Simulating the Impacts of Drought and Warming in Summer and Autumn on the Productivity of Subtropical Coniferous Forests
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Collection
2.2. Model Configuration and Simulation Setup
2.2.1. Model Overview
2.2.2. Plant Functional Type
2.2.3. Model Calibration
2.2.4. Model Verification and Validation
2.2.5. Simulation Scenarios of Disturbance Types and Severities
3. Results
3.1. Forest Productivity during and after the Drought and/or Warming Scenarios
3.2. Relationships between Available Water, Canopy Temperature, and Forest Productivity under the Disturbance Scenarios
4. Discussion
4.1. Model Performance in the Subtropical Coniferous Forests
4.2. Resistance and Resilience of the Subtropical Coniferous Forests to Seasonal Drought and/or Warming
4.3. Limitations from Soil Moisture and Canopy Temperature on Productivity
4.4. Limitations and Uncertainties
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Anderegg, W.R.; Schwalm, C.; Biondi, F.; Camarero, J.J.; Koch, G.; Litvak, M.; Ogle, K.; Shaw, J.D.; Shevliakova, E.; Williams, A. Pervasive drought legacies in forest ecosystems and their implications for carbon cycle models. Science 2015, 349, 528–532. [Google Scholar] [CrossRef] [Green Version]
- Gazol, A.; Camarero, J.J. Compound climate events increase tree drought mortality across European forests. Sci. Total Environ. 2022, 816, 151604. [Google Scholar] [CrossRef] [PubMed]
- Hartmann, H.; Moura, C.F.; Anderegg, W.R.; Ruehr, N.K.; Salmon, Y.; Allen, C.D.; Arndt, S.K.; Breshears, D.D.; Davi, H.; Galbraith, D. Research frontiers for improving our understanding of drought-induced tree and forest mortality. New Phytol. 2018, 218, 15–28. [Google Scholar] [CrossRef] [Green Version]
- Ji, S.; Ren, S.; Li, Y.; Dong, J.; Wang, L.; Quan, Q.; Liu, J. Diverse responses of spring phenology to preseason drought and warming under different biomes in the North China Plain. Sci. Total Environ. 2021, 766, 144437. [Google Scholar] [CrossRef] [PubMed]
- Nicholls, N. The changing nature of Australian droughts. Clim. Chang. 2004, 63, 323–336. [Google Scholar] [CrossRef]
- Ciais, P.; Reichstein, M.; Viovy, N.; Granier, A.; Ogée, J.; Allard, V.; Aubinet, M.; Buchmann, N.; Bernhofer, C.; Carrara, A. Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature 2005, 437, 529–533. [Google Scholar] [CrossRef] [PubMed]
- Phillips, O.L.; Aragão, L.E.; Lewis, S.L.; Fisher, J.B.; Lloyd, J.; López-González, G.; Malhi, Y.; Monteagudo, A.; Peacock, J.; Quesada, C.A. Drought sensitivity of the Amazon rainforest. Science 2009, 323, 1344–1347. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Saigusa, N.; Ichii, K.; Murakami, H.; Hirata, R.; Asanuma, J.; Den, H.; Han, S.-J.; Ide, R.; Li, S.-G.; Ohta, T. Impact of meteorological anomalies in the 2003 summer on Gross Primary Productivity in East Asia. Biogeosciences 2010, 7, 641–655. [Google Scholar] [CrossRef] [Green Version]
- Chen, G.; Tian, H.; Zhang, C.; Liu, M.; Ren, W.; Zhu, W.; Chappelka, A.H.; Prior, S.A.; Lockaby, G.B. Drought in the Southern United States over the 20th century: Variability and its impacts on terrestrial ecosystem productivity and carbon storage. Clim. Chang. 2012, 114, 379–397. [Google Scholar] [CrossRef]
- Teskey, R.; Wertin, T.; Bauweraerts, I.; Ameye, M.; McGuire, M.A.; Steppe, K. Responses of tree species to heat waves and extreme heat events. Plant Cell Environ. 2015, 38, 1699–1712. [Google Scholar] [CrossRef]
- Yuan, W.; Cai, W.; Chen, Y.; Liu, S.; Dong, W.; Zhang, H.; Yu, G.; Chen, Z.; He, H.; Guo, W. Severe summer heatwave and drought strongly reduced carbon uptake in Southern China. Sci. Rep. 2016, 6, 1–12. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Marengo, J.A.; Torres, R.R.; Alves, L.M. Drought in Northeast Brazil—Past, present, and future. Theor. Appl. Climatol. 2017, 129, 1189–1200. [Google Scholar] [CrossRef]
- Wang, S.; Huang, Y. Determinants of soil organic carbon sequestration and its contribution to ecosystem carbon sinks of planted forests. Glob. Chang. Biol. 2020, 26, 3163–3173. [Google Scholar] [CrossRef] [PubMed]
- Park Williams, A.; Allen, C.D.; Macalady, A.K.; Griffin, D.; Woodhouse, C.A.; Meko, D.M.; Swetnam, T.W.; Rauscher, S.A.; Seager, R.; Grissino-Mayer, H.D. Temperature as a potent driver of regional forest drought stress and tree mortality. Nat. Clim. Chang. 2013, 3, 292–297. [Google Scholar] [CrossRef]
- Reichstein, M.; Bahn, M.; Ciais, P.; Frank, D.; Mahecha, M.D.; Seneviratne, S.I.; Zscheischler, J.; Beer, C.; Buchmann, N.; Frank, D.C. Climate extremes and the carbon cycle. Nature 2013, 500, 287–295. [Google Scholar] [CrossRef]
