Research Article Analyses on the Changes of Grazing...

Hindawi Publishing CorporationAdvances in MeteorologyVolume 2013 Article ID 951261 9 pageshttpdxdoiorg1011552013951261

Research ArticleAnalyses on the Changes of Grazing Capacity inthe Three-River Headwaters Region of China underVarious Climate Change Scenarios

Rongrong Zhang1 Zhaohua Li1 Yongwei Yuan1 Zhihui Li23 and Fang Yin24

1 Faculty of Resources and Environmental Science Hubei University Wuhan 430062 China2 Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences No 11A Datun Road AnwaiBeijing 100101 China

3University of Chinese Academy of Sciences Beijing 100049 China4Center for Chinese Agricultural Policy CAS Beijing 100101 China

Correspondence should be addressed to Fang Yin yinf10sigsnrraccn

Received 19 May 2013 Revised 17 July 2013 Accepted 4 August 2013

Academic Editor Xiangzheng Deng

Copyright copy 2013 Rongrong Zhang et al This is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properlycited

On the livestock production in the Three-River Headwaters region (TRHR) in the macrocontext of climatic change this studyanalyzed the possible changing trends of the net primary productivity (NPP) of local grasslands under four RCPs scenarios(ie RCP26 RCP45 RCP60 and RCP85) during 2010ndash2030 with the model estimation and the grass yield and theoreticalgrazing capacity under each scenario were further qualitatively and quantitatively analyzed The results indicate that the grasslandproductivity in the TRHR will be unstable under all the four scenarios The grassland productivity will be greatly influencedby the fluctuations of precipitation and the temperature fluctuations will also play an important role during some periods Thelocal grassland productivity will decrease to some degree during 2010ndash2020 and then will fluctuate and increase slowly during2020ndash2030The theoretical grazing capacity was analyzed in this study and calculated on the basis of the grass yield The resultindicates that the theoretical grazing capacity ranges from 4 million sheep to 5 million sheep under the four scenarios and it canprovide quantitative information reference for decision making on how to determine the reasonable grazing capacity promote thesustainable development of grasslands and so forth

1 IntroductionThe net primary productivity (NPP) of vegetation reflects theproductivity of the vegetation under the natural conditions[1] The climatic change is one of the key driving forces of theinterannual change of NPP of vegetation [2] The climate isundergoing the change which is mainly characterized by theglobal warming The land surface temperature has increasedsignificantly since the 1980s especially in the northern regionof China [3ndash6] The grassland is one of the most importantland use types in China which has essential functions inthe development of the animal husbandry [7] The grasslandis greatly influenced by the climatic change and the spati-otemporal change of NPP of grasslands and the influencingmechanism of the climatic change on it have been one of theresearch focuses at home and abroad [8ndash11]

TheThree-River Headwaters region (TRHR) is the head-stream of the Yellow River Yangtze River and Lancang Riverwhich is one of themost ecologically sensitive areas in ChinaBesides it is also the largest animal husbandry productionbase in Qinghai Province with about 213 thousand km2 ofnative pasture and native grassland Many researchers haveanalyzed the change of NPP in this area from different per-spectives [12ndash16] and there have been many research workson the pattern and spatiotemporal characteristics of NPP ofecosystems However there have been few comprehensivestudies on the spatiotemporal change of NPP of grasslandsand the consequent effects in the TRHR On the one handowing to the distinctive natural ecological conditions inthis region the development of local animal husbandryalways depends on the increase of the livestock amount

2 Advances in Meteorology

which increases the income of local people and meanwhileleads to the long-term overgrazing The serious grasslanddegradation has greatly restrained the development of thelocal animal husbandry and change of landscape [17] On theother hand the global warming has led to the decrease ofthe average annual precipitation and the grass yield per unitarea also decreases slightly year by year which has threatenedthe development of local animal husbandry Therefore adescription of the climate and relevant economic activitiesin the study area is detailed and significant [18] In orderto solve the problems brought by the grassland degradationand promote the sustainable development of the local animalhusbandry in the TRHR it is necessary to carry out scientificprediction of the local grass yield determine the reasonablegrazing capacity and guide the production of local animalhusbandry

The research on NPP of the grassland is the basis for thestudy of the grass yield and prediction of grazing capacityand there have been many investigations and research workson the estimation of NPP of the grassland in China in recentyearsThemethods to estimate NPP of grassland vary greatlydue to the difference in the natural environment of the studyarea data availability and so forth There are mainly fourkinds of models to estimate NPP of grassland that is thelight use efficiency model ecosystem process model remotesensing-process coupling model and climatic statistic model[19] There are both advantages and disadvantages in thesemodels For example the light use efficiency model basedon the mechanism of vegetation photosynthesis is easy to beconstructed and has high calculation efficiency but there aresome faults in the factors taken into account parameter selec-tion calculation result and so forth The ecosystem processmodel simulates the physiological processes of vegetationand applies the technologies such as the remote sensingwhich makes it possible to carry out multiscale dynamicmonitoring of the spatiotemporal change of NPP Howeverthis model is very complex and requires high quality datawhich restrains its practicability to some degree especiallyin the regional estimation The remote sensing-process cou-pling model integrates the advantages of both the modelsmentioned above but the accuracy of its calculation resultis greatly influenced by other factors The climatic statisticmodel introduces the regressionmodels constructed with thesimple climatic factors such as temperature and precipitationand has a low data requirement This kind of models ismore practical but it is still limited by the low accuracyof the result There is great complexity uncertainties andinaccuracy in the extraction of vegetation indices and soilparameters with the remote sensing data all of which makeit very difficult and very inaccurate to calculate these datawith the light use efficiency model ecosystem process modeland ecological remote sensing coupling model Besides itis a fact that the climatic conditions have great impacts onthe livestock production in the study area Therefore theclimatic statistic model was finally used to estimate the futuregrassland productivity in the TRHR

The most widely used climate models mainly includethe Miami Model Thornthwaite Memorial Model ChikugoModel and the comprehensive model The climate model is

an effective tool in the study of climate [20] The compre-hensive model is more suitable for the estimation of NPP ofvegetation in the arid area than the Chikugo Model Besidesin comparison with other three models the comprehensiveModel has a solider theoretical foundation takes more intoaccount of the physiological processes of vegetation andconsequently can obtain a better estimation result in ZhejiangProvince [21] and Inner Mongolia [22 23]

