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Integration of eddy covariance and process-based model for the intra-annual variability of carbon fluxes in an Indian tropical forest

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Abstract

Forests play a crucial role by regulating the global and local weather through the exchange of atmospheric gases and water vapor. The present study aims to study the intra-annual variability of net ecosystem exchange (NEE) of carbon dioxide in a sal (Shorea robusta) dominated moist deciduous forest in India by integrating eddy covariance (EC) data and Biome-Biogeochemical Cycle (Biome-BGC) model. The study also attempts to address the spatial variability of NEE with respect to phenology. Monthly average NEE were estimated using a calibrated Biome-BGC model and the spatial NEE was mapped using random forest (RF) regression algorithm. Phenology metrics were generated using the moderate resolution imaging spectrometer (MODIS) enhanced vegetation index product (MOD13A2) and its relationship with the estimated NEE was studied. RF regression model for monthly average spatial NEE estimation was built with an R2 of 0.84 and % RMSE of 3.68%. The study revealed that the NEE at the regional scale can be estimated using the basic meteorological variables like mean temperature, vapor pressure deficit, minimum temperature and total precipitation. Biome-BGC model output showed that the sal forest of the study area acted as a net sink of carbon in almost all months of 2015, except April to June. Peak NEE value (− 2.80 to − 2.96 g C m−2 day−1) was observed during October month. Annual NEE of sal forest in 2015 was found to be − 526.87 g C m−2 year−1. With the start of season (end of June), sal forest showed an increasing trend in NEE while decreasing trend was observed at the end of season (end of October). The study showed the applicability of Biome-BGC model in Indian forest when integrated with EC data. The study also highlighted the utility of RF in capturing the spatial variability of NEE over large area.

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References

  • Ahongshangbam J, Patel NR, Kushwaha SPS, Watham T, Dadhwal VK (2016) Estimating gross primary production of a forest plantation area using eddy covariance data and satellite imagery. J Indian Soc Remote Sens 44(6):895–904

    Article  Google Scholar 

  • Anonymous (2014) Uttarakhand Action Plan on Climate Change. Government of Uttarakhand

  • Baishya R, Barik SK (2011) Estimation of tree biomass, carbon pool and net primary production of an old-growth Pinus kesiya Royle ex. Gordon forest in North-eastern India. Ann For Sci 68(4):727–736

    Article  Google Scholar 

  • Boisvenue C, Running SW (2006) Impacts of climate change on natural forest productivity—evidence since the middle of the 20th century. Glob Change Biol 12(5):862–882

    Article  Google Scholar 

  • Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  Google Scholar 

  • Burman PKD, Sarma D, Williams M, Karipot A, Chakraborty S (2017) Estimating gross primary productivity of a tropical forest ecosystem over North-east India using LAI and meteorological variables. J Earth Syst Sci 126(7):99

    Article  CAS  Google Scholar 

  • Champion SH, Seth SK (1968) A revised survey of the forest types of India. The Manager of Publications, Delhi

    Google Scholar 

  • Chaturvedi OP, Singh JS (1987) The structure and function of pine forest in Central Himalaya. I. Dry matter dynamics. Ann Bot 60(3):237–252

    Article  Google Scholar 

  • Chhabra A, Dadhwal VK (2004) Estimating terrestrial net primary productivity over India using satellite data. Curr Sci 86(2):269–271

    Google Scholar 

  • Chitale VS, Behera MD (2012) Can the distribution of sal (Shorea robusta Gaertn. f.) shift in the northeastern direction in India due to changing climate? Curr Sci 102:1126–1135

    Google Scholar 

  • Chu C, Bartlett M, Wang Y, He F, Weiner J, Chave J, Sack L (2016) Does climate directly influence NPP globally? Glob Change Biol 22(1):12–24

    Article  Google Scholar 

  • Dang ATN, Nandy S, Srinet R, Luong NV, Ghosh S, Kumar AS (2019) Forest aboveground biomass estimation using machine learning regression algorithm in Yok Don National Park, Vietnam. Ecol Inform 50:24–32

    Article  Google Scholar 

  • Day ME (2000) Influence of temperature and leaf-to-air vapor pressure deficit on net photosynthesis and stomatal conductance in red spruce (Picea rubens). Tree Physiol 20(1):57–63

