Downscaling global land-use/land-cover projections for use in region-level state-and-transition simulation modeling

Jason T. Sherba; Benjamin M. Sleeter; Adam W. Davis; Owen Parker; Jason T. Sherba; Benjamin M. Sleeter; Adam W. Davis; Owen Parker

doi:10.3934/environsci.2015.3.623

AIMS Environmental Science

2015, Volume 2, Issue 3: 623-647. doi: 10.3934/environsci.2015.3.623

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Research article Special Issues

Downscaling global land-use/land-cover projections for use in region-level state-and-transition simulation modeling

US Geological Survey, Western Geographic Science Center, 345 Middlefield Road, Menlo Park, CA 94025, USA

Received: 30 January 2015 Accepted: 18 June 2015 Published: 23 June 2015

Global land-use/land-cover (LULC) change projections and historical datasets are typically available at coarse grid resolutions and are often incompatible with modeling applications at local to regional scales. The difficulty of downscaling and reapportioning global gridded LULC change projections to regional boundaries is a barrier to the use of these datasets in a state-and-transition simulation model (STSM) framework. Here we compare three downscaling techniques to transform gridded LULC transitions into spatial scales and thematic LULC classes appropriate for use in a regional STSM. For each downscaling approach, Intergovernmental Panel on Climate Change (IPCC) Representative Concentration Pathway (RCP) LULC projections, at the 0.5 × 0.5 cell resolution, were downscaled to seven Level III ecoregions in the Pacific Northwest, United States. RCP transition values at each cell were downscaled based on the proportional distribution between ecoregions of (1) cell area, (2) land-cover composition derived from remotely-sensed imagery, and (3) historic LULC transition values from a LULC history database. Resulting downscaled LULC transition values were aggregated according to their bounding ecoregion and “cross-walked” to relevant LULC classes. Ecoregion-level LULC transition values were applied in a STSM projecting LULC change between 2005 and 2100. While each downscaling methods had advantages and disadvantages, downscaling using the historical land-use history dataset consistently apportioned RCP LULC transitions in agreement with historical observations. Regardless of the downscaling method, some LULC projections remain improbable and require further investigation.
- Representative Concentration Pathways (RCP),
- downscaling,
- state-and-transition simulation modeling,
- scenarios,
- land-use
Citation: Jason T. Sherba, Benjamin M. Sleeter, Adam W. Davis, Owen Parker. Downscaling global land-use/land-cover projections for use in region-level state-and-transition simulation modeling[J]. AIMS Environmental Science, 2015, 2(3): 623-647. doi: 10.3934/environsci.2015.3.623

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Abstract

Global land-use/land-cover (LULC) change projections and historical datasets are typically available at coarse grid resolutions and are often incompatible with modeling applications at local to regional scales. The difficulty of downscaling and reapportioning global gridded LULC change projections to regional boundaries is a barrier to the use of these datasets in a state-and-transition simulation model (STSM) framework. Here we compare three downscaling techniques to transform gridded LULC transitions into spatial scales and thematic LULC classes appropriate for use in a regional STSM. For each downscaling approach, Intergovernmental Panel on Climate Change (IPCC) Representative Concentration Pathway (RCP) LULC projections, at the 0.5 × 0.5 cell resolution, were downscaled to seven Level III ecoregions in the Pacific Northwest, United States. RCP transition values at each cell were downscaled based on the proportional distribution between ecoregions of (1) cell area, (2) land-cover composition derived from remotely-sensed imagery, and (3) historic LULC transition values from a LULC history database. Resulting downscaled LULC transition values were aggregated according to their bounding ecoregion and “cross-walked” to relevant LULC classes. Ecoregion-level LULC transition values were applied in a STSM projecting LULC change between 2005 and 2100. While each downscaling methods had advantages and disadvantages, downscaling using the historical land-use history dataset consistently apportioned RCP LULC transitions in agreement with historical observations. Regardless of the downscaling method, some LULC projections remain improbable and require further investigation.

