevol 305 no 5686 pp 994ndash997 2004

[41] H Shi ldquoClimatic characteristics and the spatio-temporalvariation of high temperature days in North China in1960ndash2009rdquo Scientia Geographica Sinica vol 32 no 7pp 866ndash871 2012

[42] Z Xie J Cui H Liu et al ldquoA study on the severe hot weatherin Beijing and North China part I-statistics and synoptic casestudyrdquo Climatic amp Environmental Research vol 4 1999

[43] J Wei H Yang and S Q Sun ldquoRelationship between theanomaly longitudinal position of subtropical high in theWestern Pacific and severe hot weather in North China insummerrdquo Acta Meteorologica Sinica vol 62 2004


Hindawiwwwhindawicom Volume 2018

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Submit your manuscripts atwwwhindawicom

been observed in most of China and the North China Plainis one area that has experienced the most significantwarming

Increased temperature will lead to an increase in the riskof heat-related diseases and death in humans [9] e risk ofmorbidity and accidental death among urban vulnerablepopulations and among urban and rural outdoor workersduring a period of heat stress has been identified as one ofthe key risks of climate change in the Fifth AssessmentReport of the Intergovernmental Panel on Climate Change[5] Changes in heatwaves are an important aspect of climatechange and their impact on both natural ecosystems andhuman society might be more severe than the effects of therise in average temperature [10ndash13] e latest HadleyCentre Global Historical Climatology Network Daily ob-servations show that the intensity frequency and durationof global heatwave events are increasing [14] In recent yearsheatwaves have surpassed other natural disasters such ashurricanes lightning tornadoes floods and earthquakes tobecome the primary cause of fatalities in America related tonatural disasters [15ndash17] Moreover heatwaves have killedmore people in Australia than any other natural hazard since1900 [18] It is estimated that around 40000 people incentral and western parts of continental Europe died duringa record heatwave that occurred in the summer of 2003[19 20] In 2013 heatwaves occurred frequently in mostparts of China For example Changsha Chongqing andHangzhou recorded 57 49 and 47 hot days (daily maximumtemperature ge35degC) respectively during which the highestrecorded temperatures reached 382 409 and 405degC re-spectivelyese heatwave incidents led to a direct economicloss of 8356 billion RMB [21] Research on high-temper-ature disasters has attracted widespread attention fromgovernment departments and the scientific community andit has become the focus of climate research under thebackground context of global warming

In recent years studies on population exposure tonatural disasters have achieved many results [22ndash27] Re-search on extreme climate events has developed from valueanalysis at a single site [28ndash30] to related investigations ofintensity area of impact and duration [26 31] Based on theintensity-area-duration (IAD) method Zhai et al [31] de-veloped an approach for identification of a regional extremeevent and analysis of its spatial coverage and durationExtreme heatwave events can be more serious than a singlehigh-temperature event [10] However few studies haveconsidered regional persistent extreme heatwave events andthe variation of associated population exposure

e population of the North China Plain accounts forapproximately 30 of the total population of China Fur-thermore the North China Plain represents the center ofChinarsquos political economic cultural transportation edu-cation and tourism activities erefore it is of great sci-entific and practical importance to study the evolution ofheatwave events and associated population exposure on theNorth China Plain is study was based on daily maximumtemperature data (1961ndash2017) from national meteorologicalstations on the North China Plain in combination withdemographic data and Greenhouse Gas Initiative (GGI)

Population Scenario Database of the Austrian Institute forInternational Applied Systems Analysis (IIASA) [32 33]First the frequency intensity area of impact and spatialvariation characteristics of heatwave events in North Chinaare analyzed by grid unit scale Second the temporal andspatial evolution characteristics of population exposure toheatwave events are studied Finally attribution analysis isperformed on the factors affecting population exposure emain objectives of this study were to reveal the contributionsof various factors to population exposure to past heatwaveevents on the North China Plain and to provide a scientificbasis for the development of strategies effective in helpinghumans cope with the adverse effects of such heatwaveevents