    Article  PubMed  Google Scholar 

  • Dhanda P, Nandy S, Kushwaha SPS, Ghosh S, Murthy YK, Dadhwal VK (2017) Optimizing spaceborne LiDAR and very high resolution optical sensor parameters for biomass estimation at ICESat/GLAS footprint level using regression algorithms. Prog Phys Geogr 41(3):247–267

    Article  Google Scholar 

  • Eklundh L, Jönsson P (2015) TIMESAT: a software package for time-series processing and assessment of vegetation dynamics. Remote sensing time series. Springer, Cham, pp 141–158

    Google Scholar 

  • Farquhar GV, von Caemmerer SV, Berry JA (1980) A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 149(1):78–90

    Article  CAS  PubMed  Google Scholar 

  • Han F, Zhang Q, Buyantuev A, Niu J, Liu P, Li X, Li Y (2015) Effects of climate change on phenology and primary productivity in the desert steppe of Inner Mongolia. J Arid Land 7(2):251–263

    Article  Google Scholar 

  • Hilker T, Coops NC, Hall FG, Black TA, Chen B, Krishnan P, Wulder MA, Sellers PJ, Middleton EM, Huemmrich KF (2008) A modeling approach for upscaling gross ecosystem production to the landscape scale using remote sensing data. J Geophys Res Biogeosci. https://doi.org/10.1029/2007JG000666

    Article  Google Scholar 

  • Jangra R, Gupta SR, Kumar R, Singh G (2010) Carbon sequestration in the Grevillea robusta plantation on a reclaimed sodic soil at Karnal in Northern India. Int J Ecol Environ Sci 36(1):75–86

    Google Scholar 

  • Jarvis PG (1976) The interpretation of the variations in leaf water potential and stomatal conductance found in canopies in the field. Philos Trans R Soc 273(927):593–610

    Article  CAS  Google Scholar 

  • Kaul M, Mohren GMJ, Dadhwal VK (2010) Carbon storage and sequestration potential of selected tree species in India. Mitig Adapt Strateg Glob Chang 15(5):489–510

    Article  Google Scholar 

  • Keeling HC, Phillips OL (2007) The global relationship between forest productivity and biomass. Glob Ecol Biogeogr 16(5):618–631

    Article  Google Scholar 

  • Keenan TF, Gray J, Friedl MA, Toomey M, Bohrer G, Hollinger DY, Munger JW, O’Keefe J, Schmid HP, Wing IS, Yang B (2014) Net carbon uptake has increased through warming-induced changes in temperate forest phenology. Nat Clim Change 4(7):598

    Article  CAS  Google Scholar 

  • Kumar M, Raghubanshi AS (2011) Sensitivity analysis of Biome-BGC model for dry tropical forests of Vindhyan Highlands, India. Remote Sens Spat Inf Sci 3820:129–133

    Google Scholar 

  • Kushwaha SPS, Nandy S (2012) Species diversity and community structure in sal (Shorea robusta) forests of two different rainfall regimes in West Bengal, India. Biodivers Conserv 21(5):1215–1228

    Article  Google Scholar 

  • Lai G, Zhang L, Liu Y, Yi F, Zeng X, Pan R (2012) Retrieving leaf area index and extinction coefficient of dominant vegetation canopy in Meijiang Watershed of China using ETM+ data. In: 2012 2nd international conference remote sensing, environment and transportation engineering (RSETE). IEEE, pp 1–5

  • Landsberg JJ, Waring RH (1997) A generalised model of forest productivity using simplified concepts of radiation-use efficiency, carbon balance and partitioning. For Ecol Manag 95(3):209–228

    Article  Google Scholar 

  • Law B, Turner D, Campbell J, Lefsky M, Guzy M, Sun O, Cohen W (2006) Carbon fluxes across regions: observational constraints at multiple scales. In: Scaling and uncertainty analysis in ecology. Springer, Dordrecht, pp 167–190

  • 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 Model 252:3–10

    Article  Google Scholar 

  • Mamkin V, Kurbatova J, Avilov V, Mukhartova Y, Krupenko A, Ivanov D, Levashova N, Olchev A (2016) Changes in net ecosystem exchange of CO2, latent and sensible heat fluxes in a recently clear-cut spruce forest in western Russia: results from an experimental and modeling analysis. Environ Res Lett 11(12):125012

