Spatio-Temporal Analysis of Meteorological Elements in the North China District of China during ...

19 Pages·2017·6.83 MB·English

water
Article
Spatio-Temporal Analysis of Meteorological Elements
in the North China District of China during 1960–2015
JinsongTi1,2,YuhaoYang1,2,XiaogangYin1,2,JingLiang3,LiangliangPu1,2,YulinJiang1,2,
XinyaWen1,2andFuChen1,2,*
1 CollegeofAgronomyandBiotechnology,ChinaAgriculturalUniversity,Yuan-Ming-YuanWestRoad2,
Haidian,Beijing100193,China;[email protected](J.T.);[email protected](Y.Y.);
[email protected](X.Y.);[email protected](L.P.);[email protected](Y.J.);[email protected](X.W.)
2 KeyLaboratoryofFarmingsystem,MinistryofAgricultureandRuralAffairsofthePeople’sRepublicof
China,Yuan-Ming-YuanWestRoad2,Haidian,Beijing100193,China
3 DepartmentofEnvironmentalSciences,UniversityofCalifornia,900UniversityAve,
Riverside,CA92521,USA;[email protected]
* Correspondence:[email protected];Tel.:[phone]
(cid:1)(cid:2)(cid:3)(cid:1)(cid:4)(cid:5)(cid:6)(cid:7)(cid:8)(cid:1)
(cid:1)(cid:2)(cid:3)(cid:4)(cid:5)(cid:6)(cid:7)
Received:24May2018;Accepted:11June2018;Published:15June2018
Abstract: The North China District (NCD) is one of the main grain production regions in China.
Thedoublecroppingsystemofirrigationhasbeenleadingtothegroundwatertabledeclineatthe
speedof1–2mperyear. ClimatechangeleadstouncertaintysurroundingthefutureoftheNCD
agriculturalsystem,whichwillhavegreateffectsoncropyieldsandcropwaterdemands. Inthis
research,theMeteorologicaldatasetfrom54weatherstationsitesovertheperiod1960–2015were
collectedtoquantifythelong-termspatialandtemporaltrendsofmeteorologicaldata,including
daily minimum temperature (T ), maximum temperature (T ), precipitation, solar radiation,
min max
reference evapotranspiration (ET ), and aridity index (AI). The results show that the long-term
0
wheat and maize growing season and annual average air temperatures (T and T ) showed
min max
strongnorthtosouthincreasingtrendsthroughouttheNCD.Theaverageannualprecipitationwas
632.9mmacrosstheNCD,morethan70%ofwhichwasconcentratedinthemaizegrowingseason.
The regional average annual ET was 1026.1 mm, which was 531.2 and 497.4 mm for the wheat
0
andmaizegrowingseason, respectively. Theregionalprecipitationdecreasedfromnorthwestto
southeastineachgrowingseasonandannualtimescale. Thefunnelareashavelowerprecipitation
andhigherET thantheregionalaverage,whichmayleadtotheminingofthegroundwaterfunnel
0
area. TheregionalaverageannualAIis0.63,whichliesinthehumidclass. Fortemporalanalysis,
theregionalaveragetrendsinannualT ,T ,solarradiation,ET ,precipitation,andAIwere
min max 0
0.37◦C/10a,0.15◦C/10a,−0.28MJ/day/m2/10a,−2.98mm/10a,−12.04mm/10a,and0.005/10a,
respectively. Theincreasingtrendoftemperatureandthedecreasingtrendofsolarradiationmay
haveanegativeeffectontheregionalfoodsecurity.ThefunnelareaAIshowedasignificantincreasing
trendforthewinterwheatgrowingseasonandadecreasingtrendformaize,whichindicatedthat
moreirrigationwillbeneededforthemaizegrowingseasonandthewinterfallowpolicymaylead
totheincreasingtrendprecipitationbeingwasted. Analyzingthegrowingseasonandtheannual
meteorologicalelementsofthespatiotemporaltrendscanhelpusbetterunderstandtheinfluenceof
climatechangeonthenaturalresourcesandagriculturaldevelopmentinboththepastandthefuture,
andwillprovideuswithinvaluableinformationforthemodificationofcroppingpatternstoprotect
theregionalandnationalwaterandfoodsecurity.