In order to overall forecast the changing trend of thegrassland NPP and theoretical grazing capacity in the studyarea in the context of climate change four representativeconcentration pathways (RCPs) (which represent the emis-sion trajectories under the natural and social conditionsand the corresponding scenarios) were selected to analyzethe changing trend of grassland NPP in the Three-RiverHeadwaters region This study is of both theoretical andpractical significance In theory this study extends the fieldof application of the estimation of NPP and explored thetheoretical grazing capacity in the TRHR in the future whichprovides certain references for the relevant research works inother similar regions In practice this study qualitatively andquantitatively analyzed the changing trend of the grass yieldand grazing capacity in the study area which can providesome guidance for the local grasslandutility andmanagementand the development of animal husbandry and promote theharmonious and sustainable development of the local man-land relationship

2 Study Area

TheTRHR is located in the southern part ofQinghai Provinceof China between 31∘391015840ndash36∘121015840N and 89∘451015840ndash102∘231015840E withan area of 363 thousand km2 which accounts for 43 of thetotal area of Qinghai Province The TRHR with the altituderanges from 3500m to 4800m is the headstream of theYellow River Yangtze River and Lancang River and has adense network of rivers The administrative regions cover 16counties including Yushu Xinghai Tongde Zeku MatuoMaqin Dari Gande Jiuzhi Banma Chengduo ZaduoZhiduo Qumalai Nangqian andHenan except for TanggulaMountain Town which is under the charge of Golmud City

The grassland area is 203 thousand km2 in the TRHRaccounting for 654 of the total area of this region (Figure 1)The vegetation diversity of the TRHR is the richest among theregions at the same altitude all over the world The grasslandtype changes from alpine meadow to high-cold steppe andalpine desert with the productivity also gradually decreasing[24ndash27] The grassland resource is very rich in this regionhowever the grass yield per unit area has decreased year byyear due to the climatic change and overgrazing in recentyears which has threatened the development of the localanimal husbandry

3 Methodology and Data

31 Models

311 Comprehensive Model The comprehensive modelwas developed on the basis of two well-known balance

Advances in Meteorology 3

Inner Mongolia

Heilongjiang

Liaoning

Jilin

BeijingTianjinHebei

ShanxiShandong

Tibet

Xinjiang

Qinghai

Gansu

Sichuan

Ningxia

Shaanxi Henan JiangsuAnhuiHubei

Zhejiang

HunanJiangxi

Fujian

GuangdongGuangxi

Hainan

Interpolating pointsHigh 100Low 0

0 320160(km)

Yunnan

GuizhouTaiwan

Qinghai

Tibet

Gansu

Sichuan

Xinjiang

Inner Mongolia

Inner Mongolia

90∘E88

∘E86∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E 104

∘E

90∘E88

∘E 92∘E 94

∘E 96∘E 98

∘E 100∘E 102

∘E 104∘E

36∘N

34∘N

32∘N

30∘N

36∘N

38∘N

34∘N

32∘N

30∘N

ChongqingShanghai

Figure 1 Location of the TRHR and distribution of grassland

equations that is the water balance equation and heat bala-nce equation [28 29] Zhou and Zhang deduced the regionalevapotranspiration model that links the water balanceequation and heat balance equation from the physical processduring the energy and moisture influence the vaporizationand then constructed the natural vegetation NPP modelbased on the physiological characteristics [28 29] that isthe Comprehensive Model The Comprehensive Model cancalculate the potential NPP of natural vegetation on the basisof the precipitation and net radiation received by the landsurface in the study area This model is of great significanceto the reasonable use of climatic resource and fulfillment ofthe climatic potential productivity [28] The formula of thismodel is as follows

NPP = RDI times119875119903119877119899(119875

2

119903+ 119877

2

119899+ 119875119903119877119899)

(119875119903+ 119877119899) (119875

2

119903+ 119877

2

119899)

119890

minus

radic987+625timesRDI

RDI =119877119899

119871 sdot 119875119903

(1)

where 119877119899is the annual net radiation 119875

119903is the annual

precipitation 119871 is the annual latent heat of vaporization andRDI is the radiation aridity

312 Model of the Hay Yield of Grassland There are mainlythree indictors of the grassland productivity that is thehay yield theoretical grazing capacity and animal products[30] The hay yield that is the total dry matter yield of acertain area during a certain period reflects the primaryproductivity of grassland and is a basic indicator of thegrassland productivity In this study the hay yield of grassland

during 2010ndash2030 was calculated based on NPP of grasslandwith the following formula

119861119892=

NPP119878bn (1 + 119878ug)

(2)

where 119861119892is the annual total hay yield per unit area (gsdotmminus2

sdotaminus1) NPP is the annual total NPP of grassland (gCsdotmminus2sdotaminus1)119878bn is the coefficient of the conversion coefficient of thegrassland biomass and NPP (ggC) which is 045 [31 32]and 119878ug is the proportionality coefficient of the over groundbiomass and underground biomass which varies amongdifferent vegetation types [33] 119878ug of the alpine meadowhigh-cold steppe and alpine desert is 791 425 and 789respectively According to the location of the study area 119878ugof the alpine meadow was used to calculate the grass yield

313 Model of the Theoretical Grazing Capacity of GrasslandThe theoretical grazing capacity of grassland during 2010ndash2030 was calculated on the basis of the grass yield Since thegrazing capacity of grassland is customarily represented bythe unit of livestock in China that is the number of adultlivestock that can be supported by per unit of land area everyyear and the number of sheep is generally used as the unitthe grazing capacity of grassland is also represented by thenumber of sheep per unit of land area

There have been many methods to calculate the theo-retical grazing capacity of grassland The estimation methodof ldquolimiting livestock based on grassland carrying capacityrdquocan better reflect the restriction of the practical situation in


the grazing districts on the livestock production and hencethe following formula was used [34]

CA = 119866 sdot Cuse119880119866sdot DOY (3)

where CA is the theoretical annual grazing capacity ofgrassland (unit number of sheep per unit of land area) 119866is the annual hay yield of grassland per square meter (unitkgm2) and Cuse is the utilization efficiency of grass by thelivestock varying among different grassland types [34] In thisstudy Cuse of the alpinemeadow high-cold steppe and alpinedesert shrubbery and swampmeadow is 60 50 40 and55 respectively 119880

