ImpactsofClimaticChangeonReferenceCrop ...downloads.hindawi.com/journals/amete/2020/3156460.pdf · 2.2.ClimateTrendAnalyses.Climate tendency rate is a widelyusedmethodforstudyingclimatechange,whichis

Research ArticleImpacts of Climatic Change on Reference CropEvapotranspiration across Different Climatic Zones ofNingxia at Multi-Time Scales from 1957 to 2018

Ziyang Zhao1 Hongrui Wang 1 Cheng Wang2 Wangcheng Li3 Hao Chen1

and Shuxin Gong1

1Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology College of Water SciencesBeijing Normal University Beijing 100875 China2Environment Science Division Argonne National Laboratory Lemont IL 60439 USA3School of Civil and Hydraulic Engineering Ningxia University Yinchuan 750021 China

Correspondence should be addressed to Hongrui Wang henrywangbnueducn

Received 22 August 2019 Revised 23 December 2019 Accepted 1 February 2020 Published 12 June 2020

Academic Editor Nir Y Krakauer

Copyright copy 2020 Ziyang Zhao et al is 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

e impact of global climate change on agroecosystems is growing affecting reference crop evapotranspiration (ET0) andsubsequent agricultural water management In this study the climate factors temporal trends the spatiotemporal variation andthe climate driving factors of ET0 at different time scales were evaluated across the Northern Yellow River Irrigation Area (NYR)Central Arid Zone (CAZ) and Southern Mountain Area (SMA) of Ningxia based on 20 climatic stationsrsquo daily data from 1957 to2018 e results showed that the Tmean (daily mean air temperature) Tmax (daily maximum air temperature) and Tmin (dailyminimum air temperature) all had increased significantly over the past 62 years whilst RH (relative humidity) U2 (wind speed at2m height) and SD (sunshine duration) had significantly decreasing trends across all climatic zones At monthly scale the ET0was mainly concentrated from April to September And at annual and seasonal scales the overall increasing trends were morepronounced in NX NYR and SMA while CAZ was the opposite For the spatial distribution ET0 presented a trend of rising firstand then falling at all time scales e abrupt change point for climatic factors and ET0 series was obtained at approximately 1990across all climatic zones and the ET0 had a long period of 25a and a short period of 10a at annual scale while it was 15a and 5a atseasonal scale RH and Tmax were the most sensitive climatic factors at the annual and seasonal scales while the largest con-tribution rates were Tmax and SD is study not only is important for the understanding of ET0 changes but also provides thepreliminary and elementary reference for agriculture water management in Ningxia

1 Introduction

Evapotranspiration (ET) is not only an important compo-nent of the hydrological cycle but also essential for un-derstanding land surface processes in climatology [1] Andthe productivity is closely related to actual ET in agriculturalresearch thus ET has important implications for improvinglocal agricultural water management [2] However the dataof ET are insufficient and limited due to lack of monitoringtherefore many scholars use a variety of methods to studyET from different perspectives e reference evapotrans-piration (ET0) which is defined as the potential ET of grass

can be used to prepare input data for hydrology models (egSWATmodel) schedule irrigation systems and calculate theactual evapotranspiration (ET) in a basin or a region [3]is is because the reference evapotranspiration (ET0) hassufficient moisture and it is not affected by soil factorsLinking ET0 to a specific surface (grass) can provide areference for ETon other surfaces [2] In order to revise thereference evapotranspiration (ET0) calculation criteria thePenmanndashMonteith method has been recommended by Foodand Agriculture Organization (FAO) Irrigation andDrainage Paper No 56 [4] And due to the easy calculationand ease of data access the FAO56 has become one of the

HindawiAdvances in MeteorologyVolume 2020 Article ID 3156460 23 pageshttpsdoiorg10115520203156460

bestselling and most cited publications in the field of waterresources management up to now [5]

Climate change has produced profound impact on so-ciety and environment Many global issues are likely to beaffected by climate change such as extreme hydrologicalcycles food security biodiversity loss and water scarcity[6 7] ET0 which is the link between water balance andsurface energy balance is considered as a significant indi-cator for the climate change and the water cycle [8] And aspart of changing climate ET0 trends directly affect globaland regional water resources In order to assess agriculturaldemand hydrological cycle and ecological changes moreeffectively a clear understanding of historical trends andfuture changes of ET0 and its response to climate change areessential [9 10]

Climate change is intensifying and mainly characterizedby a significant increase in temperature due to anthropo-genic emissions of active greenhouse gases (such as CO2CH4 and N2O) [11] According to the report of IPCC theaverage temperature of global land and sea surface showed alinear upward trend of rising by 085degC from 1880 to 2012the average temperature increased by 078degC from 1850 to1990 and the average temperature was projected to increaseby 15degC for the end of 21st century [12] Since the risingtrend of air temperature has always existed ET0 has beenincreasing globally in the past few decades and reported inthe Republic of Moldova [13] Greece [14] Iran [15] andFrance [16] However the ET0 is declining in some areas atthe same time such as northwest China [17] New Zealand[18] United States [19] and southwestern China [20]erefore an increase in global temperature may not lead tothe rise in ET0 under any circumstances which is called asthe ldquoevaporation paradoxrdquo To explain this problem manyscholars have done a lot of research by quantifying theinfluence of climate factors on ET0 ey concluded thatchanges in radiation and wind speed dominated the impactof ET0 in some regions [21 22] while water vapor indicatorsand temperature were the main reasons for changes in otherregions [23 24] ese contradictory explanations and re-cent studies show that it is necessary to pay attention to therelationship between ET0 and climate factors in the futureresearch

e Ningxia Hui Autonomous Region one of the im-portant parts of ldquothe Belt and Roadrdquo is located in thetemperate continental arid and semiarid areae east westand north are surrounded by Maowusu Tengger andUlanbu deserts respectively And the desertification landarea accounts for 5368 of the total area [25] e amountof water resources is limited and the demand for water hasincreased dramatically thus there is a sharp contradictionbetween economic development and limited water resources[26] In the past decade based on the Ningxia Water Re-sources Bulletin the water consumption has increased bytwo times and agriculture is the largest water-consumingsector with more than 85 of water supplied for irrigation[25] In addition water resource is being directed from theagricultural sector to industry and other sectors thus foodsecurity and agricultural water security are facing severepressure in Ningxia ET0 is a key component of the water

cycle process and plays an important role in assessing waterresources shortage [27] Consequently the analysis of ET0and its response to climate change can not only help tounderstand climate change but also improve watermanagement

In general the main objectives of this study are asfollows (1) to discuss the interannual variation trend ofclimate factors (2) to analyze monthly seasonal and an-nual ET0 of spatiotemporal changes (3) to explore theabrupt change and periodicity of ET0 series and (4) toquantify the impact of climate factors on ET0 change andidentify dominant factors is research will help to raiseawareness of climate change and provide valuable referencefor researchers and policymakers to guide regional watermanagement agricultural production and conservation ofthe environment

2 Materials and Methods

To analyze the changes in reference evapotranspiration(ET0) over Ningxia at multi-time scales an approachframework for ET0 analysis pattern selection was proposede framework mainly included the climate trend analysisET0 spatiotemporal analysis abrupt change and periodicityanalysis and ET0 influence factors analysis (Figure 1)

21 Study Area and Climate Data e Ningxia Hui Au-tonomous Region (NX) (35deg14primesim39deg23primeN 104deg17primesim107deg39primeE)is part of the Yellow River basin and is located among theAlxa Plateau the North China Plateau and the QilianMountains folds e area consists of Northern Yellow Riverirrigation area (NYR) Central Arid Zone (CAZ) andSouthern Mountain Area (SMA) and has a total area of66times104 km2 [28] (Figure 2) is region faces a seriousshortage of water resources and a huge contradiction be-tween water supply and demand And it has also led toserious environmental problems due to excessive use ofwater resources including soil erosion water pollution anddeterioration of the ecological environment [29] Moreoverthe seasons are divided into climate standards namelyspring from March to May summer from June to Augustautumn from September to November and winter fromDecember to February [30] In addition the statistical sig-nificance of the linear trend and correlation analysis in thisstudy was labeled as significance levels plt 005 (lowast) andplt 001 (lowastlowast) unless otherwise stated

In Ningxia 20 climate stations with 62 years (from 1957to 2018) of climate data were selected (Table 1) e climatedata including daily minimum air temperature (Tmin degC)daily mean air temperature (Tmean degC) daily maximum airtemperature (Tmax degC) sunshine duration (SD h) relativehumidity (RH ) and wind speed at 2m height (U2 ms)were downloaded from the National Meteorological Infor-mation Center (httpdatacmacn) e datasets are avail-able because the missing data rate handled by quality controlis less than 01 and some missing points were interpolatedbased on the regression relationship with those of the ad-jacent stations

2 Advances in Meteorology

22 Climate Trend Analyses Climate tendency rate is awidely used method for studying climate change which issimple and effective It is common practice to establish alinear regression equation

xi a + bti (i 1 2 middot middot middot n) (1)

where xi is the climate factors with a sample size of n ti is thetime corresponding to xi b is the regression coefficient and ais the regression constant b is the annual change rate ofclimate factors while its symbol indicates the change direc-tion When bgt 0 it indicates an increasing trend with timewhen blt 0 it indicates a decreasing trend with time b times 10 isknown as the climate tendency rate indicating the change rateof climate factors every 10 years

23 ET0 Estimation Analyzing the spatiotemporal charac-teristics of ET0 is helpful to understand the distribution andmanagement of agricultural water requirements e Pen-manndashMonteith (PM)method which is recommended by theFood and Agriculture Organization (FAO) has been widelyused for calculating ET0 [31] e model was used in this

study to estimate daily ET0 and cumulative monthly sea-sonal and annual ET0

ET0 0408Δ Rn minus G( 1113857 + c(900(T + 273))u2 es minus ea( 1113857

Δ + c 1 + 034u2( 1113857

(2)

where ET0 is the daily reference crop evapotranspiration(mm middot dayminus1) Δ is the slope of vapor pressure curve(kPamiddotdegCminus1) Rn is the net radiation at crop surface(MJmiddotmminus2middotdayminus1) G is the soil heat flux density(MJmiddotmminus2middotdayminus1) c is the psychrometric constant (kPamiddotdegCminus1)T is the average daily air temperature (degC) u2 is the windspeed at 2m (msminus1) es is the saturation vapor pressure (kPa)and ea is the actual vapor pressure (kPa)

24 MannndashKendall Test Analysis Analyzing the abruptchange point is helpful for understanding the evolutiontrend of ET0 and determining the demarcation point ofnatural factors and human factors Since the sample does notneed to follow a specific distribution the MannndashKendallnonparametric test is recommended by the World Climate

An approach for ET0 analysis pattern selection in Ningxia Hui Autonomous Region

Interannual M-K test

Tmean

Tmean

Tmax

Tmax

Tmin

Tmin

SD U2

U2

RH

Trend analysis Tendency rate

Clim

ate t

rend

an

alys

es

Time Space

Monthly Seasonal Annual North Central South

M-K test Wavelet analysisTrend

AbruptPeriodic

SD RH

Sensitivity ContributionPartial derivative Partial derivative

Spat

iote

mpo

ral

anal

ysis

Sens

itivi

ty an

d co

ntrib

utio

n ra

te

anal

ysis

Target-satisfying design ET0 analysis pattern for Ningxia Hui Autonomous Region

Correlation

Figure 1 Approach framework as proposed for ET0 analysis

Advances in Meteorology 3

Organization and is widely used in climate trend analysis[32]

UFk sk minus E sk( 1113857

Var sk( 1113857

1113969 k 1 2 3

sk 1113944k

i1ri

ri

1 if xi gtxj

0 else

⎧⎪⎨

⎪⎩

j 1 2 i

UBk minusUFk

k n + 1 minus k

⎧⎪⎨

⎪⎩

k 1 2 n

(3)

where UFK is a sequence of statistics calculated in time seriesx and UBK is a sequence of statistics calculated in reverseorder of time series x Ufi is a standard normal distributionif a significance level α is given and |Uf i|gtU(α2) it indicatesa significant trend change in the sequence And if there is anintersection point between the two curves of UFK and UBKthis is the moment when the abrupt changes starts

25 Continuous Wavelet Analysis Wavelet analysis offersthe possibility to better study time series problems whichcan clearly reveal the multiple change cycles hidden in the

time series fully reflects the changing trend of the system indifferent time scales and makes a qualitative estimation ofthe future development trend of the system Although thecontinuous wavelet of the Morlet function was previously inthe field of communication it has been widely used inhydrological climate research with the interdisciplinarydevelopment [33]

ψ(t) eict

eminus t22( )

Wf(a b) |a|(minus 12)

1113946Rf(t)ψ((tminus b)a)dt

Var(a) 1113946R

Wf(a b)11138681113868111386811138681113868

111386811138681113868111386811138682db

(4)

where ψ(t) is the wavelet basic function Wf(a b) is thewavelet variation coefficient Var(a) is the wavelet variancea is the scale factor of the wavelet period length b is the timefactor of time translation t is time f(t) is the time seriesnumber and R is the real number field

26 Sensitivity Analysis and Contribution Rate AssessmentIn order to quantitatively study the close relationship be-tween ET0 and different climatic factors sensitive coefficientand contribution rate are the most commonly used methodsby many scholars

261 Sensitivity Analysis Compared with other sensitivityanalysis methods the partial correlation coefficient methodcan analyze the complex nonlinear relationship of eachfactor by controlling the influence of other factors thus it issuitable for global sensitivity analysis which is widely usedin nonlinear dynamic problems [34]

0 460 920 1380 1840230km

Gansu

NeiMongol

Ningxia

Shaanxi

Shaanxi

Yinchuan

Guyuan

Wuzhong

Zhongwei

Shizuishan

0 2040 80 120 160km

Xiji

Taole

Longde

Guyuan

Yanchi

Lingwu

Haiyuan

XingrenTongxin

Weizhou

Wuzhong

DawukouHuinong

Jingyuan

ZhogningZhongwei

Yinchuan

Liupanshan

Mahuangshan

Qingtongxia

km

Regional division

nxdem30High 3525Low 956

Provincial boundary

Weather station

80deg0prime0PrimeE

50deg0prime0Prime

N40

deg0prime0Prime

N30

deg0prime0Prime

N20

deg0prime0Prime

N

50deg0prime0Prime

N40

deg0prime0Prime

N30

deg0prime0Prime

N20

deg0prime0Prime

N

39deg0prime0Prime

N38

deg0prime0Prime

N36

deg0prime0Prime

N37

deg0prime0Prime

N35

deg0prime0Prime

N

39deg0prime0Prime

N38

deg0prime0Prime

N36

deg0prime0Prime

N37

deg0prime0Prime

N35

deg0prime0Prime

N

90deg0prime0PrimeE 100deg0prime0PrimeE 110deg0prime0PrimeE 120deg0prime0PrimeE 130deg0prime0PrimeE

80deg0prime0PrimeE 90deg0prime0PrimeE 100deg0prime0PrimeE 110deg0prime0PrimeE 120deg0prime0PrimeE 130deg0prime0PrimeE



39deg0prime0Prime

N38

deg0prime0Prime

N36

deg0prime0Prime

N37

deg0prime0Prime

N35

deg0prime0Prime

N

39deg0prime0Prime

N38

deg0prime0Prime

N36

deg0prime0Prime

N37

deg0prime0Prime

N35

deg0prime0Prime

N


105deg0prime0PrimeE 106deg0prime0PrimeE 107deg0prime0PrimeE 108deg0prime0PrimeE

0 15 30 60 90 120

Provincial capital

Prefecture-level city

Provincial boundary

N N

N

(a)(b) (c)

Figure 2 Location and administrative division of the study area (a) a map of China with the study area shown in green (b) a political mapof the Ningxia Hui Autonomous Region with the location of the urban center (c) a topographical map of the Ningxia Hui AutonomousRegion with the distribution of the climate stations in the study area and climate stations are marked by solid black circle


Sx limΔx⟶ 0ΔET0ET0( 1113857

(Δxx)1113888 1113889

zET0

zxmiddot

x

ET0 (5)

where Sx is the sensitive coefficient ΔET0 and Δx are thechange values of ET0 and climate factors respectively andpositive or negative of Sx respectively indicates that ET0increases or decreases with the increase in climate factors

262 Contribution Rate In order to determine the un-derlying causes of changes in ET0 sensitivity analysis needsto be combined with actual changes in climatic factors thusit is necessary to analyze the contribution rate of climaticfactors to ET0 [35]

Cx Sx middot Rcx

Rcx n middot Trendx

xmiddot 100

(6)

where Sx is the sensitive coefficient Cx is the contributionrate of climate factors to ET0 changes Rcx is the multiyearchange rate of climate factors Trendx is the annual climatetilt rate of climate factor x x is the multiyear absolute av-erage of climate factors and n is the length of the time series

3 Results

31 Climate Factors Analysis

311 Temporal Trends of Climate Factors From the climatedata of each station from 1957 to 2018 (62 years) the averagevalue of climate factors and the climate tendency rate wasstatistically calculated (Figure 3 and Table 2) e climate inNingxia had undergone significant changes in climate factorsfrom the past 62 years Spatially averaged Tmean Tmax andTmin all increased significantly (plt 001) and the change rateswere 034degC10a 031degC10a and 051degC10a respectively

Conversely the RH and U2 had a significant downward trend(plt 005) and the change rates were 04210a and 010ms10a respectively Although the SD was also showing adownward trend the effect was not significant Across thethree different regions (NYR CAZ and SMA) Tmean Tmaxand Tmin also had significantly increased with similar varia-tion e increasing rates of Tmean (038degC10a) Tmax (036degC10a) and Tmin (054degC10a) were the largest in the NYR whilethe CAZwas the smallest (031degC10a 024degC10a and 048degC10a respectively) Although the significant downward trends(plt 001) were found for RH in NYR andU2 in CAZ the RHSD and U2 also showed a downward trend in the threedifferent regions e decreasing rates of RH (08110a) inthe NYR and SD (040 h10a) and U2 (011ms10a) in SMAwere largest while RH (00610a) and SD (004 h10a) inCAZ and U2 (008ms10a) in NYR were smallest

Generally speaking under the influence of global climatechange in recent 62 years the overall climate of Ningxia hadbeen warming and drying and the temperature has increasedsignificantly e Tmean rise rate was 034degC10a which waslower than the rise rate of 037degC10a in the northwest regionof China higher than the 023degC10a of Chinese average[36 37] also higher than the 022degC10a of global average[38 39] And the rising rate was ranked asNYRgt SMAgtCAZFurthermore the RH decreased in varying degrees while thedecreasing rate was also sorted by NYRgt SMAgtCAZe SDand U2 had similar downward trend of change and the de-creasing rate was ranked as SMACAZgtNYR

Additionally the changes in climate factors during theyear are basically consistent in all regions (Figure 4) etemperature (Tmean Tmax and Tmin) showed a rising trendand then a decreasing trend in which the maximum valueappeared in July And the temperature was ranked asCAZ gtNYR gt SMA e RH performed a trend of fallingfirst then rising and finally falling in which the maxi-mum value is September and the minimum value is April

