An integrated approach to modeling changes in land use, land cover, and disturbance and their impact on ecosystem carbon dynamics: a case study in the Sierra Nevada Mountains of California

Benjamin M. Sleeter; Jinxun Liu; Colin Daniel; Leonardo Frid; Zhiliang Zhu; Benjamin M. Sleeter; Jinxun Liu; Colin Daniel; Leonardo Frid; Zhiliang Zhu

doi:10.3934/environsci.2015.3.577

AIMS Environmental Science

2015, Volume 2, Issue 3: 577-606. doi: 10.3934/environsci.2015.3.577

Previous Article Next Article

Research article Special Issues

An integrated approach to modeling changes in land use, land cover, and disturbance and their impact on ecosystem carbon dynamics: a case study in the Sierra Nevada Mountains of California

1.
U.S. Geological Survey, Western Geographic Science Center; Tacoma, WA, USA;
2.
San Jose State University, San Jose, CA, USA;
3.
Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, ON, Canada;
4.
Apex Resource Management Solutions Ltd., Bowen Island, BC, Canada;
5.
U.S. Geological Survey, Reston, VA, USA

Received: 23 February 2015 Accepted: 14 June 2015 Published: 16 June 2015

Abstract
Full Text(HTML)
Download PDF

Increased land-use intensity (e.g. clearing of forests for cultivation, urbanization), often results in the loss of ecosystem carbon storage, while changes in productivity resulting from climate change may either help offset or exacerbate losses. However, there are large uncertainties in how land and climate systems will evolve and interact to shape future ecosystem carbon dynamics. To address this we developed the Land Use and Carbon Scenario Simulator (LUCAS) to track changes in land use, land cover, land management, and disturbance, and their impact on ecosystem carbon storage and flux within a scenario-based framework. We have combined a state-and-transition simulation model (STSM) of land change with a stock and flow model of carbon dynamics. Land-change projections downscaled from the Intergovernmental Panel on Climate Change’s (IPCC) Special Report on Emission Scenarios (SRES) were used to drive changes within the STSM, while the Integrated Biosphere Simulator (IBIS) ecosystem model was used to derive input parameters for the carbon stock and flow model. The model was applied to the Sierra Nevada Mountains ecoregion in California, USA, a region prone to large wildfires and a forestry sector projected to intensify over the next century. Three scenario simulations were conducted, including a calibration scenario, a climate-change scenario, and an integrated climate- and land-change scenario. Based on results from the calibration scenario, the LUCAS age-structured carbon accounting model was able to accurately reproduce results obtained from the process-based biogeochemical model. Under the climate-only scenario, the ecoregion was projected to be a reliable net sink of carbon, however, when land use and disturbance were introduced, the ecoregion switched to become a net source. This research demonstrates how an integrated approach to carbon accounting can be used to evaluate various drivers of ecosystem carbon change in a robust, yet transparent modeling environment.
- land use,
- carbon,
- modeling,
- scenarios,
- California,
- state-and-transition simulation model,
- stock and flow model,
- IBIS
Citation: Benjamin M. Sleeter, Jinxun Liu, Colin Daniel, Leonardo Frid, Zhiliang Zhu. An integrated approach to modeling changes in land use, land cover, and disturbance and their impact on ecosystem carbon dynamics: a case study in the Sierra Nevada Mountains of California[J]. AIMS Environmental Science, 2015, 2(3): 577-606. doi: 10.3934/environsci.2015.3.577

Related Papers:

Abstract

Increased land-use intensity (e.g. clearing of forests for cultivation, urbanization), often results in the loss of ecosystem carbon storage, while changes in productivity resulting from climate change may either help offset or exacerbate losses. However, there are large uncertainties in how land and climate systems will evolve and interact to shape future ecosystem carbon dynamics. To address this we developed the Land Use and Carbon Scenario Simulator (LUCAS) to track changes in land use, land cover, land management, and disturbance, and their impact on ecosystem carbon storage and flux within a scenario-based framework. We have combined a state-and-transition simulation model (STSM) of land change with a stock and flow model of carbon dynamics. Land-change projections downscaled from the Intergovernmental Panel on Climate Change’s (IPCC) Special Report on Emission Scenarios (SRES) were used to drive changes within the STSM, while the Integrated Biosphere Simulator (IBIS) ecosystem model was used to derive input parameters for the carbon stock and flow model. The model was applied to the Sierra Nevada Mountains ecoregion in California, USA, a region prone to large wildfires and a forestry sector projected to intensify over the next century. Three scenario simulations were conducted, including a calibration scenario, a climate-change scenario, and an integrated climate- and land-change scenario. Based on results from the calibration scenario, the LUCAS age-structured carbon accounting model was able to accurately reproduce results obtained from the process-based biogeochemical model. Under the climate-only scenario, the ecoregion was projected to be a reliable net sink of carbon, however, when land use and disturbance were introduced, the ecoregion switched to become a net source. This research demonstrates how an integrated approach to carbon accounting can be used to evaluate various drivers of ecosystem carbon change in a robust, yet transparent modeling environment.