    Article  Google Scholar 

  • Meier GA, Brown JF (2014) Remote sensing of land surface phenology. Fact Sheet 2014–3052. US Geological Survey, p 2

  • Nandy S, Ghosh S, Kushwaha SPS, Kumar AS (2019) Remote sensing-based forest biomass assessment in northwest Himalayan landscape. In: Navalgund RR, Senthil Kumar A, Nandy S (eds) Remote sensing of northwest Himalayan ecosystems. Springer, Singapore, pp 285–311

    Chapter  Google Scholar 

  • Nayak RK, Patel NR, Dadhwal VK (2010) Estimation and analysis of terrestrial net primary productivity over India by remote-sensing-driven terrestrial biosphere model. Environ Monit Assess 170(1):195–213

    Article  PubMed  Google Scholar 

  • Nayak RK, Mishra N, Dadhwal VK, Patel NR, Salim M, Rao KH, Dutt CBS (2016) Assessing the consistency between AVHRR and MODIS NDVI datasets for estimating terrestrial net primary productivity over India. J Earth Syst Sci 125(6):1189–1204

    Article  CAS  Google Scholar 

  • Nortes PA, Baille A, González-Real MM, Ruiz-Salleres I, Verhoef A, Martin-Gorriz B, Egea G (2010) Effects of high temperature and vapour pressure deficit on net ecosystem exchange and energy balance of an irrigated orange orchard in a semi-arid climate (southern Spain). In: XXVIII international horticultural congress on science and horticulture for people (IHC2010): international symposium on 922, August, pp 149–156

  • Pachauri RK, Allen MR, Barros VR, Broome J, Cramer W, Christ R, Dubash NK (2014) 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. IPCC, p 151

  • Pan S, Tian H, Dangal SR, Ouyang Z, Tao B, Ren W, Running S (2014) Modeling and monitoring terrestrial primary production in a changing global environment: toward a multiscale synthesis of observation and simulation. Adv Meteorol. https://doi.org/10.1155/2014/965936

    Article  Google Scholar 

  • Powell TL, Gholz HL, Clark KL, Starr G, Cropper WP, Martin TA (2008) Carbon exchange of a mature, naturally regenerated pine forest in north Florida. Glob Change Biol 14(11):2523–2538

    Google Scholar 

  • Raj R, Hamm NA, van der Tol C, Stein A (2014) Variance-based sensitivity analysis of Biome-BGC for gross and net primary production. Ecol Model 292:26–36

    Article  CAS  Google Scholar 

  • Rana BS, Singh SP, Singh RP (1989) Biomass and net primary productivity in Central Himalayan forests along an altitudinal gradient. For Ecol Manag 27(3–4):199–218

    Article  Google Scholar 

  • Ruiz A, Villa N (2008) Storms prediction: logistic regression vs random forest for unbalanced data

  • Ryan MG, Binkley D, Fownes JH (1997) Age-related decline in forest productivity: pattern and process. In: Advances in ecological research, vol 27. Academic, pp 213–262. https://doi.org/10.1016/s0065-2504(08)60009-4

  • Schwieder M, Leitão PJ, Pinto JRR, Teixeira AMC, Pedroni F, Sanchez M, Bustamante MM, Hostert P (2018) Landsat phenological metrics and their relation to aboveground carbon in the Brazilian Savanna. Carbon Balance Manag 13(1):7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Syed KH, Flanagan LB, Carlson PJ, Glenn AJ, Van Gaalen KE (2006) Environmental control of net ecosystem CO2 exchange in a treed, moderately rich fen in northern Alberta. Agric For Meteorol 140(1–4):97–114

    Article  Google Scholar 

  • Tan ZH, Hughes A, Sato T, Zhang YP, Han SJ, Kosugi Y, Hu YH (2017) Quantifying forest net primary production combining eddy flux inventory and metabolic theory. iForest Biogeosci For 10(2):475

    Article  Google Scholar 

  • Tatarinov FA, Cienciala E (2006) Application of Biome-BGC model to managed forests: 1. Sensitivity analysis. For Ecol Manag 237(1):267–279

    Article  Google Scholar 

  • Tripathi P, Patel NR, Kushwaha SPS (2018) Estimating net primary productivity in tropical forest plantations in India using satellite-driven ecosystem model. Geocarto Int 33(9):988–999.

