Interannual variability and trends of gross primary production and transpiration in savannas and grasslands from 2000 to 2021

Cheng MENG , Xiangming XIAO , Li PAN , Baihong PAN , Russell L. SCOTT , Pradeep WAGLE , Chenchen ZHANG , Yuan YAO , Yuanwei QIN

Front. Earth Sci. ›› 2025, Vol. 19 ›› Issue (2) : 246 -260.

PDF (8861KB)
Front. Earth Sci. ›› 2025, Vol. 19 ›› Issue (2) : 246 -260. DOI: 10.1007/s11707-024-1136-8
RESEARCH ARTICLE

Interannual variability and trends of gross primary production and transpiration in savannas and grasslands from 2000 to 2021

Author information +
History +
PDF (8861KB)

Abstract

Carbon and water fluxes of savannas and grasslands have large seasonal dynamics and inter-annual variation. In this study, we selected five savanna and grassland sites, each of them having 10+ years (11−21 years) of eddy covariance (EC) data, and a total of 85 site-years at these five sites which offers a unique opportunity for data analyses and model evaluation. We ran a long-term simulation (2000−2021) of the vegetation photosynthesis model (VPM, v3.0) and vegetation transpiration model (VTM, v2.0) to investigate the seasonal dynamics, interannual variation, and decadal trends of modeled gross primary production (GPPVPM) and transpiration (TVTM) at these sites. The seasonal dynamics of daily GPPVPM and TVTM track well with the seasonal dynamics of EC-based GPP (GPPEC, R2: 0.76−0.93) and evapotranspiration (ETEC, R2: 0.69−0.92). The inter-annual variation of annual GPPVPM tracked well that of annual GPPEC, with the linear regression slopes for GPPEC versus GPPVPM-EC ranging from 0.89 to 1.11. The simulation results of GPPVPM and TVTM using two different climate data sets (in situ climate data and European Center for Medium-Range Weather Forecasts Reanalysis v5 data set (ERA5)) were similar, suggesting that ERA5 data can be used for VPM/VTM simulations at large spatial scales. From 2000 to 2021, annual GPPVPM and TVTM had no significant inter-annual trends at one savanna and three grassland sites but increased significantly at one savanna site. The results demonstrate the potential of using VPM (v3.0) and VTM (v2.0) to predict the seasonal dynamics and inter-annual variation of GPP and T in savannas and grasslands.

Graphical abstract

Keywords

vegetation photosynthesis model / vegetation transpiration model / ERA5 / MODIS / carbon fluxes

Cite this article

Download citation ▾
Cheng MENG, Xiangming XIAO, Li PAN, Baihong PAN, Russell L. SCOTT, Pradeep WAGLE, Chenchen ZHANG, Yuan YAO, Yuanwei QIN. Interannual variability and trends of gross primary production and transpiration in savannas and grasslands from 2000 to 2021. Front. Earth Sci., 2025, 19(2): 246-260 DOI:10.1007/s11707-024-1136-8

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Alfieri J G, Xiao X, Niyogi D, Pielke R A Sr, Chen F, LeMone M A (2009). Satellite-based modeling of transpiration from the grasslands in the Southern Great Plains, USA.Global Planet Change, 67(1−2): 78–86

[2]

Baldocchi D (2014). Measuring fluxes of trace gases and energy between ecosystems and the atmosphere – The state and future of the eddy covariance method.Glob Change Biol, 20(12): 3600–3609

[3]

Baldocchi D, Ma S, Verfaillie J (2021). On the inter- and intra-annual variability of ecosystem evapotranspiration and water use efficiency of an oak savanna and annual grassland subjected to booms and busts in rainfall.Glob Change Biol, 27(2): 359–375

[4]

