Regional-scale data assimilation with the Spatially Explicit Individual-based Dynamic Global Vegetation Model (SEIB-DGVM) over Siberia

Progress in Earth and Planetary Science - Tập 8 - Trang 1-15 - 2021
Hazuki Arakida1,2, Shunji Kotsuki1,3,4, Shigenori Otsuka1,5,6, Yohei Sawada7,1, Takemasa Miyoshi1,5,6,8,9
1RIKEN Center for Computational Science, Kobe, Japan
2Hydro Technology Institute Co., Ltd., Osaka, Japan
3Center for Environmental Remote Sensing (CEReS), Chiba University, Chiba-Shi, Japan
4PRESTO, Japan Science and Technology Agency, Tokyo, Japan
5RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program, Kobe, Japan
6RIKEN Cluster for Pioneering Research, Kobe, Japan
7Institute of Engineering Innovation, The University of Tokyo, Tokyo, Japan
8Department of Atmospheric and Oceanic Science, University of Maryland, College Park, USA
9Application Laboratory, Japan Agency for Marine-Earth Science and Technology, Yokohama City, Japan

Tóm tắt

This study examined the regional performance of a data assimilation (DA) system that couples the particle filter and the Spatially Explicit Individual-based Dynamic Global Vegetation Model (SEIB-DGVM). This DA system optimizes model parameters of defoliation and photosynthetic rate, which are sensitive to phenology in the SEIB-DGVM, by assimilating satellite-observed leaf area index (LAI). The experiments without DA overestimated LAIs over Siberia relative to the satellite-observed LAI, whereas the DA system successfully reduced the error. DA provided improved analyses for the LAI and other model variables consistently, with better match with satellite observed LAI and with previous studies for spatial distributions of the estimated overstory LAI, gross primary production (GPP), and aboveground biomass. However, three main issues still exist: (1) the estimated start date of defoliation for overstory was about 40 days earlier than the in situ observation, (2) the estimated LAI for understory was about half of the in situ observation, and (3) the estimated overstory LAI and the total GPP were overestimated compared to the previous studies. Further DA and modeling studies are needed to address these issues.

Tài liệu tham khảo

Ahlström A, Schurgers G, Arneth A, Smith B (2012) Robustness and uncertainty in terrestrial ecosystem carbon response to CMIP5 climate change projections. Environ Res Lett 7(4):044008. https://doi.org/10.1088/1748-9326/7/4/044008 Arakida H, Miyoshi T, Ise T, Shima S, Kotsuki S (2017) Non-Gaussian DA of satellite-based leaf area index observations with an individual-based dynamic global vegetation model. Nonlinear Process Geophys 24(3):553–567. https://doi.org/10.5194/npg-24-553-2017 Braswell BH, Sacks WJ, Linder E, Schimel DS (2005) Estimating diurnal to annual ecosystem parameters by synthesis of a carbon flux model with eddy covariance net ecosystem exchange observations. Glob Chang Biol 11(2):335–355. https://doi.org/10.1111/j.1365-2486.2005.00897.x Cheaib A, Badeau V, Boe J, Chuine I, Delire C, Dufrêne E, François C, Gritti ES, Legay M, Pagé C, Thuiller W, Viovy N, Leadley P (2012) Climate change impacts on tree ranges: model intercomparison facilitates understanding and quantification of uncertainty. Ecol