Keywords: climatechange;trendanalysis;climatevariables;watersecurity;NorthChinaDistrict
Water2018,10,789;doi:10.3390/w10060789 www.mdpi.com/journal/water
Water2018,10,789 2of19
1. Introduction
Globalwarming,becomingarealandpressingproblem,isacceptedwidelyasthemainfeature
ofclimatechange. TheFifthAssessmentReportoftheUnitedNationsIntergovernmentalPanelon
Climate Change [1] showed that the period of 1983–2012 was the warmest 30 years over the last
800yearsfortheaverageannualNorthernHemispheretemperatures. Climatechangehasagreat
effectonthewaterdemandofplantsandcrops,thewaterconsumption,andthegrainyielddueto
increasedtemperaturesandvariablerainfall,especiallyinpooradaptabilityareas[2,3].Climatechange
isexpectedtoincreasinglyaffectcropyields[4–8]andcropwaterrequirements[9];whichimpacton
theregionalandglobalfoodsecurityandwatersecurity.
TheNorthChinaDistrict(NCD)isoneofthemostimportantgrainproducingareasinChina.
Itproduces7.31×107tonofwheatand5.73×107tonofmaizewith1.17×107haand10.00×107ha
area,whichwas56.7%ofthenation’swheatyieldsand26.1%ofitsmaizeyields[10]. Thus,NCDplays
animportantpartinensuringChina’sfoodsecurity.Thewinterwheat-summermaizecroppingsystem
isthemaincroppingsystemintheNorthChinaDistrict. Thisdoublecroppingsystemneedsabout
800–1100mmwatereveryyear[11],whiletheaverageannualprecipitationisaround500–600mmat
thesametime[12],thus,irrigationisneededinordertomeettheoptimalyield. However,thewater
resourcesarelimitedinthisareaandmostoftheirrigationwaterwasfromundergroundwater,which
causesadramaticdeclineofthewatertable. Inaddition,irrigationisrelatedtothedistributionof
theprecipitationcropwaterrequirement. Thus,assessingthespatiotemporalchangesofthedatais
beneficialforoptimizingirrigationduringthewheat-maizecroppingsystemandregionalfoodand
watersecurity.
Inthecontextofglobalwarming, thetrendofthetemperaturewasfoundtohaveincreasedfor
mostoftheworld[13–15]. Whilethesolarradiation[16],precipitation[17,18],ET0[19],andAI[20,21]
werefoundtohaveincreasedordecreasedindifferentregionsoftheworld.Earlierregionalstudiesthat
performedatrendanalysisofthemeteorologicalelementsaremorefocusedononeclimateelementin
anannualtimescale.However,cropsareaffecteddifferentlybythemeteorologicalelementsindifferent
growingseasons. Therefore,assessingmeteorologicalelementtrendsbothspatiallyandtemporallyin
differentgrowingseasonsandwithanannualtimescalemayhelpusbetterunderstandhowclimate
changeimpactsregionalfoodandwatersecurity. Theaimsofthepresentstudyare: (1)toanalyzethe
spatialmeteorologicalelementsindifferentgrowingseasonsandwithannualtimescale;(2)tocalculatethe
meteorologicalelementtrendsindifferentgrowingseasonsandwithanannualtimescale;(3)todiscuss
thepossibleimpactsofclimatechangeontheregionalwaterandfoodsecurityofNCD.
2. MaterialsandMethods
2.1. StudyAreaDescription
Thisstudyareaconsistsoffiveprovincesandcities(Beijing,Tianjin,Hebei,Shandong,Henan),
whichisagenerallysimilarareatotheNorthChinaPlain(Figure1). Theregionislocatedbetweenthe
longitudeof31and43◦Nanditisamonsoonclimatewithmediumlatitudes. Theannualprecipitation
isaroundanaveragevalueof500to600mm[22].Inthisstudy,eachyearwasdividedintotwoperiods,
comprising the winter wheat growing season and the summer maize growing season. Generally,
thewinterwheatgrowingseasonisfromOctobertoJuneandthesummermaizegrowingseasonis
fromJulytoSeptember.