Table 1 Basic information for the national climate stations used in the study area

Region Station code Name Longitude (degE) Latitude (degN) Altitude (m)

NYR

53518 Shitanjing 10645 3927 1466453519 Huinong 10646 3913 1093153615 Taole 10642 3848 1102953614 Yinchuan 10612 3828 1111653619 Lingwu 10618 3812 1117353617 Qingtongxia 10604 3802 1132253612 Wuzhong 10611 3798 1129053704 Zhongwei 10511 3732 1226653705 Zhongning 10541 3729 11844

CAZ

53723 Yanchi 10723 3748 1350453881 Weizhou 10629 3728 1382953727 Mahuangshan 10707 3717 1713053810 Tongxin 10554 3658 1340753707 Xingren 10515 3693 1698853806 Haiyuan 10539 3634 18548

SMA

53817 Guyuan 10616 36 1754253903 Xiji 10543 3558 1917953910 Liupanshan 10612 3567 2842853914 Longde 10606 3562 2079553916 Jingyuan 1062 355 19490


And the RH was sorted by SMA gtNYR gtCAZ e SDand U2 were relatively stable throughout the year withoutviolent fluctuations while they were ranked asCAZ gtNYR gt SMA

312 Abrupt Change Test Analysis of Climate FactorsAlthough there had been a trend of warming and drying inNingxia in recent 62 years when did this trend begin andwhat form did it takeeMannndashKendall abrupt change testmethod was used to analyze the climatic factors in this study(Table 2)e results showed that there were different abruptchange points in each factor Importantly since the ET0 inNX had an abrupt change point in 1990 (Figure 5) the wholeresearch period was divided into two periods (ie from 1957

to 1990 and from 1991 to 2018) for the convenience ofanalysis

In general the temperature trend changes were con-sistent in all regions e Tmean and Tmin had increasedsignificantly (plt 001) from two periods while the Tmaxdecreased significantly from 1957 to 1990 and increasedfrom 1991 to 2018 e Tmean Tmax and Tmin changed by0094degC10a minus0044degC10a and 0451degC10a from 1957 to1990 while changed by 0451degC10a 0446degC10a and0548degC10a from 1991 to 2018 in NX respectively And thisresult showed that the extreme temperatures phenomenonin Ningxia was easing e average value of Tmean from twoperiods was both ranked as NYRgtCAZgt SMA while thelargest value was 792degC10a and 919degC10a the smallest valuewas 573degC10a and 687degC10a respectively And the average

60

50

20

10

0

Fact

or v

alue

s

1960 1970 1980 1990 2000 2010 2020Year

y = 0051x ndash 105852

y = 0034x ndash 59207

y = ndash0010x + 22238

y = ndash0003x + 13068

y = 0031x ndash 39340 y = ndash0042x + 140084

TmeandegCTmaxdegCTmindegC U2ms

RHSDh

(a)

6050

20

10

0

Fact

or v

alue

s

1960 1970 1980 1990 2000 2010 2020Year

y = 0054x ndash 112294

y = 0038x ndash 66372

y = 0036x ndash 47694

y = ndash0008x + 19277

y = ndash0005x + 18643

y = ndash0081x + 214929


RHSDh

(b)

6050

20

10

0

Fact

or v

alue

s

1960 1970 1980 1990 2000 2010 2020Year

y = 0024x ndash 24640

y = 0048x ndash 101028

y = ndash0011x + 23616

y = ndash0006x + 63327

y = 0031x ndash 53876y = ndash0004x + 15391


RHSDh

(c)

7560

20

10

0

Fact

or v

alue

s

1960 1970 1980 1990 2000 2010 2020Year

y = 0049x ndash 104234

y = 0032x ndash 57373

y = 0332x ndash 45687

y = ndash0011x + 23821

y = ndash0007x + 5169

y = ndash0040x + 141996


RHSDh

(d)

Figure 3 Annual variations in spatially averaged values of climate data (average temperature (Tmean) maximum temperature (Tmax) minimumtemperature (Tmin) relative humidity (RH) sunshine duration (SD) and wind speed (U2) from 1957 to 2018 in different climate zones) (a)Ningxia (NX) (b) Northern Yellow River irrigation area (NYR) (c) Central Arid Zone (CAZ) (d) Southern Mountain Area (SMA)


value trend of Tmin was similar to Tmean Moreover the av-erage value of Tmax was sorted by CAZgtNYRgt SMA eresult showed that it is hotter in CAZ and colder in SMA RHin NX changed significantly (plt 001) by 012110a andminus159810a from two periods respectively It was also foundacross the three different climate regions and this also impliesthe trend of drying SD had an increased trend from 1957 to1991 and a decreased trend from 1991 to 2018 in all regionsbut the effect was not significant e region with largestchange was SMA where increased by 01 h10a from 1957 to1991 and decreased by 0175 h10a from 1991 to 2018 U2 haddecreased significantly (plt 005) by 0108ms10a from 1957to 1991 and 0365ms10a from 1991 to 2018 in NX re-spectively Although the three climate regions all had thesimilar trends the effect was not significant in NYR and SMA

32 Trend Analysis of ET0 ere are significant differencesin the temporal and spatial distribution of ET0 due to thevariability in the climate system and the complexity of thegeographical environment In this study the time scale wasdivided into monthly seasonal and annual scales and thespatial scale was divided into NYR CAZ and SMA

321 Analysis at Monthly Scale Monthly averaged ET0 ateach region from 1957 to 2018 is listed in Table 3 andFigure 6 e average monthly ET0 was sorted as CAZ(10806mm)gtNYR (10570mm)gtNX (10187mm)gt SMA(8302mm) e ET0 ranged from 3349mmmiddotmonthminus1 to16895mmmiddotmonthminus1 with a total value of 122241mm overthe NX while the extremum ratio was 504 In the three

climate regions both the ET0 of NYR and CAZ were largerthan NX but the extremum ratio of NYR (580) exceededCAZ (448) e ET0 and extremum ration of SMA was thesmallest among NYR CAZ and NX while its values were99619mm and 443 respectively In general ET0 showed atrend of rising first and then falling during the year across allthe regions And the time of the monthly ET0 extreme wasalmost the same in different regions the maximum valuewas in May or June and the minimum value was in De-cember In addition the ET0 from May to July was largerduring the whole year in all regions accounting for morethan 40 of the total ET0 respectively

In order to compare the spatial distribution of ET0 indifferent seasons the typical months were used for analysis(Figure 7) Because autumn in Ningxia is too short to berepresentative this study chose the typical months of spring(April) summer (August) and winter (December) foranalysis Climate and topography changed from north tosouth ET0 showed a trend of first falling then rising andfinally falling in the typical months And the ET0 was thelargest in the north and middle while the smallest in thesouth In April ET0 was the highest in WeizhouMahuangshan Xingren and Zhongning and Huinong andShitanjing were also higher e overall change ranged from90mm to 160mm while it is in line with the climatecharacteristics of Ningxiarsquos fast warming and strong wind InAugust the semiarid climate in Ningxia was characterizedby less rainfall and longer sunshine and ET0 continued toincrease with the range of variation from 100mm to165mm In December ET0 was significantly reducedcompared to April and August due to the severe cold and

Table 2 Trend analyses of climate factors with MannndashKendall test and climate tendency rate

Climate region Climate factorClimate tendency rate Climate factors average value

Change point1957sim1990 1991sim2018 1957sim2018 1957sim1990 1991sim2018 1957sim2018

NX (whole)

Tmean (degC) 0094lowastlowast 0451lowastlowast 034lowastlowast 716 833 769 1993Tmax (degC) minus0044lowastlowast 0446lowastlowast 031lowastlowast 2166 2282 2218 1995Tmin (degC) 0385lowastlowast 0548lowastlowast 051lowastlowast minus605 minus441 minus531 1989RH () 0121lowast minus1598lowast minus042lowast 5682 5559 5627 1992SD (h) 0039 minus0094 minus003 755 744 750 1994U2 (ms) minus0108lowast minus0365lowast minus010lowast 263 243 254 1988

NYR

Tmean (degC) 0145lowastlowast 0531lowastlowast 038lowastlowast 792 919 849 1992Tmax (degC) minus0051lowastlowast 0643lowastlowast 036lowastlowast 2228 2355 2285 1994Tmin (degC) 0454lowastlowast 057lowastlowast 054lowastlowast minus547 minus375 minus470 1989RH () 0036lowastlowast minus2446lowastlowast minus081lowastlowast 5570 5325 5459 1996SD (h) 0032 minus0096 minus005 798 776 788 1995U2 (ms) minus0057 minus0551 minus008 247 236 242 1991

CAZ

Tmean (degC) 0193lowastlowast 0263lowastlowast 031lowastlowast 783 892 832 1992Tmax (degC) minus0063lowastlowast 0288lowastlowast 024lowastlowast 2293 2390 2337 1995Tmin (degC) 0665lowastlowast 0135lowastlowast 048lowastlowast minus548 minus399 minus481 1988RH () minus0444 0071 minus006 5142 5145 5143 1989SD (h) 0017 minus0011 minus004 795 781 788 1989U2 (ms) minus0157lowastlowast minus0278lowastlowast minus011lowastlowast 293 271 283 1994

SMA

Tmean (degC) 0029lowastlowast 0558lowastlowast 032lowastlowast 573 687 624 1990Tmax (degC) minus0019lowastlowast 0407lowastlowast 033lowastlowast 1979 2100 2034 1992Tmin (degC) 0013lowastlowast 0939lowast 049lowastlowast minus719 minus551 minus643 1991RH () 0774lowast minus242lowast minus04lowast 6334 6208 6277 1990SD (h) 01 minus0175 minus04 673 676 675 1988U2 (ms) minus0109 minus0267lowast minus011 248 221 236 1987


although the average was between 20mm and 45mm theXingren and Haiyuan were still relatively higher

322 Analysis at Seasonal Scale Seasonal ET0 is a specialindicator that can be used to reflect changes in ET0 atdifferent stages of the year Temporal variations in seasonalET0 from each climate region from 1957 to 2018 exhibit twomain trends (Figure 8) CAZ showed a downward trend inthe four stages of spring summer autumn and winter while

NX NYR and SMA showed an upward trend And this wasconsistent with the change in the annual scale In spring theET0 was 42707mm 42356mm and 32401mm in NYRCAZ and SMA respectively accounting for 36363606 and 2758 of ET0 in NX (whole) respectively Andthe ET0 was sorted as NYRgtCAZgt SMA In addition theET0 in NX NYR and SMA all increased significantly(plt 001) and the change rate was 278mm10a 545mm10a and 151mm10a respectively Conversely the CAZhad a significant downward trend (plt 005) and the change

104 62 120 8Month

ndash20

0

20

40

60

Fact

or v

alue

s

TmeanordmCTmaxordmCTminordmC

RHSDhU2(ms)

(a)

2 4 6 8 10 120Month

ndash20

0

20

40

60

Fact

or v

alue

s


RHSDhU2(ms)

(b)

ndash20

0

20

40

60

Fact

or v

alue

s

2 4 6 8 10 120Month


RHSDhU2(ms)

(c)

ndash20

0

20

40

60

Fact

or v

alue

s

2 4 6 8 10 120Month


RHSDhU2(ms)

(d)

Figure 4 Monthly variations in spatially averaged values of climate data (average temperature (Tmean) maximum temperature (Tmax)minimum temperature (Tmin) relative humidity (RH) sunshine duration (SD) and wind speed (U2) from 1957 to 2018 in different climatezones) (a) Ningxia (NX) (b) Northern Yellow River irrigation area (NYR) (c) Central Arid Zone (CAZ) (d) Southern Mountain Area(SMA)


rate was 080mm10a Compared with the spring the trendsin summer autumn and winter were similar except for theproportion of ET0 in NYR and CAZ and they were allshowed as CAZgtNYRgt SMA (Table 4) In order to betterunderstand the changing trend of ET0 this study was di-vided into two time periods (from 1958 to 190 and from 1991to 2018) which is consistent with the analysis of climaticfactors (Table 5) ET0 tendency rate mainly showed twotrends the downward trend in the first stage and the upwardtrend in the second stage in NYR and SMA while the CAZhad always shown a downward trend Additionally thisstudy found that the ordering of ET0 tendency rate wasconsistent in the two periods of three regions which wasshowed as springgt summergtwintergt autumn

In terms of spatial distribution ET0 varies in differentregions and seasons But in general it showed a trend ofincrease first and then decreased from north to south and

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(a)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(b)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(c)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(d)

Figure 5 MannndashKendall test of each region at annual scale (a) NX (b) NYR (c) CAZ (d) SMA

Table 3 Monthly average of ET0 in Ningxia and each region

Month NYR CAZ SMA NX (whole)1 3314 4071 3232 35242 5274 5601 4265 51703 10120 10216 7728 96704 14884 14467 11039 13990

5 17703(max) 17673 13635 16880

6 17490 17975(max)

13790(max)

16895(max)

7 17469 17459 13413 166558 14784 14781 11775 141819 10382 10239 7860 983510 7705 7763 5756 733311 4661 5415 4014 475812 3049 (min) 4007 (min) 3111 (min) 3349 (min)Average 10570 10806 8302 10187Total 126834 129667 99619 122241


1 2 3 4 5 6 7 8 9 10 11 12Month

0

50

100

150

200

ET0 (

mm

)

NYRCAZ

SMANX

Figure 6 Monthly average of ET0 in Ningxia and each region

105degE 106degE 107degE


39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degNkm

0 15 30 60 90 120

90ndash104105ndash118119ndash132

ET0 (mm)133ndash146147ndash160

N

(a)


140ndash151152ndash165



39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN

km0 15 30 60 90 120

ET0 (mm)

N

(b)

Figure 7 Continued


gradually increased from east to west (Figure 9) e highvalue area of ET0 presented two distinct distribution regionsnamely the Shitanjing and Huinong in the north and theTongxin and Xingren in the middle e low value area wasmainly distributed in the south of Liupanshan Jingyuan andLongde It was worth noting that the size of ET0 in LingwuQingtongxia and Wuzhong has always been in the middleposition e ET0 in Yanchi and Mahuangshan was specialwhere the spring and autumn were relatively large and thesummer and winter were relatively small In addition theET0 in spring was significantly higher than the autumn andthe ET0 in summer was significantly higher than otherseasons Overall it was sorted as summergt springgt autumngtwinter which was consistent with the local climatecharacteristics of fast spring short summer heat cool au-tumn early and long winter and cold

323 Analysis at Annual Scale e ET0 in each climatezone had similar trend in the past 62 years (Figure 10 andTable 6) NX NYR and SMA (plt 001) showed significantgrowth trends of 519mm10a 1212mm10a and 509mm10a respectively while the CAZ decreased at a trend rate of452mm10a And the change rate of different regions was

ranked as NYRgtNXgt SMAgtCAZ In particular althoughthe temperature of CAZ was rising ET0 showed a downwardtrend indicating that there was ldquoevaporation paradoxrdquo inparts of Ningxia Overall the annual ET0 ranking was CAZ(129667mm)gtNYR (126834mm)gtNX (122241mm)gt SMA (99619mm) while the extreme ratio was ranked asNYR (130)gt SMA (129)gtCAZ (128)gtNX (127) In ad-dition it was similar to the monthly scale for the maximumand minimum of annual ET0 with the trend of both NYRand CAZ being greater than NX and CAZ less than NX

e spatial distribution characteristics of ET0 have sig-nificant regional (Figure 11) As the terrain changes fromnorth to south ET0 showed a trend of decline first then riseand finally decline Located at the southern of Ningxia theSMA is cool and humid and the Tmean was 225degC and 208degClower than NYR and CAZ respectively while the RH largerby 818 and 802 respectively us ET0 in SMA wassmaller than the NYR and CAZ CAZ located in the middle-temperate and semiarid zone is a typical continental climatewith strong drought and heavy evaporation Compared withbefore the abrupt change point of 1990 the Tmean rose by13degC and the rainfall dropped by 42 thus the ET0 of CAZwas larger than the NYR and SMA Particularly the ET0 ofNYR was larger in the north climate stations while the


36ndash4041ndash45



39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN

km0 15 30 60 90 120

ET0 (mm)

N

(c)

Figure 7 Spatial distribution of ET0 at different times (a) April (b) August (c) December


y = 0278x ndash 147669

y = ndash0234x + 966920

y = 0155x + 170088

y = ndash0082x + 397356

y = 0104x + 11213

y = ndash0055x + 246809

y = 0041x + 38831

y = ndash0080x + 583001

Spring

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

300

400

500

ET0 (m

m)

400

500

600

ET0 (m

m)

100

200

300

400

ET0 (m

m)

100

200

ET0 (m

m)

CAZNX

CAZNX

CAZNX

CAZNX

Summer

Autumn

Winter

(a)

100

0

200

ET0 (m

m)

y = 0545x ndash 655033

y = 0151x + 24724

y = 0319x ndash 136403

y = 0326x ndash 258933

y = 0209x ndash 187731

y = 0124x ndash 70641

y = 0152x ndash 185863

y = ndash0092x + 288599

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

200

300

400

500

600

ET0 (m

m)

ET0 (m

m)

300

400

500

600

100

200

300

400

ET0 (m

m)

NYRSMA

NYRSMA

NYRSMA

NYRSMA

Spring

Summer

Autumn

Winter

(b)

Figure 8 Temporal variations in seasonal ET0 (spring summer autumn and winter) from 1957 to 2018 over the NX NYR CAZ and SMA


others were relatively smaller And this was principally dueto the long SD (844 hd) and high U2 (282ms) in the northclimate stations of NYR

33 MannndashKendall Test and Wavelet Analysis of ET0

331 Analysis at Annual Scale In the past 62 years ET0 hadshown a trend of first drop and then rise in NX NYR CAZand SMA while the abrupt change points were 1990 1990(the same to NX) 1991 and 1989 respectively (Figure 5)NX and NYR had a clear upward trend in the 21st century(Zgt 196) and CAZ showed an obvious downward trend inthe 1970s (Zlt 196) while the SMA did not have a signif-icant trend of upward and downward Furthermore thesolid line is a positive value indicating that ET0 is more thanthe multiyear average value And the dashed line is negativeindicating that ET0 is less than the multiyear average valuee junction of the solid line and the dotted line indicates thechange point of ET0 e long and short periods of ET0 inNX and NYR were 25a and 10a respectively with the 10abeing the most significant (Figure 12) Similarly the periodsof ET0 in SMA were 10a and 5a Nevertheless the CAZ wasspecial with one period of 15a

332 Analysis at Seasonal Scale From the seasonal scale ofNingxia the performance of ET0 could be divided into twostages the alternate change of the early rise and decline tothe stable rise in the later period (Figure 13) e abruptchange point in spring summer and autumn was the same

