Sciences in Cold and Arid Regions ›› 2015, Vol. 7 ›› Issue (3): 216-228.doi: 10.3724/SP.J.1226.2015.00216

• ARTICLES • Previous Articles    

Comparing the seasonal variation of parameter estimation of ecosystem carbon exchange between alpine meadow and cropland in Heihe River Basin, northwestern China

HaiBo Wang1,2, MingGuo Ma3   

  1. 1. Heihe Remote Sensing Experimental Research Station, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China;
    2. Key Laboratory of Remote Sensing of Gansu Province, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China;
    3. School of Geographical Sciences, Southwest University (Beibei District), Chongqing 400715, China
  • Received:2014-06-22 Revised:2015-02-10 Published:2018-11-23
  • Contact: HaiBo Wang, wanghaibokm@163.com E-mail:wanghaibokm@163.com
  • Supported by:
    This research was funded by the National Natural Science Foundation of China (Nos. 41401412, 91125004), the Foundation for Excellent Youth Scholars of CAREERI, CAS (No. 51Y451271), and the Open Fund of the Key Laboratory of Desert and Desertification, CAS (No. KLDD-2014-007).

Abstract: Grasslands and agro-ecosystems occupy one-third of the global terrestrial area. However, great uncertainty still exists about their contributions to the global carbon cycle. This study used various combinations of a simple ecosystem respiration model and a photosynthesis model to simulate the influence of different climate factors, specifically radiation, temperature, and moisture, on the ecosystem carbon exchange at two dissimilar study sites. Using a typical alpine meadow site in a cold region and a typical cropland site in an arid region as cases, we investigated the response characteristics of productivity of grasslands and croplands to different environmental factors, and analyzed the seasonal change patterns of different model parameters. Parameter estimations and uncertainty analyses were performed based on a Bayesian approach. Our results indicated that: (1) the net ecosystem exchange (NEE) of alpine meadow and seeded maize during the growing season presented obvious diurnal and seasonal variation patterns. On the whole, the alpine meadow and seeded maize ecosystems were both apparent sinks for atmospheric CO2; (2) in the daytime, the mean NEE of the two ecosystems had the largest values in July and the lowest values in October. However, overall carbon uptake in the cropland was greater than in the alpine meadow from June to September; (3) at the alpine meadow site, temperature was the main limiting factor influencing the ecosystem carbon exchange variations during the growing season, while the sensitivity to water limitation was relatively small since there is abundant of rainfall in this region; (4) at the cropland site, both temperature and moisture were the most important limiting factors for the variations of ecosystem carbon exchanges during the growing season; and (5) some parameters had an obvious characteristic of seasonal patterns, while others had only small seasonal variations.

Key words: ecosystem carbon flux, ecosystem respiration, gross ecosystem productivity, climatic factors, alpine meadow, farmland ecosystem