    Article  Google Scholar 

  • Troup RS (1921) The silviculture of Indian forest trees, vol 1. Natraj Publishers, Dehradun, pp 55–130

    Google Scholar 

  • Tyralis H, Papacharalampous G (2017) Variable selection in time series forecasting using random forests. Algorithms 10(4):114

    Article  Google Scholar 

  • Verduzco VS, Garatuza-Payán J, Yépez EA, Watts CJ, Rodríguez JC, Robles-Morua A, Vivoni ER (2015) Variations of net ecosystem production due to seasonal precipitation differences in a tropical dry forest of northwest Mexico. J Geophys Res Biogeosci 120(10):2081–2094

    Article  Google Scholar 

  • Watham T, Patel NR, Kushwaha SPS, Dadhwal VK, Kumar AS (2017a) Evaluation of remote-sensing-based models of gross primary productivity over Indian sal forest using flux tower and MODIS satellite data. Int J Remote Sens 38(18):5069–5090

    Article  Google Scholar 

  • Watham T, Kushwaha SPS, Patel NR, Dadhwal VK, Kumar AS (2017b) Ecosystem productivity and its response to environmental variable of moist Indian sal forest. Trop Ecol 58(4):761–768

    Google Scholar 

  • Weiskittel RA, Hann WD, Kershaw AJ Jr, Vanclay KJ (2011) Forest growth and yield modeling. Wiley-Blackwell, Wiley, Oxford, pp 227–252

    Book  Google Scholar 

  • Wu J (1999) Hierarch and scaling: extrapolation information along a scaling ladder. Can J Remote Sens 25:367–380

    Article  Google Scholar 

  • Wutzler T, Lucas-Moffat A, Migliavacca M, Knauer J, Sickel K, Šigut L, Menzer O, Reichstein M (2018) Basic and extensible post-processing of eddy covariance flux data with ReddyProc. Biogeosciences 15:5015–5030

    Article  CAS  Google Scholar 

  • Yan M, Tian X, Li Z, Chen E, Li C, Fan W (2016) A long-term simulation of forest carbon fluxes over the Qilian Mountains. Int J Appl Earth Obs Geoinf 52:515–526

    Article  Google Scholar 

  • Yuan J, Jose S, Hu Z, Pang J, Hou L, Zhang S (2018) Biometric and eddy covariance methods for examining the carbon balance of a Larix principis-rupprechtii Forest in the Qinling Mountains, China. Forests 9(2):67

    Article  Google Scholar 

  • Zhang Y, Chen HY, Reich PB (2012) Forest productivity increases with evenness, species richness and trait variation: a global meta-analysis. J Ecol 100(3):742–749

    Article  Google Scholar 

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Acknowledgements

The present study was carried out as a part of soil–vegetation–atmosphere–flux (SVAF) of National Carbon Project (NCP) supported by ISRO-Geosphere-Biosphere Programme. The authors wish to acknowledge Divisional Forest Officer, Dehradun Forest Division and Staff of Barkot Forest Range, Dehradun Forest Division, Government of Uttarakhand, India and field staff of Barkot Flux Research Site for field support. Authors are thankful to the Numerical Terradynamic Simulation Group, School of Forestry, University of Montana for providing the code for the Biome-BGC model. The authors acknowledge the MODIS Science Team for the Science Algorithms, the Processing Team for Producing MODIS Data, and the GES DAAC MODIS Data Support Team for making MODIS data available to the user community.

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  1. Indian Institute of Remote Sensing, Indian Space Research Organisation, Department of Space, Government of India, Dehradun, 248001, India

    Nithin D. Pillai, Subrata Nandy, N. R. Patel, Ritika Srinet, Taibanganba Watham & Prakash Chauhan

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  1. Nithin D. Pillai
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  2. Subrata Nandy
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  4. Ritika Srinet
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Correspondence to Subrata Nandy.

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Communicated by M.D. Behera, S.K. Behera and S. Sharma.

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Pillai, N.D., Nandy, S., Patel, N.R. et al. Integration of eddy covariance and process-based model for the intra-annual variability of carbon fluxes in an Indian tropical forest. Biodivers Conserv 28, 2123–2141 (2019). https://doi.org/10.1007/s10531-019-01770-3

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