(1990) while the abrupt change point in winter was earlier(1980) ere was an obvious upward trend in the 21stcentury (zgt 196) in summer autumn and winter But inspring the trend was not significant Seasonal periods allshowed the similar trends ET0 had a long period of 15a anda short period of 5a over the entire time domain in springsummer autumn and winter (Figure 14) Take the 15aperiod in spring as an example the time point of larger ET0was the early 1960s and 1980s while the smaller ET0 was thelate 1960s 1970s and 1990s

34 Sensitive Coefficient and Contribution Rate Analysis

341 Analysis at Annual Scale Based on equation (5) thesensitive coefficients between ET0 and other six climatefactors were obtained (Table 7) All regions had the similartrends Tmean Tmax Tmin SD and U2 were positive valueswhile the RH was negative is suggested that the increasein RH caused the decline of ET0 and other climatic factorswere the opposite Moreover among the 20 climate stationsthe average minimum and maximum climate factors for thesensitive coefficient of ET0 were Tmin (008) and RH (046)respectively In general the sensitive coefficient of Ningxiawas ranked as RHgtTmaxgtU2gtTmeangt SDgtTmin Addi-tionally there were two trends of the sensitive coefficient inspatial distribution the Tmax Tmean and RH increased firstfrom north to south and then decreased while the SD Tminand U2 were the opposite us the CAZ was special wherethe sensitive coefficient of Tmax Tmean and RH was thelargest and other climate factors were the smallest

Table 4 e seasonal ET0 (mm) and proportion () from 1957 to 2018 over the NYR CAZ and SMA

Region Spring Summer Autumn WinterNYR 42707 (2636) 49725 (3580) 22748 (3566) 11654 (3240)CAZ 42356 (3606) 50179 (3613) 23417 (3670) 13715 (3812)SMA 32401 (2758) 38983 (2807) 17631 (2764) 10603 (2948)

Table 5 Trend analyses of ET0 in different seasons over the NX NYR CAZ and SMA

Climate region SeasonET0 tendency rate

1957sim1990 1991sim2018 1957sim2018

NX (whole)

Spring minus574 464 278Summer minus396 350 155Autumn minus091 039 104Winter minus294 173 041

NYR


CAZ

Spring minus527 minus502 minus080Summer minus329 minus258 minus234Autumn minus138 minus112 minus082Winter minus309 minus224 minus055

SMA



0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N

ET0 (mm)260ndash300301ndash340341ndash380


(a)

0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N



(b)

Figure 9 Continued


0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



ET0mm

N



(c)

0 30 60 90 12015Km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N



0 30 60 90 12015km

(d)

Figure 9 Spatial distribution of average ET0 at different times (a) spring (b) summer (c) autumn (d) winter

1600

1500

1400

1300

1200

1100

1000

900

ET0 (

mm

)

y = ndash0452x ndash 21940963

y = 0579x ndash 7246

CAZNX

1960 1970 1980 1990 2000 20202010Year

(a)

800

1500

1400

1300

1200

1100

1000

900

ET0 (

mm

)

y = 0509x ndash 16251


NYRSMA

1960 1970 1980 1990 2000 20202010Year

(b)

Figure 10 Temporal variations of annual ET0 from 1957 to 2018 over the NX NYR CAZ and SMA


As depicted in Figure 15 although the contribution ratesof Tmax Tmean Tmin and RH were all positive the reason forthe change in ET0 was exactly the opposite Positive sensitivecoefficient and positive climate trend rate of Tmax Tmean andTmin led to the positive contribution rate while the sensitivecoefficient and climate trend rate of RH both were negativeIn particular due to the positive sensitive coefficient andnegative climate trend rate the contribution rates of SD andU2 were both negative indicating that they led to the declineof ET0 In general the most contribution rates in NX NYRCAZ and SMA were Tmin Tmin Tmax and U2 respectivelywhile the SD was the smallest in all regions

342 Analysis at Seasonal Scale As for the seasonal scalethe sensitive coefficients of six climate factors were

consistent with the annual scale All showed that Tmean TmaxTmin SD U2 and RH were positive values while the RH wasnegative value (Table 8) In the spring and summer of NYRCAZ and NX the most sensitive coefficient of main climatefactors was Tmax while the RH (absolute value) was thelargest in autumn and wintere SMAwas different and itsmost sensitive coefficient was always RH (absolute value) infour seasons In addition there were also two cases for thesmallest sensitive coefficient of main climate factors and itwas Tmin in four seasons of CAZ and NX Moreover theNYR and SMA were Tmin in spring summer and autumnwhile SD in winter Additionally the sensitive coefficients ofthe main climate factors were also varying in the fourseasonse Tmean Tmax and SD showed an increase in trendfirst and then decrease while the U2 was the opposite As forTmin and RH they were special and Tmin appeared as analternating change in rise and decline while RH was alwaysshowing an increase in trend

As for the contribution rate depicted in Figure 16 Inspring SD and U2 were negative values whilst the TmeanTmax Tmin and RH were positive values And the largestcontribution rate in NYR and CAZwas RH while theU2 waslargest in SMA and NXMoreover there was no regularity insummer and autumn In summer the largest contributionrates in NYR CAZ SMA and NX were U2 Tmax Tmin andU2 respectively And in autumn the largest contributionrates were Tmax Tmax Tmean and U2 respectively In winterit was extremely consistent in different regions in which theTmax was always the dominant factor In addition the cli-mate factors varied from spring to winter but it could beroughly divided into two situations e Tmean Tmax andTmin were relatively regular which showed a trend of de-creasing first and then increasing On the contrary the SDU2 and RH did not show obvious regularity

4 Discussion

41 Variation inClimate Factors In the past thousand yearsthe temperature had showed an unprecedented upwardtrend indicating the global warming as an indisputable factis study found that the Ningxia was suffering from climatewarming (ie 034degC10a in Tmean 031degC10a in Tmax and051degC10a in Tmin (Table 2)) which is consistent with mostresearch in the past few decades [40] and almost triple theglobal temperature increasing rate shown in the IPCC FifthAssessment Report (012degC10a) e rise in temperaturemay be due to two reasons one is the increase in solarradiation caused by the destruction of the ozone layer andthe other is the large amount of greenhouse gas emissionscaused by economic development and population growth[41]

e U2 showed a downward trend in Ningxia based onthis study (010ms10a Table 2) which was reported insome relevant studies [42] e natural reason internal roleof the natural system is the main reason for the decrease inwind speed including the weakening of the East Asianwinter monsoon and summer monsoon in recent decades[43] Additionally the SD also showed a decreasing trend inNingxia (003 h10a Table 2) which is generally consistent

Table 6 Multiyear average statistic value of ET0mm

Region Minimum Maximum AverageNYR 110530 143491 126834CAZ 112386 143419 129667SMA 84399 109192 99619NX (whole) 106342 135642 122241

0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N


1152ndash12831284ndash1416

Figure 11 Spatial distribution of annual average ET0 in Ningxia


1960 1970 1980 1990 2000 2010

10

20

30Ye

ars

Year

100 100

100

(a)

Year

s

1960 1970 1980 1990 2000 2010

10

20

30

Year

50

50

50

50

5050

(b)

1960 1970 1980 1990 2000 2010

10

20

30

Year100

100

100

Year

s

(c)

1960 1970 1980 1990 2000 2010

10

20

30

Yearndash2

0 ndash2080

ndash20

ndash20

ndash20

ndash20

ndash20 ndash20ndash20

8080

ndash20

Year

s

(d)

Figure 12 Wavelet analysis of each region at annual scale (a) NX (b) NYR (c) CAZ (d) SMA

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(a)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(b)

Figure 13 Continued


ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(c)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(d)

Figure 13 MannndashKendall test of Ningxia at seasonal scale (a) spring (b) summer (c) autumn (d) winter

1960 1970 1980 1990 2000 2010

10

20

30

Tim

e sca

le (a

)

Year

1040

0

10

10

10

10 1010

1010

10

(a)

Tim

e sca

le (a

)

1960 1970 1980 1990 2000 2010

10

20

30

Year

050

50

0

0 0 0 0

0 0 0

0 0 0 0 0 0 000

500

(b)

Tim

e sca

le (a

)

1960 1970 1980 1990 2000 2010

10

20

30

Year

10

10

10

10

10

10 10

10

10

40

(c)

Tim

e sca

le (a

)

1960 1970 1980 1990 2000 2010

10

20

30

Year

1010

10

10

10

1010

4010

10

10 10

(d)

Figure 14 Wavelet analysis of each region at seasonal scale (a) spring (b) summer (c) autumn (d) winter

Table 7 Sensitive coefficient of the main climate factors to ET0 from 1957 to 2018 in Ningxia at annual scale

Region Tmean Tmax Tmin SD U2 RHNYR 025 037 009 015 029 minus045CAZ 026 044 004 014 020 minus056SMA 023 032 009 015 030 minus042NX 024 035 008 015 028 minus046


with the existing finding [44]ere are different views on thereduction of sunshine hours and decreased visibility due torising aerosol content in the troposphere may be the maincause [45] Similar toU2 and SD the RH showed a downwardtrend from 1957 to 2018 (04210a Table 2) And this resultis coincident to the other studies [46] e impact of climatechange on RH is a complex issue involving temperaturesurface runoff vegetation types and even the undergroundwater [47] erefore it is difficult to determine the funda-mental causes of RH change Furthermore this study foundthat the 1990s seemed to be a time code of abrupt change inwhich the six climate factors were beginning to change beforeand after it e temporal trend of the six climate factors inNingxia is consistent withmost of the findings while there areminor differences in spatial distribution which is mainly dueto the form of climate data and the area size

42 ET0 Trends and ldquoEvaporation Paradoxrdquo As the rise in Tand the decrease in U2 RH and SD the ET0 is generallyconsidered to gradually increase However the performance

of different regions in Ningxia is inconsistent e annualand seasonal ET0 showed an increasing trend in NX(579mm10a) NYR (1224mm10a) and SMA (509mm10a) while it was decreasing in CAZ (452mm10a) isimplied that there was ldquoevaporation paradoxrdquo in the CAZ ofNingxia and this estimate for ET0 was higher than the entireChina (35mm10a) [34] and the Northern Loess Plateau ofChina (33mm10a) [9] For decades many scholars studiedthe causes of the ldquoevaporation paradoxrdquo in different regionsand have reached different conclusions [48 49] It is gen-erally believed that there are three main reasons for thedecline of ET0 e decrease in solar radiation was caused bythe increase in atmospheric cloud amount (aerosol) and thedecrease in water vapor pressure was caused by the increasein air humidity and the decrease in wind speed

In order to obtain the cause of the evaporation paradoxin the CAZ of Ningxia the MODIS satellite data (MOD08)from 2000 to 2018 were selected to analyze the distributionand variation in cloud optical thickness and aerosol opticalthickness As depicted in Figure 17 the CAZrsquos annual

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

12

Con

trib

utio

n ra

te (m

ma

)

Figure 15 Contribution rate of the main climate factors in Ningxia at annual scale (mma)

Table 8 Sensitive coefficient of the main climate factors to ET0 from 1957 to 2018 in Ningxia at seasonal scale

Region Season Tmean Tmax Tmin SD U2 RH

NYR

Spring 026 047 006 024 028 minus030Summer 032 052 013 035 019 minus033Autumn 024 042 006 020 030 minus054Winter 011 010 012 004 042 minus059

CAZ


SMA


NX



average cloud optical thickness is 24 in 2018 and the upwardtrend is very significant compared to 16 in 2000 Generallythe increase in the thickness of the cloud optics reduces thetotal amount of radiation reaching the ground is studyanalyzed the total solar radiation variation in CAZ since1957e total radiation of CAZ had a significant downwardtrend from 1957 to 2018 (minus13418MJm2a) In general theincrease in cloud optical thickness in CAZ led to a decreasein total solar radiation which is the main reason for thedecline in ET0

43 e Impacts of Climate Change on the Variation in ET0e impact of climate factors on the contribution of ET0changes is not only related to the sensitivity of ET0 but alsoto the changes in climate factors themselves e sensitivityanalysis at annual scale in this study showed that the ET0 inNingxia was most sensitive to RH (minus046) followed by Tmax(035) U2 (028) Tmean (024) SD (015) and Tmin (008)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(a)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(b)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(c)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash24

ndash16

ndash08

00

08

16

24

Con

trib

utio

n ra

te (m

ma

)

(d)

Figure 16 Contribution rate of the main climate factors in Ningxia at seasonal scale (mma) (a) spring (b) summer (c) autumn (d) winter

Clou

d op

tical

thic

knes

s

y =0386x ndash 755403

28

24

20

16

Cloud optical thicknessTotal solar radiation

y = ndash13418 + 32542427

1980 2000 20201970 19901960 2010Year

5200

5600

6000

6400To

tal s

olar

radi

atio

n (M

Jm2 )

Figure 17 Annual variations in spatially averaged values of cloudoptical thickness and total solar radiation


which is consistent with some relevant studies [41 50]Simultaneously the positive changes in ET0 in Ningxia dueto the relative increase in Tmax was larger than other cli-mate factors And the relative contribution rate of RH wassmall although it was the most sensitive Additionallyanother explanation for what is known as ldquoglobal dim-mingrdquo in previous studies was the reduction in SD whichwas the most important controlling factor leading to thereduction in ET0 [51] Nevertheless this study found thatthe SD had less contribution rate to ET0 in Ningxiacompared to other climate factors And this result is inaccordance with Qi [30] who indicated that the SD hadless influence on ET0 in Northeast China Compared to theannual scale the sensitivity analysis in this study illus-trated that the ET0 in Ningxia at seasonal scale had noobvious regularity e Tmax was the most sensitive inspring (042) and summer (046) while the RH was thelargest in autumn (minus054) and winter (minus055) and this isdifferent from Wang [41] who found that Tmax was themost sensitive to ET0 in four seasons As for the contri-bution rate of Ningxia it was similar to the sensitivity Andthis is inconsistent with omas [52] who emphasizedthat the U2 changes ET0 in water-limited areas of westChina e possible reason for this is that the climatedifference among the three regions (NYR CAZ and SMA)of Ningxia was relatively large [53]

44e Future Study of ET0 ET0 is significant for the waterand energy balance of terrestrial ecosystems and reasonableprediction of future ET0 is not only beneficial to water re-sources management but also important for guiding agri-cultural production [54] Although Ningxia is deeply inlandin the northwest of China the ET0 is significant affected bythe climate us we can study the ET0 from two per-spectives in the future

First there are many atmospheric teleconnection pat-terns (ie Atlantic Oscillation (AO) the Indian OceanDipole (IOD) Pacific Decadal Oscillation (PDO) and ElNintildeo-Southern Oscillation indices (ENSO)) in the globalclimate system and the effects of these atmospheric circu-lations have broken through time and space constraintsat is to say the influence can occur simultaneously orsequentially in time and the change can be far apart in spaceerefore these atmospheric teleconnection models canchange ET0 in annual and seasonal scales by affecting cli-matic factors With a deep understanding of the atmosphericcirculation system in the future it is possible to take ad-vantage of the atmospheric teleconnection models to predictfuture ET0 [23]

Second the air temperature had increased over the past50 years and is reported in Xinjiang [55] Gansu [56] andMongolia [57] AndMcVicar observed a drying trend due tothe reduced rainfall in northwest China [58] e futurestudy is necessarily motivated by the abovementionedconsiderations to assess sensitivity of the evapotranspirationdue to plusmn20 change in several climatic factors Specially theIPCC report for the 21st century can be considered in thefuture study which can describe and quantify the impacts of

climatic factors on seasonal and annual ET0 based on theclimate change [59]

5 Conclusions

e climate factors temporal trends the spatiotemporalvariation of ET0 at different time scales and its climaticdriving factors across different climatic zones of Ningxiawere investigated with the climate tendency rate Man-nndashKendall test continuous wavelet analysis sensitivityanalysis and contribution rate assessment based on dailydata of 20 climatic stations from 1957 to 2018 e mainconclusions of this study are as follows

(1) Tmean Tmax and Tmin all have increased significantlyover the past 62 years in Ningxia whilst RH U2 andSD have significantly decreasing trends And thistrend has becomemore pronounced with 1990 as theabrupt change point

(2) e ET0 is mainly concentrated from April toSeptember in a year In NX NYR and SMA the ET0series has a significant increase in both annual andseasonal scales while CAZ is the opposite In termsof spatial distribution at monthly seasonal andannual scales there is a trend of increasing first andthen decreasing from north to south

(3) An abrupt change point in annual ET0 is detectedaround the year of 1990 and the annual ET0 de-creased significantly from 1957 to 1990 while itincreased significantly from 1991 to 2018 e ET0has a long period of 25a and a short period of 10a atannual scale while it is 15a and 5a at seasonal scale

(4) At the annual and seasonal scales the most sensitiveclimatic factors are RH and Tmax while the largestcontribution rates are Tmax and SD

e results of this study can not only help to guide theagricultural water management in Ningxia but also con-tribute to agricultural production and environmental pro-tection In the future work the relationship betweenatmospheric circulation and ET0 can be analyzed for ET0prediction which is most significant for the researchers anddecision makers

Data Availability

e data used in this paper are provided by the NationalMeteorological Information Center (httpdatacmacn)

Conflicts of Interest

e authors declare that they have no conflicts of interest

Authorsrsquo Contributions

Ziyang Zhao contributed substantially to conceptualizationmethodology validation data curation data interpretationand writing All authors participated in drafting the article orrevising it critically and gave final approval of the version tobe submitted


Acknowledgments

is research was supported by the National Natural ScienceFoundation of China (Grant nos 51879010 and 51479003)the National Key Research and Development Program ofChina (2018YFC0407900 and 2019YFC0408902) theGraduate Innovation Fund in Beijing Key Laboratory ofUrban Hydrological Cycle and Sponge City Technology(HYD2020IFDC03) and the 111 Project (Grant noB18006)

References

[1] S Xu Z Yu C Yang X Ji and K Zhang ldquoTrends inevapotranspiration and their responses to climate change andvegetation greening over the upper reaches of the YellowRiver basinrdquo Agricultural and Forest Meteorology vol 263pp 118ndash129 2018

[2] D Chen G Gao C Xu J Guo and G Ren ldquoComparison ofthe ornthwaite method and pan data with the standardPenman-Monteith estimates of reference evapotranspirationin Chinardquo Climate Research vol 28 no 2 pp 123ndash132 2005

[3] L Gong C-y Xu D Chen S Halldin and Y D ChenldquoSensitivity of the PenmanndashMonteith reference evapotrans-piration to key climatic variables in the Changjiang (YangtzeRiver) basinrdquo Journal of Hydrology vol 329 no 3ndash4pp 620ndash629 2006

[4] R G Allen L S Pereira M Smith D Raes and J L WrightldquoFAO-56 dual crop coefficient method for estimating evapo-ration from soil and application extensionsrdquo Journal of Irri-gation and Drainage Engineering vol 131 no 1 pp 2ndash13 2005

[5] L S Pereira R G Allen M Smith and D Raes ldquoCropevapotranspiration estimation with FAO56 past and futurerdquoAgricultural Water Management vol 147 pp 4ndash20 2015