119866is the hay quantity needed by per unit

of sheep every day (unit kgd) which was set to be 20 kgaccording to the relevant criterion [35] DOY (unit d) is 365Since the grassland is the main vegetation type in the Three-River Headwaters region Cuse of the high-cold steppe wasused to estimate the theoretical grazing capacity of grasslandduring 2010ndash2030

32 Data Source The data of precipitation and near-surfaceair temperature in the study area was simulated with themodels of CMIP5 (Coupled Model Intercomparison ProjectPhase 5) There are three steps in the data processing (1)The data was first selected and downloaded including themodel (CCSM4) modeling realm (atmosphere) ensemble(r6i1p1 and r5i1p1) and climatic variables (precipitation andnear-surface air temperature) (2) The data of study areawas then extracted and calculated The annual average valuewas calculated based on the monthly data and the annualprecipitation was calculated as the sum of the monthlyprecipitation and then extracted 112 points covering the studyarea (3) The point data with the spatial resolution 09 times 125degree were interpolated in 1 km times 1 km raster using theKriging method and were projected with the Albers 1940coordinate system

4 Results and Analyses

41 Changing Trend of NPP of Grassland in the TRHRThere is significant spatial heterogeneity of the NPP in theTRHR decreasing from the southeast to the northwest onthe whole (Figure 2) The results indicate that the NPP ofgrassland mainly increases in the east and southeast partwhile it decreases significantly in the northwest southwestand middle part There is no significant change of the NPPof grassland in most of other parts The changing trends ofNPP during every ten years indicate that the NPP changessignificantly under the RCP26 scenario andRCP45 scenarioincreasing in the east and southeast part to some degree anddecreasing in the south part to some extentTheNPP changesslightly under the RCP60 scenario and RCP85 scenarioUnder the RCP45 scenario the NPP decreases obviously inthe middle and south part during 2010ndash2020 and increasesslightly during 2020ndash2030 indicating that there is seriousdesertification of the local grassland Besides the increase ofNPP by 2030 suggests that there is some improvement of theconditions of the local grassland

In this study the influence of temperature and precipita-tion on the change of NPP was analyzed The result indicatesthat the NPP of grassland will range from 100 g sdotmminus2 sdot aminus1 to130 g sdotmminus2 sdot aminus1 during 2010ndash2030The results under differentscenarios are shown as follows (Figure 3)

The result under the RCP26 scenario indicates thatthe temperature and precipitation would present a decreas-ing trend during 2015ndash2020 and 2025ndash2030 and shows anopposed trend during 2010ndash2015 and 2020ndash2025 (Figure 3)The precipitation will fluctuate more greatly than the tem-perature on the whole By contrast the NPP will changein an opposite way during these periods but with smalleramplitude of fluctuation Therefore there is a significantnegative relationship between the NPP and temperaturewhile there is only a weak relationship between the NPP andprecipitation under this scenario

The result under the RCP45 scenario indicates that theNPP and precipitation show a similar changing trend thatis a concave-down parabolic trajectory on the whole Theprecipitation will fluctuate most greatly during 2010ndash2020while the NPP first decrease with the precipitation and thenincreases rapidly after reaching a relatively low level TheNPP will decrease by 84 from 2010 to 2015 but it willincrease by 82 from 2015 to 2020 Then the NPP willincrease slowly while fluctuating slightly during 2020ndash2030Therefore there is a significant negative relationship betweenthe NPP and precipitation under this scenario while therelationship between the NPP and temperature is very weak

The result under the RCP60 scenario indicates that theNPP will first increase and then decrease during 2015ndash2025while temperature will show an opposite changing trendduring this period as during other period they will change ina similar wayThe precipitation will show an increasing trendduring 2010ndash2015 and 2020ndash2030TheNPP and precipitationwill both decline obviously during 2015ndash2020 and reach thebottom around 2020 The NPP will decrease by 10 in 2020when compared with 2015 which indicates that the changeof NPP is greatly influenced by the change of precipitationduring this period and they are strongly correlated Theresult suggests that the changing trends of the NPP areconsistent with those of the precipitation on the whole butthe fluctuation range of the NPP is small indicating thatthere is some lag in the response of the NPP to the change ofprecipitation under this scenario According to the analysisabove the NPP responds more sensitively to the change ofprecipitation than to the change of temperature

Under the RCP85 scenario the temperature changesslightly during 2010ndash2025 and 2025ndash2030 and shows anincreasing trend during 2020ndash2025 Besides the NPP alsofluctuates slightly during 2010ndash2025 and 2025ndash2030 indicat-ing that the temperature plays a dominant role in influencingthe NPP The NPP and precipitation both fluctuate signif-icantly during 2015ndash2020 and there is an obvious low ebbaround 2020 The NPP decreases by 153 in 2020 in com-parison to 2015 indicating that there is a strong correlationbetween the change of NPP and the change of precipitationDuring 2020ndash2025 the NPP temperature and precipitationall show an obvious increasing trend The NPP increases


RCP26

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

36∘N

34∘N

32∘N

36∘N

34∘N

32∘N

RCP26

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

36∘N

34∘N

32∘N

36∘N

34∘N

32∘N

RCP26

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

36∘N

34∘N

32∘N

36∘N

34∘N

32∘N

RCP45

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

36∘N

34∘N

32∘N

36∘N

34∘N

32∘N

RCP60

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

36∘N

34∘N

32∘N

36∘N

34∘N

32∘N

RCP85

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

36∘N

34∘N

32∘N

36∘N

34∘N

32∘N

RCP85

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

36∘N

34∘N

32∘N

36∘N

34∘N

32∘N

High 260Low 0

High 260Low 0

High 260Low 0

0 150 300(km)

RCP85

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

36∘N

34∘N

32∘N

36∘N

34∘N

32∘N

RCP60

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

36∘N

34∘N

32∘N

36∘N

34∘N

32∘N

RCP60

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

36∘N

34∘N

32∘N

36∘N

34∘N

32∘N

RCP45

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

36∘N

34∘N

32∘N

36∘N

34∘N

32∘N

RCP45

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

36∘N

34∘N

32∘N

36∘N

34∘N

32∘N

0 150 300(km)

0 150 300(km)

Figure 2 The NPP map of the TRHR in 2010 2020 and 2030 under the four RCPs scenarios

by 204 from 2020 to 2025 and reaches a significant peakaround 2025 indicating that there is significant relationshipbetween the change of NPP and the changes of both theprecipitation and temperature Therefore the temperatureplays a key role in influencing the change of NPP during2010ndash2020 and 2025ndash2030 while the precipitation plays adominant role during 2015ndash2020 Besides during 2020ndash2025both the temperature and precipitation greatly influence theNPP