Biederman J A, Scott R L, Bell T W, Bowling D R, Dore S, Garatuza-Payan J, Kolb T E, Krishnan P, Krofcheck D J, Litvak M E, Maurer G E, Meyers T P, Oechel W C, Papuga S A, Ponce-Campos G E, Rodriguez J C, Smith W K, Vargas R, Watts C J, Yepez E A, Goulden M L (2017). CO2 exchange and evapotranspiration across dryland ecosystems of southwestern North America.Glob Change Biol, 23(10): 4204–4221

[5]

Chang Q, Xiao X, Doughty R, Wu X, Jiao W, Qin Y (2021). Assessing variability of optimum air temperature for photosynthesis across site-years, sites and biomes and their effects on photosynthesis estimation.Agric For Meteorol, 298–299: 108277

[6]

Chang Q, Xiao X, Wu X, Doughty R, Jiao W, Bajgain R, Qin Y, Wang J (2020). Estimating site-specific optimum air temperature and assessing its effect on the photosynthesis of grasslands in mid- to high-latitudes.Environ Res Lett, 15(3): 034064

[7]

Cooley S S, Fisher J B, Goldsmith G R (2022). Convergence in water use efficiency within plant functional types across contrasting climates.Nat Plants, 8(4): 341–345

[8]

Dong J, Xiao X, Wagle P, Zhang G, Zhou Y, Jin C, Torn M S, Meyers T P, Suyker A E, Wang J, Yan H, Biradar C, Moore B III (2015). Comparison of four EVI-based models for estimating gross primary production of maize and soybean croplands and tallgrass prairie under severe drought.Remote Sens Environ, 162: 154–168

[9]

Dye D G (2004). Spectral composition and quanta-to-energy ratio of diffuse photosynthetically active radiation under diverse cloud conditions.J Geophys Res, 109(D10): 2003JD004251

[10]

Gentine P, Green J K, Guérin M, Humphrey V, Seneviratne S I, Zhang Y, Zhou S (2019). Coupling between the terrestrial carbon and water cycles—a review.Environ Res Lett, 14(8): 083003

[11]

Goetz S J, Prince S D, Goward S N, Thawley M M, Small J (1999). Satellite remote sensing of primary production: an improved production efficiency modeling approach.Ecol Modell, 122(3): 239–255

[12]

He H, Liu M, Xiao X, Ren X, Zhang L, Sun X, Yang Y, Li Y, Zhao L, Shi P, Du M, Ma Y, Ma M, Zhang Y, Yu G (2014). Large-scale estimation and uncertainty analysis of gross primary production in Tibetan alpine grasslands.J Geophys Res Biogeosci, 119(3): 466–486

[13]

Huete A R, Liu H Q, Batchily K, van Leeuwen W (1997). A comparison of vegetation indices over a global set of TM images for EOS-MODIS.Remote Sens Environ, 59(3): 440–451

[14]

Huete A, Didan K, Miura T, Rodriguez E P, Gao X, Ferreira L G (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices.Remote Sens Environ, 83(1−2): 195–213

[15]

Jiang C, Ryu Y (2016). Multi-scale evaluation of global gross primary productivity and evapotranspiration products derived from Breathing Earth System Simulator (BESS).Remote Sens Environ, 186: 528–547

[16]

Kemp D R, Guodong H, Xiangyang H, Michalk D L, Fujiang H, Jianping W, Yingjun Z (2013). Innovative grassland management systems for environmental and livelihood benefits.Proc Natl Acad Sci USA, 110(21): 8369–8374

[17]

Kendall M G (1948). Rank Correlation Methods.Griffin, Oxford: England

[18]

Konapala G, Mishra A K, Wada Y, Mann M E (2020). Climate change will affect global water availability through compounding changes in seasonal precipitation and evaporation.Nat Commun, 11(1): 3044

[19]

Li X, Gentine P, Lin C, Zhou S, Sun Z, Zheng Y, Liu J, Zheng C (2019). A simple and objective method to partition evapotranspiration into transpiration and evaporation at eddy-covariance sites.Agric For Meteorol, 265: 171–182

[20]

