The effects of air temperature and precipitation on the net primary productivity in China during the early 21st century

Qianfeng WANG, Jingyu ZENG, Song LENG, Bingxiong FAN, Jia TANG, Cong JIANG, Yi HUANG, Qing ZHANG, Yanping QU, Wulin WANG, Wei SHUI

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Front. Earth Sci. ›› 2018, Vol. 12 ›› Issue (4) : 818-833. DOI: 10.1007/s11707-018-0697-9
RESEARCH ARTICLE
RESEARCH ARTICLE

The effects of air temperature and precipitation on the net primary productivity in China during the early 21st century

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Abstract

Research on how terrestrial ecosystems respond to climate change can reveal the complex interactions between vegetation and climate. net primary productivity (NPP), an important vegetation parameter and ecological indicator, fluctuates within any given ecological environment or regional carbon budget. In this study, spatial interpolation was used to generate a spatial dataset, with 1-km spatial resolution, with meteorological data from 736 observation stations across China. An improved CASA model was used to simulate NPP over the period of 2001–2013 by taking into account land-cover change in every year during the same period. We propose the grid-based spatial patterns and dynamics of annual NPP, annual average temperature, and annual total precipitation based on the model. We also used the model to demonstrate the spatial correlation between NPP, temperature, and precipitation in the study area with special focus on the impact of climate change in the early 21st century. Results showed that the spatial pattern of NPP over all of China is characterized by higher values in the southeast and lower values in the northwest. The spatial pattern of temperature indicates substantial latitudinal differences across the country, and the spatial pattern of precipitation shows a ribbon of decline from the southeast coast to the northwest inland. Most areas show an upward trend in NPP. Temperatures appear to decrease across the country during the global warming hiatus (1998–2008), and are accompanied by an increase in precipitation over most regions. The correlation between NPP and annual average temperature is weak. Alternatively, NPP and annual total precipitation are positively correlated in northern and central China at a coefficient above 0.64 (p<0.01) yet negatively correlated in the eastern parts of the Qinghai-Tibet Plateau and Sichuan Basin. Results can provide useful information for improving parameters for calibration of the terrestrial ecosystem process model.

Keywords

impact / air temperature / precipitation / NPP / China

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Qianfeng WANG, Jingyu ZENG, Song LENG, Bingxiong FAN, Jia TANG, Cong JIANG, Yi HUANG, Qing ZHANG, Yanping QU, Wulin WANG, Wei SHUI. The effects of air temperature and precipitation on the net primary productivity in China during the early 21st century. Front. Earth Sci., 2018, 12(4): 818‒833 https://doi.org/10.1007/s11707-018-0697-9