- Laginha Pinto Correia, D.; Bouchard, M.; Filotas, É.; Raulier, F. Disentangling the effect of drought on stand mortality and productivity in northern temperate and boreal forests. J. Appl. Ecol. 2019, 56, 758–768. [Google Scholar] [CrossRef]
- Adams, H.D.; Macalady, A.K.; Breshears, D.D.; Allen, C.D.; Stephenson, N.L.; Saleska, S.R.; Huxman, T.E.; McDowell, N.g. Climate-induced tree mortality: Earth system consequences. Eos Trans. Am. Geophys. Union 2010, 91, 153–154. [Google Scholar] [CrossRef]
- Bigler, C.; Gavin, D.G.; Gunning, C.; Veblen, T.T. Drought induces lagged tree mortality in a subalpine forest in the Rocky Mountains. Oikos 2007, 116, 1983–1994. [Google Scholar] [CrossRef]
- McDowell, N.; Pockman, W.T.; Allen, C.D.; Breshears, D.D.; Cobb, N.; Kolb, T.; Plaut, J.; Sperry, J.; West, A.; Williams, D.G. Mechanisms of plant survival and mortality during drought: Why do some plants survive while others succumb to drought? New Phytol. 2008, 178, 719–739. [Google Scholar] [CrossRef]
- Asner, G.P.; Alencar, A. Drought impacts on the Amazon forest: The remote sensing perspective. New Phytol. 2010, 187, 569–578. [Google Scholar] [CrossRef]
- Lewis, S.L.; Brando, P.M.; Phillips, O.L.; Van Der Heijden, G.M.; Nepstad, D. The 2010 Amazon drought. Science 2011, 331, 554. [Google Scholar] [CrossRef] [PubMed]
- Yang, Y.; Saatchi, S.S.; Xu, L.; Yu, Y.; Choi, S.; Phillips, N.; Kennedy, R.; Keller, M.; Knyazikhin, Y.; Myneni, R.B. Post-drought decline of the Amazon carbon sink. Nat. Commun. 2018, 9, 1–9. [Google Scholar] [CrossRef] [PubMed]
- Yang, J.; Tian, H.; Pan, S.; Chen, G.; Zhang, B.; Dangal, S. Amazon drought and forest response: Largely reduced forest photosynthesis but slightly increased canopy greenness during the extreme drought of 2015/2016. Glob. Chang. Biol. 2018, 24, 1919–1934. [Google Scholar] [CrossRef] [PubMed]
- Xiong, Q.; Sun, Z.; Cui, W.; Lei, J.; Fu, X.; Wu, L. A Study on Sensitivities of Tropical Forest GPP Responding to the Characteristics of Drought—A Case Study in Xishuangbanna, China. Water 2022, 14, 157. [Google Scholar] [CrossRef]
- Liu, Z.; Wang, T.; Qu, Y.; Liu, H.; Wu, X.; Wen, Y. Prediction of high-quality MODIS-NPP product data. Remote Sens. 2019, 11, 1458. [Google Scholar] [CrossRef] [Green Version]
- Zhou, G.; Wei, X.; Wu, Y.; Liu, S.; Huang, Y.; Yan, J.; Zhang, D.; Zhang, Q.; Liu, J.; Meng, Z. Quantifying the hydrological responses to climate change in an intact forested small watershed in Southern C hina. Glob. Chang. Biol. 2011, 17, 3736–3746. [Google Scholar] [CrossRef]
- IPCC. 2021: Summary for Policymakers. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change; Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S.L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M.I., et al., Eds.; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2021; pp. 3–32. [Google Scholar] [CrossRef]
- Wang, H.-J.; Sun, J.-Q.; Chen, H.-P.; Zhu, Y.-L.; Zhang, Y.; Jiang, D.-B.; Lang, X.-M.; Fan, K.; Yu, E.-T.; Yang, S. Extreme climate in China: Facts, simulation and projection. Meteorol. Z. 2012, 21, 279. [Google Scholar] [CrossRef]
- Li, X.; Piao, S.; Wang, K.; Wang, X.; Wang, T.; Ciais, P.; Chen, A.; Lian, X.; Peng, S.; Peñuelas, J. Temporal trade-off between gymnosperm resistance and resilience increases forest sensitivity to extreme drought. Nat. Ecol. Evol. 2020, 4, 1075–1083. [Google Scholar] [CrossRef]
- Lloret, F.; Keeling, E.G.; Sala, A. Components of tree resilience: Effects of successive low-growth episodes in old ponderosa pine forests. Oikos 2011, 120, 1909–1920. [Google Scholar] [CrossRef]
- MacGillivray, C.; Grime, J.; Team, T.I.S.P. Testing predictions of the resistance and resilience of vegetation subjected to extreme events. Funct. Ecol. 1995, 9, 640–649. [Google Scholar] [CrossRef]
- Stuart-Haëntjens, E.; De Boeck, H.J.; Lemoine, N.P.; Mänd, P.; Kröel-Dulay, G.; Schmidt, I.K.; Jentsch, A.; Stampfli, A.; Anderegg, W.R.; Bahn, M. Mean annual precipitation predicts primary production resistance and resilience to extreme drought. Sci. Total Environ. 2018, 636, 360–366. [Google Scholar] [CrossRef] [PubMed]
- Zhang, L.; Xiao, J.; Zhou, Y.; Zheng, Y.; Li, J.; Xiao, H. Drought events and their effects on vegetation productivity in China. Ecosphere 2016, 7, e01591. [Google Scholar] [CrossRef]
- Sun, X.; Wen, X.; Yu, G.; Liu, Y.; Liu, Q. Seasonal drought effects on carbon sequestration of a mid-subtropical planted forest of southeastern China. Sci. China Ser. D Earth Sci. 2006, 49, 110–118. [Google Scholar] [CrossRef]