Luo X, Zhou H, Satriawan T W, Tian J, Zhao R, Keenan T F, Griffith D M, Sitch S, Smith N G, Still C J (2024). Mapping the global distribution of C4 vegetation using observations and optimality theory.Nat Commun, 15(1): 1219

[21]

Ma S, Baldocchi D, Wolf S, Verfaillie J (2016). Slow ecosystem responses conditionally regulate annual carbon balance over 15 years in Californian oak-grass savanna.Agric For Meteorol, 228–229: 252–264

[22]

Ma S, Eichelmann E, Wolf S, Rey-Sanchez C, Baldocchi D D (2020). Transpiration and evaporation in a Californian oak-grass savanna: field measurements and partitioning model results.Agric For Meteorol, 295: 108204

[23]

Mann H B (1945). Nonparametric tests against trend.Econometrica, 13(3): 245–259

[24]

Nelson J A, Pérez-Priego O, Zhou S, Poyatos R, Zhang Y, Blanken P D, Gimeno T E, Wohlfahrt G, Desai A R, Gioli B, Limousin J M, Bonal D, Paul-Limoges E, Scott R L, Varlagin A, Fuchs K, Montagnani L, Wolf S, Delpierre N, Berveiller D, Gharun M, Belelli Marchesini L, Gianelle D, Šigut L, Mammarella I, Siebicke L, Andrew Black T, Knohl A, Hörtnagl L, Magliulo V, Besnard S, Weber U, Carvalhais N, Migliavacca M, Reichstein M, Jung M (2020). Ecosystem transpiration and evaporation: insights from three water flux partitioning methods across FLUXNET sites.Glob Change Biol, 26(12): 6916–6930

[25]

NobelP S (2020) Physicochemical and Environmental Plant Physiology (5th Ed). New York: Academic Press

[26]

O’Mara F P (2012). The role of grasslands in food security and climate change.Ann Bot (Lond), 110(6): 1263–1270

[27]

Pan L, Xiao X, Pan B, Meng C, Staebler R M, Zhang C, Qin Y (2024). Interannual variations and trends of gross primary production and transpiration of four mature deciduous broadleaf forest sites during 2000–2020.Remote Sens Environ, 304: 114042

[28]