References

[1]
Alexandrov G, Oikawa T, Yamagata Y (2002). The scheme for globalization of a process-based model explaining gradations in terrestrial NPP and its application. Ecol Modell, 148(3): 293–306
CrossRef Google scholar
[2]
Bondeau A, Kicklighter D W, Kaduk J, the Participants of the Potsdam NPP model intercomparison (1999). Comparing global models of terrestrial net primary productivity (NPP): importance of vegetation structure on seasonal NPP estimates. Glob Change Biol, 5(S1): 35–45
CrossRef Google scholar
[3]
Brown S M, Petrone R M, Mendoza C, Devito K J (2010). Surface vegetation controls on evapotranspiration from a sub-humid Western Boreal Plain wetland. Hydrol Processes, 24(8): 1072–1085
CrossRef Google scholar
[4]
Burn D H, Hag Elnur M A (2002). Detection of hydrologic trends and variability. J Hydrol (Amst), 255(1–4): 107–122
CrossRef Google scholar
[5]
Cao M, Tao B, Li K, Shao X, Stephen D (2003). Interannual variation in terrestrial ecosystem carbon fluxes in China from 1981 to 1998. Acta Bot Sin, 45(5): 552–560
[6]
Cao M, Woodward F I (1998). Dynamic responses of terrestrial ecosystem carbon cycling to global climate change. Nature, 393(6682): 249–252
CrossRef Google scholar
[7]
Chandrappa R, Gupta S, Kulshrestha U C (2011). Industrial Revolutions, Climate Change and Asia, Coping with Climate Change. Berlin: Springer
[8]
Chen L, Liu G, Feng X (2000). Estimation of net primary productivity of terrestrial vegetation in China by remote sensing. Acta Bot Sin, 43(11): 1191–1198
[9]
Crabtree R, Potter C, Mullen R, Sheldon J, Huang S, Harmsen J, Rodman A, Jean C (2009). A modeling and spatio-temporal analysis framework for monitoring environmental change using NPP as an ecosystem indicator. Remote Sens Environ, 113(7): 1486–1496
CrossRef Google scholar
[10]
Cui Y (2013). Preliminary estimation of the realistic optimum temperature for vegetation growth in China. Environ Manage, 52(1): 151–162
CrossRef Google scholar
[11]
Dong M, Yu M (2008). Simulation analysis on net primary productivity of grassland communities along a water gradient and their responses to climate change. J Plant Ecol, 32(3): 531–543
[12]
Easterling D R, Wehner M F (2009). Is the climate warming or cooling? Geophys Res Lett, 36(8): L08706
CrossRef Google scholar
[13]
Fang O, Wang Y, Shao X (2016). The effect of climate on the net primary productivity (NPP) of Pinus koraiensis in the Changbai Mountains over the past 50 years. Trees (Berl), 30(1): 281–294
CrossRef Google scholar
[14]
Field C B, Randerson J T, Malmström C M (1995). Global net primary production: combining ecology and remote sensing. Remote Sens Environ, 51(1): 74–88
CrossRef Google scholar
[15]
Gang C, Zhang Y, Wang Z, Chen Y, Yang Y, Li J, Cheng J, Qi J, Odeh I (2017). Modeling the dynamics of distribution, extent, and NPP of global terrestrial ecosystems in response to future climate change. Global Planet Change, 148: 153–165
CrossRef Google scholar
[16]
Gang C, Zhou W, Wang Z, Chen Y, Li J, Chen J, Qi J, Odeh I, Groisman P (2015). Comparative assessment of grassland NPP dynamics in response to climate change in China, North America, Europe and Australia from 1981 to 2010. J Agron Crop Sci, 201(1): 57–68
CrossRef Google scholar
[17]
He M, Ju W, Zhou Y, Chen J, He H, Wang S, Wang H, Guan D, Yan J, Li Y, Hao Y, Zhao F (2013). Development of a two-leaf light use efficiency model for improving the calculation of terrestrial gross primary productivity. Agric Meteorol, 173: 28–39
CrossRef Google scholar
[18]
Jones H G (1992 ). Plant and Microclimate. A Quantitative Approach to Environmental Plant Physiology (2nd ed). Camberidge University Press
[19]
Kaufmann R K, Kauppi H, Mann M L, Stock J H (2011). Reconciling anthropogenic climate change with observed temperature 1998–2008. Proc Natl Acad Sci USA, 108(29): 11790–11793
CrossRef Google scholar
[20]
Kendall M G (1948). Rank Correlation Methods. London: Charles Griffin
[21]
King D A, Turner D P, Ritts W D (2011). Parameterization of a diagnostic carbon cycle model for continental scale application. Remote Sens Environ, 115(7): 1653–1664
CrossRef Google scholar
[22]
Li J, Cui Y, Liu J, Shi W, Qin Y (2013). Estimation and analysis of net primary productivity by integrating MODIS remote sensing data with a light use efficiency model. Ecol Modell, 252(1): 3–10