2 Data and Methods

21 Study Area e area of the North China Plain con-sidered in this study comprised the region bounded by33degndash42degN 110degndash120degE which includes Beijing Tianjin InnerMongolia Hebei Henan Shanxi Shandong Jiangsu Anhuiand Shaanxi (Figure 1) is region covers an area of ap-proximately 90times105 km2 and it contains approximately30 of the total population of Chinae North China Plainhas a warm temperate semiarid monsoon climate which hasthe characteristics of cold and snow in winter a dry andwindy spring hot rainy summer and cool dry autumn

22 Data Description e climate dataset used in this studywas derived from daily maximum temperature data(1961ndash2017) recorded at national meteorological stations onthe North China Plain which were integrated by the Na-tional Meteorological Information Center of the ChineseMeteorological Administration Following quality controlthe daily maximum temperature data were interpolated to a05deg times 05deg grid using the inverse distance weighting method

e North China Plain population data were obtainedfrom the Chinese Demographic Yearbook Using the GGIPopulation Scenario Database of the IIASA (which hasspatial resolution of 05deg times 05deg and temporal resolution of10a) as the background of population distribution thepopulation data of the North China Plain provinces fromthe Chinese Statistical Yearbook were interpolated to a05deg times 05deg grid using the grid weight calculation method

23 Method

231 Heatwave Event Identification In accordance withthe definition of heatwaves by the China MeteorologicalAdministration this study considered a weather processwith a maximum temperature ge35degC for more than threeconsecutive days as a heatwave event is definition fo-cuses on the hazard of persistently high air temperatureWhen a grid met this definition it was assumed that therewas a heatwave at that grid us we analyzed the fre-quency of heatwave events (ie number of heatwaveevents) intensity of heatwave events (ie maximum dailymaximum temperature of a heatwave event) and areas







POPij 1113944m

j11113944

n

i1

Pij

Pj

Xj (1)




(2)








times 100 (5)



40degN

42degN

38degN

36degN

34degN

Shanxi

MongoliaMongolia

Taih

ang m

ount

ains


Low 1High 3000

Height (m)

Shaa

nxi


Shandong

Henan

AnhuiJiangshu



3 Results






0

10

20

30

40

50

60

70

1961 1966 1971 1976 1981 1986 1991 1996 2001 2006 2011 2016

Freq

uenc

y (ti

mes

)


36

365

37

375

38

385

1961 1966 1971 1976 1981 1986 1991 1996 2001 2006 2011 2016

Inte

nsity

(degC)


002040608

112141618

1961 1966 1971 1976 1981 1986 1991 1996 2001 2006 2011 2016

Are

a (times1

06 km

2 )








42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E

01~10

11~20gt20

(a)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


01~10

11~20gt20

(b)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


01~10

11~20gt20

(c)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


01~10

11~20gt20

(d)42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


01~10

11~20gt20

(e)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg


frequency (times)

01~10

11~20gt20

(f )

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


01~10

11~20gt20

(g)







0

01

02

03

04

05

06

07

08

1961 1966 1971 1976 1981 1986 1991 1996 2001 2006 2011 2016Po

pula

tion

expo

sure

(bill

ion)



42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E

00~1010~20 gt40

20~3030~40

(a)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


00~1010~20 gt40

20~3030~40

(b)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


00~1010~20 gt40

20~3030~40

(c)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


00~1010~20 gt40

20~3030~40

(d)42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


00~1010~20 gt40

20~3030~40

(e)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


00~1010~20 gt40

20~3030~40

(f )

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


00~1010~20 gt40

20~3030~40

(g)





42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E

15~20gt20

lt0505~1010~15


(a)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E

15~20gt20

lt0505~1010~15


(b)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E

15~20gt20

lt0505~1010~15


(c)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E

15~20gt20

lt0505~1010~15


(d)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E

15~20gt20

lt0505~1010~15


(e)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E

15~20gt20

lt0505~1010~15


(f )




