References

[1]	Feddema J, Oleson KW, Bonan G, et al. (2005) Importance of land cover change in simulating future climates. Science 310: 1674-1678. doi: 10.1126/science.1118160
[2]	Houghton RA, Hackler JL, Lawrence KT (1999) The U.S. carbon budget: contributions from land-use Change. Science 285: 574-578.
[3]	Caspersen JP, Pacala SW, Jenkins JC, et al. (2000) Contributions of land-use history to carbon accumulation in U.S. forests. Science 290: 1148-1151.
[4]	Vitousek PM, Mooney HA, Lubchenco J, et al. (1997) Human domination of earth's ecosystems. Science 277: 494-499. doi: 10.1126/science.277.5325.494
[5]	Foley JA, Defries R, Asner GP, et al. (2005) Global consequences of land use. Science 309: 570-574. doi: 10.1126/science.1111772
[6]	Pan Y, Birdsey RA, Fang J, et al. (2011) A large and persistent carbon sink in the world's forests. Science 333: 988-993. doi: 10.1126/science.1201609
[7]	U.S. Environmental Protection Agency (2014) Inventory of U.S. greenhouse gas emissions and sinks: 1990 - 2012. Available from: http://www.epa.gov/climatechange/emissions/usinventoryreport.html
[8]	Sleeter BM, Wilson TS, Soulard CE, et al. (2011) Estimation of late twentieth century land-cover change in California. Environ Monit Assess 173: 251-266. doi: 10.1007/s10661-010-1385-8
[9]	Liu J, Vogelmann JE, Zhu Z, et al. (2011) Estimating California ecosystem carbon change using process model and land cover disturbance data: 1951-2000. Ecol Model 222: 2333-2341. doi: 10.1016/j.ecolmodel.2011.03.042
[10]	Winrock International, PIER Energy-Related Environmental Research. California Energy Commission, 500-04-068F. 2014. Availalble from: http://www.energy.ca.gov/pier/project_reports/500-04-068.html
[11]	Birdsey RA, Lewis GM (2003) Carbon in U.S. forests and wood products, 1987-1997: state-by-state estimates. USDA Forest Service, Northeastern Research Station. Gen Tech Rep NE-310: 42.
[12]	State of California, Department of Finance, Report P-1 (Total Population): State and county population projections, 2010-2060. Sacramento CA, December 2014, Available from: http://www.dof.ca.gov/research/demographic/reports/projections/P-1/.
[13]	Nakicenovic N, Swart R, (eds) (2000) Special Report on Emission Scenarios: A special report of Working Group III of the Intergovernmental Panel on Climate Change. Cambridge University Press, UK.
[14]	Millenium Ecosystem Assessment (2005) Ecosystems and Human Well-being: Scenarios, Volume 2. Carpenter SR, Pingali PL, Bennett EM et al., (eds), Island Press, 1718 Connecticut Avenue, Suite 300, NW, Washington, DC 20009.
[15]	Moss RH, Edmonds JA, Hibbard KA, et al. (2010) The next generation of scenarios for climate change research and assessment. Nature 463: 747-756. doi: 10.1038/nature08823
[16]	Sleeter BM, Sohl TL, Bouchard MA, et al. (2012) Scenarios of land use and land cover change in the conterminous United States: Utilizing the special report on emission scenarios at ecoregional scales. Global Environ Chang 22: 896-914. doi: 10.1016/j.gloenvcha.2012.03.008
[17]	Radeloff VC, Nelson E, Plantiga AJ, et al. (2012) Economic-based projections of future land use in the conterminous United States under alternative policy scenarios. Ecol Appl 22: 1036-1049. doi: 10.1890/11-0306.1
[18]	van Vuuren DP, Lucas PL, Hilderink H (2007) Downscaling drivers of global environmental change: Enabling use of global SRES scenarios at the national and grid levels. Global Environ Chang 17: 114-130. doi: 10.1016/j.gloenvcha.2006.04.004
[19]	Verburg PH, Schulp CJE, Witte N, et al. (2006) Downscaling of land use change scenarios to assess the dynamics of European landscapes. Agr Ecosyst Environ 114: 39-56. doi: 10.1016/j.agee.2005.11.024