Water2018,10,789 3of19
Figure1.LocationofNorthChinaDistrictandtheweatherstationsite.
2.2. InputDataSources
Thedailymeteorologicaldatafrom54weatherstationsitesacrosstheNCDcontainsthemaximum
airtemperature,minimumairtemperature,averagerelativehumidity,precipitation,brightsunshine
hours,andwindspeed,whichwereobtainedfromtheChinaMeteorologicalDataSharingService
System. Most selected weather stations possess data from 1960 to 2015. The Mengjin, Qingdao,
and Changdao weather stations possess data from 1961 to 2015, and the Weihai station, from
1940 to 2015 (Figure 1). The groundwater contiguous groundwater funnel area is collected from
theAnnouncementofOutlawsArea,RestrictedArea,andProhibitedAreaofeachprovince.
2.3. CalculationofET andAridityIndex
0
Referencecropevapotranspiration(ET )expressestheevaporatingpoweroftheatmosphereata
0
specificlocationandperiod. ET isaclimateparameterandisonlyaffectedbyclimateparameters.
0
There are many equations to estimate ET , such as the Hargreaves Equation [23], Priestley-Taylor
0
Equation[24], Penman-MonteithEquation[25], andFAOPenman-MonteithEquation[26]. Inthis
study,weusetheFAOPenman-MonteithEquation,whichwasmostwidelyusedtoestimatetheET
0
aroundtheworld. TheFAOPenman-MonteithEquationcanbeexpressedas:
0.408(cid:52)(R −G)+γ 900 µ (e −e )
ET = n T+273 2 s a (1)
0 (cid:52)+γ(1+0.34µ )
2
whereRnisthenetradiationatthecropsurface(MJ/m2/day),whichwascalculatedfromthesunshine
hoursusingtheequationdevelopedbyRietveld[27]; Gisthesoilheatfluxdensity(MJ/day/m2);
T is the air temperature at a 2 m height (◦C); µ is the wind speed at a 2 m height (m/s); e is the
2 s
saturationvaporpressure(kPa);e istheactualvaporpressure(kPa);∆istheslopeofthesaturation
a
vaporpressure-temperaturecurve(kPa/◦C);andγisthepsychometricconstant(kPa/◦C).TheET
0
wascalculatedbytheSIMETAWmodel,whichwasdevelopedbasedontheFAOPenman-Monteith
equation[28],andhaswidelyusedinNCD[29–32].
Aridity Index (AI) is an index for assessing drought risk in the crop growing period,
whichconsidersthetotalprecipitationandevapotranspiration. Therearemanymethodstoevaluate
Water2018,10,789 4of19
anAI.Inthisresearch,theAIwascalculatedaccordingtotheUNESCO[33]method,whichcanbe
computedas
P
Aridityindex(AI) = (2)
ET
0
wherePistheprecipitation(mm)thatisobtainedfromeachweatherstationsite.TheannualPandET
0
ofthewinter-wheatandsummer-maizegrowingseasonisobtainedbysummingthedailyET rasters.
0
Anysitecanbeclassifiedintothefivecategoriesofhyper-arid,arid,semi-arid,dry-subhumid,andhumid
accordingtothemagnitudeoftheAI,whichisclassifiedbythenumeric0.03,0.2,0.5and0.65.
2.4. Mann-KendallTrendTestandSlopEstimator
TheMann-Kendall(M-K)isoneofthewidelyusednon-parametrictestsfordetectingsignificant
trends and slope in time series [34,35]. There are many methods to detect the significant trend of
theconsideredmeteorologicalelementsintimeseriesoftheselectedstations,theMann-Kendalland
Spearman’sRhotestaremostwidelyused. Manytheseshavediscussedthedifferencebetweenthe
Mann-Kendall and Spearman’s Rho test [36–38], the results show a similar performance of these
twomethods. InthisstudyweusedtheM-KmethodtodetectthesignificanttrendandSen’smethod
todetecttrendmagnitudes,whichweresuggestedbytheWorldMeteorologicalOrganization[39].