Lett 15(6):533–544. https://doi.org/10.1111/j.1461-0248.2012.01764.x Delbart N, Kergoat L, Toan TL, Lhermitte J, Picard G (2005) Determination of phenological dates in boreal regions using normalized difference water index. Remote Sens Environ 97(1):26–38. https://doi.org/10.1016/j.rse.2005.03.011 Demarty J, Chevallier F, Friend AD, Viovy N, Piao S, Ciais P (2007) Assimilation of global MODIS leaf area index retrievals within a terrestrial biosphere model. Geophys Res Lett 34(15):L15402. https://doi.org/10.1029/2007GL030014 Eriksson HM, Eklundh L, Kuusk A, Nilson T (2006) Impact of understory vegetation on forest canopy reflectance and remotely sensed LAI estimates. Remote Sens Environ 103(4):408–418. https://doi.org/10.1016/j.rse.2006.04.005 European Commission, Joint Research Centre (2003) The global land cover map for the year 2000, GLC2000 database. European commision joint research centre https://forobs.jrc.ec.europa.eu/products/glc2000/glc2000.php. Accessed 6 Jan 2017 Fisher RA, Koven CD, Anderegg WRL, Christoffersen BO, Dietze MC, Farrior CE, Holm JA, Hurtt GC, Knox RG, Lawrence PJ, Lichstein JW, Longo M, Matheny AM, Medvigy D, Muller-Landau HC, Powell TL, Serbin SP, Sato H, Shuman JK, Smith B, Trugman AT, Viskari T, Verbeeck H, Weng E, Xu C, Xu X, Zhang T, Moorcroft PR (2017) Vegetation demographics in earth system models: A review of progress and priorities. Glob Chang Biol 24(1):35–54. https://doi.org/10.1111/gcb.13910 Frankenberg C, Fisher JB, Worden J, Badgley G, Saatchi SS, Lee JE, Toon GC, Butz A, Jung M, Kuze A, Yokota T (2011) New global observations of the terrestrial carbon cycle from GOSAT: Patterns of plant fluorescence with gross primary productivity. Geophys Res Lett 38(17):17. https://doi.org/10.1029/2011GL048738 Friend AD, Lucht W, Rademacher TT, Keribin R, Betts R, Cadule P, Ciais P, Clark DB, Dankers R, Falloon PD, Ito A, Kahana R, Kleidon A, Lomas MR, Nishina K, Ostberg S, Pavlick R, Peylin P, Schaphoff S, Vuichard N, Warszawski L, Wiltshire A, Woodward FI (2014) Carbon residence time dominates uncertainty in terrestrial vegetation responses to future climate and atmospheric CO2. Proc Natl Acad Sci 111(9):3280–3285. https://doi.org/10.1073/pnas.1222477110 Gao C, Wang H, Weng E, Lakshmivarahan S, Zhang Y, Luo Y (2011) Assimilation of multiple data sets with the ensemble Kalman filter to improve forecasts of forest carbon dynamics. Ecol Appl 21(5):1461–1473. https://doi.org/10.1890/09-1234.1 Gordon NJ, Salmond DJ, Smith AFM (1993) Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proc F 140(2):107–113. https://doi.org/10.1049/ip-f-2.1993.0015 Iida S, Ohta T, Matsumoto K, Nakai T, Kuwada T, Kononov AV, Maximov TC, van der Molen MK, Dolman H, Tanaka H, Yabuki H (2009) Evapotranspiration from understory vegetation in an eastern Siberian boreal larch forest. Agric Forest Met 149(6-7):1129–1139. https://doi.org/10.1016/j.agrformet.2009.02.003 Ise T, Ikeda S, Watanabe S, Ichii K (2018) Regional-scale data assimilation of a terrestrial ecosystem model: leaf phenology parameters are dependent on local climatic conditions. Front Environ Sci 6:95. https://doi.org/10.3389/fenvs.2018.00095 Ito A, Nishina K, Reyer CPO, François L, Henrot AJ, Munhoven G, Jacquemin