42 Changing Trend of Grass Yield of Grassland The resultunder the RCP26 scenario indicates that the changing trend

and fluctuation range of the grass yield are both prettyconsistent with those of the NPP mentioned above that isboth increase during 2015ndash2020 and 2025ndash2030 and decreaseduring 2010ndash2015 and 2020ndash2025 (Figure 4) On the wholethe grass yield is generally above 63 million tons underthis scenario except for the period around 2025 and thefluctuation range is not great and the average yield level is verystable

The result under the RCP45 scenario indicates that thegrassland yield will fluctuate greatly but will still increaseslightly on the whole during 2010ndash2015 The grass yieldwill keep a stable increasing trend during 2015ndash2030 In


400

425

450

475

500

2010 2015 2020 2025 2030

Prec

ipita

tion

(mm

)

100105110115120125130

2010 2015 2020 2025 2030

RCP26RCP45

RCP60RCP85

2010 2015 2020 2025 2030

minus25

minus22

minus19

minus16

minus13

minus1

NPP

(gm

2)

Tem

pera

ture

(∘C)

Figure 3 Changing trends of the NPP of grassland temperatureand precipitation (the average number in every year) in the TRHRduring 2010ndash2030

540

570

600

630

660

690

2010 2015 2020 2025 2030

Gra

ssla

nd y

ield

(ton

s)

RCP26RCP45

RCP60RCP85

Figure 4 Changing trends of grass yield of grassland in the TRHRduring 2010ndash2030 under the four scenarios

comparison to the changing trend of NPP mentioned abovethe changing trend of the grass yield is consistent with thatof the NPP during 2015ndash2030 but they are not closely relatedduring 2010ndash2015

The result under the RCP60 scenario indicates that thegrass yield will decline during 2010ndash2020 and then tends

330

360

390

420

450

480

2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030

RCP26 RCP45RCP60 RCP852 per mov avg (RCP26) 2 per mov avg (RCP45)2 per mov avg (RCP60) 2 per mov avg (RCP85)

Figure 5 Theoretical grazing capacity in the TRHR during 2010ndash2030 (ten thousand sheep)

to increase slowly after reaching a low level around 2020indicating that the grass yield of the TRHR will fluctuategreatly under this scenario

The result under the RCP85 scenario suggests that thegrass yield of the TRHR will fluctuate slightly during 2010ndash2015 then declines significantly and thereafter keeps anincreasing trend but the increment will gradually declineand there may even be some slight decrease According tothe analysis above it is predictable that the grass yield willfluctuate obviously around 2020 and decline to a very lowlevel and will only fluctuate slightly during other periodsunder this scenario

To sum up there is a positive relationship between thegrass yield and NPP of grassland in the TRHR The changeof the NPP of grassland has an impact on the grass yieldbut its effects vary among different RCPs scenariosThe grassyield is very stable under the RCP26 scenario generallyabove 63 million tons every year Under the RCP45 andRCP60 scenarios the changing trends of the grass yield andNPP of grassland are generally similar during most periodsexcept for 2015ndash2020 during which their changing trendsare contrary Under the RCP85 scenario the grass yieldfluctuates most greatly and the precipitation grass yield andNPP of grassland will all descend to the bottom around 2020indicating that the grass yield is most greatly influenced bythe precipitation under this scenario

43 Analysis of the Grazing Capacity of Grassland The theo-retical grazing capacity during 2010ndash2030 was analyzed inthis studyThe grazing capacity of grassland in the TRHRwascalculated on the basis of the grass yield The result indicatesthat the theoretical grazing capacity ranges from 4 millionsheep to 5 million sheep under the four scenarios (Figure 5)

The result under the RCP26 scenario indicates that thetheoretical grazing capacity in the TRHR will show a signi-ficant decreasing trend and reach the minimum in 2017 andit will then increase rapidly during 2018ndash2021 but will there-after keep a decline trend on the whole (Figure 5) Besides


the inter-annual fluctuation range is very great under thisscenario According to the changing trends of the tempera-ture and precipitation mentioned above the grazing capacityresponds very slowly to the change of temperature within acertain scope and there is no significant relationship betweenthem while the grazing capacity shows a changing trend sim-ilar to that of the precipitationThe result indicates that underthe condition of no great fluctuation in the temperature thegrazing capacity mainly depends on the precipitation whichis consistent with the actual condition that the local animalhusbandry is mainly restricted by the water resource

The result under the RCP45 scenario indicates that thegrazing capacity fluctuates greatly during 2010ndash2015 butthe fluctuation range will gradually decrease with the timeIt shows an increasing trend during 2015ndash2025 especiallyduring 2019ndash2025 and there will be a stable and continuousincrease The grazing capacity will first decrease sharply andthen increase rapidly during 2025ndash2030 On the whole thegrazing capacity fluctuates very greatly and the stability isvery low under this scenario It suggests that the grazingcapacity of the local grassland increases with the precipitationwithin a certain scope beyond which the temperature willplay a more important role In comparison to the changingtrends of the temperature and precipitationmentioned aboveit can be seen that the influence of the temperature on thegrazing capacity is always very significant under this scenariowhile that of the precipitation is only significant during 2015ndash2025

The result under the RCP60 scenario indicates that thegrazing capacity shows a decreasing trend on the wholeduring 2010ndash2020 during which there is great fluctuationThe grazing capacity will first increase and then decreaseduring 2020ndash2030 and it shows a decreasing trend on thewhole under this scenario In comparison to the changingtrends of the temperature and precipitationmentioned abovethe changing trend of the grazing capacity is more consistentwith that of the precipitation However during 2020ndash2030the change of the grazing capacity is negatively related withthe change of temperature and it responds very slowly to thechange of precipitation and even not obviously It indicatesthat the precipitation has more important impacts on thegrazing capacity when the temperature is within a certainrange but on condition that the temperature decreases by acertain degree the precipitation will only play a secondaryrole

The result under the RCP80 scenario indicates that thelocal grazing capacity will fluctuate slightly during 2010ndash2015 but without significant change on the whole It willcontinually decrease during 2016ndash2020 and reach the bottomaround 2020 and then will keep increasing and finallyfluctuate around 45million sheep According to the changingtrends of the temperature and precipitation the changingtrend of the grazing capacity is more consistent with thatof the precipitation indicating that the precipitation plays amore important role on influencing the grazing capacity thanthe temperature does