TheadvantageoftheMann-Kendalltrendtestisthatitisnotcontaminatedbyasmallnumberof
outliersandthesampledoesnotneedtorankwithinacertaindistribution,soitiswidelyusedto
estimatestreamflow,precipitation,andtemperaturetrend. TheM-KteststatisticSiscalculatedas
S=∑n−1∑n sgn(x −x) (3)
i=1 j=i+1 j i
where n is the number of data points, x and x are the data values in the time series i and j (j > i),
i j
respectively,andsgn(x −x)isthesignfunctionas
j i

 +1, ifxj−xi >0
(cid:0) (cid:1)
sgn x −x = 0, ifx −x =0 (4)
j i j i
 −1, ifx −x >0
j i
Thevarianceiscomputedas
n(n−1)(2n+5)−∑m t(t −1)(2t +5)
Var(S)= i=1 i i i (5)
18
wherenisthenumberofdatapoints,misthenumberoftiedgroups,andt denotesthenumberofties
i
ofextenti. Atiedgroupisasetofsampledatahavingthesamevalue. Incaseswherethesamplesize
n>10,thestandardnormalteststatisticZ iscomputedas
S
 √S−1 , ifS>0
 Var(S)
Z = 0, ifS=0 (6)
S
 √S−1 , ifS<0
Var(S)
PositivevaluesofZ indicateincreasingtrendswhilenegativeZ valuesshowdecreasingtrends.
S S
The testing trends are done at the specific α significance level. When |ZS| > Z(1−α⁄2), the null
hypothesisisrejectedandasignificanttrendexistsinthetimeseries. Z(1−α⁄2) isobtainedfromthe
standardnormaldistributiontable. Inthisstudy,asignificancelevelofα=0.05wasused. Atthe5%
significancelevel,thenullhypothesisofnotrendisrejectedif|Z |>1.96.
S
Water2018,10,789 5of19
Sen’s slope estimator [40] can be used for estimating time series data trend magnitudes.
Theadvantageofthismethodisthatitlimitstheinfluenceoftheoutliersontheslopeincomparison
withlinearregression. ThisestimateisgivenbytheTheil-SenEstimatoras
(cid:18)x −x (cid:19)
j z
b=Median (7)
j−1
wherex andx arethedatavaluesattimesjandz(j>z),respectively;bdenotestheannualincrement
j z
underthehypothesisofalineartrend. bprovidestherealslopeofthetendencyandcanvaryslightly
fromtheslopeobtainedfromlinearregression. Sen’sslopeestimatoriswidelyusedtoestimatethe
magnitudesoftrendsintheclimaticfactors.
Inthisstudy,theM-Ktestisusedtoanalyzethetrendsofairtemperature,dailytemperature
range,solarradiation,precipitation,ET ,andAIintheNCD.TheM-Ktrendtestandslopestimatorof
0
eachweatherstationsiteiscalculatedbyMATLABR2017b.
3. Results
3.1. SpatialandTemporalVariationofTemperature
3.1.1. SpatialAnalysis
The temperature significantly influences crop yields and crop water use. Global warming has
increasedtheinstabilityofagricultureandthefluctuationofcropyields.Theincreaseintemperaturehas
delayedautumnfrostsandledtolatersowingandearlierharvestingofthewinter-wheatgrowingseason
intheNorthChinaDistrict[41].Moreover,higherdailymaximtemperatureshavealsocausedheatand
droughtstressduringthewheatandmaizegrowingseason,whichhighlynegativelyaffectedthecrop
yields[2,42–44].Ontheotherhand,higherdailyminimumtemperatureshaveacceleratedtherespiration
ofcrops,whichthus,havecausedyieldloss.Furthermore,temperatureisoneoftheimportantinputdata
formanyhydrologyandcropmodels.Thus,itisnecessarytoevaluatetheregionaltemperaturetrend
bothspatiallyandtemporally.