[20]	Wear D (2011) Forecasts of county-level land uses under three future scenarios: a technical document supporting the Forest Service 2010 RPA Assessment. Department of Agriculture Forest Service, Southern Research Station. Gen Tech Rep SRS-141: 41.
[21]	Zhu Z, et al, (ed) (2010) A method for assessing carbon stocks, carbon sequestration, and greenhouse-gas fluxes in ecosystems of the United States under present conditions and future scenarios. U.S. Geological Survey Scientific Investigations Report 2010-5233, Reston, VA, Available from: http://pubs.usgs.gov/sir/2010/5233/
[22]	IPCC (2007) Climate Change 2007: Synthesis Report. Contribution of Working Groups I, II, and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Geneva, Switzerland: IPCC. 104.
[23]	Foley JA, Prentice C, Ramankutty N, et al. (1996) An integrated biosphere model of land surface processes, terrestrial carbon balance, and vegetation dynamics. Global Biogeochem Cy 10: 603-628. doi: 10.1029/96GB02692
[24]	Baker WL (1989) A review of models of landscape change. Landscape Ecol 2: 11-133.
[25]	Daniel CJ, Frid L (2012) Predicting Landscape Vegetation Dynamics Using State-and-Transition Simulation Models. In: Kerns BK, Shlisky AJ, Daniel CJ, (eds). U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, General Technical Report PNW-GTR-869; 5-22.
[26]	APEX Resource Management Solutions,ST-Sim State and Transition Simulator modeling software. 2014. Available from: http://www.apexrms.com/stsm, version 2.4.6.
[27]	IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Volume 4: agriculture, forestry and other land use. Paustian K, Ravindranath N.H., Amstel A (eds), Available from: http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol4.html
[28]	Kucharik CJ, Foley JA, Delire C, et al. (2000) Testing the performance of a dynamic global ecosystem model: Water balance, carbon balance, and vegetation structure. Global Biogeochem Cy 14: 795-825. doi: 10.1029/1999GB001138
[29]	Farquhar GD, von Caemmerer S, Berry JA (1980) A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 149: 78-90. doi: 10.1007/BF00386231
[30]	Ball JT, Woodrow I, Berry J (1987) A Model Predicting Stomatal Conductance and its Contribution to the Control of Photosynthesis under Different Environmental Conditions. In: Biggins J, editor. Progress in Photosynthesis Research: Springer Netherlands. 221-224.
[31]	Botta A, Viovy N, Ciais P, et al. (2000) A global prognostic scheme of leaf onset using satellite data. Global Change Biol 6: 709-725. doi: 10.1046/j.1365-2486.2000.00362.x
[32]	Parton WJ, Scurlock JMO, Ojima DS, et al. (1993) Observations and modeling of biomass and soil organic matter dynamics for the grassland biome worldwide. Global Biogeochem Cy 7: 785-809. doi: 10.1029/93GB02042
[33]	Verberne E, Hassink J, De Willigen P, et al. (1990) Modelling organic matter dynamics in different soils. Neth J Agr Sci 38: 221-238.
[34]	Parton WJ, Schimel DS, Cole C, et al. (1987) Division s-3-soil microbiology and biochemistry. Soil Sci Soc Am J 51: 1173-1179. doi: 10.2136/sssaj1987.03615995005100050015x
[35]	Liu J, Price DT, Chen JM (2005) Nitrogen controls on ecosystem carbon sequestration: a model implementation and application to Saskatchewan, Canada. Ecol Model 186: 178-195. doi: 10.1016/j.ecolmodel.2005.01.036
[36]	Zhu Q, Liu J, Peng C, et al. (2014) Modelling methane emissions from natural wetlands by development and application of the TRIPLEX-GHG model. Geosci Model Dev 7: 981-999. doi: 10.5194/gmd-7-981-2014
[37]	Anderson JR, Hardy EE, Roach JT, et al. (1976) A Land Use And Land Cover Classification System For Use With Remote Sensor Data. U.S. Geological Survey Professional Paper 964.