[6] N S Christensen A W Wood N Voisin D P Lettenmaierand R N Palmer ldquoe effects of climate change on thehydrology and water resources of the Colorado River basinrdquoClimatic Change vol 62 no 1ndash3 pp 337ndash363 2004

[7] G-R Walther E Post P Convey et al ldquoEcological responsesto recent climate changerdquo Nature vol 416 no 6879pp 389ndash395 2002

[8] X-e Tao H Chen C-y Xu Y-k Hou and M-x JieldquoAnalysis and prediction of reference evapotranspiration withclimate change in Xiangjiang River basin Chinardquo WaterScience and Engineering vol 8 no 4 pp 273ndash281 2015

[9] T Ning Z Li W Liu and X Han ldquoEvolution of potentialevapotranspiration in the northern Loess Plateau of Chinarecent trends and climatic driversrdquo International Journal ofClimatology vol 36 no 12 pp 4019ndash4028 2016

[10] C Li P Wu X Li et al ldquoSpatial and temporal evolution ofclimatic factors and its impacts on potential evapotranspi-ration in Loess Plateau of Northern Shaanxi Chinardquo Scienceof the Total Environment vol 589 pp 165ndash172 2017

[11] M Malte M Nicolai H William et al ldquoGreenhouse-gasemission targets for limiting global warming to 2degCrdquo Naturevol 458 no 7242 pp 1158ndash1162 2009

[12] R K Pachauri M R Allen V R Barros et al Climate Change2014 Synthesis Report Contribution of Working Groups I IIand III to the Fifth Assessment Report of the IntergovernmentalPanel on Climate Change IPCC Geneva Switzerland 2014

[13] A Piticar D Mihaila L G Lazurca P I BistriceanA Putuntica and A E Briciu ldquoSpatiotemporal distribution ofreference evapotranspiration in the Republic of Moldovardquo

eoretical amp Applied Climatology vol 124 no 3ndash4pp 1133ndash1144 2016

[14] G Papaioannou G Kitsara and S Athanasatos ldquoImpact ofglobal dimming and brightening on reference evapotrans-piration in Greecerdquo Journal of Geophysical Research vol 116no D9 2011

[15] A-A Sabziparvar H Tabari A Aeini and M GhafourildquoEvaluation of class a pan coefficient models for estimation ofreference crop evapotranspiration in cold semi-arid and warmarid climatesrdquo Water Resources Management vol 24 no 5pp 909ndash920 2010

[16] K Chaouche L Neppel C Dieulin et al ldquoAnalyses of pre-cipitation temperature and evapotranspiration in a FrenchMediterranean region in the context of climate changerdquoComptes Rendus Geoscience vol 342 no 3 pp 234ndash243 2010

[17] L Xu Z Shi Y Wang et al ldquoSpatiotemporal variation anddriving forces of reference evapotranspiration in Jing Riverbasin northwest Chinardquo Hydrological Processes vol 29no 23 pp 4846ndash4862 2015

[18] M L Roderick and G D Farquhar ldquoChanges in New Zealandpan evaporation since the 1970srdquo International Journal ofClimatology vol 25 no 15 pp 2031ndash2039 2005

[19] M T Hobbins J A Ramırez and T C Brown ldquoTrends in panevaporation and actual evapotranspiration across the con-terminous US paradoxical or complementaryrdquo GeophysicalResearch Letters vol 31 no 13 pp 405ndash407 2004

[20] Z Li F Qi L Wei et al ldquoSpatial and temporal trend ofpotential evapotranspiration and related driving forces inSouthwestern China during 1961ndash2009rdquo Quaternary Inter-national vol 336 no 127ndash144 pp 127ndash144 2014

[21] T C Peterson V S Golubev and P Y Groisman ldquoEvap-oration losing its strengthrdquo Nature vol 377 no 6551pp 687-688 1995

[22] D Jhajharia Y Dinpashoh E Kahya V P Singh andA Fakheri-Fard ldquoTrends in reference evapotranspiration inthe humid region of Northeast Indiardquo Hydrological Processesvol 26 no 3 pp 421ndash435 2012

[23] R Chai S Sun H Chen and S Zhou ldquoChanges in referenceevapotranspiration over China during 1960ndash2012 attribu-tions and relationships with atmospheric circulationrdquo Hy-drological Processes vol 32 no 19 pp 3032ndash3048 2018

[24] Z Dan and X Liu ldquoAssessing the effect of climate change onreference evapotranspiration in Chinardquo Stochastic Environ-mental Research amp Risk Assessment vol 27 no 8 pp 1871ndash1881 2013

[25] S Xu Y Liu X Wang and G Zhang ldquoScale effect on spatialpatterns of ecosystem services and associations among themin semi-arid area a case study in Ningxia Hui autonomousregion Chinardquo Science of e Total Environment vol 598pp 297ndash306 2017

[26] Y L Zhang P X Liu and Y Wang ldquoTemporal and spatialvariations of the drought in Ningxia based on aridity indexand Morlet wavelet analysisrdquo Chinese Journal of Ecologyvol 34 no 8 pp 2373ndash2380 2015

[27] J Liu L XWangM A Li-WenW UWan-Li Y L Liu andY C Sun ldquoA loss estimation method of monitoring andestimating the yield loss of wheat by drought in dry farmingareas in Northwest of Chinardquo Scientia Agricultura Sinicavol 37 no 2 pp 201ndash207 2004

[28] Y Guan L Kang C Shao P Wang and M Ju ldquoMeasuringcounty-level heterogeneity of CO2 emissions attributed toenergy consumption a case study in Ningxia Hui autono-mous region Chinardquo Journal of Cleaner Production vol 142pp 3471ndash3481 2017


[29] R Lyu J Zhang M Xu and J Li ldquoImpacts of urbanization onecosystem services and their temporal relations a case studyin Northern Ningxia Chinardquo Land Use Policy vol 77pp 163ndash173 2018

[30] H Qi F Pan X Pan et al ldquoDry-wet variations and causeanalysis in Northeast China at multi-time scalesrdquo eoreticalamp Applied Climatology vol 133 no 3ndash4 pp 1ndash12 2017

[31] R G Allen L S Pereira D Raes and M Smith CropEvapotranspiration-Guidelines for Computing Crop WaterRequirements-FAO Irrigation and Drainage Paper 56 FAOvol 300 no 9 Rome Italy 1998

[32] Z Liu Y Wang M Shao X Jia and X Li ldquoSpatiotemporalanalysis of multiscalar drought characteristics across the LoessPlateau of Chinardquo Journal of Hydrology vol 534 pp 281ndash2992016

[33] L Chen Y Wang B Touati et al ldquoTemporal characteristicsdetection and attribution analysis of hydrological time-seriesvariation in the seagoing river of southern China under en-vironmental changerdquo Acta Geophysica vol 66 no 5pp 1151ndash1170 2018

[34] J Fan L Wu F Zhang Y Xiang and J Zheng ldquoClimatechange effects on reference crop evapotranspiration acrossdifferent climatic zones of China during 1956ndash2015rdquo Journalof Hydrology vol 542 pp 923ndash937 2016

[35] P Lin Z He J Du L Chen Z Xi and L Jing ldquoImpacts ofclimate change on reference evapotranspiration in the QilianMountains of China historical trends and projected changesrdquoInternational Journal of Climatology vol 38 no 7pp 2980ndash2993 2018

[36] Y Ding G Ren Z Zhao et al ldquoDetection causes andprojection of climate change over China an overview ofrecent progressrdquo Advances in Atmospheric Sciences vol 24no 6 pp 954ndash971 2007

[37] P Shilong C Philippe H Yao et al ldquoe impacts of climatechange on water resources and agriculture in Chinardquo Naturevol 467 no 7311 pp 43ndash51 2010

[38] B Nijssen G M OrsquoDonnell A F Hamlet andD P Lettenmaier ldquoHydrologic sensitivity of global rivers toclimate changerdquo Climatic Change vol 50 no 1ndash2pp 143ndash175 2001

[39] J Gao ldquoSome Views on Trend and Causes of Global ClimateChangerdquo Rural Eco-Environment vol 13 no 4 pp 43ndash471997

[40] L Cui L Wang Z Lai Q Tian W Liu and J Li ldquoInnovativetrend analysis of annual and seasonal air temperature andrainfall in the Yangtze River basin China during 1960ndash2015rdquoJournal of Atmospheric and Solar-Terrestrial Physics vol 164pp 48ndash59 2017

[41] Z Wang A Ye L Wang K Liu and L Cheng ldquoSpatial andtemporal characteristics of reference evapotranspiration andits climatic driving factors over China from 1979ndash2015rdquoAgricultural Water Management vol 213 pp 1096ndash11082019

[42] R Yu B Wang and T Zhou ldquoTropospheric cooling andsummer monsoon weakening trend over East Asiardquo Geo-physical Research Letters vol 31 no 22 Article ID L222122004

[43] Y JIANG Y Luo Z Zhao et al ldquoProjections of wind changesfor 21st century in China by three regional climate modelsrdquoChinese Geographical Science vol 20 no 3 pp 226ndash235 2010

[44] H C Power ldquoTrends in solar radiation over Germany and anassessment of the role of aerosols and sunshine durationrdquoeoretical amp Applied Climatology vol 76 no 1ndash2 pp 47ndash632003

[45] Z Li W Zhang Y He et al ldquoDecreasing trend of sunshinehours and related driving forces in Southwestern Chinardquoeoretical and Applied Climatology vol 109 no 1ndash2pp 305ndash321 2012

[46] B Xie Q Zhang and Y Ying ldquoTrends in precipitable waterand relative humidity in China 1979ndash2005rdquo Journal of Ap-plied Meteorology and Climatology vol 50 no 10pp 1985ndash1994 2011

[47] A J Simmons K M Willett P D Jones P W orne andD P Dee ldquoLow-frequency variations in surface atmospherichumidity temperature and precipitation inferences fromreanalyses and monthly gridded observational data setsrdquoJournal of Geophysical Research vol 115 no D1 2010

[48] Y Zhang C Liu Y Tang and Y Yang ldquoTrends in panevaporation and reference and actual evapotranspirationacross the Tibetan Plateaurdquo Journal of Geophysical Researchvol 112 no D12 2007

[49] Z Zhu S Piao Y Xu A Bastos P Ciais and S Peng ldquoeeffects of teleconnections on carbon fluxes of global terrestrialecosystemsrdquo Geophysical Research Letters vol 44 no 7pp 3209ndash3218 2017

[50] C Liu D Zhang X Liu and C Zhao ldquoSpatial and temporalchange in the potential evapotranspiration sensitivity tometeorological factors in China (1960ndash2007)rdquo Journal ofGeographical Sciences vol 22 no 1 pp 3ndash14 2012

[51] G Kitsara G Papaioannou A Papathanasiou and A RetalisldquoDimmingbrightening in athens trends in sunshine dura-tion cloud cover and reference evapotranspirationrdquo WaterResources Management vol 27 no 6 pp 1623ndash1633 2013

[52] A omas ldquoSpatial and temporal characteristics of potentialevapotranspiration trends over Chinardquo International Journalof Climatology vol 20 no 4 pp 381ndash396 2015

[53] Y H Ding W Wang X G Chen G F Zheng J Shao andJ I Xiao-Ling ldquoAnalyses on climate characteristic and var-iation rule of rainstorm in Ningxia in recent 44 yearsrdquo PlateauMeteorology vol 26 no 3 pp 630ndash636 2007

[54] Z Zhao H Wang C Wang W Li H Chen and C DengldquoChanges in reference evapotranspiration over Northwest Chinafrom 1957 to 2018 variation characteristics cause analysis andrelationships with atmospheric circulationrdquo Agricultural WaterManagement vol 231 Article ID 105958 2020

[55] Y Xue P Han and G Feng ldquoChange trend of the precipi-tation and air temperature in Xinjiang since recent 50 yearsrdquoArid Zone Research vol 20 no 2 pp 127ndash130 2003

[56] R Dou J Yan and P Wang ldquoSpatiotemporal distribution oftemperature in Gansu province under global climate changeduring the period from 1956 to 2012rdquo Arid Zone Researchvol 32 no 1 pp 73ndash79 2015

[57] Z Ma H Yu Q Zhang and C Cao ldquoCharacteristics andabrupt change of temperature and precipitation in innerMongolia area over the period 1960ndash2016rdquo Research of Soiland Water Conservation vol 26 no 3 pp 114ndash121 2019

[58] T R McVicar M L Roderick R J Donohue et al ldquoGlobalreview and synthesis of trends in observed terrestrial near-surface wind speeds implications for evaporationrdquo Journal ofHydrology vol 416-417 pp 182ndash205 2012

[59] S Adnan K Ullah A H Khan and S Gao ldquoMeteorologicalimpacts on evapotranspiration in different climatic zones ofPakistanrdquo Journal of Arid Land vol 9 no 6 pp 938ndash9522017


bestselling and most cited publications in the field of waterresources management up to now [5]

Climate change has produced profound impact on so-ciety and environment Many global issues are likely to beaffected by climate change such as extreme hydrologicalcycles food security biodiversity loss and water scarcity[6 7] ET0 which is the link between water balance andsurface energy balance is considered as a significant indi-cator for the climate change and the water cycle [8] And aspart of changing climate ET0 trends directly affect globaland regional water resources In order to assess agriculturaldemand hydrological cycle and ecological changes moreeffectively a clear understanding of historical trends andfuture changes of ET0 and its response to climate change areessential [9 10]

Climate change is intensifying and mainly characterizedby a significant increase in temperature due to anthropo-genic emissions of active greenhouse gases (such as CO2CH4 and N2O) [11] According to the report of IPCC theaverage temperature of global land and sea surface showed alinear upward trend of rising by 085degC from 1880 to 2012the average temperature increased by 078degC from 1850 to1990 and the average temperature was projected to increaseby 15degC for the end of 21st century [12] Since the risingtrend of air temperature has always existed ET0 has beenincreasing globally in the past few decades and reported inthe Republic of Moldova [13] Greece [14] Iran [15] andFrance [16] However the ET0 is declining in some areas atthe same time such as northwest China [17] New Zealand[18] United States [19] and southwestern China [20]erefore an increase in global temperature may not lead tothe rise in ET0 under any circumstances which is called asthe ldquoevaporation paradoxrdquo To explain this problem manyscholars have done a lot of research by quantifying theinfluence of climate factors on ET0 ey concluded thatchanges in radiation and wind speed dominated the impactof ET0 in some regions [21 22] while water vapor indicatorsand temperature were the main reasons for changes in otherregions [23 24] ese contradictory explanations and re-cent studies show that it is necessary to pay attention to therelationship between ET0 and climate factors in the futureresearch

e Ningxia Hui Autonomous Region one of the im-portant parts of ldquothe Belt and Roadrdquo is located in thetemperate continental arid and semiarid areae east westand north are surrounded by Maowusu Tengger andUlanbu deserts respectively And the desertification landarea accounts for 5368 of the total area [25] e amountof water resources is limited and the demand for water hasincreased dramatically thus there is a sharp contradictionbetween economic development and limited water resources[26] In the past decade based on the Ningxia Water Re-sources Bulletin the water consumption has increased bytwo times and agriculture is the largest water-consumingsector with more than 85 of water supplied for irrigation[25] In addition water resource is being directed from theagricultural sector to industry and other sectors thus foodsecurity and agricultural water security are facing severepressure in Ningxia ET0 is a key component of the water

cycle process and plays an important role in assessing waterresources shortage [27] Consequently the analysis of ET0and its response to climate change can not only help tounderstand climate change but also improve watermanagement

In general the main objectives of this study are asfollows (1) to discuss the interannual variation trend ofclimate factors (2) to analyze monthly seasonal and an-nual ET0 of spatiotemporal changes (3) to explore theabrupt change and periodicity of ET0 series and (4) toquantify the impact of climate factors on ET0 change andidentify dominant factors is research will help to raiseawareness of climate change and provide valuable referencefor researchers and policymakers to guide regional watermanagement agricultural production and conservation ofthe environment

2 Materials and Methods

To analyze the changes in reference evapotranspiration(ET0) over Ningxia at multi-time scales an approachframework for ET0 analysis pattern selection was proposede framework mainly included the climate trend analysisET0 spatiotemporal analysis abrupt change and periodicityanalysis and ET0 influence factors analysis (Figure 1)

21 Study Area and Climate Data e Ningxia Hui Au-tonomous Region (NX) (35deg14primesim39deg23primeN 104deg17primesim107deg39primeE)is part of the Yellow River basin and is located among theAlxa Plateau the North China Plateau and the QilianMountains folds e area consists of Northern Yellow Riverirrigation area (NYR) Central Arid Zone (CAZ) andSouthern Mountain Area (SMA) and has a total area of66times104 km2 [28] (Figure 2) is region faces a seriousshortage of water resources and a huge contradiction be-tween water supply and demand And it has also led toserious environmental problems due to excessive use ofwater resources including soil erosion water pollution anddeterioration of the ecological environment [29] Moreoverthe seasons are divided into climate standards namelyspring from March to May summer from June to Augustautumn from September to November and winter fromDecember to February [30] In addition the statistical sig-nificance of the linear trend and correlation analysis in thisstudy was labeled as significance levels plt 005 (lowast) andplt 001 (lowastlowast) unless otherwise stated

In Ningxia 20 climate stations with 62 years (from 1957to 2018) of climate data were selected (Table 1) e climatedata including daily minimum air temperature (Tmin degC)daily mean air temperature (Tmean degC) daily maximum airtemperature (Tmax degC) sunshine duration (SD h) relativehumidity (RH ) and wind speed at 2m height (U2 ms)were downloaded from the National Meteorological Infor-mation Center (httpdatacmacn) e datasets are avail-able because the missing data rate handled by quality controlis less than 01 and some missing points were interpolatedbased on the regression relationship with those of the ad-jacent stations








Δ + c 1 + 034u2( 1113857

(2)





Tmean

Tmean

Tmax

Tmax

Tmin

Tmin

SD U2

U2

RH


Clim

ate t

rend

an

alys

es

Time Space



AbruptPeriodic

SD RH


Spat

iote

mpo

ral

anal

ysis

Sens

itivi

ty an

d co

ntrib

utio

n ra

te

anal

ysis


Correlation





Var sk( 1113857

1113969 k 1 2 3

sk 1113944k

i1ri

ri

1 if xi gtxj

0 else

⎧⎪⎨

⎪⎩

j 1 2 i

UBk minusUFk

k n + 1 minus k

⎧⎪⎨

⎪⎩

k 1 2 n

(3)




ψ(t) eict

eminus t22( )



Var(a) 1113946R

Wf(a b)11138681113868111386811138681113868

111386811138681113868111386811138682db

(4)