During1960–2015,theaveragedailyminimumtemperature(T )waslowerinthenorthcompared
min
tothesouththroughthewheatandmaizegrowingseason(Figure2a–c). Thefigureindicatesthatthe
averagedailyT ofthemaizegrowingseasonishigherthanwheat,whichwasabovezerooverthe
min
studyarea. ThewheatandmaizegrowingseasonT rangedfrom−9.0to6.6◦Candfrom10.3to
min
22.1 ◦C; and from −2.6 to 11.8 ◦C when averaged annually. The average daily T of the Henan
min
provinceisthehighest,whichis4.6,20.3,and9.8◦Cforwheat,themaizegrowingseason,andannually;
theHebeiprovincewasthelowest(Table1). SimilartoT ,theHebeiandHenanprovincesarethe
min
highestandlowestT forthethreeperiods.Theaveragedailymaximumairtemperature(T )ofthe
max max
studyregionhasasouth-to-northdecreasingtrendforthewheatgrowingseasonandannually,ranging
from−2.6to11.5◦Cand3.7to16.2◦C.Unlikethewheatgrowingstage,theT formaizehasshowna
max
middle-surrounddecreasingtrendintheNPD,whichrangedfrom22.3to30.6◦C.
Table1.Theprovince-wide,funnelareaandregionalstatisticsforwheat,maize,andtheannualtemperature.
Beijing Tianjin Hebei Henan Shandong FunnelArea Regional
T
min
Wheatgrowingseason 1.3 2.7 −1.2 4.6 2.8 2.4 1.9
Maizegrowingseason 19.3 20.9 17.4 20.3 19.3 19.9 19.0
Annual 7.3 8.8 5.0 9.8 8.3 8.2 7.6
Tmax
Wheatgrowingseason 12.3 11.8 10.8 15.1 11.8 13.5 12.5
Maizegrowingseason 29.4 28.9 28.2 29.9 27.3 29.9 28.6
Annual 18.0 17.5 16.6 20.1 17.0 18.9 17.8
Water2018,10,789 6of19
Figure2. Thespatialdistributionof(a–c)long-termaverageminimumairtemperature(T )and
min
(d–f)maximumairtemperature(Tmax)onthewinter-wheatgrowingseason,summer-maizegrowing
season,andannualbasis.
3.1.2. TemporalAnalysis
Thetrendsinthewinter-wheat, summer-maizegrowingseason, andtheannualtimescalefrom
1961to2015forT ,andT areshowninFigure3. Themapsalsoshowthestatisticalsignificance
min max
oftheweatherstationtrendsduringtheresearchperiod. Thedifferentcolorcirclesindicatethevalue
ofthetemporaltrendsandthepositiveandnegativenumbersrepresenttheincreasinganddecreasing
trend. The black points in the maps indicate that the trend quantified for the weather station was
statisticallysignificantatα=0.05,whiletheredrosepointrepresentsthesignificanceoftheincreasing
trends,whichdistinguishesthemfromthestationwithinsignificanttrendsthatareassignedwithawhite
point.Table2showsthetrendstatisticsforthegrowingseasonsandtheannualtimescaleT andT
min min
onaprovince-wide,funnel,andregionalbasis. Figure3a–cshowthatmostofthestudyregionsshow
significantincreasingtrendsinthewheatandmaizegrowingseasonsandtheannualT . Onlyone
min
weatherstationshowsadecreasingtrendinthewinter-wheatgrowingseasonandtheannualtimescale.