- Mi, N.; Wen, X.; Cai, F.; Zhang, Y.; Wang, H. Influence of seasonal drought on ecosystem water use efficiency in a subtropical evergreen coniferous plantation. Appl. Ecol. Environ. Res. 2016, 14, 33–50. [Google Scholar] [CrossRef]
- Wen, X.F.; Wang, H.M.; Wang, J.L.; Yu, G.R.; Sun, X.M. Ecosystem carbon exchanges of a subtropical evergreen coniferous plantation subjected to seasonal drought, 2003–2007. Biogeosciences 2010, 7, 357–369. [Google Scholar] [CrossRef] [Green Version]
- Zhao, M.; Running, S.W. Drought-induced reduction in global terrestrial net primary production from 2000 through 2009. Science 2010, 329, 940–943. [Google Scholar] [CrossRef] [Green Version]
- Schwinning, S.; Starr, B.; Ehleringer, J. Summer and winter drought in a cold desert ecosystem (Colorado Plateau) part II: Effects on plant carbon assimilation and growth. J. Arid. Environ. 2005, 61, 61–78. [Google Scholar] [CrossRef]
- Chao, Z.; Boliang, L. Spatio-temporal characteristics of persistent regional meteorological drought in summer and autumn in Hunan. J. Arid. Meteorol. 2021, 39, 193. [Google Scholar]
- Hember, R.A.; Kurz, W.A.; Coops, N.C. Relationships between individual-tree mortality and water-balance variables indicate positive trends in water stress-induced tree mortality across North America. Glob. Chang. Biol. 2017, 23, 1691–1710. [Google Scholar] [CrossRef]
- Jiang, H.; Song, L.; Li, Y.; Ma, M.; Fan, L. Monitoring the reduced resilience of forests in southwest China using long-term remote sensing data. Remote Sens. 2021, 14, 32. [Google Scholar] [CrossRef]
- Longo, M.; Knox, R.G.; Levine, N.M.; Alves, L.F.; Bonal, D.; Camargo, P.B.; Fitzjarrald, D.R.; Hayek, M.N.; Restrepo-Coupe, N.; Saleska, S.R. Ecosystem heterogeneity and diversity mitigate Amazon forest resilience to frequent extreme droughts. New Phytol. 2018, 219, 914–931. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Shaw, J.D.; Steed, B.E.; DeBlander, L.T. Forest inventory and analysis (FIA) annual inventory answers the question: What is happening to pinyon-juniper woodlands? J. For. 2005, 103, 280–285. [Google Scholar]
- Frankenberg, C.; Fisher, J.B.; Worden, J.; Badgley, G.; Saatchi, S.S.; Lee, J.E.; Toon, G.C.; Butz, A.; Jung, M.; Kuze, A. New global observations of the terrestrial carbon cycle from GOSAT: Patterns of plant fluorescence with gross primary productivity. Geophys. Res. Lett. 2011, 38, L17706. [Google Scholar] [CrossRef] [Green Version]
- Wei, X.; Wang, X.; Wei, W.; Wan, W. Use of sun-induced chlorophyll fluorescence obtained by OCO-2 and GOME-2 for GPP estimates of the Heihe River basin, China. Remote Sens. 2018, 10, 2039. [Google Scholar] [CrossRef] [Green Version]
- Moorcroft, P.R.; Hurtt, G.C.; Pacala, S.W. A method for scaling vegetation dynamics: The Ecosystem Demography model (ED). Ecol. Monogr. 2001, 71, 557–586. [Google Scholar] [CrossRef]
- Medvigy, D.; Wofsy, S.; Munger, J.; Hollinger, D.; Moorcroft, P. Mechanistic scaling of ecosystem function and dynamics in space and time: Ecosystem Demography model version 2. J. Geophys. Res. Biogeosci. 2009, 114, G01002. [Google Scholar] [CrossRef] [Green Version]
- Longo, M.; Knox, R.G.; Medvigy, D.M.; Levine, N.M.; Dietze, M.C.; Kim, Y.; Swann, A.L.; Zhang, K.; Rollinson, C.R.; Bras, R.L. The biophysics, ecology, and biogeochemistry of functionally diverse, vertically and horizontally heterogeneous ecosystems: The Ecosystem Demography model, version 2.2–Part 1: Model description. Geosci. Model Dev. 2019, 12, 4309–4346. [Google Scholar] [CrossRef] [Green Version]
- Longo, M.; Knox, R.G.; Levine, N.M.; Swann, A.L.S.; Medvigy, D.M.; Dietze, M.C.; Kim, Y.; Zhang, K.; Bonal, D.; Burban, B.; et al. The biophysics, ecology, and biogeochemistry of functionally diverse, vertically and horizontally heterogeneous ecosystems: The Ecosystem Demography model, version 2.2–Part 2: Model evaluation for tropical South America. Geosci. Model Dev. 2019, 12, 4347–4374. [Google Scholar] [CrossRef] [Green Version]
- Xiao, M.; Peng, S.; Han, B. Analysis on the characteristics of climate change in the Hengshao arid corridor from 1961 to 2018. J. Anhui Agric. Sci. 2022, 50, 201–204. [Google Scholar]
- Gelaro, R.; McCarty, W.; Suárez, M.J.; Todling, R.; Molod, A.; Takacs, L.; Randles, C.A.; Darmenov, A.; Bosilovich, M.G.; Reichle, R. The Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2). J. Clim. 2017, 30, 5419–5454. [Google Scholar] [CrossRef]
- GMAO. MERRA-2 tavg1_2d_slv_Nx: 2d, 1-Hourly, Time-Averaged, Single-Level, Assimilation, Single-Level Diagnostics V5. 12.4. 2015. Available online: https://disc.gsfc.nasa.gov/datasets/M2T1NXSLV_5.12.4/summary (accessed on 16 March 2022).