Pastorello G, Trotta C, Canfora E, Chu H, Christianson D, Cheah Y W, Poindexter C, Chen J, Elbashandy A, Humphrey M, Isaac P, Polidori D, Reichstein M, Ribeca A, van Ingen C, Vuichard N, Zhang L, Amiro B, Ammann C, Arain M A, Ardö J, Arkebauer T, Arndt S K, Arriga N, Aubinet M, Aurela M, Baldocchi D, Barr A, Beamesderfer E, Marchesini L B, Bergeron O, Beringer J, Bernhofer C, Berveiller D, Billesbach D, Black T A, Blanken P D, Bohrer G, Boike J, Bolstad P V, Bonal D, Bonnefond J M, Bowling D R, Bracho R, Brodeur J, Brümmer C, Buchmann N, Burban B, Burns S P, Buysse P, Cale P, Cavagna M, Cellier P, Chen S, Chini I, Christensen T R, Cleverly J, Collalti A, Consalvo C, Cook B D, Cook D, Coursolle C, Cremonese E, Curtis P S, D’Andrea E, da Rocha H, Dai X, Davis K J, Cinti B D, Grandcourt A, Ligne A D, De Oliveira R C, Delpierre N, Desai A R, Di Bella C M, Tommasi P, Dolman H, Domingo F, Dong G, Dore S, Duce P, Dufrêne E, Dunn A, Dušek J, Eamus D, Eichelmann U, ElKhidir H A M, Eugster W, Ewenz C M, Ewers B, Famulari D, Fares S, Feigenwinter I, Feitz A, Fensholt R, Filippa G, Fischer M, Frank J, Galvagno M, Gharun M, Gianelle D, Gielen B, Gioli B, Gitelson A, Goded I, Goeckede M, Goldstein A H, Gough C M, Goulden M L, Graf A, Griebel A, Gruening C, Grünwald T, Hammerle A, Han S, Han X, Hansen B U, Hanson C, Hatakka J, He Y, Hehn M, Heinesch B, Hinko-Najera N, Hörtnagl L, Hutley L, Ibrom A, Ikawa H, Jackowicz-Korczynski M, Janouš D, Jans W, Jassal R, Jiang S, Kato T, Khomik M, Klatt J, Knohl A, Knox S, Kobayashi H, Koerber G, Kolle O, Kosugi Y, Kotani A, Kowalski A, Kruijt B, Kurbatova J, Kutsch W L, Kwon H, Launiainen S, Laurila T, Law B, Leuning R, Li Y, Liddell M, Limousin J M, Lion M, Liska A J, Lohila A, López-Ballesteros A, López-Blanco E, Loubet B, Loustau D, Lucas-Moffat A, Lüers J, Ma S, Macfarlane C, Magliulo V, Maier R, Mammarella I, Manca G, Marcolla B, Margolis H A, Marras S, Massman W, Mastepanov M, Matamala R, Matthes J H, Mazzenga F, McCaughey H, McHugh I, McMillan A M S, Merbold L, Meyer W, Meyers T, Miller S D, Minerbi S, Moderow U, Monson R K, Montagnani L, Moore C E, Moors E, Moreaux V, Moureaux C, Munger J W, Nakai T, Neirynck J, Nesic Z, Nicolini G, Noormets A, Northwood M, Nosetto M, Nouvellon Y, Novick K, Oechel W, Olesen J E, Ourcival J M, Papuga S A, Parmentier F J, Paul-Limoges E, Pavelka M, Peichl M, Pendall E, Phillips R P, Pilegaard K, Pirk N, Posse G, Powell T, Prasse H, Prober S M, Rambal S, Rannik Ü Raz-Yaseef N, Rebmann C, Reed D, Dios V R, Restrepo-Coupe N, Reverter B R, Roland M, Sabbatini S, Sachs T, Saleska S R, Sánchez-Cañete E P, Sanchez-Mejia Z M, Schmid H P, Schmidt M, Schneider K, Schrader F, Schroder I, Scott R L, Sedlák P, Serrano-Ortíz P, Shao C, Shi P, Shironya I, Siebicke L, Šigut L, Silberstein R, Sirca C, Spano D, Steinbrecher R, Stevens R M, Sturtevant C, Suyker A, Tagesson T, Takanashi S, Tang Y, Tapper N, Thom J, Tomassucci M, Tuovinen J P, Urbanski S, Valentini R, van der Molen M, van Gorsel E, van Huissteden K, Varlagin A, Verfaillie J, Vesala T, Vincke C, Vitale D, Vygodskaya N, Walker J P, Walter-Shea E, Wang H, Weber R, Westermann S, Wille C, Wofsy S, Wohlfahrt G, Wolf S, Woodgate W, Li Y, Zampedri R, Zhang J, Zhou G, Zona D, Agarwal D, Biraud S, Torn M, Papale D (2020). ., 7(1): 225

[29]

Peng S, Piao S, Shen Z, Ciais P, Sun Z, Chen S, Bacour C, Peylin P, Chen A (2013). Precipitation amount, seasonality and frequency regulate carbon cycling of a semi-arid grassland ecosystem in Inner Mongolia, China: a modeling analysis.Agric For Meteorol, 178–179: 46–55

[30]

Running S W, Zhao M (2019). Daily GPP and Annual NPP (MOD17A2H/A3H) and Year-end Gap- Filled (MOD17A2HGF/A3HGF) Products NASA Earth Observing System MODIS Land Algorithm (For Collection 6). In: The Numerical Terradynamic Simulation Group, Missoula, MT, USA

[31]

Ryu Y, Jiang C, Kobayashi H, Detto M (2018). MODIS-derived global land products of shortwave radiation and diffuse and total photosynthetically active radiation at 5km resolution from 2000.Remote Sens Environ, 204: 812–825