Itwashigherinthewheatgrowingseasonthaninthemaizegrowingseason,withthehighestT trend
min
provincebeingBeijinginthewheatgrowingseason(Table2).Theregionalaverageincreasingtrendsin
thewheatandmaizegrowingseasonsandtheannualT were0.44,0.24,and0.37◦C/10a,respectively.
min
LikeT , thetrendsforboththewinter-wheatgrowingseasonandtheannualtimescaleT were
min max
positive,whilethecentralpartsoftheNCDshownon-significantincreasingtrendsforthewheatgrowing
seasonandtheannualtimescale(Figure3d–f).Ontheotherhand,themainpartsoftheHenanprovince
showdecreasingtrendsforthemaizegrowingseason,whichwasdifferenttotheT . Table2shows
min
thattheaverageT intheHenanprovinceforthemaizegrowingseasonistheonlydecreasinggrowing
max
seasonT ,province-wide,whichwas−0.08◦C/10a.
max
Water2018,10,789 7of19
Table2. Theprovince-wide, funnelarea, andregionalstatisticsforthewheat, maize, andannual
temperaturetrends.
Beijing Tianjin Hebei Henan Shandong FunnelArea Regional
T (◦C/10a)
min
Wheatgrowingseason 0.68* 0.26* 0.53* 0.37* 0.41* 0.53* 0.44
Maizegrowingseason 0.53* 0.14* 0.32* 0.17* 0.21* 0.28* 0.24
Annual 0.64* 0.22* 0.46* 0.30* 0.34* 0.42* 0.37
Tmax(◦C/10a)
Wheatgrowingseason 0.25* 0.36* 0.23* 0.23* 0.24* 0.20* 0.23
Maizegrowingseason 0.19* 0.23* 0.12* −0.08 0.08 0.02 0.04
Annual 0.25* 0.32* 0.17* 0.10* 0.18* 0.11* 0.15
*Indicatesthatthetemporaltrendissignificant(p<0.05).
Figure3.Thespatialdistributionandstatisticalsignificanceofthetrendsin(a–c)Tminand(d–f)Tmax
onthewinter-wheatgrowing-season,thesummer-maizegrowing-season,andtheannualbasis.
3.2. SpatialandTemporalVariationofSolarRadiation
3.2.1. SpatialAnalysis
Solarradiationisthemostimportantsourceofrenewableandenvironmentalenergyontheplanet.
Becauseofitsabundance,solarenergycanplayaprominentroleinourfutureenergyandreducethe
dependencyonfossilfuel. Inaddition,manyresearchershavediscoveredtheimportanceandpositive
effectofsolarradiationoncropyield[45,46].During1960to2015,theaveragedailysolarradiationwas
higherinthenorthcomparedtothesouthforthewheatandmaizegrowingseasonsandtheannual
timescale.Itvariedbetween12.5and14.9MJ/day/m2inthewheatgrowingseason(Figure4a),whichwas
lowerthanthemaizegrowingseason(Figure4b).Table3showsthedescriptivestatisticsfortheaverage
dailysolarradiation.TheShandongprovincehadthehighestsolarradiationforthewheatgrowingseason
fromthefiveprovinces.While,formaizegrowingseason,theTianjinandHebeiprovinceswerethehighest.
Therewaslittledifferenceinthefunnelareaandregionalaveragedailysolarradiation.
Water2018,10,789 8of19
Figure4.Thespatialdistributionofthelong-termaveragedailysolarradiation(R)for(a)thewinter-wheat
growingseason;(b)thesummer-maizegrowingseasonand(c)theannualbasis.
Table3. Theprovince-wide, funnelarea, andregionalstatisticsforwheat, maize, andtheannual
averagedailysolarradiation.
R(MJ/day/m2) Beijing Tianjin Hebei Henan Shandong FunnelArea Regional
Wheatgrowingseason 13.6 13.4 13.6 12.9 14.0 13.4 13.5
Maizegrowingseason 18.0 18.2 18.2 17.1 18.1 17.7 17.8
Annual 15.5 15.5 15.6 14.7 15.8 15.3 15.4
3.2.2. TemporalAnalysis
MostoftheNCDdailysolarradiationshowsasignificantdecreasingtrendforthewheatgrowing
season,exceptforthewestpartoftheHenanprovincewheretheQinlingMountainsexist(Figure5a).