- GMAO. MERRA-2 tavg1_2d_lnd_nx: 2d, 1-Hourly, Time-Averaged, Single-Level, Assimilation, Land Surface Diagnostics v5. 12.4. 2015. Available online: https://disc.gsfc.nasa.gov/datasets/M2T1NXLND_5.12.4/summary (accessed on 16 March 2022).
- GMAO. MERRA-2 tavg1_2d_flx_Nx: 2D, 1-Hourly, Time-Averaged, Single-Level, Assimilation, Surface Flux Diagnostics V5. 12.4. 2015. Available online: https://disc.gsfc.nasa.gov/datasets/M2T1NXFLX_5.12.4/summary (accessed on 16 March 2022).
- GMAO. MERRA-2 tavg1_2d_rad_nx: 2d, 1-Hourly, Time-Averaged, Single-Level, Assimilation, Radiation Diagnostics v5. 12.4. 2015. Available online: https://disc.gsfc.nasa.gov/datasets/M2T1NXRAD_5.12.4/summary (accessed on 16 March 2022).
- General Administration of Quality Supervision, Inspection and Quarantine of the People’s Republic of China. GB/T 20481-2017; Standardization Administration of the People’s Republic of China. Grades of meteorological drought. 2017; p. 32. Available online: https://www.chinesestandard.net/PDF/BOOK.aspx/GBT20481-2017 (accessed on 10 November 2022).
- Hengl, T.; de Jesus, J.M.; Heuvelink, G.B.M.; Ruiperez Gonzalez, M.; Kilibarda, M.; Blagotic, A.; Shangguan, W.; Wright, M.N.; Geng, X.; Bauer Marschallinger, B.; et al. SoilGrids250m: Global gridded soil information based on machine learning. PLoS ONE 2017, 12, e0169748. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Poggio, L.; de Sousa, L.M.; Batjes, N.H.; Heuvelink, G.B.M.; Kempen, B.; Ribeiro, E.; Rossiter, D. SoilGrids 2.0: Producing soil information for the globe with quantified spatial uncertainty. Soil 2021, 7, 217–240. [Google Scholar] [CrossRef]
- Pelletier, J.D.; Broxton, P.D.; Hazenberg, P.; Zeng, X.; Troch, P.A.; Niu, G.Y.; Williams, Z.; Brunke, M.A.; Gochis, D. A gridded global data set of soil, intact regolith, and sedimentary deposit thicknesses for regional and global land surface modeling. J. Adv. Model. Earth Syst. 2016, 8, 41–65. [Google Scholar] [CrossRef]
- Bonan, G. Climate Change and Terrestrial Ecosystem Modeling; Cambridge University Press: Cambridge, UK, 2019. [Google Scholar]
- Rollinson, C.R.; Liu, Y.; Raiho, A.; Moore, D.J.; McLachlan, J.; Bishop, D.A.; Dye, A.; Matthes, J.H.; Hessl, A.; Hickler, T. Emergent climate and CO2 sensitivities of net primary productivity in ecosystem models do not agree with empirical data in temperate forests of eastern North America. Glob. Chang. Biol. 2017, 23, 2755–2767. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Jiang, Y.; Kim, J.B.; Trugman, A.T.; Kim, Y.; Still, C.J. Linking tree physiological constraints with predictions of carbon and water fluxes at an old-growth coniferous forest. Ecosphere 2019, 10, e02692. [Google Scholar] [CrossRef] [Green Version]
- Shiklomanov, A.N.; Dietze, M.C.; Fer, I.; Viskari, T.; Serbin, S.P. Cutting out the middleman: Calibrating and validating a dynamic vegetation model (ED2-PROSPECT5) using remotely sensed surface reflectance. Geosci. Model Dev. 2021, 14, 2603–2633. [Google Scholar] [CrossRef]
- Meunier, F.; Krishna Moorthy, S.M.; Peaucelle, M.; Calders, K.; Terryn, L.; Verbruggen, W.; Liu, C.; Saarinen, N.; Origo, N.; Nightingale, J. Using terrestrial laser scanning to constrain forest ecosystem structure and functions in the Ecosystem Demography model (ED2. 2). Geosci. Model Dev. 2022, 15, 4783–4803. [Google Scholar] [CrossRef]