0 460 920 1380 1840230km

Gansu

NeiMongol

Ningxia

Shaanxi

Shaanxi

Yinchuan

Guyuan

Wuzhong

Zhongwei

Shizuishan

0 2040 80 120 160km

Xiji

Taole

Longde

Guyuan

Yanchi

Lingwu

Haiyuan

XingrenTongxin

Weizhou

Wuzhong

DawukouHuinong

Jingyuan

ZhogningZhongwei

Yinchuan

Liupanshan

Mahuangshan

Qingtongxia

km

Regional division


Provincial boundary

Weather station

80deg0prime0PrimeE

50deg0prime0Prime

N40

deg0prime0Prime

N30

deg0prime0Prime

N20

deg0prime0Prime

N

50deg0prime0Prime

N40

deg0prime0Prime

N30

deg0prime0Prime

N20

deg0prime0Prime

N

39deg0prime0Prime

N38

deg0prime0Prime

N36

deg0prime0Prime

N37

deg0prime0Prime

N35

deg0prime0Prime

N

39deg0prime0Prime

N38

deg0prime0Prime

N36

deg0prime0Prime

N37

deg0prime0Prime

N35

deg0prime0Prime

N





39deg0prime0Prime

N38

deg0prime0Prime

N36

deg0prime0Prime

N37

deg0prime0Prime

N35

deg0prime0Prime

N

39deg0prime0Prime

N38

deg0prime0Prime

N36

deg0prime0Prime

N37

deg0prime0Prime

N35

deg0prime0Prime

N



0 15 30 60 90 120

Provincial capital


Provincial boundary

N N

N

(a)(b) (c)




(Δxx)1113888 1113889

zET0

zxmiddot

x

ET0 (5)



Cx Sx middot Rcx

Rcx n middot Trendx

xmiddot 100

(6)


3 Results








NYR


CAZ


SMA







60

50

20

10

0

Fact

or v

alue

s

1960 1970 1980 1990 2000 2010 2020Year

y = 0051x ndash 105852

y = 0034x ndash 59207

y = ndash0010x + 22238

y = ndash0003x + 13068

y = 0031x ndash 39340 y = ndash0042x + 140084


RHSDh

(a)

6050

20

10

0

Fact

or v

alue

s

1960 1970 1980 1990 2000 2010 2020Year

y = 0054x ndash 112294

y = 0038x ndash 66372

y = 0036x ndash 47694

y = ndash0008x + 19277

y = ndash0005x + 18643

y = ndash0081x + 214929


RHSDh

(b)

6050

20

10

0

Fact

or v

alue

s

1960 1970 1980 1990 2000 2010 2020Year

y = 0024x ndash 24640

y = 0048x ndash 101028

y = ndash0011x + 23616

y = ndash0006x + 63327

y = 0031x ndash 53876y = ndash0004x + 15391


RHSDh

(c)

7560

20

10

0

Fact

or v

alue

s

1960 1970 1980 1990 2000 2010 2020Year

y = 0049x ndash 104234

y = 0032x ndash 57373

y = 0332x ndash 45687

y = ndash0011x + 23821

y = ndash0007x + 5169

y = ndash0040x + 141996


RHSDh

(d)











NX (whole)


NYR


CAZ


SMA






104 62 120 8Month

ndash20

0

20

40

60

Fact

or v

alue

s


RHSDhU2(ms)

(a)

2 4 6 8 10 120Month

ndash20

0

20

40

60

Fact

or v

alue

s


RHSDhU2(ms)

(b)

ndash20

0

20

40

60

Fact

or v

alue

s

2 4 6 8 10 120Month


RHSDhU2(ms)

(c)

ndash20

0

20

40

60

Fact

or v

alue

s

2 4 6 8 10 120Month


RHSDhU2(ms)

(d)





ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(a)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(b)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(c)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(d)




5 17703(max) 17673 13635 16880

6 17490 17975(max)

13790(max)

16895(max)



1 2 3 4 5 6 7 8 9 10 11 12Month

0

50

100

150

200

ET0 (

mm

)

NYRCAZ

SMANX




39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degNkm

0 15 30 60 90 120



N

(a)





39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN

km0 15 30 60 90 120

ET0 (mm)

N

(b)

Figure 7 Continued







36ndash4041ndash45



39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN

km0 15 30 60 90 120

ET0 (mm)

N

(c)



y = 0278x ndash 147669

y = ndash0234x + 966920

y = 0155x + 170088

y = ndash0082x + 397356

y = 0104x + 11213

y = ndash0055x + 246809

y = 0041x + 38831

y = ndash0080x + 583001

Spring

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

300

400

500

ET0 (m

m)

400

500

600

ET0 (m

m)

100

200

300

400

ET0 (m

m)

100

200

ET0 (m

m)

CAZNX

CAZNX

CAZNX

CAZNX

Summer

Autumn

Winter

(a)

100

0

200

ET0 (m

m)

y = 0545x ndash 655033

y = 0151x + 24724

y = 0319x ndash 136403

y = 0326x ndash 258933

y = 0209x ndash 187731

y = 0124x ndash 70641

y = 0152x ndash 185863

y = ndash0092x + 288599

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

200

300

400

500

600

ET0 (m

m)

ET0 (m

m)

300

400

500

600

100

200

300

400

ET0 (m

m)

NYRSMA

NYRSMA

NYRSMA

NYRSMA

Spring

Summer

Autumn

Winter

(b)














1957sim1990 1991sim2018 1957sim2018

NX (whole)


NYR


CAZ


SMA



0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N



(a)

0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N



(b)

Figure 9 Continued


0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



ET0mm

N



(c)

0 30 60 90 12015Km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N



0 30 60 90 12015km

(d)


1600

1500

1400

1300

1200

1100

1000

900

ET0 (

mm

)


y = 0579x ndash 7246

CAZNX

1960 1970 1980 1990 2000 20202010Year

(a)

800

1500

1400

1300

1200

1100

1000

900

ET0 (

mm

)

y = 0509x ndash 16251


NYRSMA

1960 1970 1980 1990 2000 20202010Year

(b)







4 Discussion





0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N


1152ndash12831284ndash1416



1960 1970 1980 1990 2000 2010

10

20

30Ye

ars

Year

100 100

100

(a)

Year

s

1960 1970 1980 1990 2000 2010

10

20

30

Year

50

50

50

50

5050

(b)

1960 1970 1980 1990 2000 2010

10

20

30

Year100

100

100

Year

s

(c)

1960 1970 1980 1990 2000 2010

10

20

30

Yearndash2

0 ndash2080

ndash20

ndash20

ndash20

ndash20


8080

ndash20

Year

s

(d)


ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(a)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(b)

Figure 13 Continued


ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(c)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(d)


1960 1970 1980 1990 2000 2010

10

20

30

Tim

e sca

le (a

)

Year

1040

0

10

10

10

10 1010

1010

10

(a)

Tim

e sca

le (a

)

1960 1970 1980 1990 2000 2010

10

20

30

Year

050

50

0

0 0 0 0

0 0 0

0 0 0 0 0 0 000

500

(b)

Tim

e sca

le (a

)

1960 1970 1980 1990 2000 2010

10

20

30

Year

10

10

10

10

10

10 10

10

10

40

(c)

Tim

e sca

le (a

)

1960 1970 1980 1990 2000 2010

10

20

30

Year

1010

10

10

10

1010

4010

10

10 10

(d)









NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

12

Con

trib

utio

n ra

te (m

ma

)




NYR


CAZ


SMA


NX





NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(a)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(b)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(c)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash24

ndash16

ndash08

00

08

16

24

Con

trib

utio

n ra

te (m

ma

)

(d)


Clou

d op

tical

thic

knes

s

y =0386x ndash 755403

28

24

20

16


y = ndash13418 + 32542427

1980 2000 20201970 19901960 2010Year

5200

5600

6000

6400To

tal s

olar

radi

atio

n (M

Jm2 )








5 Conclusions







Data Availability







Acknowledgments


References





































































Δ + c 1 + 034u2( 1113857

(2)





Tmean

Tmean

Tmax

Tmax

Tmin

Tmin

SD U2

U2

RH


Clim

ate t

rend

an

alys

es

Time Space



AbruptPeriodic

SD RH


Spat

iote

mpo

ral

anal

ysis

Sens

itivi

ty an

d co

ntrib

utio

n ra

te

anal

ysis


Correlation





Var sk( 1113857

1113969 k 1 2 3

sk 1113944k

i1ri

ri

1 if xi gtxj

0 else

⎧⎪⎨

⎪⎩

j 1 2 i

UBk minusUFk

k n + 1 minus k

⎧⎪⎨

⎪⎩

k 1 2 n

(3)




ψ(t) eict

eminus t22( )



Var(a) 1113946R

Wf(a b)11138681113868111386811138681113868

111386811138681113868111386811138682db

(4)




0 460 920 1380 1840230km

Gansu

NeiMongol

Ningxia

Shaanxi

Shaanxi

Yinchuan

Guyuan

Wuzhong

Zhongwei

Shizuishan

0 2040 80 120 160km

Xiji

Taole

Longde

Guyuan

Yanchi

Lingwu

Haiyuan

XingrenTongxin

Weizhou

Wuzhong

DawukouHuinong

Jingyuan

ZhogningZhongwei

Yinchuan

Liupanshan

Mahuangshan

Qingtongxia

km

Regional division


Provincial boundary

Weather station

80deg0prime0PrimeE

50deg0prime0Prime

N40

deg0prime0Prime

N30

deg0prime0Prime

N20

deg0prime0Prime

N

50deg0prime0Prime

N40

deg0prime0Prime

N30

deg0prime0Prime

N20

deg0prime0Prime

N

39deg0prime0Prime

N38

deg0prime0Prime

N36

deg0prime0Prime

N37

deg0prime0Prime

N35

deg0prime0Prime

N

39deg0prime0Prime

N38

deg0prime0Prime

N36

deg0prime0Prime

N37

deg0prime0Prime

N35

deg0prime0Prime

N





39deg0prime0Prime

N38

deg0prime0Prime

N36

deg0prime0Prime

N37

deg0prime0Prime

N35

deg0prime0Prime

N

39deg0prime0Prime

N38

deg0prime0Prime

N36

deg0prime0Prime

N37

deg0prime0Prime

N35

deg0prime0Prime

N



0 15 30 60 90 120

Provincial capital


Provincial boundary

N N

N

(a)(b) (c)




(Δxx)1113888 1113889

zET0

zxmiddot

x

ET0 (5)



Cx Sx middot Rcx

Rcx n middot Trendx

xmiddot 100

(6)


3 Results








NYR


CAZ


SMA







60

50

20

10

0

Fact

or v

alue

s

1960 1970 1980 1990 2000 2010 2020Year

y = 0051x ndash 105852

y = 0034x ndash 59207

y = ndash0010x + 22238

y = ndash0003x + 13068

y = 0031x ndash 39340 y = ndash0042x + 140084


RHSDh

(a)

6050

20

10

0

Fact

or v

alue

s

1960 1970 1980 1990 2000 2010 2020Year

y = 0054x ndash 112294

y = 0038x ndash 66372

y = 0036x ndash 47694

y = ndash0008x + 19277

y = ndash0005x + 18643

y = ndash0081x + 214929


RHSDh

(b)

6050

20

10

0

Fact

or v

alue

s

1960 1970 1980 1990 2000 2010 2020Year

y = 0024x ndash 24640

y = 0048x ndash 101028

y = ndash0011x + 23616

y = ndash0006x + 63327

y = 0031x ndash 53876y = ndash0004x + 15391


RHSDh

(c)

7560

20

10

0

Fact

or v

alue

s

1960 1970 1980 1990 2000 2010 2020Year

y = 0049x ndash 104234

y = 0032x ndash 57373

y = 0332x ndash 45687

y = ndash0011x + 23821

y = ndash0007x + 5169

y = ndash0040x + 141996


RHSDh

(d)











NX (whole)


NYR


CAZ


SMA






104 62 120 8Month

ndash20

0

20

40

60

Fact

or v

alue

s


RHSDhU2(ms)

(a)

2 4 6 8 10 120Month

ndash20

0

20

40

60

Fact

or v

alue

s


RHSDhU2(ms)

(b)

ndash20

0

20

40

60

Fact

or v

alue

s

2 4 6 8 10 120Month


RHSDhU2(ms)

(c)

ndash20

0

20

40

60

Fact

or v

alue

s

2 4 6 8 10 120Month


RHSDhU2(ms)

(d)





ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(a)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(b)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(c)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(d)




5 17703(max) 17673 13635 16880

6 17490 17975(max)

13790(max)

16895(max)



1 2 3 4 5 6 7 8 9 10 11 12Month

0

50

100

150

200

ET0 (

mm

)

NYRCAZ

SMANX




39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degNkm

0 15 30 60 90 120



N

(a)





39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN

km0 15 30 60 90 120

ET0 (mm)

N

(b)

Figure 7 Continued







36ndash4041ndash45



39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN

km0 15 30 60 90 120

ET0 (mm)

N

(c)



y = 0278x ndash 147669

y = ndash0234x + 966920

y = 0155x + 170088

y = ndash0082x + 397356

y = 0104x + 11213

y = ndash0055x + 246809

y = 0041x + 38831

y = ndash0080x + 583001

Spring

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

300

400

500

ET0 (m

m)

400

500

600

ET0 (m

m)

100

200

300

400

ET0 (m

m)

100

200

ET0 (m

m)

CAZNX

CAZNX

CAZNX

CAZNX

Summer

Autumn

Winter

(a)

100

0

200

ET0 (m

m)

y = 0545x ndash 655033

y = 0151x + 24724

y = 0319x ndash 136403

y = 0326x ndash 258933

y = 0209x ndash 187731

y = 0124x ndash 70641

y = 0152x ndash 185863

y = ndash0092x + 288599

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

200

300

400

500

600

ET0 (m

m)

ET0 (m

m)

300

400

500

600

100

200

300

400

ET0 (m

m)

NYRSMA

NYRSMA

NYRSMA

NYRSMA

Spring

Summer

Autumn

Winter

(b)














1957sim1990 1991sim2018 1957sim2018

NX (whole)


NYR


CAZ


SMA



0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N



(a)

0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N



(b)

Figure 9 Continued


0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



ET0mm

N



(c)

0 30 60 90 12015Km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N



0 30 60 90 12015km

(d)


1600

1500

1400

1300

1200

1100

1000

900

ET0 (

mm

)


y = 0579x ndash 7246

CAZNX

1960 1970 1980 1990 2000 20202010Year

(a)

800

1500

1400

1300

1200

1100

1000

900

ET0 (

mm

)

y = 0509x ndash 16251


NYRSMA

1960 1970 1980 1990 2000 20202010Year

(b)







4 Discussion





0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N


1152ndash12831284ndash1416



1960 1970 1980 1990 2000 2010

10

20

30Ye

ars

Year

100 100

100

(a)

Year

s

1960 1970 1980 1990 2000 2010

10

20

30

Year

50

50

50

50

5050

(b)

1960 1970 1980 1990 2000 2010

10

20

30

Year100

100

100

Year

s

(c)

1960 1970 1980 1990 2000 2010

10

20

30

Yearndash2

0 ndash2080

ndash20

ndash20

ndash20

ndash20


8080

ndash20

Year

s

(d)


ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(a)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(b)

Figure 13 Continued


ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(c)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(d)


1960 1970 1980 1990 2000 2010

10

20

30

Tim

e sca

le (a

)

Year

1040

0

10

10

10

10 1010

1010

10

(a)

Tim

e sca

le (a

)

1960 1970 1980 1990 2000 2010

10

20

30

Year

050

50

0

0 0 0 0

0 0 0

0 0 0 0 0 0 000

500

(b)

Tim

e sca

le (a

)

1960 1970 1980 1990 2000 2010

10

20

30

Year

10

10

10

10

10

10 10

10

10

40

(c)

Tim

e sca

le (a

)

1960 1970 1980 1990 2000 2010

10

20

30

Year

1010

10

10

10

1010

4010

10

10 10

(d)









NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

12

Con

trib

utio

n ra

te (m

ma

)




NYR


CAZ


SMA


NX





NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(a)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(b)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(c)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash24

ndash16

ndash08

00

08

16

24

Con

trib

utio

n ra

te (m

ma

)

(d)


Clou

d op

tical

thic

knes

s

y =0386x ndash 755403

28

24

20

16


y = ndash13418 + 32542427

1980 2000 20201970 19901960 2010Year

5200

5600

6000

6400To

tal s

olar

radi

atio

n (M

Jm2 )








5 Conclusions







Data Availability







Acknowledgments


References

































































Var sk( 1113857

1113969 k 1 2 3

sk 1113944k

i1ri

ri

1 if xi gtxj

0 else

⎧⎪⎨

⎪⎩

j 1 2 i

UBk minusUFk

k n + 1 minus k

⎧⎪⎨

⎪⎩

k 1 2 n

(3)




ψ(t) eict

eminus t22( )



Var(a) 1113946R

Wf(a b)11138681113868111386811138681113868

111386811138681113868111386811138682db

(4)




0 460 920 1380 1840230km

Gansu

NeiMongol

Ningxia

Shaanxi

Shaanxi

Yinchuan

Guyuan

Wuzhong

Zhongwei

Shizuishan

0 2040 80 120 160km

Xiji

Taole

Longde

Guyuan

Yanchi

Lingwu

Haiyuan

XingrenTongxin

Weizhou

Wuzhong

DawukouHuinong

Jingyuan

ZhogningZhongwei

Yinchuan

Liupanshan

Mahuangshan

Qingtongxia

km

Regional division


Provincial boundary

Weather station

80deg0prime0PrimeE

50deg0prime0Prime

N40

deg0prime0Prime

N30

deg0prime0Prime

N20

deg0prime0Prime

N

50deg0prime0Prime

N40

deg0prime0Prime

N30

deg0prime0Prime

N20

deg0prime0Prime

N

39deg0prime0Prime

N38

deg0prime0Prime

N36

deg0prime0Prime

N37

deg0prime0Prime

N35

deg0prime0Prime

N

39deg0prime0Prime

N38

deg0prime0Prime

N36

deg0prime0Prime

N37

deg0prime0Prime

N35

deg0prime0Prime

N





39deg0prime0Prime

N38

deg0prime0Prime

N36

deg0prime0Prime

N37

deg0prime0Prime

N35

deg0prime0Prime

N

39deg0prime0Prime

N38

deg0prime0Prime

N36

deg0prime0Prime

N37

deg0prime0Prime

N35

deg0prime0Prime

N



0 15 30 60 90 120

Provincial capital


Provincial boundary

N N

N

(a)(b) (c)




(Δxx)1113888 1113889

zET0

zxmiddot

x

ET0 (5)



Cx Sx middot Rcx

Rcx n middot Trendx

xmiddot 100

(6)


3 Results








NYR


CAZ


SMA







60

50

20

10

0

Fact

or v

alue

s

1960 1970 1980 1990 2000 2010 2020Year

y = 0051x ndash 105852

y = 0034x ndash 59207

y = ndash0010x + 22238

y = ndash0003x + 13068

y = 0031x ndash 39340 y = ndash0042x + 140084


RHSDh

(a)