I, Tian H, Yang J, Pan S, Morfopoulos C, Betts R, Hickler T, Steinkamp J, Ostberg S, Schaphoff S, Ciais P, Chang J, Rafique R, Zeng N, Zhao F (2017) Photosynthetic productivity and its efficiencies in ISIMIP2a biome models: benchmarking for impact assessment studies. Environ Res Lett 12(8):085001. https://doi.org/10.1088/1748-9326/aa7a19 Jung M, Reichstein M, Schwalm CR, Huntingford C, Sitch S, Ahlström A, Arneth A, Camps-Valls G, Ciais P, Friedlingstein P, Gans F, Ichii K, Jain AK, Kato E, Papale D, Poulter B, Raduly B, Rödenbeck C, Tramontana G, Viovy N, Wang YP, Weber U, Zaehle S, Zeng N (2017) Compensatory water effects link yearly global land CO2 sink changes to temperature. Nature 541(7638):516–520. https://doi.org/10.1038/nature20780 Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Iredell M, Saha S, White G, Woollen J, Zhu Y, Chelliah M, Ebisuzaki W, Higgins W, Janowiak J, Mo KC, Ropelewski C, Wang J, Leetmaa A, Reynolds R, Jenne R, Joseph D (1996) The NCEP/NCAR 40-year reanalysis project. B Am Meteorol Soc 77:437–471. http://www.forobs.jrc.ec.europa.eu/products/glc2000/glc2000.php. Kaminski T, Knorr W, Schürmann G, Scholze M, Rayner PJ, Zaehle S, Blessing S, Dorigo W, Gayler V, Giering R, Gobron N, Grant JP, Heimann M, Hooker-Stroud A, Houweling S, Kato T, Kattge J, Kelley D, Kemp S, Koffi EN, Köstler C, Mathieu P-P, Pinty B, Reick CH, Rödenbeck C, Schnur R, Scipal K, Sebald C, Stacke T, Terwisscha van Scheltinga A, Vossbeck M, Widmann H, Ziehn T (2013) The BETHY/JSBACH Carbon Cycle DA System: experiences and challenges. J Geophys Res Biogeo 118(4):1414–1426. https://doi.org/10.1002/jgrg.20118 Kato T, Knorr W, Scholze M, Veenendaal E, Kaminski T, Kattge J, Gobron N (2013) Simultaneous assimilation of satellite and eddy covariance data for improving terrestrial water and carbon simulations at a semi-arid woodland site in Botswana. Biogeosciences 10(2):789–802. https://doi.org/10.5194/bg-10-789-2013 Kitagawa G (1998) A self-organizing state-space model. J Am Stat Assoc 93(443):1203–1215. https://doi.org/10.2307/2669862 Knorr W, Kattge J (2005) Inversion of terrestrial ecosystem model parameter values against eddy covariance measurements by Monte Carlo sampling. Glob Chang Biol 11(8):1333–1351. https://doi.org/10.1111/j.1365-2486.2005.00977.x Knyazikhin Y, Glassy J, Privette JL, Tian Y, Lotsch A, Zhang Y, Wang Y, Morisette JT, Votava P, Myneni RB, Nemani RR, Running SW (1999) MODIS Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation Absorbed by Vegetation (FPAR) product (MOD15) Algorithm. Theoretical Basis Document. NASA Goddard Space Flight Center, Greenbelt Kobayashi H, Delbart N, Suzuki R, Kushida K (2010) A satellite-based method for monitoring seasonality in the overstory leaf area index of Siberian larch forest. J Geophys Res 115(G1):G01002. https://doi.org/10.1029/2009JG000939 Kobayashi H, Suzuki R, Kobayashi S (2007) Reflectance seasonality and its relation to the canopy leaf area index in an eastern Siberian larch forest: Multi-satellite data and radiative transfer analyses. Remote Sens Environ 106(2):238–252. https://doi.org/10.1016/j.rse.2006.08.011 Kotani A, Saito A, Kononov AV, Petrov RE, Maximov TC, Iijima Y, Ohta T (2019) Impact of unusually wet