- Zhu, Y.; Liu, S.; Yan, W.; Deng, D.; Zhou, G.; Zhao, M.; Gao, F.; Zhu, L.; Wang, Z.; Xie, M. Impact of ice-storms and subsequent salvage logging on the productivity of Cunninghamia lanceolata (Chinese Fir) forests. Forests 2022, 13, 296. [Google Scholar] [CrossRef]
- Berzaghi, F.; Longo, M.; Ciais, P.; Blake, S.; Bretagnolle, F.; Vieira, S.; Scaranello, M.; Scarascia-Mugnozza, G.; Doughty, C.E. Carbon stocks in central African forests enhanced by elephant disturbance. Nat. Geosci. 2019, 12, 725–729. [Google Scholar] [CrossRef]
- di Porcia E Brugnera, M.; Meunier, F.; Longo, M.; Krishna Moorthy, S.M.; De Deurwaerder, H.; Schnitzer, S.A.; Bonal, D.; Faybishenko, B.; Verbeeck, H. Modeling the impact of liana infestation on the demography and carbon cycle of tropical forests. Glob. Chang. Biol. 2019, 25, 3767–3780. [Google Scholar] [CrossRef]
- Longo, M.; Saatchi, S.; Keller, M.; Bowman, K.; Ferraz, A.; Moorcroft, P.R.; Morton, D.C.; Bonal, D.; Brando, P.; Burban, B. Impacts of degradation on water, energy, and carbon cycling of the Amazon tropical forests. J. Geophys. Res. Biogeosci. 2020, 125, e2020JG005677. [Google Scholar] [CrossRef] [PubMed]
- Xu, X.; Konings, A.G.; Longo, M.; Feldman, A.; Xu, L.; Saatchi, S.; Wu, D.; Wu, J.; Moorcroft, P. Leaf surface water, not plant water stress, drives diurnal variation in tropical forest canopy water content. New Phytol. 2021, 231, 122–136. [Google Scholar] [CrossRef] [PubMed]
- Medvigy, D.; Jeong, S.J.; Clark, K.L.; Skowronski, N.S.; Schäfer, K.V. Effects of seasonal variation of photosynthetic capacity on the carbon fluxes of a temperate deciduous forest. J. Geophys. Res. Biogeosci. 2013, 118, 1703–1714. [Google Scholar] [CrossRef]
- The Forestry Department of Hunan Province. Hunan Provincial Protocol of National Forest Inventory of China. 2014. [Google Scholar]
- Onoda, Y.; Wright, I.J.; Evans, J.R.; Hikosaka, K.; Kitajima, K.; Niinemets, U.; Poorter, H.; Tosens, T.; Westoby, M. Physiological and structural tradeoffs underlying the leaf economics spectrum. New Phytol. 2017, 214, 1447–1463. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Wang, H.; Harrison, S.P.; Prentice, I.C.; Yang, Y.; Bai, F.; Togashi, H.F.; Wang, M.; Zhou, S.; Ni, J. The China plant trait database: Toward a comprehensive regional compilation of functional traits for land plants. Ecology 2018, 99, 500. [Google Scholar] [CrossRef] [PubMed]
- Zhao, M.; Xiang, W.; Peng, C.; Tian, D. Simulating age-related changes in carbon storage and allocation in a Chinese fir plantation growing in southern China using the 3-PG model. For. Ecol. Manag. 2009, 257, 1520–1531. [Google Scholar] [CrossRef]
- Kattge, J.; Bönisch, G.; Díaz, S.; Lavorel, S.; Prentice, I.C.; Leadley, P.; Tautenhahn, S.; Werner, G.D.; Aakala, T.; Abedi, M. TRY plant trait database–enhanced coverage and open access. Glob. Chang. Biol. 2020, 26, 119–188. [Google Scholar] [CrossRef] [Green Version]
- Kattge, J.; Diaz, S.; Lavorel, S.; Prentice, I.C.; Leadley, P.; Bönisch, G.; Garnier, E.; Westoby, M.; Reich, P.B.; Wright, I.J. TRY–a global database of plant traits. Glob. Chang. Biol. 2011, 17, 2905–2935. [Google Scholar] [CrossRef]
- Running, S.; Zhao, M. MOD17A3HGF MODIS/Terra Net Primary Production Gap-Filled Yearly L4 Global 500 m SIN Grid V006 [Data set]. NASA EOSDIS Land Processes DAAC. Available online: https://doi.org/10.5067/MODIS/MOD17A3HGF.0064 (accessed on 17 September 2021).