6050

20

10

0

Fact

or v

alue

s

1960 1970 1980 1990 2000 2010 2020Year

y = 0054x ndash 112294

y = 0038x ndash 66372

y = 0036x ndash 47694

y = ndash0008x + 19277

y = ndash0005x + 18643

y = ndash0081x + 214929


RHSDh

(b)

6050

20

10

0

Fact

or v

alue

s

1960 1970 1980 1990 2000 2010 2020Year

y = 0024x ndash 24640

y = 0048x ndash 101028

y = ndash0011x + 23616

y = ndash0006x + 63327

y = 0031x ndash 53876y = ndash0004x + 15391


RHSDh

(c)

7560

20

10

0

Fact

or v

alue

s

1960 1970 1980 1990 2000 2010 2020Year

y = 0049x ndash 104234

y = 0032x ndash 57373

y = 0332x ndash 45687

y = ndash0011x + 23821

y = ndash0007x + 5169

y = ndash0040x + 141996


RHSDh

(d)











NX (whole)


NYR


CAZ


SMA






104 62 120 8Month

ndash20

0

20

40

60

Fact

or v

alue

s


RHSDhU2(ms)

(a)

2 4 6 8 10 120Month

ndash20

0

20

40

60

Fact

or v

alue

s


RHSDhU2(ms)

(b)

ndash20

0

20

40

60

Fact

or v

alue

s

2 4 6 8 10 120Month


RHSDhU2(ms)

(c)

ndash20

0

20

40

60

Fact

or v

alue

s

2 4 6 8 10 120Month


RHSDhU2(ms)

(d)





ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(a)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(b)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(c)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(d)




5 17703(max) 17673 13635 16880

6 17490 17975(max)

13790(max)

16895(max)



1 2 3 4 5 6 7 8 9 10 11 12Month

0

50

100

150

200

ET0 (

mm

)

NYRCAZ

SMANX




39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degNkm

0 15 30 60 90 120



N

(a)





39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN

km0 15 30 60 90 120

ET0 (mm)

N

(b)

Figure 7 Continued







36ndash4041ndash45



39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN

km0 15 30 60 90 120

ET0 (mm)

N

(c)



y = 0278x ndash 147669

y = ndash0234x + 966920

y = 0155x + 170088

y = ndash0082x + 397356

y = 0104x + 11213

y = ndash0055x + 246809

y = 0041x + 38831

y = ndash0080x + 583001

Spring

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

300

400

500

ET0 (m

m)

400

500

600

ET0 (m

m)

100

200

300

400

ET0 (m

m)

100

200

ET0 (m

m)

CAZNX

CAZNX

CAZNX

CAZNX

Summer

Autumn

Winter

(a)

100

0

200

ET0 (m

m)

y = 0545x ndash 655033

y = 0151x + 24724

y = 0319x ndash 136403

y = 0326x ndash 258933

y = 0209x ndash 187731

y = 0124x ndash 70641

y = 0152x ndash 185863

y = ndash0092x + 288599

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

200

300

400

500

600

ET0 (m

m)

ET0 (m

m)

300

400

500

600

100

200

300

400

ET0 (m

m)

NYRSMA

NYRSMA

NYRSMA

NYRSMA

Spring

Summer

Autumn

Winter

(b)














1957sim1990 1991sim2018 1957sim2018

NX (whole)


NYR


CAZ


SMA



0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N



(a)

0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N



(b)

Figure 9 Continued


0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



ET0mm

N



(c)

0 30 60 90 12015Km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N



0 30 60 90 12015km

(d)


1600

1500

1400

1300

1200

1100

1000

900

ET0 (

mm

)


y = 0579x ndash 7246

CAZNX

1960 1970 1980 1990 2000 20202010Year

(a)

800

1500

1400

1300

1200

1100

1000

900

ET0 (

mm

)

y = 0509x ndash 16251


NYRSMA

1960 1970 1980 1990 2000 20202010Year

(b)







4 Discussion





0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N


1152ndash12831284ndash1416



1960 1970 1980 1990 2000 2010

10

20

30Ye

ars

Year

100 100

100

(a)

Year

s

1960 1970 1980 1990 2000 2010

10

20

30

Year

50

50

50

50

5050

(b)

1960 1970 1980 1990 2000 2010

10

20

30

Year100

100

100

Year

s

(c)

1960 1970 1980 1990 2000 2010

10

20

30

Yearndash2

0 ndash2080

ndash20

ndash20

ndash20

ndash20


8080

ndash20

Year

s

(d)


ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(a)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(b)

Figure 13 Continued


ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(c)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(d)


1960 1970 1980 1990 2000 2010

10

20

30

Tim

e sca

le (a

)

Year

1040

0

10

10

10

10 1010

1010

10

(a)

Tim

e sca

le (a

)

1960 1970 1980 1990 2000 2010

10

20

30

Year

050

50

0

0 0 0 0

0 0 0

0 0 0 0 0 0 000

500

(b)

Tim

e sca

le (a

)

1960 1970 1980 1990 2000 2010

10

20

30

Year

10

10

10

10

10

10 10

10

10

40

(c)

Tim

e sca

le (a

)

1960 1970 1980 1990 2000 2010

10

20

30

Year

1010

10

10

10

1010

4010

10

10 10

(d)









NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

12

Con

trib

utio

n ra

te (m

ma

)




NYR


CAZ


SMA


NX





NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(a)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(b)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(c)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash24

ndash16

ndash08

00

08

16

24

Con

trib

utio

n ra

te (m

ma

)

(d)


Clou

d op

tical

thic

knes

s

y =0386x ndash 755403

28

24

20

16


y = ndash13418 + 32542427

1980 2000 20201970 19901960 2010Year

5200

5600

6000

6400To

tal s

olar

radi

atio

n (M

Jm2 )








5 Conclusions







Data Availability







Acknowledgments


References
































































(Δxx)1113888 1113889

zET0

zxmiddot

x

ET0 (5)



Cx Sx middot Rcx

Rcx n middot Trendx

xmiddot 100

(6)


3 Results








NYR


CAZ


SMA







60

50

20

10

0

Fact

or v

alue

s

1960 1970 1980 1990 2000 2010 2020Year

y = 0051x ndash 105852

y = 0034x ndash 59207

y = ndash0010x + 22238

y = ndash0003x + 13068

y = 0031x ndash 39340 y = ndash0042x + 140084


RHSDh

(a)

6050

20

10

0

Fact

or v

alue

s

1960 1970 1980 1990 2000 2010 2020Year

y = 0054x ndash 112294

y = 0038x ndash 66372

y = 0036x ndash 47694

y = ndash0008x + 19277

y = ndash0005x + 18643

y = ndash0081x + 214929


RHSDh

(b)

6050

20

10

0

Fact

or v

alue

s

1960 1970 1980 1990 2000 2010 2020Year

y = 0024x ndash 24640

y = 0048x ndash 101028

y = ndash0011x + 23616

y = ndash0006x + 63327

y = 0031x ndash 53876y = ndash0004x + 15391


RHSDh

(c)

7560

20

10

0

Fact

or v

alue

s

1960 1970 1980 1990 2000 2010 2020Year

y = 0049x ndash 104234

y = 0032x ndash 57373

y = 0332x ndash 45687

y = ndash0011x + 23821

y = ndash0007x + 5169

y = ndash0040x + 141996


RHSDh

(d)











NX (whole)


NYR


CAZ


SMA






104 62 120 8Month

ndash20

0

20

40

60

Fact

or v

alue

s


RHSDhU2(ms)

(a)

2 4 6 8 10 120Month

ndash20

0

20

40

60

Fact

or v

alue

s


RHSDhU2(ms)

(b)

ndash20

0

20

40

60

Fact

or v

alue

s

2 4 6 8 10 120Month


RHSDhU2(ms)

(c)

ndash20

0

20

40

60

Fact

or v

alue

s

2 4 6 8 10 120Month


RHSDhU2(ms)

(d)





ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(a)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(b)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(c)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(d)




5 17703(max) 17673 13635 16880

6 17490 17975(max)

13790(max)

16895(max)



1 2 3 4 5 6 7 8 9 10 11 12Month

0

50

100

150

200

ET0 (

mm

)

NYRCAZ

SMANX




39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degNkm

0 15 30 60 90 120



N

(a)





39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN

km0 15 30 60 90 120

ET0 (mm)

N

(b)

Figure 7 Continued







36ndash4041ndash45



39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN

km0 15 30 60 90 120

ET0 (mm)

N

(c)



y = 0278x ndash 147669

y = ndash0234x + 966920

y = 0155x + 170088

y = ndash0082x + 397356

y = 0104x + 11213

y = ndash0055x + 246809

y = 0041x + 38831

y = ndash0080x + 583001

Spring

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

300

400

500

ET0 (m

m)

400

500

600

ET0 (m

m)

100

200

300

400

ET0 (m

m)

100

200

ET0 (m

m)

CAZNX

CAZNX

CAZNX

CAZNX

Summer

Autumn

Winter

(a)

100

0

200

ET0 (m

m)

y = 0545x ndash 655033

y = 0151x + 24724

y = 0319x ndash 136403

y = 0326x ndash 258933

y = 0209x ndash 187731

y = 0124x ndash 70641

y = 0152x ndash 185863

y = ndash0092x + 288599

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

200

300

400

500

600

ET0 (m

m)

ET0 (m

m)

300

400

500

600

100

200

300

400

ET0 (m

m)

NYRSMA

NYRSMA

NYRSMA

NYRSMA

Spring

Summer

Autumn

Winter

(b)














1957sim1990 1991sim2018 1957sim2018

NX (whole)


NYR


CAZ


SMA



0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N



(a)

0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N



(b)

Figure 9 Continued


0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



ET0mm

N



(c)

0 30 60 90 12015Km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N



0 30 60 90 12015km

(d)


1600

1500

1400

1300

1200

1100

1000

900

ET0 (

mm

)


y = 0579x ndash 7246

CAZNX

1960 1970 1980 1990 2000 20202010Year

(a)

800

1500

1400

1300

1200

1100

1000

900

ET0 (

mm

)

y = 0509x ndash 16251


NYRSMA

1960 1970 1980 1990 2000 20202010Year

(b)







4 Discussion





0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N


1152ndash12831284ndash1416



1960 1970 1980 1990 2000 2010

10

20

30Ye

ars

Year

100 100

100

(a)

Year

s

1960 1970 1980 1990 2000 2010

10

20

30

Year

50

50

50

50

5050

(b)

1960 1970 1980 1990 2000 2010

10

20

30

Year100

100

100

Year

s

(c)

1960 1970 1980 1990 2000 2010

10

20

30

Yearndash2

0 ndash2080

ndash20

ndash20

ndash20

ndash20


8080

ndash20

Year

s

(d)


ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(a)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(b)

Figure 13 Continued


ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(c)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(d)


1960 1970 1980 1990 2000 2010

10

20

30

Tim

e sca

le (a

)

Year

1040

0

10

10

10

10 1010

1010

10

(a)

Tim

e sca

le (a

)

1960 1970 1980 1990 2000 2010

10

20

30

Year

050

50

0

0 0 0 0

0 0 0

0 0 0 0 0 0 000

500

(b)

Tim

e sca

le (a

)

1960 1970 1980 1990 2000 2010

10

20

30

Year

10

10

10

10

10

10 10

10

10

40

(c)

Tim

e sca

le (a

)

1960 1970 1980 1990 2000 2010

10

20

30

Year

1010

10

10

10

1010

4010

10

10 10

(d)









NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

12

Con

trib

utio

n ra

te (m

ma

)




NYR


CAZ


SMA


NX





NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(a)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(b)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(c)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash24

ndash16

ndash08

00

08

16

24

Con

trib

utio

n ra

te (m

ma

)

(d)


Clou

d op

tical

thic

knes

s

y =0386x ndash 755403

28

24

20

16


y = ndash13418 + 32542427

1980 2000 20201970 19901960 2010Year

5200

5600

6000

6400To

tal s

olar

radi

atio

n (M

Jm2 )








5 Conclusions







Data Availability







Acknowledgments


References



































































60

50

20

10

0

Fact

or v

alue

s

1960 1970 1980 1990 2000 2010 2020Year

y = 0051x ndash 105852

y = 0034x ndash 59207

y = ndash0010x + 22238

y = ndash0003x + 13068

y = 0031x ndash 39340 y = ndash0042x + 140084


RHSDh

(a)

6050

20

10

0

Fact

or v

alue

s

1960 1970 1980 1990 2000 2010 2020Year

y = 0054x ndash 112294

y = 0038x ndash 66372

y = 0036x ndash 47694

y = ndash0008x + 19277

y = ndash0005x + 18643

y = ndash0081x + 214929


RHSDh

(b)

6050

20

10

0

Fact

or v

alue

s

1960 1970 1980 1990 2000 2010 2020Year

y = 0024x ndash 24640

y = 0048x ndash 101028

y = ndash0011x + 23616

y = ndash0006x + 63327

y = 0031x ndash 53876y = ndash0004x + 15391


RHSDh

(c)

7560

20

10

0

Fact

or v

alue

s

1960 1970 1980 1990 2000 2010 2020Year

y = 0049x ndash 104234

y = 0032x ndash 57373

y = 0332x ndash 45687

y = ndash0011x + 23821

y = ndash0007x + 5169

y = ndash0040x + 141996


RHSDh

(d)











NX (whole)


NYR


CAZ


SMA






104 62 120 8Month

ndash20

0

20

40

60

Fact

or v

alue

s


RHSDhU2(ms)

(a)

2 4 6 8 10 120Month

ndash20

0

20

40

60

Fact

or v

alue

s


RHSDhU2(ms)

(b)

ndash20

0

20

40

60

Fact

or v

alue

s

2 4 6 8 10 120Month


RHSDhU2(ms)

(c)

ndash20

0

20

40

60

Fact

or v

alue

s

2 4 6 8 10 120Month


RHSDhU2(ms)

(d)





ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(a)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(b)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(c)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(d)




5 17703(max) 17673 13635 16880

6 17490 17975(max)

13790(max)

16895(max)



1 2 3 4 5 6 7 8 9 10 11 12Month

0

50

100

150

200

ET0 (

mm

)

NYRCAZ

SMANX




39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degNkm

0 15 30 60 90 120



N

(a)





39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN

km0 15 30 60 90 120

ET0 (mm)

N

(b)

Figure 7 Continued







36ndash4041ndash45



39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN

km0 15 30 60 90 120

ET0 (mm)

N

(c)



y = 0278x ndash 147669

y = ndash0234x + 966920

y = 0155x + 170088

y = ndash0082x + 397356

y = 0104x + 11213

y = ndash0055x + 246809

y = 0041x + 38831

y = ndash0080x + 583001

Spring

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

300

400

500

ET0 (m

m)

400

500

600

ET0 (m

m)

100

200

300

400

ET0 (m

m)

100

200

ET0 (m

m)

CAZNX

CAZNX

CAZNX

CAZNX

Summer

Autumn

Winter

(a)

100

0

200

ET0 (m

m)

y = 0545x ndash 655033

y = 0151x + 24724

y = 0319x ndash 136403

y = 0326x ndash 258933

y = 0209x ndash 187731

y = 0124x ndash 70641

y = 0152x ndash 185863

y = ndash0092x + 288599

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

200

300

400

500

600

ET0 (m

m)

ET0 (m

m)

300

400

500

600

100

200

300

400

ET0 (m

m)

NYRSMA

NYRSMA

NYRSMA

NYRSMA

Spring

Summer

Autumn

Winter

(b)














1957sim1990 1991sim2018 1957sim2018

NX (whole)


NYR


CAZ


SMA



0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N



(a)

0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N



(b)

Figure 9 Continued


0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



ET0mm

N



(c)

0 30 60 90 12015Km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N



0 30 60 90 12015km

(d)


1600

1500

1400

1300

1200

1100

1000

900

ET0 (

mm

)


y = 0579x ndash 7246

CAZNX

1960 1970 1980 1990 2000 20202010Year

(a)

800

1500

1400

1300

1200

1100

1000

900

ET0 (

mm

)

y = 0509x ndash 16251


NYRSMA

1960 1970 1980 1990 2000 20202010Year

(b)







4 Discussion





0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N


1152ndash12831284ndash1416



1960 1970 1980 1990 2000 2010

10

20

30Ye

ars

Year

100 100

100

(a)

Year

s

1960 1970 1980 1990 2000 2010

10

20

30

Year

50

50

50

50

5050

(b)

1960 1970 1980 1990 2000 2010

10

20

30

Year100

100

100

Year

s

(c)

1960 1970 1980 1990 2000 2010

10

20

30

Yearndash2

0 ndash2080

ndash20

ndash20

ndash20

ndash20


8080

ndash20

Year

s

(d)


ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(a)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(b)

Figure 13 Continued


ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(c)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(d)


1960 1970 1980 1990 2000 2010

10

20

30

Tim

e sca

le (a

)

Year

1040

0

10

10

10

10 1010

1010

10

(a)

Tim

e sca

le (a

)

1960 1970 1980 1990 2000 2010

10

20

30

Year

050

50

0

0 0 0 0

0 0 0

0 0 0 0 0 0 000

500

(b)

Tim

e sca

le (a

)

1960 1970 1980 1990 2000 2010

10

20

30

Year

10

10

10

10

10

10 10

10

10

40

(c)

Tim

e sca

le (a

)

1960 1970 1980 1990 2000 2010

10

20

30

Year

1010

10

10

10

1010

4010

10

10 10

(d)









NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

12

Con

trib

utio

n ra

te (m

ma

)




NYR


CAZ


SMA


NX





NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(a)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(b)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(c)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash24

ndash16

ndash08

00

08

16

24

Con

trib

utio

n ra

te (m

ma

)

(d)


Clou

d op

tical

thic

knes

s

y =0386x ndash 755403

28

24

20

16


y = ndash13418 + 32542427

1980 2000 20201970 19901960 2010Year

5200

5600

6000

6400To

tal s

olar

radi

atio

n (M

Jm2 )








5 Conclusions







Data Availability







Acknowledgments


References







































































NX (whole)


NYR


CAZ


SMA






104 62 120 8Month

ndash20

0

20

40

60

Fact

or v

alue

s


RHSDhU2(ms)

(a)

2 4 6 8 10 120Month

ndash20

0

20

40

60

Fact

or v

alue

s


RHSDhU2(ms)

(b)

ndash20

0

20

40

60

Fact

or v

alue

s

2 4 6 8 10 120Month


RHSDhU2(ms)

(c)

ndash20

0

20

40

60

Fact

or v

alue

s

2 4 6 8 10 120Month


RHSDhU2(ms)

(d)





ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(a)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(b)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(c)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(d)




5 17703(max) 17673 13635 16880

6 17490 17975(max)

13790(max)

16895(max)



1 2 3 4 5 6 7 8 9 10 11 12Month

0

50

100

150

200

ET0 (

mm

)

NYRCAZ

SMANX




39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degNkm

0 15 30 60 90 120



N

(a)





39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN

km0 15 30 60 90 120

ET0 (mm)

N

(b)