[38]	Loveland TR, Sohl TL, Stehman SV, et al. (2002) A strategy for estimating the rates of recent land cover changes in the United States. Photogramm Eng Rem S 68: 1091-1099.
[39]	Sleeter BM, Sohl TL, Loveland TR, et al. (2013) Land-cover change in the conterminous United States from 1973 to 2000. Global Environ Chang 23: 733-748. doi: 10.1016/j.gloenvcha.2013.03.006
[40]	Omernik JM (1987) Ecoregions of the Conterminous United States. Ann Assoc Am Geogr 77: 118-125. doi: 10.1111/j.1467-8306.1987.tb00149.x
[41]	Kim SJ, Flato GM, Boer GJ (2003) A coupled climate model simulation of the Last Glacial Maximum, Part 2: approach to equilibrium. Clim Dynam 20: 635-661.
[42]	Kim SJ, Flato GM, Boer GJ, et al. (2002) A coupled climate model simulation of the Last Glacial Maximum, Part 1: transient multi-decadal response. Clim Dynam 19: 515-537. doi: 10.1007/s00382-002-0243-y
[43]	Flato GM, Boer GJ (2001) Warming symmetry in climate change simulations. Geophys Res Lett 28: 195-198. doi: 10.1029/2000GL012121
[44]	Price DT, McKenney DW, Papadopol P, et al. (2004) High resolution future scenario climate data for North America; Proceedings of the American Meteorological Society 26th Conference on Agricultural and Forest Meteorology, Vancouver, BC, Canada, 23-26 August 2004. 13. http://ams.confex.com/ams/pdfpapers/78202.pdf
[45]	Cohen WB, Spies TA (1992) Estimating structural attributes of Douglas Fir/Western Hemlock forest stands from Landsat and SPOT imagery. Remote Sens Environ 41: 1-17. doi: 10.1016/0034-4257(92)90056-P
[46]	Kellndorfer J, Walker W, LaPoint E, et al. (2012) NACP Aboveground Biomass and Carbon Baseline Data (NBCD 2000), U.S.A., 2000. Available on-line at http://daac.ornl.gov: ORNL DAAC Oak Ridge, Tennessee, U.S.A.
[47]	Sleeter R, Acevedo W, Soulard CE, et al. (2015) Methods used to parameterize the spatially explicit components of a state and transition simulation model. AIMS Environ Sci [submitted].
[48]	Wilson BT, Woodall CW, Griffith DM (2013) Imputing forest carbon stock estimates from inventory plots to a nationally continuous coverage. Carbon Balance Manage 8: 1. doi: 10.1186/1750-0680-8-1
[49]	Zhu Z (ed) (2012) Baseline and Projected Future Carbon Storage and Greenhouse-Gas Fluxes in Ecosystems of the Western United States, U.S. Geological Survey Professional Paper 1797, 192 . Available from: http://pubs.usgs.gov/pp/1797/
[50]	Blackard J, Finco M, Helmer E, et al. (2008) Mapping U.S. forest biomass using nationwide forest inventory data and moderate resolution information. Remote Sens Environ 112: 1658-1677.
[51]	Soil Survey Staff, Natural Resources Conservation Service, United States Department of Agriculture. Soil Survey Geographic (SSURGO) Database. Available from: http://sdmdataaccess.nrcs.usda.gov/
[52]	Daly C, Halbleib M, Smith JI, et al. (2008) Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. Int J Climatol 28: 2031-2064. doi: 10.1002/joc.1688
[53]	Kurz WA, Apps MJ (1999) A 70-year retrospective analysis of carbon fluxes in the Canadian forest sector. Ecol Appl 9: 526-547. doi: 10.1890/1051-0761(1999)009[0526:AYRAOC]2.0.CO;2
[54]	Kurz WA, Dymond CC, White TM, et al. (2009) CBM-CFS3: A model of carbon-dynamics in forestry and land-use change implementing IPCC standards. Ecol Model 220: 480-504. doi: 10.1016/j.ecolmodel.2008.10.018
[55]	Bachelet D, Neilson RP, Lenihan JM, et al. (2001) Climate Change Effects on Vegetation Distribution and Carbon Budget in the United States. Ecosystems 4: 164-185. doi: 10.1007/s10021-001-0002-7
[56]	Westerling AL, Bryant BP (2007) Climate change and wildfire in California. Climatic Change 87: 231-249.

Reader Comments

your name:*

Email:*
© 2015 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)