In summary the precipitation plays a dominant rolein influencing the grazing capacity under the RCP26 sce-nario and the water resource is the main limiting factor of

the development of the local animal husbandry The precip-itation has limited impacts on the development of the localanimal husbandry under theRCP45 scenarioThe theoreticalgrazing capacity increases with the precipitation within acertain scope beyondwhich the temperature will play amoreimportant role The precipitation and temperature both havesome influence on the grazing capacity under the RCP60scenarioThe precipitation plays a more important role whenthe temperature reaches a certain scope and vice versa Theprecipitation plays a more important role in influencing thegrazing capacity under the RCP85 scenario On the wholethe theoretical grazing capacity in the TRHR ranges from 4million to 5 million sheep

5 Conclusion and Discussion

This study estimated theNPPof grassland in theTRHRunderfour RCPs scenarios based on the comprehensive modeland estimated the local grass yield and theoretical grazingcapacity in the future Besides the future changing trendsof the NPP grass yield and grazing capacity were analyzedunder four scenarios In this paper we draw the followingconclusions

There are very complex influences of the precipitation andtemperature on the grassland productivity and the effects ofthe precipitation and temperature on theNPP grass yield andgrazing capacity are very complex and unstable under differ-ent scenarios For example the theoretical grazing capacity in2029 is 41072million sheep under the RCP26 scenario whileit is 46527 million sheep under the RCP45 scenario whichalso differs greatly under another two scenarios

The grassland productivity in the TRHR is unstableon the whole The grass yield is greatly influenced by thefluctuation of the precipitation and the temperature whichalso plays a more important role and subsequently influencesthe grazing capacity This conclusion is consistent with thatof the previous research on the changing trend of vegetationNPP in the past 50 years in the Yellow River HeadwaterArea which was carried out by Yao et al [36] indicatingthat the precipitation plays a dominant role in influencing thegrassland productivity in theThree-River Headwaters region

The grassland productivity in the TRHR will decreaseslightly during 2010ndash2020 especially around 2020when therewill be a minimum while the grazing capacity will firstincrease and then decrease during this period under allthe scenarios except the RCP85 scenario According to theanalysis of the changing trend of the grazing capacity there isa dramatic change in the grazing capacity in the TRHR due tothe influence of the climatic factors Therefore it is necessaryto reinforce the control on the grazing capacity eliminatesome livestock species in time and replace the dominantgrass species with the grass species that can better adapt tothe climatic change Besides it is necessary to prepare forthe various responses to the climatic change and formulatethe artificial intervention mechanism as early as possible soas to reasonably guide the development of the local animalhusbandry

This study forecasted and analyzed the grassland NPPhay yield of grasslands and theoretical grazing capacity with


the comprehensive model on the basis of the simulationof temperature and precipitation under the four scenariosThe research result is only obtained on the basis of thehydrothermal conditions while in fact various factors suchas the soil terrain and solar radiation all have some impactson the grasslandNPPTherefore there is still some limitationsin the result of this study and it is necessary to carry outmore in-depth research works on the modification of thesimulation result with the comprehensive model throughincluding more other factors

Acknowledgments

This research was supported by the National Basic ResearchProgram of China (973 Program) (no 012CB95570001) andChina National Natural Science Funds for DistinguishedYoung Scholar (Grant no 71225005) and the ExploratoryForefront Project for the Strategic Science Plan in IGSNRRCAS are also appreciated

References

[1] J Liu J M Chen J Cihlar andW Chen ldquoNet primary produc-tivity distribution in the BOREAS region from a process modelusing satellite and surface datardquo Journal of Geophysical Researchvol 104 no 22 pp 27735ndash27754 1999

[2] S Piao J Fang and J He ldquoVariations in vegetation net primaryproduction in the Qinghai-Xizang Plateau China from 1982 to1999rdquo Climatic Change vol 74 no 1ndash3 pp 253ndash267 2006

[3] G Ren J Guo and M Xu ldquoClimate changes of Chinarsquos main-land over the past half centuryrdquo Acta Meteorologica Sinica vol63 no 6 pp 942ndash956 2005 (Chinese)

[4] H Zuo and Y Hu ldquoVariations trend of yearly mean air temper-ature and precipitation in China in the last 50 yearsrdquo PlateauMeteorology vol 23 no 2 pp 238ndash244 2004 (Chinese)

[5] ZWang Y Ding and J He ldquoAn updating analysis of the climatechange in China in recent 50 yearsrdquo Acta Meteorologica Sinicavol 62 no 2 pp 228ndash236 2004 (Chinese)

[6] Chinarsquos National Climate Change Team China Climate ChangeCountry Study Tsinghua University Press 2000 (Chinese)

[7] F Wu X Deng F Yin and Y Yuan ldquoProjected changes ofgrassland productivity along the representative concentrationpathways during 2010ndash2050 inChinardquoAdvances inMeteorologyvol 2013 Article ID 812723 9 pages 2013

[8] X Cui Z Chen and Z Du ldquoStudy on and water-use charac-teristics of main plants in semiarid Stepperdquo Acta PrataculturaeSinica vol 10 no 2 pp 14ndash21 2001 (Chinese)

[9] M Cao S D Prince K Li B Tao J Small and X ShaoldquoResponse of terrestrial carbon uptake to climate interannualvariability in Chinardquo Global Change Biology vol 9 no 4 pp536ndash546 2003

[10] J Fang S Piao and C B Field ldquoIncreasing net primary pro-duction in China from 1982 to 1999rdquo Frontiers in Ecology andthe Environment vol 1 no 6 pp 293ndash297 2003

[11] S Piao J Fang and Q Guo ldquoApplication of CASAmodel to theestimation of Chinese terrestrial net primary productivityrdquoActaPhytoecologica Sinica no 5 pp 603ndash608 2001 (Chinese)

[12] T Xiao and J Wang ldquoVulnerability of grassland ecosystems inthe Sanjiangyuan region based on NPPrdquo Resources Science vol32 no 2 pp 323ndash330 2010 (Chinese)

[13] T Xiao J Liu and Q Shao ldquoA simulation on changes invegetation productivity in ldquoThreeRiver Sourcesrdquo nature reserveQinghai province over past 20 yearsrdquo Journal of Geo-Infor-mation Science vol 11 no 5 pp 558ndash564 2009 (Chinese)