Allweatherstationsolarradiationshowedsignificantdecreasingtrendsforthemaizegrowingseason,
which varied between −0.87 and −0.16 MJ/day/m2/10a (Figure 5b). All provincial solar radiation
showedsignificantdecreasingtrendsforthewheatandmaizegrowingseasonsandtheannualtimescale.
Thetendencyofthemaizegrowingseasonwashigherthanthatofthewheatgrowingseasonandthe
funnelareawashigherthantheregionalscaleforboththewheatandmaizegrowingseasons(Table4).
Theannualsolarradiationdecreasedduring1960to2015atarateof−0.35and−0.28MJ/day/m2/10ain
thefunnelareaandtheregionalbasis,respectively.
Figure5.Thespatialdistributionandstatisticalsignificanceofthetrendsinthedailysolarradiation(R)
on(a)thewinter-wheatgrowingseason;(b)thesummer-maizegrowingseasonand(c)annualbasis.
Table4.Theprovince-wide,funnelarea,andregionalstatisticsforthewheat,maize,andannualdaily
solarradiationaveragetrends.
R(MJ/day/m2/10a) Beijing Tianjin Hebei Henan Shandong FunnelArea Regional
Wheatgrowingseason −0.18* −0.28* −0.19* −0.20* −0.20* −0.25* −0.20*
Maizegrowingseason −0.54* −0.65* −0.42* −0.58* −0.4* −0.56* −0.48*
Annual −0.30* −0.43* −0.26* −0.33* −0.26* −0.35* −0.28*
*Indicatesthatthetemporaltrendissignificant(p<0.05).
Water2018,10,789 9of19
3.3. SpatialandTemporalVariationofPrecipitation
3.3.1. SpatialAnalysis
The quantification of the precipitation spatiotemporal variability in any region is important
to enhance the capacity and level of the agriculture drought monitoring, prediction, and impact
assessment, especially in arid and semi-arid regions such as NCD where the groundwater table
declinesby1–2mperyear[47]. Duringtheperiodof1960–2015,theaverageprecipitationdecreased
fromthenorthwesttothesoutheastineachofthegrowingseasonsandtheannualtimescale(Figure6).
Thedescriptivestatisticsofthewinter-wheatandsummer-maizegrowingseasonsandtheannual
precipitation for the province-wide area are presented in Table 5. Regionally, the wheat growing
season precipitation varies from 83.2 to 461.6 mm, which, for the maize growing season and the
annual timescale, was from 297.2 to 609.0 mm and 380.6 to 1070.0 mm, respectively. Averaged by
province,thevalleywheatandmaizegrowingseasonprecipitationswereobservedinBeijingand
Hebei,respectively,whilethemaximumvalueswereobservedinHenanandShandong. Theminimum
valueannualprecipitationwasobservedinHebeitoo,whichwas520.7mm,whilethepeakannual
precipitationwasobservedintheHenanprovince. Theregionalmeanprecipitationwas632.9mm,
whileforthefunnelarea,itwas570.4mm.
Figure6.Thespatialdistributionofthelong-termaverageprecipitation(P)for(a)thewinter-wheat
growingseason;(b)thesummer-maizegrowingseasonand(c)theannualbasis.
Table5. Theprovince-wide, funnelarea, andregionalstatisticsforthewheat, maize, andannual
averageprecipitations.
P(mm) Beijing Tianjin Hebei Henan Shandong FunnelArea Regional
Wheatgrowingseason 99.7 109.8 108.3 260.8 185.6 138.7 176.4
Maizegrowingseason 445.3 441.4 412.5 470.7 507.4 431.7 458.0
Annual 545.0 551.2 520.7 730.8 688.1 570.4 632.9
3.3.2. TemporalAnalysis
Thetrendsinthewinter-wheatandsummer-maizegrowingseasonsandtheannualprecipitation
havebeencomputedfortheperiod1960–2015usinganon-parametrictest(Figure7).Thewinter-wheat
growingseasonprecipitationtrendsarepositiveforthestudyareaandmorethan44.4%oftheweather
stationshowedasignificantincrease. AsshowninFigure7a,thetrendoftheprecipitationwashigher
intheeastoftheNCDthanthewest. Unlikethewheatgrowingseasonprecipitationtrends,themaize
growing season precipitation trends show negative trends in the majority of the NCD and there
wereonly9weatherstationsshowingsignificantdecreasingprecipitationtrends. Itisnoteworthy
thatFigure7bshowsthatmostincreasedweatherstationwasconcentrationintheHenanprovince.