CrossRef Google scholar
[23]
Liang W, Yang Y T, Fan D M, Guan H D, Zhang T, Long D, Zhou Y, Bai D (2015). Analysis of spatial and temporal patterns of net primary production and their climate controls in China from 1982 to 2010. Agric Meteorol, 204: 22–36
CrossRef Google scholar
[24]
Liu C, Dong X, Liu Y (2015). Changes of NPP and their relationship to climate factors based on the transformation of different scales in Gansu, China. Catena, 125: 190–199
CrossRef Google scholar
[25]
Los S O, Justice C, Tucker C (1994). A global 1° by 1° NDVI data set for climate studies derived from the GIMMS continental NDVI data. Int J Remote Sens, 15(17): 3493–3518
CrossRef Google scholar
[26]
Mahadevan P, Wofsy S C, Matross D M, Xiao X, Dunn A L, Lin J C, Gerbig C, Munger J W, Chow V Y, Gottlieb E W (2008). A satellite-based biosphere parameterization for net ecosystem CO2 exchange: vegetation photosynthesis and respiration model (VPRM). Global Biogeochem Cycles, 22(2): GB2005
CrossRef Google scholar
[27]
Mann H B (1945). Nonparametric tests against trend. Econometrica, 13(3): 245–259
CrossRef Google scholar
[28]
Mao D, Wang Z, Han J, Ren C (2012). Spatio-temporal pattern of net primary productivity and its driven factors in Northeast China in 1982–2010. Dili Kexue, 32: 1106–1111
[29]
Nagler P L, Glenn E P, Kim H, Emmerich W, Scott R L, Huxman T E, Huete A R (2007). Relationship between evapotranspiration and precipitation pulses in a semiarid rangeland estimated by moisture flux towers and MODIS vegetation indices. J Arid Environ, 70(3): 443–462
CrossRef Google scholar
[30]
Nemani R R, Keeling C D, Hashimoto H, Jolly W M, Piper S C, Tucker C J, Myneni R B, Running S W (2003). Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science, 300(5625): 1560–1563
[31]
Olesen J E, Carter T, Diaz-Ambrona C, Fronzek S, Heidmann T, Hickler T, Holt T, Minguez M, Morales P, Palutikof J, Quemada M, Ruiz-Ramos M, Rubæk G H, Sau F, Smith B, Sykes M T (2007). Uncertainties in projected impacts of climate change on European agriculture and terrestrial ecosystems based on scenarios from regional climate models. Clim Change, 81(S1): 123–143
CrossRef Google scholar
[32]
Pachauri R K, Allen M R, Barros V R, Broome J, Cramer W, Christ R, Church J A, Clarke L, Dahe Q, Dasgupta P (2014). IPCC, Climate Change 2014: Synthesis Report, Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (Intergovernmental Panel on Climate Change, 2014), 1–151
[33]
Pachavo G, Murwira A (2014). Remote sensing net primary productivity (NPP) estimation with the aid of GIS modelled shortwave radiation (SWR) in a Southern African Savanna. Int J Appl Earth Obs Geoinf, 30(1): 217–226
CrossRef Google scholar
[34]
Palut M P, Canziani O F (2007). Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press
[35]
Pan S, Tian H, Dangal S R, Ouyang Z, Lu C, Yang J, Tao B, Ren W, Banger K, Yang Q, Zhang B (2015). Impacts of climate variability and extremes on global net primary production in the first decade of the 21st century. J Geogr Sci, 25(9): 1027–1044
CrossRef Google scholar
[36]
Piao S, Ciais P, Friedlingstein P, Peylin P, Reichstein M, Luyssaert S, Margolis H, Fang J, Barr A, Chen A, Grelle A, Hollinger D Y, Laurila T, Lindroth A, Richardson A D, Vesala T (2008). Net carbon dioxide losses of northern ecosystems in response to autumn warming. Nature, 451(7174): 49–52
CrossRef Google scholar
[37]
Piao S, Nan H, Huntingford C, Ciais P, Friedlingstein P, Sitch S, Peng S, Ahlstrom Am Canadell J G, Cong N, Levis S, Levy P E, Liu L, Lomas M R, Mao J, Myneni R B, Peylin P, Poulter B, Shi X, Yin G, Viovy N, Wang T, Wang X, Zaehle S, Zeng N, Zeng Z, Chen A (2014). Evidence for a weakening relationship between interannual temperature variability and northern vegetation activity. Nat Commun, 5(5018): 1–7
[38]
Ran Y, Li X, Lu L, Li Z (2012). Large-scale land cover mapping with the integration of multi-source information based on the Dempster–Shafer theory. Int J Geogr Inf Sci, 26(1): 169–191
CrossRef Google scholar
[39]
Running S W, Nemani R R, Heinsch F A, Zhao M, Reeves M, Hashimoto H (2004). A continuous satellite-derived measure of global terrestrial primary production. Bioscience, 54(6): 547–560
CrossRef Google scholar
[40]
Sen P K (1968). Estimates of the regression coefficient based on Kendall’s Tau. J Am Stat Assoc, 63(324): 1379–1389