permafrost soil on understory vegetation and CO2 exchange in a larch forest in eastern Siberia. Agric Forest Met 265:295–309. https://doi.org/10.1016/j.agrformet.2018.11.025 Liu YY, van Dijk AIJM, de Jeu RAM, Canadell JG, McCabe MF, Evans JP, Wang G (2015) Recent reversal in loss of global terrestrial biomass. Nat Clim Chang 5(5):470–474. https://doi.org/10.1038/nclimate2581 Luo Y, Ogle K, Tucker C, Fei S, Gao C, LaDeau S, Clark JS, Schimel DS (2011) Ecological forecasting and DA in a data-rich era. Ecol Appl 21(5):1429–1442. https://doi.org/10.1890/09-1275.1 MacBean N, Maignan F, Peylin P, Bacour C, Bréon FM, Ciais P (2015) Using satellite data to improve the leaf phenology of a global terrestrial biosphere model. Biogeosciences 12(23):7185–7208. https://doi.org/10.5194/bg-12-7185-2015 Nakai Y, Matsuura T, Kajimoto T, Abaimov AP, Yamamoto S, Zyryanova OA (2008) Eddy covariance CO2 flux above a Gmelin larch forest on continuous permafrost in central Siberia during a growing season. Theor Appl Climatol 93(3-4):133–147. https://doi.org/10.1007/s00704-007-0337-x Ohta T, Hiyama T, Tanaka H, Kuwada T, Maximov TC, Ohata T, Fukushima Y (2001) Seasonal variation in the energy and water exchanges above and below a larch forest in eastern Siberia. Hydrol Process 15(8):1459–1476. https://doi.org/10.1002/hyp.219 Ohta T, Kotani A, Iijima Y, Maximov TC, Ito S, Hanamura M, Kononov AV, Maximov AP (2014) Effects of waterlogging on water and carbon dioxide fluxes and environmental variables in a Siberian larch forest, 1998–2011. Agric For Meteorol 188:64–75. https://doi.org/10.1016/j.agrformet.2013.12.012 Ohta T, Maximov TC, Dolman AJ, Nakai T, van der Molen MK, Kononov AV, Maximov AP, Hiyama T, Iijima Y, Moors EJ, Tanaka H, Toba T, Yabuki H (2008) Interannual variation of water balance and summer evapotranspiration in an eastern Siberian larch forest over a 7-year period (1998–2006). Agric For Meteorol 148(12):1941–1953. https://doi.org/10.1016/j.agrformet.2008.04.012 Peng C (2000) From static biogeographical model to dynamic global vegetation model: a global perspective on modelling vegetation dynamics. Ecol Model 135(1):33–54. https://doi.org/10.1016/S0304-3800(00)00348-3 Peng C, Guiot J, Wu H, Jiang H, Luo Y (2011) Integrating models with data in ecology and palaeoecology: advances towards a model–data fusion approach. Ecol Lett 14(5):522–536. https://doi.org/10.1111/j.1461-0248.2011.01603.x Ponomarev EI, Kharuk VI, Ranson KJ (2016) Wildfires dynamics in Siberian larch forests. Forests 7(12):125. https://doi.org/10.3390/f7060125 Rayner PJ, Scholze M, Knorr W, Kaminski T, Giering R, Widmann H (2005) Two decades of terrestrial carbon fluxes from a carbon cycle DA system (CCDAS). Global Biogeochem Cy 19(2):GB2026. https://doi.org/10.1029/2004GB002254 Reichstein M, Falge E, Baldocchi D, Papale D, Aubinet M, Berbigier P, Bernhofer C, Buchmann N, Gilmanov T, Granier A, Grünwald T, Havránková K, Ilvesniemi H, Janous D, Knohl A, Laurila T, Lohila A, Loustau D, Matteucci G, Meyers T, Miglietta F, Ourcival JM, Pumpanen J, Rambal S, Rotenberg E, Sanz M, Tenhunen J, Seufert G, Vaccari F, Vesala T, Yakir D, Valentini R (2005) On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm. Glob Chang Biol 