Figure7cshowsthenatureandsignificanceoftheannualprecipitationtrendsoverthestudyarea.
There were 31 weather stations showing negative trends in annual precipitation, which is a total
Water2018,10,789 10of19
proportionof57.4%. Mostoftheweatherstationprecipitationsshowednon-significanttrendsinthe
studyregion, withonlyoneweatherstationshowingdominantpositivetrends, andtwoshowing
dominantnegativetrends. Thedescriptivestatisticsfortheprecipitationtrendwhenaveragedfor
province-wide,funnelarea,andregionalarelistedinTable6. Themagnitudeofthewheatgrowing
seasonprecipitationtrendswascalculatedas6.94mmperdecadeonthefunnelareaaveragebasis,
whichwashigherthanthestudyregion. Additionally,theprecipitationtrendwaslowerinthefunnel
areathanintheregionalbasisforthemaizegrowingseason,whichwere−14.72and−9.63mm/10a,
respectively. Alltheprovincesshowedapositivetrendforthewheatgrowingseasonandanegative
trend for the maize growing season, with the Henan province being the only one out of the five
provinces to show a positive trend for the annual precipitation. Table 6 shows that the annual
precipitationdecreasedduringtheperiodof1960–2015atarateof−5.69and−2.98mm/10ainthe
funnelareaandtheregionalbasis,respectively.
Figure7.Thespatialdistributionandstatisticalsignificanceofthetrendsintheprecipitation(P)ofthe
(a)winter-wheatgrowingseason;(b)thesummer-maizegrowingseasonand(c)theannualbasis.
Table6. Theprovince-wide, funnelarea, andregionalstatisticsforthewheat, maize, andannual
precipitationaveragetrend.
P(mm/10a) Beijing Tianjin Hebei Henan Shandong FunnelArea Regional
Wheatgrowingseason 12.16* 8.46* 6.84* 4.01 6.69 6.94* 6.85*
Maizegrowingseason −19.38 −20.53* −17.32* −2.43 −17.65 −14.72* −9.63*
Annual −4.95 −12.59 −6.93 6.05 −9.68 −5.69 −2.98
*Indicatesthatthetemporaltrendissignificant(p<0.05).
3.4. SpatialandTemporalVariationofReferenceEvapotranspiration
3.4.1. SpatialAnalysis
ET isanimportantpartofthehydrologicalcycleandoccupiesamoreimportantproportion
0
intheregionalwaterresourcebalance. Differentfromtheprecipitationeffectsoftheregionalwater
resourcesupply,theET mainlyaffectsconsumption,especiallythecropwaterrequirement.Theresults
0
showthatthemeanwinter-wheatgrowingseasonET overa56-yearperiodvariedfrom420.3mm
0
to670.0mmacrosstheNCD(Figure8a). Averagedbyprovince, thevalleywheatgrowingseason
ET wasobservedintheHebeiprovince,whichwas508.6mm(Table7),whilethemaximumvalues
0
wereobservedinBeijing. ThefunnelareaandregionalET were545.9and531.2mmforthewheat
0
growingseason.Figure8bshowsthatthecentralpartoftheNCDET washigherthanthesurrounding
0
parts for the maize growing season. The average funnel area and regional maize growing season
ET were513.8and497.4mm,respectively,whichwaslowerthanthatofthewheatgrowingseason.
0
TheJinanweatherstationhadthemaximumannualET ,whichwas1240.9mm. TheannualET from
0 0
thelowesttothehighest,province-wide,areasfollows: Hebei,Henan,Shandong,Beijing,andTianjin.