Data Availability




Acknowledgments



References















































Journal of










Volume 2018





Geophysics


Journal of




Microbiology





Agronomy




















POPij 1113944m

j11113944

n

i1

Pij

Pj

Xj (1)




(2)








times 100 (5)



40degN

42degN

38degN

36degN

34degN

Shanxi

MongoliaMongolia

Taih

ang m

ount

ains


Low 1High 3000

Height (m)

Shaa

nxi


Shandong

Henan

AnhuiJiangshu



3 Results






0

10

20

30

40

50

60

70

1961 1966 1971 1976 1981 1986 1991 1996 2001 2006 2011 2016

Freq

uenc

y (ti

mes

)


36

365

37

375

38

385

1961 1966 1971 1976 1981 1986 1991 1996 2001 2006 2011 2016

Inte

nsity

(degC)


002040608

112141618

1961 1966 1971 1976 1981 1986 1991 1996 2001 2006 2011 2016

Are

a (times1

06 km

2 )








42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E

01~10

11~20gt20

(a)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


01~10

11~20gt20

(b)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


01~10

11~20gt20

(c)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


01~10

11~20gt20

(d)42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


01~10

11~20gt20

(e)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg


frequency (times)

01~10

11~20gt20

(f )

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


01~10

11~20gt20

(g)







0

01

02

03

04

05

06

07

08

1961 1966 1971 1976 1981 1986 1991 1996 2001 2006 2011 2016Po

pula

tion

expo

sure

(bill

ion)



42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E

00~1010~20 gt40

20~3030~40

(a)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


00~1010~20 gt40

20~3030~40

(b)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


00~1010~20 gt40

20~3030~40

(c)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


00~1010~20 gt40

20~3030~40

(d)42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


00~1010~20 gt40

20~3030~40

(e)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


00~1010~20 gt40

20~3030~40

(f )

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


00~1010~20 gt40

20~3030~40

(g)





42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E

15~20gt20

lt0505~1010~15


(a)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E

15~20gt20

lt0505~1010~15


(b)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E

15~20gt20

lt0505~1010~15


(c)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E

15~20gt20

lt0505~1010~15


(d)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E

15~20gt20

lt0505~1010~15


(e)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E

15~20gt20

lt0505~1010~15


(f )




























Data Availability




Acknowledgments



References















































Journal of










Volume 2018





Geophysics


Journal of




Microbiology





Agronomy















3 Results






0

10

20

30

40

50

60

70

1961 1966 1971 1976 1981 1986 1991 1996 2001 2006 2011 2016

Freq

uenc

y (ti

mes

)


36

365

37

375

38

385

1961 1966 1971 1976 1981 1986 1991 1996 2001 2006 2011 2016

Inte

nsity

(degC)


002040608

112141618

1961 1966 1971 1976 1981 1986 1991 1996 2001 2006 2011 2016

Are

a (times1

06 km

2 )








42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E

01~10

11~20gt20

(a)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


01~10

11~20gt20

(b)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


01~10

11~20gt20

(c)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


01~10

11~20gt20

(d)42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


01~10

11~20gt20

(e)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg


frequency (times)

01~10

11~20gt20

(f )

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


01~10

11~20gt20

(g)







0

01

02

03

04

05

06

07

08

1961 1966 1971 1976 1981 1986 1991 1996 2001 2006 2011 2016Po

pula

tion

expo

sure

(bill

ion)



42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E

00~1010~20 gt40

20~3030~40

(a)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


00~1010~20 gt40

20~3030~40

(b)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


00~1010~20 gt40

20~3030~40

(c)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


00~1010~20 gt40

20~3030~40

(d)42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


00~1010~20 gt40

20~3030~40

(e)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


00~1010~20 gt40

20~3030~40

(f )

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


00~1010~20 gt40

20~3030~40

(g)





42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E

15~20gt20

lt0505~1010~15


(a)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E

15~20gt20

lt0505~1010~15


(b)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E

15~20gt20

lt0505~1010~15


(c)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E

15~20gt20

lt0505~1010~15


(d)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E

15~20gt20

lt0505~1010~15


(e)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E

15~20gt20

lt0505~1010~15


(f )




