11(9):1424–1439. https://doi.org/10.1111/j.1365-2486.2005.001002.x Rogers A, Medlyn BE, Dukes JS, Bonan G, von Caemmerer S, Dietze MC, Kattge J, Leakey ADB, Mercado LM, Niinemets Ü, Prentice IC, Serbin SP, Sitch S, Way DA, Zaehle S (2017) A roadmap for improving the representation of photosynthesis in Earth system models. New Phytol 213(1):22–42. https://doi.org/10.1111/nph.14283 Sato H, Ise T (2012) Effect of plant dynamic processes on African vegetation responses to climate change: Analysis using the spatially explicit individual-based dynamic global vegetation model (SEIB-DGVM). J Geophys Res 117(G3):G03017. https://doi.org/10.1029/2012JG002056 Sato H, Itoh A, Kohyama T (2007) SEIB–DGVM: A new Dynamic Global Vegetation Model using a spatially explicit individualbased approach. Ecol Model 200(3-4):279–307. https://doi.org/10.1016/j.ecolmodel.2006.09.006 Sato H, Kobayashi H, Delbart N (2010) Simulation study of the vegetation structure and function in eastern Siberian larch forests using the individual-based vegetation model SEIB-DGVM. Forest Ecol Manag 259(3):301–311. https://doi.org/10.1016/j.foreco.2009.10.019 Sato H, Kobayashi H, Iwahana G, Ohta T (2016) Endurance of larch forest ecosystems in eastern Siberia under warming trends. Ecol Evol 6(16):5690–5704. https://doi.org/10.1002/ece3.2285 Stöckli R, Rutishauser T, Baker I, Liniger MA, Denning AS (2011) A global reanalysis of vegetation phenology. J Geophys Res 116(G3):G03020. https://doi.org/10.1029/2010JG001545 Suzuki K, Kubota J, Yabuki H, Ohata T, Vuglinsky V (2007) Moss beneath a leafless larch canopy: influence on water and energy balances in the southern mountainous taiga of eastern Siberia. Hydrol Process 21(15):1982–1991. https://doi.org/10.1002/hyp.6709 Suzuki R, Yoshikawa K, Maximov TC (2001) Phenological photographs of Siberian larch forest from 1997 to 2000 at Spasskaya Pad, Republic of Sakha, Russia. ACDAP, JAMSTEC, Digital Media, Yokosuka Tramontana G, Jung M, Schwalm CR, Ichii K, Camps-Valls G, Ráduly B, Reichstein M, Arain MA, Cescatti A, Kiely G, Merbold L, Serrano-Ortiz P, Sickert S, Wolf S, Papale D (2016) Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms. Biogeosciences 13(14):4291–4313. https://doi.org/10.5194/bg-13-4291-2016 University of East Anglia Climatic Research Unit, Harris IC, Jones PD (2015) CRU TS3.23: Climatic Research Unit (CRU) Time-Series (TS) version 3.23 of high resolution gridded data of month-by-month variation in climate (Jan. 1901–Dec. 2014). Centre for Environmental Data Analysis. https://doi.org/10.5285/4c7fdfa6-f176-4c58-acee-683d5e9d2ed5 Wang Q, Tenhunen J, Dinh NQ, Reichstein M, Otieno D, Granier A, Pilegarrd K (2005) Evaluation of seasonal variation of MODIS derived leaf area index at two European deciduous broadleaf forest sites. Remote Sens Environ 96(3-4):475–484. https://doi.org/10.1016/j.rse.2005.04.003 Williams M, Schwarz PA, Law BE, Irvine J, Kurpius MR (2005) An improved analysis of forest carbon dynamics using DA. Glob Chang Biol 11(1):89–105. https://doi.org/10.1111/j.1365-2486.2004.00891.x Yan M, Tian X, Li Z, Chen E, Wang X, Han Z, Sun H (2016) Simulation of forest carbon fluxes using model incorporation and DA. Remote Sens 8(7):567. https://doi.org/10.3390/rs8070567