- Jia, B.; Xie, Z.; Zeng, Y.; Wang, L.; Wang, Y.; Xie, J.; Xie, Z. Diurnal and seasonal variations of CO2 fluxes and their climate controlling factors for a subtropical forest in Ningxiang. Adv. Atmos. Sci. 2015, 32, 553–564. [Google Scholar] [CrossRef]
- Xie, Z.; Wang, L.; Jia, B.; Yuan, X. Measuring and modeling the impact of a severe drought on terrestrial ecosystem CO2 and water fluxes in a subtropical forest. J. Geophys. Res. Biogeosci. 2016, 121, 2576–2587. [Google Scholar] [CrossRef] [Green Version]
- Zhang, J.; Liao, Y.; Wu, H.; Zhang, J.; Zhao, H. Characteristics of atmospheric circulation anomalies and drought in summer and autumn in Hunan Province. J. Arid. Meteorol. 2018, 36, 353–364. [Google Scholar]
- Zhou, Y.; Zeng, G.; Pang, L.; Li, Y.; Xiong, H.; Tang, L.; Zhang, G.; Ju, G.; Han, Q. Characteristics of weather and climate during dought periods in South China. J. Appl. Meteorol. Sci. 2003, 14, 118–125. [Google Scholar]
- Levine, N.M.; Zhang, K.; Longo, M.; Baccini, A.; Phillips, O.L.; Lewis, S.L.; Alvarez-Dávila, E.; de Andrade, A.C.S.; Brienen, R.J.; Erwin, T.L. Ecosystem heterogeneity determines the ecological resilience of the Amazon to climate change. Proc. Natl. Acad. Sci. USA 2016, 113, 793–797. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Huang, Y.; Deng, X.; Zhao, Z.; Xiang, W.; Yan, W.; Ouyang, S.; Lei, P. Monthly radial growth model of Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.), and the relationships between radial increment and climate factors. Forests 2019, 10, 757. [Google Scholar] [CrossRef]
- Feng, X.; Uriarte, M.; González, G.; Reed, S.; Thompson, J.; Zimmerman, J.K.; Murphy, L. Improving predictions of tropical forest response to climate change through integration of field studies and ecosystem modeling. Glob. Chang. Biol. 2018, 24, e213–e232. [Google Scholar] [CrossRef] [PubMed]
- Stocker, B.D.; Zscheischler, J.; Keenan, T.F.; Prentice, I.C.; Seneviratne, S.I.; Peñuelas, J. Drought impacts on terrestrial primary production underestimated by satellite monitoring. Nat. Geosci. 2019, 12, 264–270. [Google Scholar] [CrossRef] [Green Version]
- Granier, A.; Reichstein, M.; Bréda, N.; Janssens, I.; Falge, E.; Ciais, P.; Grünwald, T.; Aubinet, M.; Berbigier, P.; Bernhofer, C. Evidence for soil water control on carbon and water dynamics in European forests during the extremely dry year: 2003. Agric. For. Meteorol. 2007, 143, 123–145. [Google Scholar] [CrossRef]
- Reichstein, M.; Ciais, P.; Papale, D.; Valentini, R.; Running, S.; Viovy, N.; Cramer, W.; Granier, A.; Ogée, J.; Allard, V. Reduction of ecosystem productivity and respiration during the European summer 2003 climate anomaly: A joint flux tower, remote sensing and modelling analysis. Glob. Chang. Biol. 2007, 13, 634–651. [Google Scholar] [CrossRef]
- Salomón, R.L.; Peters, R.L.; Zweifel, R.; Sass-Klaassen, U.G.; Stegehuis, A.I.; Smiljanic, M.; Poyatos, R.; Babst, F.; Cienciala, E.; Fonti, P. The 2018 European heatwave led to stem dehydration but not to consistent growth reductions in forests. Nat. Commun. 2022, 13, 1–11. [Google Scholar]
- Shestakova, T.A.; Gutiérrez, E.; Kirdyanov, A.V.; Camarero, J.J.; Génova, M.; Knorre, A.A.; Linares, J.C.; Resco de Dios, V.; Sánchez-Salguero, R.; Voltas, J. Forests synchronize their growth in contrasting Eurasian regions in response to climate warming. Proc. Natl. Acad. Sci. USA 2016, 113, 662–667. [Google Scholar] [CrossRef] [Green Version]
- Steppe, K.; Sterck, F.; Deslauriers, A. Diel growth dynamics in tree stems: Linking anatomy and ecophysiology. Trends Plant Sci. 2015, 20, 335–343. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Anderegg, W.R.; Berry, J.A.; Field, C.B. Linking definitions, mechanisms, and modeling of drought-induced tree death. Trends Plant Sci. 2012, 17, 693–700. [Google Scholar] [CrossRef] [PubMed]