Data Availability




Acknowledgments



References















































Journal of










Volume 2018





Geophysics


Journal of




Microbiology





Agronomy




















42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E

01~10

11~20gt20

(a)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


01~10

11~20gt20

(b)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


01~10

11~20gt20

(c)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


01~10

11~20gt20

(d)42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


01~10

11~20gt20

(e)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg


frequency (times)

01~10

11~20gt20

(f )

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


01~10

11~20gt20

(g)







0

01

02

03

04

05

06

07

08

1961 1966 1971 1976 1981 1986 1991 1996 2001 2006 2011 2016Po

pula

tion

expo

sure

(bill

ion)



42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E

00~1010~20 gt40

20~3030~40

(a)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


00~1010~20 gt40

20~3030~40

(b)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


00~1010~20 gt40

20~3030~40

(c)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


00~1010~20 gt40

20~3030~40

(d)42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


00~1010~20 gt40

20~3030~40

(e)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


00~1010~20 gt40

20~3030~40

(f )

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


00~1010~20 gt40

20~3030~40

(g)





42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E

15~20gt20

lt0505~1010~15


(a)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E

15~20gt20

lt0505~1010~15


(b)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E

15~20gt20

lt0505~1010~15


(c)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E

15~20gt20

lt0505~1010~15


(d)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E

15~20gt20

lt0505~1010~15


(e)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E

15~20gt20

lt0505~1010~15


(f )




























Data Availability




Acknowledgments



References















































Journal of










Volume 2018





Geophysics


Journal of




Microbiology





Agronomy



















0

01

02

03

04

05

06

07

08

1961 1966 1971 1976 1981 1986 1991 1996 2001 2006 2011 2016Po

pula

tion

expo

sure

(bill

ion)



42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E

00~1010~20 gt40

20~3030~40

(a)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


00~1010~20 gt40

20~3030~40

(b)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


00~1010~20 gt40

20~3030~40

(c)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


00~1010~20 gt40

20~3030~40

(d)42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


00~1010~20 gt40

20~3030~40

(e)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


00~1010~20 gt40

20~3030~40

(f )

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E


00~1010~20 gt40

20~3030~40

(g)





42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E

15~20gt20

lt0505~1010~15


(a)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E

15~20gt20

lt0505~1010~15


(b)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E

15~20gt20

lt0505~1010~15


(c)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E

15~20gt20

lt0505~1010~15


(d)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E

15~20gt20

lt0505~1010~15


(e)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E

15~20gt20

lt0505~1010~15


(f )




























Data Availability




Acknowledgments



References















































Journal of










Volume 2018





Geophysics


Journal of




Microbiology





Agronomy

















42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E

15~20gt20

lt0505~1010~15


(a)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E

15~20gt20

lt0505~1010~15


(b)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E

15~20gt20

lt0505~1010~15


(c)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E

15~20gt20

lt0505~1010~15


(d)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E

15~20gt20

lt0505~1010~15


(e)

42degN

40degN

38degN

36degN

34degN

110deg

E

112deg

E

114deg

E

116deg

E

118deg

E

120deg

E

15~20gt20

lt0505~1010~15


(f )




























Data Availability




Acknowledgments



References















































Journal of










Volume 2018





Geophysics


Journal of




Microbiology





Agronomy






























Data Availability




Acknowledgments



References















































Journal of










Volume 2018





Geophysics


Journal of




Microbiology





Agronomy


















Data Availability




Acknowledgments



References















































Journal of










Volume 2018





Geophysics


Journal of




Microbiology





Agronomy













































Journal of










Volume 2018





Geophysics


Journal of




Microbiology





Agronomy
















Journal of










Volume 2018





Geophysics


Journal of




Microbiology





Agronomy















Documents

StudyontheChangeCharacteristicsofandPopulation ...downloads.hindawi.com/journals/amete/2019/7069195.pdf · 2019. 8. 17. · e population of the North China Plain accounts for approximately