[14] J Fan Q Shao J Wang Z Chen and H Zhong ldquoAnalysis oftemporal-spatial dynamics of grazing pressure on grassland inThree-River Headwaters regionrdquo Chinese Journal of Grasslandvol 33 no 3 pp 64ndash70 2011 (Chinese)

[15] J Wang J Liu Q Shao R Liu J Fan and Z Chen ldquoSpatial-Temporal patterns of net primary productivity for 1988ndash2004based onGlopem-Cevsamodel in the lsquoThree-RiverHeadwatersrsquoregion of Qinghai province Chinardquo Chinese Journal of PlantEcology vol 33 no 2 pp 254ndash269 2009

[16] X Xu J Liu Q Shao and J Fan ldquoThe dynamic changes ofecosystem spatial pattern and structure in the Three-RiverHeadwaters region in Qinghai province during recent 30 yearsrdquoGeographical Research vol 27 no 4 pp 829ndash838 2008 (Chi-nese)

[17] J Zhan X Deng and T Yue ldquoLandscape change detection inYulin prefecturerdquo Journal of Geographical Sciences vol 14 no 1pp 47ndash55 2004

[18] M Gonzalez M Cariaga and M Skansi ldquoSome factors thatinfluence seasonal precipitation in Argentinean Chaordquo Adva-nces in Meteorology vol 2012 Article ID 359164 13 pages 2012

[19] M Zhang W Jiang and Q Chen ldquoResearch progress in theestimation models of grassland net primary productivityrdquo ActaAgrestia Sinica vol 19 no 2 pp 357ndash364 2011 (Chinese)

[20] X Deng C Zhao and H Yan ldquoSystematic modeling of impactsof land use and land cover changes on regional climate aReviewrdquo Advances in Meteorology vol 2013 Article ID 31767811 pages 2013

[21] S Shan S Zhou J Shi and Y Liu ldquoCalculation and comparisonof vegetation net primary productivity (NPP) in Zhejiangprovince with three modelsrdquo Chinese Journal of Agrometeorol-ogy vol 31 no 2 pp 271ndash276 2010 (Chinese)

[22] C Zhao G Chen and S Zhou ldquoSpatial distribution of thenet primary productivity of natural vegetation in the northwestregionrdquo Journal of Lanzhou University vol 45 no 1 pp 42ndash492009 (Chinese)

[23] Z Li Z Liu Z Chen and Z Yang ldquoThe effects climate changeson the productivity in the innerMongolia steppe of ChinardquoActaPrataculturae Sinica vol 12 no 1 pp 4ndash10 2003 (Chinese)

[24] Editorial Board of Qinghai Vegetation Qinghai Peoplersquos Publish-ing House 1994 (Chinese)

[25] Qinghai Grassland Station Qinghai Grassland Resource QinghaiPeoplersquos Publishing House 1981 (Chinese)

[26] K Wang F Hong and J Zong ldquoResource resources and theirsustainable utility in the ldquoThree-River Headwatersrdquo regionrdquoActa Agrestia Sinica vol 13 pp 28ndash311 2005 (Chinese)

[27] G Chen X Liu M Peng and Y Zhao ldquoThe essential featureand protect of ecosystem in theThree-River Headwaters regionofQinghai provincerdquoQinghai Science and Technology vol 4 pp14ndash171 2003 (Chinese)

[28] G Zhou and X Zhang ldquoStudy on NPP of natural vegetation inChina under global climate changerdquo Acta Phytoecologica Sinicevol 20 no 1 pp 11ndash19 1996 (Chinese)

[29] G Zhou and X Zhang ldquoA natural vegetation NPPmodelrdquo ActaPhytoecologica Sinica vol 19 no 3 pp 193ndash200 1995 (Chinese)

[30] J Gambiza and C Nyama Country PastureForage ResourceProfiles FAO 2006


[31] J Yun G Liu and S Xu ldquoChinarsquos terrestrial ecosystem carboncycle and global significancerdquo

[32] G Wang and Y Wen Concentrations of Greenhouse Gases andEmissions Monitoring and Related Process China Environmen-tal Science Press 1996 (Chinese)

[33] S Piao J Fang J He and Y Xiao ldquoSpatial distribution ofgrassland biomass in ChinardquoActa Phytoecologica Sinica vol 28no 4 pp 491ndash498 2004 (Chinese)

[34] B Chen Chinarsquos Agricultural Resources Comprehensive Produc-tivity and Population Carrying Capacity China MeteorologicalPress 2001 (Chinese)

[35] Y Tian Based on Grid of Chinese Terrestrial Ecosystem FoodFunction Evaluation Graduate School of Chinese Academy ofSciences 2005 (Chinese)

[36] Y Yao J Yang and G Xiao ldquoChange feature of net primaryproductivity of natural vegetation and its impact factor inthe source region of Yellow River in recent 50 yearsrdquo PlateauMeteorology vol 30 no 6 pp 1594ndash1603 2011 (Chinese)

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ClimatologyJournal of

EcologyInternational Journal of


EarthquakesJournal of


Hindawi Publishing Corporationhttpwwwhindawicom

Applied ampEnvironmentalSoil Science

Volume 2014

Mining


Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal of

Geophysics

OceanographyInternational Journal of


Journal of Computational Environmental SciencesHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal ofPetroleum Engineering


GeochemistryHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Atmospheric SciencesInternational Journal of


OceanographyHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in


MineralogyInternational Journal of


MeteorologyAdvances in

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Paleontology JournalHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ScientificaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Geological ResearchJournal of


Geology Advances in


which increases the income of local people and meanwhileleads to the long-term overgrazing The serious grasslanddegradation has greatly restrained the development of thelocal animal husbandry and change of landscape [17] On theother hand the global warming has led to the decrease ofthe average annual precipitation and the grass yield per unitarea also decreases slightly year by year which has threatenedthe development of local animal husbandry Therefore adescription of the climate and relevant economic activitiesin the study area is detailed and significant [18] In orderto solve the problems brought by the grassland degradationand promote the sustainable development of the local animalhusbandry in the TRHR it is necessary to carry out scientificprediction of the local grass yield determine the reasonablegrazing capacity and guide the production of local animalhusbandry