CrossRef Google scholar
[41]
Turner D, Ritts W, Styles J, Yang Z, Cohen W, Law B, Thornton P (2006). A diagnostic carbon flux model to monitor the effects of disturbance and interannual variation in climate on regional NEP. Tellus B Chem Phys Meterol, 58(5): 476–490
CrossRef Google scholar
[42]
Veroustraete F, Sabbe H, Eerens H (2002). Estimation of carbon mass fluxes over Europe using the C-Fix model and Euroflux data. Remote Sens Environ, 83(3): 376–399
CrossRef Google scholar
[43]
Wang F, Xu Y J, Dean T J (2011). Projecting climate change effects on forest net primary productivity in subtropical Louisiana, USA. Ambio, 40(5): 506–520
CrossRef Google scholar
[44]
Wang J, Dong J, Yi Y, Lu G, Oyler J, Simth W K, Zhao M, Liu J, Running S (2017a). Decreasing net primary production due to drought and slight decreases in solar radiation in China from 2000 to 2012. J Geophys Res, 122(1)
CrossRef Google scholar
[45]
Wang Q, Wu J, Li X, Zhou H, Yang J, Geng G, An X, Liu L, Tang Z (2017b). A comprehensively quantitative method of evaluating the impact of drought on crop yield using daily multi-scale SPEI and crop growth process model. Int J Biometeorol, 61(4): 685–699
CrossRef Google scholar
[46]
Xiao X, Hollinger D, Aber J, Goltz M, Davidson E A, Zhang Q, Moore B III (2004). Satellite-based modeling of gross primary production in an evergreen needleleaf forest. Remote Sens Environ, 89(4): 519–534
CrossRef Google scholar
[47]
Xie B, Qin Z, Wang Y, Chang Q (2014). Spatial and temporal variation in terrestrial net primary productivity on Chinese Loess Plateau and its influential factors. Transactions of the Chinese society of Agricultural Engineering, 30(11): 244–253
[48]
Xu Z, Chen Y, Li J (2004). Impact of climate change on water resources in the Tarim River basin. Water Resour Manage, 18(5): 439–458
CrossRef Google scholar
[49]
Yu D, Zhu W, Pan Y (2008). The role of atmospheric circulation system playing in coupling relationship between spring NPP and precipitation in East Asia area. Environ Monit Assess, 145(1–3): 135–143
CrossRef Google scholar
[50]
Yuan W, Liu S, Zhou G, Zhou G, Tieszen L L, Baldocchi D, Bernhofer C, Gholz H, Goldstein A H, Goulden M L, Hollinger D Y, Hu Y, Law B E, Stoy P C, Vesala T, Wofsy S C (2007). Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes. Agric Meteorol, 143(3–4): 189–207
CrossRef Google scholar
[51]
Yue T X, Zhao N, Ramsey R D, Wang C L, Fan Z M, Chen C F, Lu Y M, Li B L (2013). Climate change trend in China, with improved accuracy. Clim Change, 120(1‒2): 137–151
CrossRef Google scholar
[52]
Zhang F, Feng Q, Li X, Wei Y (2014a). Remotely-sensed estimation of NPP and its spatial-temporal characteristics in the Heihe River Basin. J Desert Res, 34: 1657–1664
[53]
Zhang Y, Jia W, Zhao Y, Liu Y, Zhao Z, Chen J (2014b). Spatial-temporal variations of net primary productivity of Qilian mountains vegetation based on CASA model. Acta Botanica Boreali-Occidentalia Sinica, 34: 2085–2091
[54]
Zhang Y, Qi W, Zhou C, Ding M, Liu L, Gao J, Bai W, Wang Z, Zheng D (2014c). Spatial and temporal variability in the net primary production of alpine grassland on the Tibetan Plateau since 1982. J Geogr Sci, 24(2): 269–287
CrossRef Google scholar
[55]
Zhao M, Running S (2010). Drought-induced reduction in global terrestrial net primary production from 2000 through 2009. Science, 329(5994): 940–943
CrossRef Google scholar
[56]
Zhu W, Pan Y, He H, Yu D, Hu H (2006). Simulation of maximum light use efficiency for some typical vegetation types in China. Chin Sci Bull, 51(4): 457–463
CrossRef Google scholar
[57]
Zhu W, Pan Y, Zhang J (2007). Estimation of net primary productivity of Chinese terrestrial vegetation based on remote sensing. J Plant Ecol, 31(3): 413–424
CrossRef Google scholar

Acknowledgements

This research received financial support from the National Basic Research Program of China (No. 2016YFC0502900), the National Natural Science Foundation of China (Nos. 41601562, 41401643, and 41401050), the Research Project for Young Teachers of Fujian Province (No. JAT160085), and the Scientific Research Foundation of Fuzhou University (No. XRC-1536). We would like to thank the editors and reviewers for providing helpful suggestions and support.

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