- Martinez-Vilalta, J.; Anderegg, W.R.; Sapes, G.; Sala, A. Greater focus on water pools may improve our ability to understand and anticipate drought-induced mortality in plants. New Phytol. 2019, 223, 22–32. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Leuning, R. A critical appraisal of a combined stomatal-photosynthesis model for C3 plants. Plant Cell Environ. 1995, 18, 339–355. [Google Scholar] [CrossRef]
- McDowell, N.G.; Allen, C.D. Darcy’s law predicts widespread forest mortality under climate warming. Nat. Clim. Chang. 2015, 5, 669–672. [Google Scholar] [CrossRef]
- Williams, A.P.; Cook, E.R.; Smerdon, J.E.; Cook, B.I.; Abatzoglou, J.T.; Bolles, K.; Baek, S.H.; Badger, A.M.; Livneh, B. Large contribution from anthropogenic warming to an emerging North American megadrought. Science 2020, 368, 314–318. [Google Scholar] [CrossRef]
- Bita, C.E.; Gerats, T. Plant tolerance to high temperature in a changing environment: Scientific fundamentals and production of heat stress-tolerant crops. Front. Plant Sci. 2013, 4, 273. [Google Scholar] [CrossRef] [Green Version]
- Sharkey, T.D.; Zhang, R. High temperature effects on electron and proton circuits of photosynthesis. J. Integr. Plant Biol. 2010, 52, 712–722. [Google Scholar] [CrossRef]
- Todorov, D.; Karanov, E.; Smith, A.; Hall, M. Chlorophyllase activity and chlorophyll content in wild and mutant plants of Arabidopsis thaliana. Biol. Plant. 2003, 46, 125–127. [Google Scholar] [CrossRef]
- Barnabás, B.; Jäger, K.; Fehér, A. The effect of drought and heat stress on reproductive processes in cereals. Plant Cell Environ. 2008, 31, 11–38. [Google Scholar] [CrossRef]
- Sellers, P.; Randall, D.; Collatz, G.; Berry, J.; Field, C.; Dazlich, D.; Zhang, C.; Collelo, G.; Bounoua, L. A revised land surface parameterization (SiB2) for atmospheric GCMs. Part I: Model formulation. J. Clim. 1996, 9, 676–705. [Google Scholar] [CrossRef]
- Zhou, S.; Gao, Q.; Xu, D.; Zhou, C. Temporal and spatial variations of the extreme drought in Hunan during the last 61 years. In Proceedings of the 32nd Annual Meeting of the Chinese Meteorological Society, Tianjin, China, 14–16 October 2015; pp. 269–273. [Google Scholar]
- LeBauer, D.S.; Wang, D.; Richter, K.T.; Davidson, C.C.; Dietze, M.C. Facilitating feedbacks between field measurements and ecosystem models. Ecol. Monogr. 2013, 83, 133–154. [Google Scholar] [CrossRef]
- Dietze, M.C.; Serbin, S.P.; Davidson, C.; Desai, A.R.; Feng, X.; Kelly, R.; Kooper, R.; LeBauer, D.; Mantooth, J.; McHenry, K. A quantitative assessment of a terrestrial biosphere model’s data needs across North American biomes. J. Geophys. Res. Biogeosci. 2014, 119, 286–300. [Google Scholar] [CrossRef]
- Fer, I.; Kelly, R.; Moorcroft, P.R.; Richardson, A.D.; Cowdery, E.M.; Dietze, M.C. Linking big models to big data: Efficient ecosystem model calibration through Bayesian model emulation. Biogeosciences 2018, 15, 5801–5830. [Google Scholar] [CrossRef]
Plot | Stand Origin | Forest Type | Elevation [m] | Mean Age [a] | Mean DBH [cm] | Canopy Height [m] |
---|---|---|---|---|---|---|
1 | Plantation forest | C. lanceolata | 540 | 17 | 11.8 | 12.2 |
2 | Natural forest | P. massoniana | 220 | 23 | 11.1 | 10.0 |
3 | Plantation forest | C. lanceolata | 600 | 14 | 12.2 | 8.3 |
4 | Natural forest | P. massoniana | 230 | 17 | 12.8 | 8.0 |
5 | Natural forest | P. massoniana | 550 | 24 | 10.2 | 7.7 |
6 | Plantation forest | P. massoniana | 160 | 19 | 10.2 | 8.0 |
7 | Natural forest | P. massoniana | 400 | 20 | 11.8 | 12.5 |
8 | Natural forest | P. massoniana | 390 | 22 | 10.8 | 10.6 |
9 | Natural forest | P. massoniana | 290 | 31 | 14.0 | 11.7 |
10 | Plantation forest | P. massoniana | 430 | 31 | 12.2 | 9.8 |
Plant Functional Type | SLA [m2 kgC−1] | b1Ht [m] | b2Ht [cm−1] |
---|---|---|---|
PFT2 | 24.5 | 0.026 | 0.715 |
PFT4 | 36.6 | 0.026 | 0.717 |
PFT7 | 7.4 | 51.037 | −0.014 |
Plant Functional Type | Allometric Equation | RMSE | MAE |
---|---|---|---|
PFT2 | Height = 61.7·(1 − exp(−0.035·DBH0.695)) A | 2.58 | 6.66 |
Height = 61.7·(1 − exp(−0.026·DBH0.715)) B | 1.68 | 2.84 | |
−34.7% | −57.4% | ||
PFT4 | Height = 61.7·(1 − exp(−0.042·DBH0.522)) A | 2.58 | 6.67 |