The research on NPP of the grassland is the basis for thestudy of the grass yield and prediction of grazing capacityand there have been many investigations and research workson the estimation of NPP of the grassland in China in recentyearsThemethods to estimate NPP of grassland vary greatlydue to the difference in the natural environment of the studyarea data availability and so forth There are mainly fourkinds of models to estimate NPP of grassland that is thelight use efficiency model ecosystem process model remotesensing-process coupling model and climatic statistic model[19] There are both advantages and disadvantages in thesemodels For example the light use efficiency model basedon the mechanism of vegetation photosynthesis is easy to beconstructed and has high calculation efficiency but there aresome faults in the factors taken into account parameter selec-tion calculation result and so forth The ecosystem processmodel simulates the physiological processes of vegetationand applies the technologies such as the remote sensingwhich makes it possible to carry out multiscale dynamicmonitoring of the spatiotemporal change of NPP Howeverthis model is very complex and requires high quality datawhich restrains its practicability to some degree especiallyin the regional estimation The remote sensing-process cou-pling model integrates the advantages of both the modelsmentioned above but the accuracy of its calculation resultis greatly influenced by other factors The climatic statisticmodel introduces the regressionmodels constructed with thesimple climatic factors such as temperature and precipitationand has a low data requirement This kind of models ismore practical but it is still limited by the low accuracyof the result There is great complexity uncertainties andinaccuracy in the extraction of vegetation indices and soilparameters with the remote sensing data all of which makeit very difficult and very inaccurate to calculate these datawith the light use efficiency model ecosystem process modeland ecological remote sensing coupling model Besides itis a fact that the climatic conditions have great impacts onthe livestock production in the study area Therefore theclimatic statistic model was finally used to estimate the futuregrassland productivity in the TRHR

The most widely used climate models mainly includethe Miami Model Thornthwaite Memorial Model ChikugoModel and the comprehensive model The climate model is

an effective tool in the study of climate [20] The compre-hensive model is more suitable for the estimation of NPP ofvegetation in the arid area than the Chikugo Model Besidesin comparison with other three models the comprehensiveModel has a solider theoretical foundation takes more intoaccount of the physiological processes of vegetation andconsequently can obtain a better estimation result in ZhejiangProvince [21] and Inner Mongolia [22 23]

In order to overall forecast the changing trend of thegrassland NPP and theoretical grazing capacity in the studyarea in the context of climate change four representativeconcentration pathways (RCPs) (which represent the emis-sion trajectories under the natural and social conditionsand the corresponding scenarios) were selected to analyzethe changing trend of grassland NPP in the Three-RiverHeadwaters region This study is of both theoretical andpractical significance In theory this study extends the fieldof application of the estimation of NPP and explored thetheoretical grazing capacity in the TRHR in the future whichprovides certain references for the relevant research works inother similar regions In practice this study qualitatively andquantitatively analyzed the changing trend of the grass yieldand grazing capacity in the study area which can providesome guidance for the local grasslandutility andmanagementand the development of animal husbandry and promote theharmonious and sustainable development of the local man-land relationship

2 Study Area

TheTRHR is located in the southern part ofQinghai Provinceof China between 31∘391015840ndash36∘121015840N and 89∘451015840ndash102∘231015840E withan area of 363 thousand km2 which accounts for 43 of thetotal area of Qinghai Province The TRHR with the altituderanges from 3500m to 4800m is the headstream of theYellow River Yangtze River and Lancang River and has adense network of rivers The administrative regions cover 16counties including Yushu Xinghai Tongde Zeku MatuoMaqin Dari Gande Jiuzhi Banma Chengduo ZaduoZhiduo Qumalai Nangqian andHenan except for TanggulaMountain Town which is under the charge of Golmud City

The grassland area is 203 thousand km2 in the TRHRaccounting for 654 of the total area of this region (Figure 1)The vegetation diversity of the TRHR is the richest among theregions at the same altitude all over the world The grasslandtype changes from alpine meadow to high-cold steppe andalpine desert with the productivity also gradually decreasing[24ndash27] The grassland resource is very rich in this regionhowever the grass yield per unit area has decreased year byyear due to the climatic change and overgrazing in recentyears which has threatened the development of the localanimal husbandry

3 Methodology and Data

31 Models

311 Comprehensive Model The comprehensive modelwas developed on the basis of two well-known balance


Inner Mongolia

Heilongjiang

Liaoning

Jilin

BeijingTianjinHebei

ShanxiShandong

Tibet

Xinjiang

Qinghai

Gansu

Sichuan

Ningxia


Zhejiang

HunanJiangxi

Fujian

GuangdongGuangxi

Hainan


0 320160(km)

Yunnan

GuizhouTaiwan

Qinghai

Tibet

Gansu

Sichuan

Xinjiang

Inner Mongolia

Inner Mongolia

90∘E88

∘E86∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E 104

∘E

90∘E88

∘E 92∘E 94

∘E 96∘E 98

∘E 100∘E 102

∘E 104∘E

36∘N

34∘N

32∘N

30∘N

36∘N

38∘N

34∘N

32∘N

30∘N

ChongqingShanghai



NPP = RDI times119875119903119877119899(119875

2

119903+ 119877

2

119899+ 119875119903119877119899)

(119875119903+ 119877119899) (119875

2

119903+ 119877

2

119899)

119890

minus


RDI =119877119899

119871 sdot 119875119903

(1)


119903is the annual




119861119892=

NPP119878bn (1 + 119878ug)

(2)




















RCP26

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

36∘N

34∘N

32∘N

36∘N

34∘N

32∘N

RCP26

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

36∘N

34∘N

32∘N

36∘N

34∘N

32∘N

RCP26

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

36∘N

34∘N

32∘N

36∘N

34∘N

32∘N

RCP45

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

36∘N

34∘N

32∘N

36∘N

34∘N

32∘N

RCP60

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

36∘N

34∘N

32∘N

36∘N

34∘N

32∘N

RCP85

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

36∘N

34∘N

32∘N

36∘N

34∘N

32∘N

RCP85

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

36∘N

34∘N

32∘N

36∘N

34∘N

32∘N

High 260Low 0

High 260Low 0

High 260Low 0

0 150 300(km)

RCP85

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

36∘N

34∘N

32∘N

36∘N

34∘N

32∘N

RCP60

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

36∘N

34∘N

32∘N

36∘N

34∘N

32∘N

RCP60

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

36∘N

34∘N

32∘N

36∘N

34∘N

32∘N

RCP45

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

36∘N

34∘N

32∘N

36∘N

34∘N

32∘N

RCP45

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

36∘N

34∘N

32∘N

36∘N

34∘N

32∘N

0 150 300(km)