Figure 7 Continued







36ndash4041ndash45



39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN

km0 15 30 60 90 120

ET0 (mm)

N

(c)



y = 0278x ndash 147669

y = ndash0234x + 966920

y = 0155x + 170088

y = ndash0082x + 397356

y = 0104x + 11213

y = ndash0055x + 246809

y = 0041x + 38831

y = ndash0080x + 583001

Spring

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

300

400

500

ET0 (m

m)

400

500

600

ET0 (m

m)

100

200

300

400

ET0 (m

m)

100

200

ET0 (m

m)

CAZNX

CAZNX

CAZNX

CAZNX

Summer

Autumn

Winter

(a)

100

0

200

ET0 (m

m)

y = 0545x ndash 655033

y = 0151x + 24724

y = 0319x ndash 136403

y = 0326x ndash 258933

y = 0209x ndash 187731

y = 0124x ndash 70641

y = 0152x ndash 185863

y = ndash0092x + 288599

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

200

300

400

500

600

ET0 (m

m)

ET0 (m

m)

300

400

500

600

100

200

300

400

ET0 (m

m)

NYRSMA

NYRSMA

NYRSMA

NYRSMA

Spring

Summer

Autumn

Winter

(b)














1957sim1990 1991sim2018 1957sim2018

NX (whole)


NYR


CAZ


SMA



0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N



(a)

0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N



(b)

Figure 9 Continued


0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



ET0mm

N



(c)

0 30 60 90 12015Km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N



0 30 60 90 12015km

(d)


1600

1500

1400

1300

1200

1100

1000

900

ET0 (

mm

)


y = 0579x ndash 7246

CAZNX

1960 1970 1980 1990 2000 20202010Year

(a)

800

1500

1400

1300

1200

1100

1000

900

ET0 (

mm

)

y = 0509x ndash 16251


NYRSMA

1960 1970 1980 1990 2000 20202010Year

(b)







4 Discussion





0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N


1152ndash12831284ndash1416



1960 1970 1980 1990 2000 2010

10

20

30Ye

ars

Year

100 100

100

(a)

Year

s

1960 1970 1980 1990 2000 2010

10

20

30

Year

50

50

50

50

5050

(b)

1960 1970 1980 1990 2000 2010

10

20

30

Year100

100

100

Year

s

(c)

1960 1970 1980 1990 2000 2010

10

20

30

Yearndash2

0 ndash2080

ndash20

ndash20

ndash20

ndash20


8080

ndash20

Year

s

(d)


ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(a)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(b)

Figure 13 Continued


ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(c)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(d)


1960 1970 1980 1990 2000 2010

10

20

30

Tim

e sca

le (a

)

Year

1040

0

10

10

10

10 1010

1010

10

(a)

Tim

e sca

le (a

)

1960 1970 1980 1990 2000 2010

10

20

30

Year

050

50

0

0 0 0 0

0 0 0

0 0 0 0 0 0 000

500

(b)

Tim

e sca

le (a

)

1960 1970 1980 1990 2000 2010

10

20

30

Year

10

10

10

10

10

10 10

10

10

40

(c)

Tim

e sca

le (a

)

1960 1970 1980 1990 2000 2010

10

20

30

Year

1010

10

10

10

1010

4010

10

10 10

(d)









NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

12

Con

trib

utio

n ra

te (m

ma

)




NYR


CAZ


SMA


NX





NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(a)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(b)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(c)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash24

ndash16

ndash08

00

08

16

24

Con

trib

utio

n ra

te (m

ma

)

(d)


Clou

d op

tical

thic

knes

s

y =0386x ndash 755403

28

24

20

16


y = ndash13418 + 32542427

1980 2000 20201970 19901960 2010Year

5200

5600

6000

6400To

tal s

olar

radi

atio

n (M

Jm2 )








5 Conclusions







Data Availability







Acknowledgments


References


































































104 62 120 8Month

ndash20

0

20

40

60

Fact

or v

alue

s


RHSDhU2(ms)

(a)

2 4 6 8 10 120Month

ndash20

0

20

40

60

Fact

or v

alue

s


RHSDhU2(ms)

(b)

ndash20

0

20

40

60

Fact

or v

alue

s

2 4 6 8 10 120Month


RHSDhU2(ms)

(c)

ndash20

0

20

40

60

Fact

or v

alue

s

2 4 6 8 10 120Month


RHSDhU2(ms)

(d)





ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(a)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(b)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(c)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(d)




5 17703(max) 17673 13635 16880

6 17490 17975(max)

13790(max)

16895(max)



1 2 3 4 5 6 7 8 9 10 11 12Month

0

50

100

150

200

ET0 (

mm

)

NYRCAZ

SMANX




39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degNkm

0 15 30 60 90 120



N

(a)





39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN

km0 15 30 60 90 120

ET0 (mm)

N

(b)

Figure 7 Continued







36ndash4041ndash45



39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN

km0 15 30 60 90 120

ET0 (mm)

N

(c)



y = 0278x ndash 147669

y = ndash0234x + 966920

y = 0155x + 170088

y = ndash0082x + 397356

y = 0104x + 11213

y = ndash0055x + 246809

y = 0041x + 38831

y = ndash0080x + 583001

Spring

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

300

400

500

ET0 (m

m)

400

500

600

ET0 (m

m)

100

200

300

400

ET0 (m

m)

100

200

ET0 (m

m)

CAZNX

CAZNX

CAZNX

CAZNX

Summer

Autumn

Winter

(a)

100

0

200

ET0 (m

m)

y = 0545x ndash 655033

y = 0151x + 24724

y = 0319x ndash 136403

y = 0326x ndash 258933

y = 0209x ndash 187731

y = 0124x ndash 70641

y = 0152x ndash 185863

y = ndash0092x + 288599

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

200

300

400

500

600

ET0 (m

m)

ET0 (m

m)

300

400

500

600

100

200

300

400

ET0 (m

m)

NYRSMA

NYRSMA

NYRSMA

NYRSMA

Spring

Summer

Autumn

Winter

(b)














1957sim1990 1991sim2018 1957sim2018

NX (whole)


NYR


CAZ


SMA



0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N



(a)

0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N



(b)

Figure 9 Continued


0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



ET0mm

N



(c)

0 30 60 90 12015Km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N



0 30 60 90 12015km

(d)


1600

1500

1400

1300

1200

1100

1000

900

ET0 (

mm

)


y = 0579x ndash 7246

CAZNX

1960 1970 1980 1990 2000 20202010Year

(a)

800

1500

1400

1300

1200

1100

1000

900

ET0 (

mm

)

y = 0509x ndash 16251


NYRSMA

1960 1970 1980 1990 2000 20202010Year

(b)







4 Discussion





0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N


1152ndash12831284ndash1416



1960 1970 1980 1990 2000 2010

10

20

30Ye

ars

Year

100 100

100

(a)

Year

s

1960 1970 1980 1990 2000 2010

10

20

30

Year

50

50

50

50

5050

(b)

1960 1970 1980 1990 2000 2010

10

20

30

Year100

100

100

Year

s

(c)

1960 1970 1980 1990 2000 2010

10

20

30

Yearndash2

0 ndash2080

ndash20

ndash20

ndash20

ndash20


8080

ndash20

Year

s

(d)


ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(a)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(b)

Figure 13 Continued


ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(c)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(d)


1960 1970 1980 1990 2000 2010

10

20

30

Tim

e sca

le (a

)

Year

1040

0

10

10

10

10 1010

1010

10

(a)

Tim

e sca

le (a

)

1960 1970 1980 1990 2000 2010

10

20

30

Year

050

50

0

0 0 0 0

0 0 0

0 0 0 0 0 0 000

500

(b)

Tim

e sca

le (a

)

1960 1970 1980 1990 2000 2010

10

20

30

Year

10

10

10

10

10

10 10

10

10

40

(c)

Tim

e sca

le (a

)

1960 1970 1980 1990 2000 2010

10

20

30

Year

1010

10

10

10

1010

4010

10

10 10

(d)









NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

12

Con

trib

utio

n ra

te (m

ma

)




NYR


CAZ


SMA


NX





NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(a)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(b)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(c)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash24

ndash16

ndash08

00

08

16

24

Con

trib

utio

n ra

te (m

ma

)

(d)


Clou

d op

tical

thic

knes

s

y =0386x ndash 755403

28

24

20

16


y = ndash13418 + 32542427

1980 2000 20201970 19901960 2010Year

5200

5600

6000

6400To

tal s

olar

radi

atio

n (M

Jm2 )








5 Conclusions







Data Availability







Acknowledgments


References

































































ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(a)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(b)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(c)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(d)




5 17703(max) 17673 13635 16880

6 17490 17975(max)

13790(max)

16895(max)



1 2 3 4 5 6 7 8 9 10 11 12Month

0

50

100

150

200

ET0 (

mm

)

NYRCAZ

SMANX




39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degNkm

0 15 30 60 90 120



N

(a)





39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN

km0 15 30 60 90 120

ET0 (mm)

N

(b)

Figure 7 Continued







36ndash4041ndash45



39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN

km0 15 30 60 90 120

ET0 (mm)

N

(c)



y = 0278x ndash 147669

y = ndash0234x + 966920

y = 0155x + 170088

y = ndash0082x + 397356

y = 0104x + 11213

y = ndash0055x + 246809

y = 0041x + 38831

y = ndash0080x + 583001

Spring

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

300

400

500

ET0 (m

m)

400

500

600

ET0 (m

m)

100

200

300

400

ET0 (m

m)

100

200

ET0 (m

m)

CAZNX

CAZNX

CAZNX

CAZNX

Summer

Autumn

Winter

(a)

100

0

200

ET0 (m

m)

y = 0545x ndash 655033

y = 0151x + 24724

y = 0319x ndash 136403

y = 0326x ndash 258933

y = 0209x ndash 187731

y = 0124x ndash 70641

y = 0152x ndash 185863

y = ndash0092x + 288599

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

200

300

400

500

600

ET0 (m

m)

ET0 (m

m)

300

400

500

600

100

200

300

400

ET0 (m

m)

NYRSMA

NYRSMA

NYRSMA

NYRSMA

Spring

Summer

Autumn

Winter

(b)














1957sim1990 1991sim2018 1957sim2018

NX (whole)


NYR


CAZ


SMA



0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N



(a)

0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N



(b)

Figure 9 Continued


0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



ET0mm

N



(c)

0 30 60 90 12015Km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N



0 30 60 90 12015km

(d)


1600

1500

1400

1300

1200

1100

1000

900

ET0 (

mm

)


y = 0579x ndash 7246

CAZNX

1960 1970 1980 1990 2000 20202010Year

(a)

800

1500

1400

1300

1200

1100

1000

900

ET0 (

mm

)

y = 0509x ndash 16251


NYRSMA

1960 1970 1980 1990 2000 20202010Year

(b)







4 Discussion





0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N


1152ndash12831284ndash1416



1960 1970 1980 1990 2000 2010

10

20

30Ye

ars

Year

100 100

100

(a)

Year

s

1960 1970 1980 1990 2000 2010

10

20

30

Year

50

50

50

50

5050

(b)

1960 1970 1980 1990 2000 2010

10

20

30

Year100

100

100

Year

s

(c)

1960 1970 1980 1990 2000 2010

10

20

30

Yearndash2

0 ndash2080

ndash20

ndash20

ndash20

ndash20


8080

ndash20

Year

s

(d)


ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(a)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(b)

Figure 13 Continued


ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(c)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(d)


1960 1970 1980 1990 2000 2010

10

20

30

Tim

e sca

le (a

)

Year

1040

0

10

10

10

10 1010

1010

10

(a)

Tim

e sca

le (a

)

1960 1970 1980 1990 2000 2010

10

20

30

Year

050

50

0

0 0 0 0

0 0 0

0 0 0 0 0 0 000

500

(b)

Tim

e sca

le (a

)

1960 1970 1980 1990 2000 2010

10

20

30

Year

10

10

10

10

10

10 10

10

10

40

(c)

Tim

e sca

le (a

)

1960 1970 1980 1990 2000 2010

10

20

30

Year

1010

10

10

10

1010

4010

10

10 10

(d)









NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

12

Con

trib

utio

n ra

te (m

ma

)




NYR


CAZ


SMA


NX





NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(a)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(b)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(c)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash24

ndash16

ndash08

00

08

16

24

Con

trib

utio

n ra

te (m

ma

)

(d)


Clou

d op

tical

thic

knes

s

y =0386x ndash 755403

28

24

20

16


y = ndash13418 + 32542427

1980 2000 20201970 19901960 2010Year

5200

5600

6000

6400To

tal s

olar

radi

atio

n (M

Jm2 )








5 Conclusions







Data Availability







Acknowledgments


References































































1 2 3 4 5 6 7 8 9 10 11 12Month

0

50

100

150

200

ET0 (

mm

)

NYRCAZ

SMANX




39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degNkm

0 15 30 60 90 120



N

(a)





39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN

km0 15 30 60 90 120

ET0 (mm)

N

(b)

Figure 7 Continued







36ndash4041ndash45



39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN

km0 15 30 60 90 120

ET0 (mm)

N

(c)



y = 0278x ndash 147669

y = ndash0234x + 966920

y = 0155x + 170088

y = ndash0082x + 397356

y = 0104x + 11213

y = ndash0055x + 246809

y = 0041x + 38831

y = ndash0080x + 583001

Spring

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

300

400

500

ET0 (m

m)

400

500

600

ET0 (m

m)

100

200

300

400

ET0 (m

m)

100

200

ET0 (m

m)

CAZNX

CAZNX

CAZNX

CAZNX

Summer

Autumn

Winter

(a)

100

0

200

ET0 (m

m)

y = 0545x ndash 655033

y = 0151x + 24724

y = 0319x ndash 136403

y = 0326x ndash 258933

y = 0209x ndash 187731

y = 0124x ndash 70641

y = 0152x ndash 185863

y = ndash0092x + 288599

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

200

300

400

500

600

ET0 (m

m)

ET0 (m

m)

300

400

500

600

100

200

300

400

ET0 (m

m)

NYRSMA

NYRSMA

NYRSMA

NYRSMA

Spring

Summer

Autumn

Winter

(b)














1957sim1990 1991sim2018 1957sim2018

NX (whole)


NYR


CAZ


SMA



0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N



(a)

0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N



(b)

Figure 9 Continued


0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



ET0mm

N



(c)

0 30 60 90 12015Km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N



0 30 60 90 12015km

(d)


1600

1500

1400

1300

1200

1100

1000

900

ET0 (

mm

)


y = 0579x ndash 7246

CAZNX

1960 1970 1980 1990 2000 20202010Year

(a)

800

1500

1400

1300

1200

1100

1000

900

ET0 (

mm

)

y = 0509x ndash 16251


NYRSMA

1960 1970 1980 1990 2000 20202010Year

(b)







4 Discussion





0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N


1152ndash12831284ndash1416



1960 1970 1980 1990 2000 2010

10

20

30Ye

ars

Year

100 100

100

(a)

Year

s

1960 1970 1980 1990 2000 2010

10

20

30

Year

50

50

50

50

5050

(b)

1960 1970 1980 1990 2000 2010

10

20

30

Year100

100

100

Year

s

(c)

1960 1970 1980 1990 2000 2010

10

20

30

Yearndash2

0 ndash2080

ndash20

ndash20

ndash20

ndash20


8080

ndash20

Year

s

(d)


ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(a)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(b)

Figure 13 Continued


ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(c)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(d)


1960 1970 1980 1990 2000 2010

10

20

30

Tim

e sca

le (a

)

Year

1040

0

10

10

10

10 1010

1010

10

(a)

Tim

e sca

le (a

)

1960 1970 1980 1990 2000 2010

10

20

30

Year

050

50

0

0 0 0 0

0 0 0

0 0 0 0 0 0 000

500

(b)

Tim

e sca

le (a

)

1960 1970 1980 1990 2000 2010

10

20

30

Year

10

10

10

10

10

10 10

10

10

40

(c)

Tim

e sca

le (a

)

1960 1970 1980 1990 2000 2010

10

20

30

Year

1010

10

10

10

1010

4010

10

10 10

(d)









NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

12

Con

trib

utio

n ra

te (m

ma

)




NYR


CAZ


SMA


NX





NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(a)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(b)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(c)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash24

ndash16

ndash08

00

08

16

24

Con

trib

utio

n ra

te (m

ma

)

(d)


Clou

d op

tical

thic

knes

s

y =0386x ndash 755403

28

24

20

16


y = ndash13418 + 32542427

1980 2000 20201970 19901960 2010Year

5200

5600

6000

6400To

tal s

olar

radi

atio

n (M

Jm2 )








5 Conclusions







Data Availability







Acknowledgments


References




































































36ndash4041ndash45



39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN

km0 15 30 60 90 120

ET0 (mm)

N

(c)



y = 0278x ndash 147669

y = ndash0234x + 966920

y = 0155x + 170088

y = ndash0082x + 397356

y = 0104x + 11213

y = ndash0055x + 246809

y = 0041x + 38831

y = ndash0080x + 583001

Spring

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

300

400

500

ET0 (m

m)

400

500

600

ET0 (m

m)

100

200

300

400

ET0 (m

m)

100

200

ET0 (m

m)

CAZNX

CAZNX

CAZNX

CAZNX

Summer

Autumn

Winter

(a)

100

0

200

ET0 (m

m)

y = 0545x ndash 655033

y = 0151x + 24724

y = 0319x ndash 136403

y = 0326x ndash 258933

y = 0209x ndash 187731

y = 0124x ndash 70641

y = 0152x ndash 185863

y = ndash0092x + 288599

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

200

300

400

500

600

ET0 (m

m)

ET0 (m

m)

300

400

500

600

100

200

300

400

ET0 (m

m)

NYRSMA

NYRSMA

NYRSMA

NYRSMA

Spring

Summer

Autumn

Winter

(b)














1957sim1990 1991sim2018 1957sim2018

NX (whole)


NYR


CAZ


SMA



0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N



(a)

0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N



(b)

Figure 9 Continued


0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



ET0mm

N



(c)

0 30 60 90 12015Km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N



0 30 60 90 12015km

(d)


1600

1500

1400

1300

1200

1100

1000

900

ET0 (

mm

)


y = 0579x ndash 7246

CAZNX

1960 1970 1980 1990 2000 20202010Year

(a)

800

1500

1400

1300

1200

1100

1000

900

ET0 (

mm

)

y = 0509x ndash 16251


NYRSMA

1960 1970 1980 1990 2000 20202010Year

(b)







4 Discussion





0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N


1152ndash12831284ndash1416



1960 1970 1980 1990 2000 2010

10

20

30Ye

ars

Year

100 100

100

(a)

Year

s

1960 1970 1980 1990 2000 2010

10

20

30

Year

50

50

50

50

5050

(b)

1960 1970 1980 1990 2000 2010

10

20

30

Year100

100

100

Year

s

(c)

1960 1970 1980 1990 2000 2010

10

20

30

Yearndash2

0 ndash2080

ndash20

ndash20

ndash20

ndash20


8080

ndash20

Year

s

(d)


ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(a)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(b)

Figure 13 Continued


ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(c)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(d)


1960 1970 1980 1990 2000 2010

10

20

30

Tim

e sca

le (a

)

Year

1040

0

10

10

10

10 1010

1010

10

(a)

Tim

e sca

le (a

)

1960 1970 1980 1990 2000 2010

10

20

30

Year

050

50

0

0 0 0 0

0 0 0

0 0 0 0 0 0 000

500

(b)

Tim

e sca

le (a

)

1960 1970 1980 1990 2000 2010

10

20

30

Year

10

10

10

10

10

10 10

10

10

40

(c)

Tim

e sca

le (a

)

1960 1970 1980 1990 2000 2010

10

20

30

Year

1010

10

10

10

1010

4010

10

10 10

(d)









NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

12

Con

trib

utio

n ra

te (m

ma

)




NYR


CAZ


SMA


NX





NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(a)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(b)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(c)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash24

ndash16

ndash08

00

08

16

24

Con

trib

utio

n ra

te (m

ma

)

(d)


Clou

d op

tical

thic

knes

s

y =0386x ndash 755403

28

24

20

16


y = ndash13418 + 32542427

1980 2000 20201970 19901960 2010Year

5200

5600

6000

6400To

tal s

olar

radi

atio

n (M

Jm2 )








5 Conclusions







Data Availability







Acknowledgments


References































































y = 0278x ndash 147669

y = ndash0234x + 966920

y = 0155x + 170088

y = ndash0082x + 397356

y = 0104x + 11213

y = ndash0055x + 246809

y = 0041x + 38831

y = ndash0080x + 583001

Spring

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

300

400

500

ET0 (m

m)

400

500

600

ET0 (m

m)

100

200

300

400

ET0 (m

m)

100

200

ET0 (m

m)

CAZNX

CAZNX

CAZNX

CAZNX

Summer

Autumn

Winter

(a)

100

0

200

ET0 (m

m)

y = 0545x ndash 655033

y = 0151x + 24724

y = 0319x ndash 136403

y = 0326x ndash 258933

y = 0209x ndash 187731

y = 0124x ndash 70641

y = 0152x ndash 185863

y = ndash0092x + 288599

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

1970 1980 1990 2000 2010 20201960Year

200

300

400

500

600

ET0 (m

m)

ET0 (m

m)

300

400

500

600

100

200

300

400

ET0 (m

m)

NYRSMA

NYRSMA

NYRSMA

NYRSMA

Spring

Summer

Autumn

Winter

(b)














1957sim1990 1991sim2018 1957sim2018

NX (whole)


NYR


CAZ


SMA



0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N



(a)

0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N



(b)

Figure 9 Continued


0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



ET0mm

N



(c)

0 30 60 90 12015Km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N



0 30 60 90 12015km

(d)


1600

1500

1400

1300

1200

1100

1000

900

ET0 (

mm

)


y = 0579x ndash 7246

CAZNX

1960 1970 1980 1990 2000 20202010Year

(a)

800

1500

1400

1300

1200

1100

1000

900

ET0 (

mm

)

y = 0509x ndash 16251


NYRSMA

1960 1970 1980 1990 2000 20202010Year

(b)







4 Discussion





0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N


1152ndash12831284ndash1416



1960 1970 1980 1990 2000 2010

10

20

30Ye

ars

Year

100 100

100

(a)

Year

s

1960 1970 1980 1990 2000 2010

10

20

30

Year

50

50

50

50

5050

(b)

1960 1970 1980 1990 2000 2010

10

20

30

Year100

100

100

Year

s

(c)

1960 1970 1980 1990 2000 2010

10

20

30

Yearndash2

0 ndash2080

ndash20

ndash20

ndash20

ndash20


8080

ndash20

Year

s

(d)


ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(a)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(b)

Figure 13 Continued


ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(c)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(d)


1960 1970 1980 1990 2000 2010

10

20

30

Tim

e sca

le (a

)

Year

1040

0

10

10

10

10 1010

1010

10

(a)

Tim

e sca

le (a

)

1960 1970 1980 1990 2000 2010

10

20

30

Year

050

50

0

0 0 0 0

0 0 0

0 0 0 0 0 0 000

500

(b)

Tim

e sca

le (a

)

1960 1970 1980 1990 2000 2010

10

20

30

Year

10

10

10

10

10

10 10

10

10

40

(c)

Tim

e sca

le (a

)

1960 1970 1980 1990 2000 2010

10

20

30

Year

1010

10

10

10

1010

4010

10

10 10

(d)









NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

12

Con

trib

utio

n ra

te (m

ma

)




NYR


CAZ


SMA


NX





NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(a)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(b)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(c)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash24

ndash16

ndash08

00

08

16

24

Con

trib

utio

n ra

te (m

ma

)

(d)


Clou

d op

tical

thic

knes

s

y =0386x ndash 755403

28

24

20

16


y = ndash13418 + 32542427

1980 2000 20201970 19901960 2010Year

5200

5600

6000

6400To

tal s

olar

radi

atio

n (M

Jm2 )








5 Conclusions







Data Availability







Acknowledgments


References










































































1957sim1990 1991sim2018 1957sim2018

NX (whole)


NYR


CAZ


SMA



0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N



(a)

0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N



(b)

Figure 9 Continued


0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



ET0mm

N



(c)

0 30 60 90 12015Km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N



0 30 60 90 12015km

(d)


1600

1500

1400

1300

1200

1100

1000

900

ET0 (

mm

)


y = 0579x ndash 7246

CAZNX

1960 1970 1980 1990 2000 20202010Year

(a)

800

1500

1400

1300

1200

1100

1000

900

ET0 (

mm

)

y = 0509x ndash 16251


NYRSMA

1960 1970 1980 1990 2000 20202010Year

(b)







4 Discussion





0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N


1152ndash12831284ndash1416



1960 1970 1980 1990 2000 2010

10

20

30Ye

ars

Year

100 100

100

(a)

Year

s

1960 1970 1980 1990 2000 2010

10

20

30

Year

50

50

50

50

5050

(b)

1960 1970 1980 1990 2000 2010

10

20

30

Year100

100

100

Year

s

(c)

1960 1970 1980 1990 2000 2010

10

20

30

Yearndash2

0 ndash2080

ndash20

ndash20

ndash20

ndash20


8080

ndash20

Year

s

(d)


ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(a)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(b)

Figure 13 Continued


ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(c)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(d)


1960 1970 1980 1990 2000 2010

10

20

30

Tim

e sca

le (a

)

Year

1040

0

10

10

10

10 1010

1010

10

(a)

Tim

e sca

le (a

)

1960 1970 1980 1990 2000 2010

10

20

30

Year

050

50

0

0 0 0 0

0 0 0

0 0 0 0 0 0 000

500

(b)

Tim

e sca

le (a

)

1960 1970 1980 1990 2000 2010

10

20

30

Year

10

10

10

10

10

10 10

10

10

40

(c)

Tim

e sca

le (a

)

1960 1970 1980 1990 2000 2010

10

20

30

Year

1010

10

10

10

1010

4010

10

10 10

(d)









NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

12

Con

trib

utio

n ra

te (m

ma

)




NYR


CAZ


SMA


NX





NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(a)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(b)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(c)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash24

ndash16

ndash08

00

08

16

24

Con

trib

utio

n ra

te (m

ma

)

(d)


Clou

d op

tical

thic

knes

s

y =0386x ndash 755403

28

24

20

16


y = ndash13418 + 32542427

1980 2000 20201970 19901960 2010Year

5200

5600

6000

6400To

tal s

olar

radi

atio

n (M

Jm2 )








5 Conclusions







Data Availability







Acknowledgments


References































































0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N



(a)

0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N



(b)

Figure 9 Continued


0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



ET0mm

N



(c)

0 30 60 90 12015Km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N



0 30 60 90 12015km

(d)


1600

1500

1400

1300

1200

1100

1000

900

ET0 (

mm

)


y = 0579x ndash 7246

CAZNX

1960 1970 1980 1990 2000 20202010Year

(a)

800

1500

1400

1300

1200

1100

1000

900

ET0 (

mm

)

y = 0509x ndash 16251


NYRSMA

1960 1970 1980 1990 2000 20202010Year

(b)







4 Discussion





0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N


1152ndash12831284ndash1416



1960 1970 1980 1990 2000 2010

10

20

30Ye

ars

Year

100 100

100

(a)

Year

s

1960 1970 1980 1990 2000 2010

10

20

30

Year

50

50

50

50

5050

(b)

1960 1970 1980 1990 2000 2010

10

20

30

Year100

100

100

Year

s

(c)

1960 1970 1980 1990 2000 2010

10

20

30

Yearndash2

0 ndash2080

ndash20

ndash20

ndash20

ndash20


8080

ndash20

Year

s

(d)


ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(a)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(b)

Figure 13 Continued


ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(c)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(d)


1960 1970 1980 1990 2000 2010

10

20

30

Tim

e sca

le (a

)

Year

1040

0

10

10

10

10 1010

1010

10

(a)

Tim

e sca

le (a

)

1960 1970 1980 1990 2000 2010

10

20

30

Year

050

50

0

0 0 0 0

0 0 0

0 0 0 0 0 0 000

500

(b)

Tim

e sca

le (a

)

1960 1970 1980 1990 2000 2010

10

20

30

Year

10

10

10

10

10

10 10

10

10

40

(c)

Tim

e sca

le (a

)

1960 1970 1980 1990 2000 2010

10

20

30

Year

1010

10

10

10

1010

4010

10

10 10

(d)









NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

12

Con

trib

utio

n ra

te (m

ma

)




NYR


CAZ


SMA


NX





NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(a)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(b)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(c)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash24

ndash16

ndash08

00

08

16

24

Con

trib

utio

n ra

te (m

ma

)

(d)


Clou

d op

tical

thic

knes

s

y =0386x ndash 755403

28

24

20

16


y = ndash13418 + 32542427

1980 2000 20201970 19901960 2010Year

5200

5600

6000

6400To

tal s

olar

radi

atio

n (M

Jm2 )








5 Conclusions







Data Availability







Acknowledgments


References































































0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



ET0mm

N



(c)

0 30 60 90 12015Km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N



0 30 60 90 12015km

(d)


1600

1500

1400

1300

1200

1100

1000

900

ET0 (

mm

)


y = 0579x ndash 7246

CAZNX

1960 1970 1980 1990 2000 20202010Year

(a)

800

1500

1400

1300

1200

1100

1000

900

ET0 (

mm

)

y = 0509x ndash 16251


NYRSMA

1960 1970 1980 1990 2000 20202010Year

(b)







4 Discussion





0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N


1152ndash12831284ndash1416



1960 1970 1980 1990 2000 2010

10

20

30Ye

ars

Year

100 100

100

(a)

Year

s

1960 1970 1980 1990 2000 2010

10

20

30

Year

50

50

50

50

5050

(b)

1960 1970 1980 1990 2000 2010

10

20

30

Year100

100

100

Year

s

(c)

1960 1970 1980 1990 2000 2010

10

20

30

Yearndash2

0 ndash2080

ndash20

ndash20

ndash20

ndash20


8080

ndash20

Year

s

(d)


ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(a)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(b)

Figure 13 Continued


ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(c)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(d)


1960 1970 1980 1990 2000 2010

10

20

30

Tim

e sca

le (a

)

Year

1040

0

10

10

10

10 1010

1010

10

(a)

Tim

e sca

le (a

)

1960 1970 1980 1990 2000 2010

10

20

30

Year

050

50

0

0 0 0 0

0 0 0

0 0 0 0 0 0 000

500

(b)

Tim

e sca

le (a

)

1960 1970 1980 1990 2000 2010

10

20

30

Year

10

10

10

10

10

10 10

10

10

40

(c)

Tim

e sca

le (a

)

1960 1970 1980 1990 2000 2010

10

20

30

Year

1010

10

10

10

1010

4010

10

10 10

(d)









NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

12

Con

trib

utio

n ra

te (m

ma

)




NYR


CAZ


SMA


NX





NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(a)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(b)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(c)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash24

ndash16

ndash08

00

08

16

24

Con

trib

utio

n ra

te (m

ma

)

(d)


Clou

d op

tical

thic

knes

s

y =0386x ndash 755403

28

24

20

16


y = ndash13418 + 32542427

1980 2000 20201970 19901960 2010Year

5200

5600

6000

6400To

tal s

olar

radi

atio

n (M

Jm2 )








5 Conclusions







Data Availability







Acknowledgments


References



































































4 Discussion





0 30 60 90 12015km

39degN

38degN

37degN

36degN

35degN

39degN

38degN

37degN

36degN

35degN



N


1152ndash12831284ndash1416



1960 1970 1980 1990 2000 2010

10

20

30Ye

ars

Year

100 100

100

(a)

Year

s

1960 1970 1980 1990 2000 2010

10

20

30

Year

50

50

50

50

5050

(b)

1960 1970 1980 1990 2000 2010

10

20

30

Year100

100

100

Year

s

(c)

1960 1970 1980 1990 2000 2010

10

20

30

Yearndash2

0 ndash2080

ndash20

ndash20

ndash20

ndash20


8080

ndash20

Year

s

(d)


ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(a)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(b)

Figure 13 Continued


ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(c)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(d)


1960 1970 1980 1990 2000 2010

10

20

30

Tim

e sca

le (a

)

Year

1040

0

10

10

10

10 1010

1010

10

(a)

Tim

e sca

le (a

)

1960 1970 1980 1990 2000 2010

10

20

30

Year

050

50

0

0 0 0 0

0 0 0

0 0 0 0 0 0 000

500

(b)

Tim

e sca

le (a

)

1960 1970 1980 1990 2000 2010

10

20

30

Year

10

10

10

10

10

10 10

10

10

40

(c)

Tim

e sca

le (a

)

1960 1970 1980 1990 2000 2010

10

20

30

Year

1010

10

10

10

1010

4010

10

10 10

(d)









NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

12

Con

trib

utio

n ra

te (m

ma

)




NYR


CAZ


SMA


NX





NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(a)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(b)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(c)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash24

ndash16

ndash08

00

08

16

24

Con

trib

utio

n ra

te (m

ma

)

(d)


Clou

d op

tical

thic

knes

s

y =0386x ndash 755403

28

24

20

16


y = ndash13418 + 32542427

1980 2000 20201970 19901960 2010Year

5200

5600

6000

6400To

tal s

olar

radi

atio

n (M

Jm2 )








5 Conclusions







Data Availability







Acknowledgments


References































































1960 1970 1980 1990 2000 2010

10

20

30Ye

ars

Year

100 100

100

(a)

Year

s

1960 1970 1980 1990 2000 2010

10

20

30

Year

50

50

50

50

5050

(b)

1960 1970 1980 1990 2000 2010

10

20

30

Year100

100

100

Year

s

(c)

1960 1970 1980 1990 2000 2010

10

20

30

Yearndash2

0 ndash2080

ndash20

ndash20

ndash20

ndash20


8080

ndash20

Year

s

(d)


ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(a)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(b)

Figure 13 Continued


ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(c)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(d)


1960 1970 1980 1990 2000 2010

10

20

30

Tim

e sca

le (a

)

Year

1040

0

10

10

10

10 1010

1010

10

(a)

Tim

e sca

le (a

)

1960 1970 1980 1990 2000 2010

10

20

30

Year

050

50

0

0 0 0 0

0 0 0

0 0 0 0 0 0 000

500

(b)

Tim

e sca

le (a

)

1960 1970 1980 1990 2000 2010

10

20

30

Year

10

10

10

10

10

10 10

10

10

40

(c)

Tim

e sca

le (a

)

1960 1970 1980 1990 2000 2010

10

20

30

Year

1010

10

10

10

1010

4010

10

10 10

(d)









NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

12

Con

trib

utio

n ra

te (m

ma

)




NYR


CAZ


SMA


NX





NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(a)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(b)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(c)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash24

ndash16

ndash08

00

08

16

24

Con

trib

utio

n ra

te (m

ma

)

(d)


Clou

d op

tical

thic

knes

s

y =0386x ndash 755403

28

24

20

16


y = ndash13418 + 32542427

1980 2000 20201970 19901960 2010Year

5200

5600

6000

6400To

tal s

olar

radi

atio

n (M

Jm2 )








5 Conclusions







Data Availability







Acknowledgments


References































































ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(c)

ndash4

ndash2

0

2

4

Z

1960 1970 1980 1990 2000 20202010Year

UFUB

(d)


1960 1970 1980 1990 2000 2010

10

20

30

Tim

e sca

le (a

)

Year

1040

0

10

10

10

10 1010

1010

10

(a)

Tim

e sca

le (a

)

1960 1970 1980 1990 2000 2010

10

20

30

Year

050

50

0

0 0 0 0

0 0 0

0 0 0 0 0 0 000

500

(b)

Tim

e sca

le (a

)

1960 1970 1980 1990 2000 2010

10

20

30

Year

10

10

10

10

10

10 10

10

10

40

(c)

Tim

e sca

le (a

)

1960 1970 1980 1990 2000 2010

10

20

30

Year

1010

10

10

10

1010

4010

10

10 10

(d)









NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

12

Con

trib

utio

n ra

te (m

ma

)




NYR


CAZ


SMA


NX





NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(a)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(b)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(c)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash24

ndash16

ndash08

00

08

16

24

Con

trib

utio

n ra

te (m

ma

)

(d)


Clou

d op

tical

thic

knes

s

y =0386x ndash 755403

28

24

20

16


y = ndash13418 + 32542427

1980 2000 20201970 19901960 2010Year

5200

5600

6000

6400To

tal s

olar

radi

atio

n (M

Jm2 )








5 Conclusions







Data Availability







Acknowledgments


References



































































NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

12

Con

trib

utio

n ra

te (m

ma

)




NYR


CAZ


SMA


NX





NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(a)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(b)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(c)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash24

ndash16

ndash08

00

08

16

24

Con

trib

utio

n ra

te (m

ma

)

(d)


Clou

d op

tical

thic

knes

s

y =0386x ndash 755403

28

24

20

16


y = ndash13418 + 32542427

1980 2000 20201970 19901960 2010Year

5200

5600

6000

6400To

tal s

olar

radi

atio

n (M

Jm2 )








5 Conclusions







Data Availability







Acknowledgments


References

































































NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(a)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(b)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash08

ndash04

00

04

08

Con

trib

utio

n ra

te (m

ma

)

(c)

NYR CAZ SMA NX

TmeanSDTmax

U2TminRH

ndash24

ndash16

ndash08

00

08

16

24

Con

trib

utio

n ra

te (m

ma

)

(d)


Clou

d op

tical

thic

knes

s

y =0386x ndash 755403

28

24

20

16


y = ndash13418 + 32542427

1980 2000 20201970 19901960 2010Year

5200

5600

6000

6400To

tal s

olar

radi

atio

n (M

Jm2 )








5 Conclusions







Data Availability







Acknowledgments


References




































































5 Conclusions







Data Availability







Acknowledgments


References































































Acknowledgments


References































































































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