Height = 61.7·(1 − exp(−0.026·DBH0.717)) B | 1.66 | 2.75 | |
−35.8% | −58.8% |
Type | Scenario | Precipitation Rate [kg m−2 s−1] | Air Temperature [K] |
---|---|---|---|
Default | Baseline (B0) | P0 | T0 |
Drought-only scenario | Mild drought (D1) | P0 × 0.6 | T0 |
Moderate drought (D2) | P0 × 0.4 | T0 | |
Severe drought (D3) | P0 × 0.2 | T0 | |
Warming-only scenario | Mild warming (W1) | P0 | T0 + 1.0 |
Moderate warming (W2) | P0 | T0 + 1.5 | |
Severe warming (W3) | P0 | T0 + 2.0 | |
Drought-warming scenario | Mild drought-warming (D1 + W1) | P0 × 0.6 | T0 + 1.0 |
Moderate drought-warming (D2 + W2) | P0 × 0.4 | T0 + 1.5 | |
Severe drought-warming (D3 + W3) | P0 × 0.2 | T0 + 2.0 |
Month | NPP [kgC m−2 yr−1] | Absolute Change [kgC m−2 yr−1] | Relative Rate of Change [%] | |
---|---|---|---|---|
Baseline | Disturbance | |||
July | 1.014 | 0.986 (D1) | −0.028 | −2.76 |
0.966 (D2) | −0.048 | −4.73 | ||
0.935 (D3) | −0.079 | −7.79 | ||
0.941 (W1) | −0.073 | −7.20 | ||
0.905 (W2) | −0.109 | −10.75 | ||
0.869 (W3) | −0.145 | −14.30 | ||
0.910 (D1 + W1) | −0.104 | −10.26 | ||
0.847 (D2 + W2) | −0.167 | −16.47 | ||
0.768 (D3 + W3) | −0.246 | −24.26 | ||
August | 1.048 | 1.008 (D1) | −0.040 | −3.82 |
0.970 (D2) | −0.078 | −7.44 | ||
0.905 (D3) | −0.143 | −13.65 | ||
0.988 (W1) | −0.060 | −5.73 | ||
0.958 (W2) | −0.090 | −8.59 | ||
0.928 (W3) | −0.120 | −11.45 | ||
0.941 (D1 + W1) | −0.107 | −10.21 | ||
0.857 (D2 + W2) | −0.191 | −18.23 | ||
0.736 (D3 + W3) | −0.312 | −29.77 | ||
September | 1.078 | 1.055 (D1) | −0.023 | −2.13 |
1.007 (D2) | −0.071 | −6.59 | ||
0.841 (D3) | −0.237 | −21.99 | ||
1.023 (W1) | −0.055 | −5.10 | ||
0.994 (W2) | −0.084 | −7.79 | ||
0.965 (W3) | −0.113 | −10.48 | ||
0.992 (D1 + W1) | −0.086 | −7.98 | ||
0.887 (D2 + W2) | −0.191 | −17.72 | ||
0.526 (D3 + W3) | −0.552 | −51.21 |
Month | NPP (kgC m−2 yr−1) | Absolute Change (kgC m−2 yr−1) | Relative Rate of Change (%) | |
---|---|---|---|---|
Baseline | Disturbance | |||
October | 1.034 | 1.006 (D1) | −0.028 | −2.71 |
0.894 (D2) | −0.140 | −13.54 | ||
0.469 (D3) | −0.565 | −54.64 | ||
1.032 (W1) | −0.002 | −0.19 | ||
1.031 (W2) | −0.003 | −0.29 | ||
1.031 (W3) | −0.003 | −0.29 | ||
0.993 (D1 + W1) | −0.041 | −3.97 | ||
0.796 (D2 + W2) | −0.238 | −23.02 | ||
0.329 (D3 + W3) | −0.705 | −68.18 | ||
November | 0.750 | 0.741 (D1) | −0.009 | −1.20 |
0.718 (D2) | −0.032 | −4.27 | ||
0.689 (D3) | −0.061 | −8.13 | ||
0.749 (W1) | −0.001 | −0.13 | ||
0.749 (W2) | −0.001 | −0.13 | ||
0.748 (W3) | −0.002 | −0.27 | ||
0.736 (D1 + W1) | −0.014 | −1.87 | ||
0.707 (D2 + W2) | −0.043 | −5.73 | ||
0.669 (D3 + W3) | −0.081 | −10.80 | ||
December | 0.720 | 0.716 (D1) | −0.004 | −0.56 |
0.708 (D2) | −0.012 | −1.67 | ||
0.695 (D3) | −0.025 | −3.47 | ||
0.719 (W1) | −0.001 | −0.14 | ||
0.718 (W2) | −0.002 | −0.28 | ||
0.718 (W3) | −0.002 | −0.28 | ||
0.714 (D1 + W1) | −0.006 | −0.83 | ||
0.704 (D2 + W2) | −0.016 | −2.22 | ||
0.681 (D3 + W3) | −0.039 | −5.42 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Xie, M.; Zhu, Y.; Liu, S.; Deng, D.; Zhu, L.; Zhao, M.; Wang, Z. Simulating the Impacts of Drought and Warming in Summer and Autumn on the Productivity of Subtropical Coniferous Forests. Forests 2022, 13, 2147. https://doi.org/10.3390/f13122147
Xie M, Zhu Y, Liu S, Deng D, Zhu L, Zhao M, Wang Z. Simulating the Impacts of Drought and Warming in Summer and Autumn on the Productivity of Subtropical Coniferous Forests. Forests. 2022; 13(12):2147. https://doi.org/10.3390/f13122147
Chicago/Turabian StyleXie, Menglu, Yu Zhu, Shuguang Liu, Deming Deng, Liangjun Zhu, Meifang Zhao, and Zhao Wang. 2022. "Simulating the Impacts of Drought and Warming in Summer and Autumn on the Productivity of Subtropical Coniferous Forests" Forests 13, no. 12: 2147. https://doi.org/10.3390/f13122147
APA StyleXie, M., Zhu, Y., Liu, S., Deng, D., Zhu, L., Zhao, M., & Wang, Z. (2022). Simulating the Impacts of Drought and Warming in Summer and Autumn on the Productivity of Subtropical Coniferous Forests. Forests, 13(12), 2147. https://doi.org/10.3390/f13122147