Andrew ES, Verma SB, 2001. Year-round observations of the net ecosystem exchange of carbon dioxide in a native tallgrass prairie. Global Change Biology, 7: 279-289. DOI: 10.1046/j.1365-2486.2001.00407.x.
Anthoni PM, Unsworth MH, Law BE, et al., 2002. Seasonal differences in carbon and water vapor exchange in young and old-growth ponderosa pine ecosystem. Agricultural and Forest Meteorology, 111: 203-222. DOI: 10.1016/S0168-1923 (02)00021-7.
Bai YF, Wang J, Zhang BC, et al., 2012. Comparing the impact of cloudiness on carbon dioxide exchange in a grassland and a maize cropland in northwestern China. Ecological Research, 27: 615-623. DOI: 10.1007/s11284-012-0930-z.
Baldocchi D, Falge E, Gu L, et al., 2001. FLUXNET: A new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities. Bulletin of the American Meteorological Society, 82: 2415-2434. DOI: 10.1175/1520-0477(2001)082<2415:FANTTS>2.3.CO;2.
Boyer JS, 1982. Plant productivity and environment. Science, 218: 443-448. DOI: 10.1126/science.218.4571.443.
Braswell BH, Sacks WJ, Linder E, et al., 2005. Estimating diurnal to annual ecosystem parameters by synthesis of a carbon flux model with eddy covariance net ecosystem exchange observations. Global Change Biology, 11(2): 335-355. DOI: 10.1111/j.1365-2486.2005.00897.x.
Fang C, Moncrieff JB, 2001. The dependence of soil CO2 efflux on temperature. Soil Biology & Biochemistry, 33(2): 155-165. DOI: 10.1016/S0038-0717(00)00125-5.
Follett RF, Schuman GE, 2005. Grazing land contributions to carbon sequestration. In: McGilloway DA (ed.). Grassland: A Global Resource. Wageningen, the Netherlands: Wageningen Academic Publishers, pp. 265-277.
Gallagher M, Doherty J, 2007. Parameter estimation and uncertainty analysis for a watershed model. Environmental Modelling & Software, 22: 1000-1020. DOI: 10.1016/ j.envsoft.2006.06.007.
Gilmanov TG, Aires L, Barcza Z, et al., 2010. Productivity, respiration, and light-response parameters of world grassland and agroecosystems derived from flux-tower measurements. Rangeland Ecology & Management, 63: 16-39. DOI: 10.2111/REM-D-09-00072.1.
Hastings WK, 1970. Monte Carlo sampling methods using Markov chain and their applications. Biometrika, 57: 97-109. DOI: 10.1093/biomet/57.1.97.
Hollinger DY, Goltz SM, Davidson EA, et al., 1999. Seasonal patterns and environmental control of carbon dioxide and water vapor exchange in an ecotonal boreal forest. Global Change Biology, 5: 891-902. DOI: 10.1046/j.1365-2486.1999.00281.x.
Law BE, Falge E, Gu L, et al., 2002. Environmental controls over carbon dioxide and water vapor exchange of terrestrial vegetation. Agriculture and Forest Meteorology, 113: 97-120. DOI: 10.1016/ S0168-1923(02)00104-1.
Li X, Li XW, Li ZY, et al., 2009. Watershed allied telemetry experimental research. Journal of Geophysical Research: Atmospheres, 114: D22103. DOI: 10.1029/2008JD011590.
Lieth H, 1975. Modeling the primary productivity of the world. In: Lieth H, Whittaker RH (eds.). Primary Productivity of the Biosphere. New York: Springer-Verlag, pp. 237-263.
Metropolis N, Rosenbluth AW, Rosenbluth MN, et al., 1953. Equations of state calculations by fast computing machines. The Journal of Chemical Physics, 21(6): 1087-1092. DOI: 10.1063/1.1699114.
Mosegaard K, Sambridge M, 2002. Monte Carlo analysis of inverse problems. Inverse Problems, 18: 29-54. DOI: 10.1088/ 0266-5611/18/3/201.
Reichstein M, Falge E, Baldocchi D, et al., 2005. On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm. Global Change Biology, 11: 1424-1439. DOI: 10.1111/j.1365-2486. 2005.001002.x.
Ruimy A, Javis PG, Baldocchi DD, et al., 1995. CO2 fluxes over plant canopies and solar radiation: A review. Advances in Ecological Research, 26: 1-69. DOI: 10.1016/S0065-2504(08)60063-X.
Schwarz G, 1978. Estimating the dimensions of a model. The Annals of Statistics, 6(2): 461-464. DOI: 10.1214/aos/1176344136.
Smith P, Falloon P, 2005. Carbon sequestration in European croplands. In: Griffiths H, Jarvis PG (eds.). The Carbon Balance of Forest Biomes. New York: Taylor & Francis, pp. 47-55.
Van Oijen M, Rougier J, Smith R, 2005. Bayesian calibration of process-based forest models: Bridging the gap between models and data. Tree Physiology, 25: 915-927. DOI: 10.1093/treephys/25.7.915.
Van't Hoff JH, 1898. Lectures on Theoretical and Physical Phemistry. Part I. Chemical Dynamics (trans. by Lehfeldt RA). London: Edward Arnold, pp. 224-229.
Wofsy SC, Goulden ML, Munger JW, et al., 1993. Net exchange of CO2 in a mid-latitude forest. Science, 260: 1314-1317. DOI: 10.1126/science.260.5112.1314.
Xu LL, Zhang XZ, Shi PL, et al., 2005. Establishment of apparent quantum yield and maximum ecosystem assimilation on Tibetan Plateau alpine meadow ecosystem. Science in China (Series D: Earth Science), 48(Supp. I): 141-147.
Zhang FW, Li YN, Li HQ, et al., 2007. The comparative study of the apparent quantum yield and maximum photosynthesis rates of 3 typical vegetation types on Qinghai Tibetan Plateau. Acta Agrestia Sinica, 15(5): 442-448.
Zhang LM, Yu GR, Sun XM, et al., 2006. Seasonal variation of ecosystem apparent quantum yield (a) and maximum photosynthesis rate (Pmax) of different forest ecosystems in China. Agricultural and Forest Meteorology, 137: 176-187. DOI: 10.1016/j.agrformet.2006.02.006.
Zhang ZH, Wang WZ, Ma MG, et al., 2010. The processing methods of eddy covariance flux data and products in ""WATER"" test. Remote Sensing Technology and Application, 25(6): 788-796.
Zobitz JM, Desai AR, Moore DJP, et al., 2011. A primer for data assimilation with ecological models using Markov Chain Monte Carlo (MCMC). Oecologia, 167(3): 599-611. DOI: 10.1007/S00422-011-2017-9.
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