0 150 300(km)







400

425

450

475

500

2010 2015 2020 2025 2030

Prec

ipita

tion

(mm

)

100105110115120125130

2010 2015 2020 2025 2030

RCP26RCP45

RCP60RCP85

2010 2015 2020 2025 2030

minus25

minus22

minus19

minus16

minus13

minus1

NPP

(gm

2)

Tem

pera

ture

(∘C)


540

570

600

630

660

690

2010 2015 2020 2025 2030

Gra

ssla

nd y

ield

(ton

s)

RCP26RCP45

RCP60RCP85




330

360

390

420

450

480

2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030























Acknowledgments


References















































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in


Inner Mongolia

Heilongjiang

Liaoning

Jilin

BeijingTianjinHebei

ShanxiShandong

Tibet

Xinjiang

Qinghai

Gansu

Sichuan

Ningxia


Zhejiang

HunanJiangxi

Fujian

GuangdongGuangxi

Hainan


0 320160(km)

Yunnan

GuizhouTaiwan

Qinghai

Tibet

Gansu

Sichuan

Xinjiang

Inner Mongolia

Inner Mongolia

90∘E88

∘E86∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E 104

∘E

90∘E88

∘E 92∘E 94

∘E 96∘E 98

∘E 100∘E 102

∘E 104∘E

36∘N

34∘N

32∘N

30∘N

36∘N

38∘N

34∘N

32∘N

30∘N

ChongqingShanghai



NPP = RDI times119875119903119877119899(119875

2

119903+ 119877

2

119899+ 119875119903119877119899)

(119875119903+ 119877119899) (119875

2

119903+ 119877

2

119899)

119890

minus


RDI =119877119899

119871 sdot 119875119903

(1)


119903is the annual




119861119892=

NPP119878bn (1 + 119878ug)

(2)




















RCP26

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

36∘N

34∘N

32∘N

36∘N

34∘N

32∘N

RCP26

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

36∘N

34∘N

32∘N

36∘N

34∘N

32∘N

RCP26

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

36∘N

34∘N

32∘N

36∘N

34∘N

32∘N

RCP45

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

36∘N

34∘N

32∘N

36∘N

34∘N

32∘N

RCP60

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

36∘N

34∘N

32∘N

36∘N

34∘N

32∘N

RCP85

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

36∘N

34∘N

32∘N

36∘N

34∘N

32∘N

RCP85

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

36∘N

34∘N

32∘N

36∘N

34∘N

32∘N

High 260Low 0

High 260Low 0

High 260Low 0

0 150 300(km)

RCP85

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

36∘N

34∘N

32∘N

36∘N

34∘N

32∘N

RCP60

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

36∘N

34∘N

32∘N

36∘N

34∘N

32∘N

RCP60

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

36∘N

34∘N

32∘N

36∘N

34∘N

32∘N

RCP45

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

36∘N

34∘N

32∘N

36∘N

34∘N

32∘N

RCP45

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

36∘N

34∘N

32∘N

36∘N

34∘N

32∘N

0 150 300(km)

0 150 300(km)







400

425

450

475

500

2010 2015 2020 2025 2030

Prec

ipita

tion

(mm

)

100105110115120125130

2010 2015 2020 2025 2030

RCP26RCP45

RCP60RCP85

2010 2015 2020 2025 2030

minus25

minus22

minus19

minus16

minus13

minus1

NPP

(gm

2)

Tem

pera

ture

(∘C)


540

570

600

630

660

690

2010 2015 2020 2025 2030

Gra

ssla

nd y

ield

(ton

s)

RCP26RCP45

RCP60RCP85




330

360

390

420

450

480

2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030























Acknowledgments


References















































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in
















RCP26

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

36∘N

34∘N

32∘N

36∘N

34∘N

32∘N

RCP26

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

36∘N

34∘N

32∘N

36∘N

34∘N

32∘N

RCP26

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

36∘N

34∘N

32∘N

36∘N

34∘N

32∘N

RCP45

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

36∘N

34∘N

32∘N

36∘N

34∘N

32∘N

RCP60

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

36∘N

34∘N

32∘N

36∘N

34∘N

32∘N

RCP85

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

36∘N

34∘N

32∘N

36∘N

34∘N

32∘N

RCP85

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

36∘N

34∘N

32∘N

36∘N

34∘N

32∘N

High 260Low 0

High 260Low 0

High 260Low 0

0 150 300(km)

RCP85

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

36∘N

34∘N

32∘N

36∘N

34∘N

32∘N

RCP60

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

36∘N

34∘N

32∘N

36∘N

34∘N

32∘N

RCP60

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

36∘N

34∘N

32∘N

36∘N

34∘N

32∘N

RCP45

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

36∘N

34∘N

32∘N

36∘N

34∘N

32∘N

RCP45

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

90∘E 92

∘E 94∘E 96

∘E 98∘E 100

∘E 102∘E

36∘N

34∘N

32∘N

36∘N

34∘N

32∘N

0 150 300(km)

0 150 300(km)







400

425

450

475

500

2010 2015 2020 2025 2030

Prec

ipita

tion

(mm

)

100105110115120125130

2010 2015 2020 2025 2030

RCP26RCP45

RCP60RCP85

2010 2015 2020 2025 2030

minus25

minus22

minus19

minus16

minus13

minus1

NPP

(gm

2)

Tem

pera

ture

(∘C)


540

570

600

630

660

690

2010 2015 2020 2025 2030

Gra

ssla

nd y

ield

(ton

s)

RCP26RCP45

RCP60RCP85




330

360

390

420

450

480

2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030























Acknowledgments


References















































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in


400

425

450

475

500

2010 2015 2020 2025 2030

Prec

ipita

tion

(mm

)

100105110115120125130

2010 2015 2020 2025 2030

RCP26RCP45

RCP60RCP85

2010 2015 2020 2025 2030

minus25

minus22

minus19

minus16

minus13

minus1

NPP

(gm

2)

Tem

pera

ture

(∘C)


540

570

600

630

660

690

2010 2015 2020 2025 2030

Gra

ssla

nd y

ield

(ton

s)

RCP26RCP45

RCP60RCP85




330

360

390

420

450

480

2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030























Acknowledgments


References















































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in
















Acknowledgments


References















































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in

















Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in

Research Article Analyses on the Changes of Grazing...

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