[32]

Scott R L, Biederman J A (2017). Partitioning evapotranspiration using long-term carbon dioxide and water vapor fluxes.Geophys Res Lett, 44(13): 6833–6840

[33]

Scott R L, Biederman J A, Hamerlynck E P, Barron-Gafford G A (2015). The carbon balance pivot point of southwestern U. S. semiarid ecosystems: insights from the 21st century drought.J Geophys Res Biogeosci, 120(12): 2612–2624

[34]

Scott R L, Johnston M R, Knowles J F, MacBean N, Mahmud K, Roby M C, Dannenberg M P (2023). Interannual variability of spring and summer monsoon growing season carbon exchange at a semiarid savanna over nearly two decades.Agric For Meteorol, 339: 109584

[35]

Sen P K (1968). Estimates of the regression coefficient based on Kendall’s Tau.J Am Stat Assoc, 63(324): 1379–1389

[36]

Shen H, Li X, Cheng Q, Zeng C, Yang G, Li H, Zhang L (2015). Missing information reconstruction of remote sensing data: a technical review.IEEE Geosci Remote Sens Mag, 3(3): 61–85

[37]

Sjöström M, Zhao M, Archibald S, Arneth A, Cappelaere B, Falk U, de Grandcourt A, Hanan N, Kergoat L, Kutsch W, Merbold L, Mougin E, Nickless A, Nouvellon Y, Scholes R J, Veenendaal E M, Ardö J (2013). Evaluation of MODIS gross primary productivity for Africa using eddy covariance data.Remote Sens Environ, 131: 275–286

[38]

Still C J, Berry J A, Collatz G J, DeFries R S (2003). Global distribution of C3 and C4 vegetation: carbon cycle implications.Global Biogeochemical Cycles 17, 6–1–6–14

[39]

Stoy P C, El-Madany T S, Fisher J B, Gentine P, Gerken T, Good S P, Klosterhalfen A, Liu S, Miralles D G, Perez-Priego O, Rigden A J, Skaggs T H, Wohlfahrt G, Anderson R G, Coenders-Gerrits A M J, Jung M, Maes W H, Mammarella I, Mauder M, Migliavacca M, Nelson J A, Poyatos R, Reichstein M, Scott R L, Wolf S (2019). Reviews and syntheses: turning the challenges of partitioning ecosystem evaporation and transpiration into opportunities.Biogeosciences, 16(19): 3747–3775

[40]

Swain D L, Langenbrunner B, Neelin J D, Hall A (2018). Increasing precipitation volatility in twenty-first-century California.Nat Clim Chang, 8(5): 427–433

[41]

Tucker C J (1979). Red and photographic infrared linear combinations for monitoring vegetation.Remote Sens Environ, 8(2): 127–150

[42]

Turner D P, Ritts W D, Cohen W B, Gower S T, Zhao M, Running S W, Wofsy S C, Urbanski S, Dunn A L, Munger J W (2003). Scaling Gross Primary Production (GPP) over boreal and deciduous forest landscapes in support of MODIS GPP product validation.Remote Sens Environ, 88(3): 256–270

[43]

Wagle P, Kakani V G (2014). Environmental control of daytime net ecosystem exchange of carbon dioxide in switchgrass.Agric Ecosyst Environ, 186: 170–177

[44]

Wagle P, Xiao X, Scott R L, Kolb T E, Cook D R, Brunsell N, Baldocchi D D, Basara J, Matamala R, Zhou Y, Bajgain R (2015a). Biophysical controls on carbon and water vapor fluxes across a grassland climatic gradient in the United States.Agric For Meteorol, 214–215: 293–305

[45]

Wagle P, Xiao X, Suyker A E (2015b). Estimation and analysis of gross primary production of soybean under various management practices and drought conditions.ISPRS J Photogramm Remote Sens, 99: 70–83

[46]

Wagle P, Xiao X, Torn M S, Cook D R, Matamala R, Fischer M L, Jin C, Dong J, Biradar C (2014). Sensitivity of vegetation indices and gross primary production of tallgrass prairie to severe drought.Remote Sens Environ, 152: 1–14

[47]

Wang X, Xiao X, Zou Z, Chen B, Ma J, Dong J, Doughty R B, Zhong Q, Qin Y, Dai S, Li X, Zhao B, Li B (2020). Tracking annual changes of coastal tidal flats in China during 1986–2016 through analyses of Landsat images with Google Earth Engine.Remote Sens Environ, 238: 110987

[48]

Winslow J C, Hunt E R Jr, Piper S C (2003). The influence of seasonal water availability on global C3 versus C4 grassland biomass and its implications for climate change research.Ecol Modell, 163(1−2): 153–173

[49]

Xiao X (2006). Light absorption by leaf chlorophyll and maximum light use efficiency.IEEE Trans Geosci Remote Sens, 44(7): 1933–1935

[50]

Xiao X, Biradar C M, Czarnecki C, Alabi T, Keller M (2009). A simple algorithm for large-scale mapping of evergreen forests in tropical America, Africa and Asia.Remote Sens (Basel), 1(3): 355–374

[51]

Xiao X, Hollinger D, Aber J, Goltz M, Davidson E A, Zhang Q, Moore B III (2004a). Satellite-based modeling of gross primary production in an evergreen needleleaf forest.Remote Sens Environ, 89(4): 519–534

[52]

Xiao X, Zhang Q, Braswell B, Urbanski S, Boles S, Wofsy S, Moore B, Ojima D (2004b). Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data.Remote Sens Environ, 91(2): 256–270

[53]

Xin F, Xiao X, Zhao B, Miyata A, Baldocchi D, Knox S, Kang M, Shim K, Min S, Chen B, Li X, Wang J, Dong J, Biradar C (2017). Modeling gross primary production of paddy rice cropland through analyses of data from CO2 eddy flux tower sites and MODIS images.Remote Sens Environ, 190: 42–55

[54]

Xu L, Baldocchi D D (2004). Seasonal variation in carbon dioxide exchange over a Mediterranean annual grassland in California.Agric For Meteorol, 123(1−2): 79–96

[55]

Yan H, Fu Y, Xiao X, Huang H Q, He H, Ediger L (2009). Modeling gross primary productivity for winter wheat–maize double cropping system using MODIS time series and CO2 eddy flux tower data.Agric Ecosyst Environ, 129(4): 391–400

[56]

Zhang Y, Xiao X, Wu X, Zhou S, Zhang G, Qin Y, Dong J (2017). A global moderate resolution dataset of gross primary production of vegetation for 2000–2016.Sci Data, 4(1): 170165

[57]

Zhao M, Heinsch F A, Nemani R R, Running S W (2005). Improvements of the MODIS terrestrial gross and net primary production global data set.Remote Sens Environ, 95(2): 164–176

[58]

Zheng Y, Shen R, Wang Y, Li X, Liu S, Liang S, Chen J M, Ju W, Zhang L, Yuan W (2020). Improved estimate of global gross primary production for reproducing its long-term variation, 1982–2017.Earth Syst Sci Data, 12(4): 2725–2746

[59]

Zhou S, Yu B, Zhang Y, Huang Y, Wang G (2016). Partitioning evapotranspiration based on the concept of underlying water use efficiency.Water Resour Res, 52(2): 1160–1175

[60]

Zhu X, Pei Y, Zheng Z, Dong J, Zhang Y, Wang J, Chen L, Doughty R B, Zhang G, Xiao X (2018). Underestimates of grassland gross primary production in MODIS standard products.Remote Sens (Basel), 10(11): 1771

RIGHTS & PERMISSIONS

Higher Education Press

AI Summary AI Mindmap
PDF (8861KB)

Supplementary files

FES-24136-OF-MC_suppl_1

318

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/