References

[1]	Zaehle S, Bondeau A, Carter TR, et al. (2007) Projected changes in terrestrial carbon storage in Europe under climate and land-use change, 1990-2100. Ecosystems 10: 380-401. doi: 10.1007/s10021-007-9028-9
[2]	Kim J, Choi J, Choi C, et al. (2013) Impacts of changes in climate and land use/land cover under IPCC RCP scenarios on streamflow in the Hoeya River Basin, Korea. Sci Total Environ 452: 181-195.
[3]	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
[4]	Hurtt G, Chini LP, Frolking S, et al. (2011) Harmonization of land-use scenarios for the period 1500-2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands. Climatic Change 109: 117-161. doi: 10.1007/s10584-011-0153-2
[5]	Pitcher HM (2009) The future of scenarios: issues in developing new climate change scenarios. Environ Res Lett 4: 1-7.
[6]	Nakicenovic N, Swart R (2000) Special report on emission scenarios. Edited by Nakicenovic N, Swart R, pp. 612. ISBN 0521804930. Cambridge, UK: Cambridge University Press.
[7]	Lambin EF, Turner BL, Geist HJ, et al. (2001) The causes of land-use and land-cover change: moving beyond the myths. Global Environ Chang 11: 261-269. doi: 10.1016/S0959-3780(01)00007-3
[8]	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
[9]	Van Vuuren D, Edmonds J, Kainuma M, et al. (2011) The representative concentration pathways: an overview. Climatic Change 109: 5-31. doi: 10.1007/s10584-011-0148-z
[10]	Van Vuuren DP, Smith SJ, Riahi K (2010) Downscaling socioeconomic and emissions scenarios for global environmental change research: a review. Wiley Interdiscip Rev Clim Change 1: 393-404. doi: 10.1002/wcc.50
[11]	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
[12]	Dendoncker N, Bogaert P, Rounsevell M (2006) A statistical method to downscale aggregated land use data and scenarios. J Land Use Sci 1: 63-82. doi: 10.1080/17474230601058302
[13]	West T, Le Page Y, Huang M, et al. (2014) Downscaling global land cover projections from an integrated assessment model for use in regional analyses: results and evaluation for the US from 2005 to 2095. Environ Res Lett 9: 064004. doi: 10.1088/1748-9326/9/6/064004
[14]	Rounsevell M, Ewert F, Reginster I, et al. (2005) Future scenarios of European agricultural land use: II. Projecting changes in cropland and grassland. Agr Ecosyst Environ 107: 117-135.
[15]	Abildtrup J, Audsley E, Fekete-Farkas M, et al. (2006) Socio-economic scenario development for the assessment of climate change impacts on agricultural land use: a pairwise comparison approach. Environ Sci Policy 9: 101-115. doi: 10.1016/j.envsci.2005.11.002
[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]	Sohl TL, Sleeter BM, Zhu Z, et al. (2012) A land-use and land-cover modeling strategy to support a national assessment of carbon stocks and fluxes. Appl Geogr 34: 111-124. doi: 10.1016/j.apgeog.2011.10.019
[18]	IPCC (2013) Working Group I Contribution to the IPCC Fifth Assessment Report (AR5), Climate Change 2013: The Physical Science Basis. Intergovernmental Panel on Climate Change, Geneva, Switzerland.
[19]	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
[20]	Gallant AL, Loveland TR, Sohl TL, et al. (2004) Using an ecoregion framework to analyze land-cover and land-use dynamics. Environ Manage 34: 89-110. doi: 10.1007/s00267-003-0145-3
[21]	Wilson TS, Sleeter BM, Sleeter RR, et al. (2014) Land-use threats and protected areas: A scenario-based, landscape level approach. Land 3: 362-389. doi: 10.3390/land3020362
[22]	Masui T, Matsumoto K, Hijioka Y, et al. (2011) An emission pathway for stabilization at 6 Wm- 2 radiative forcing. Climatic Change 109: 59-76. doi: 10.1007/s10584-011-0150-5
[23]	Sleeter BM, Wilson TS, Acevedo W, Status and trends of land change in the Western United States—1973 to 2000. U.S. Geological Survey, 2012. Available from: http://pubs.usgs.gov/pp/1794/a/
[24]	Hurtt GC, Chini LP, Frolking S, et al. (2009) Harmonization of global land-use scenarios for the period 1500-2100 for IPCC-AR5. ILEAPS Newslett 7: 6-8.
[25]	Soulard CE, Acevedo W, Auch RF, et al., Land Cover Trends Dataset, 1973-2000. U.S. Geological Survey, 2014. Available from: http://dx.doi.org/10.3133/ds844
[26]	Fry JA, Xian G, Jin S, et al. (2011) Completion of the 2006 national land cover database for the conterminous United States. Photogramm Eng Rem S 77: 858-864.
[27]	Loveland T, Sohl T, Stehman S, et al. (2002) A Strategy for Estimating the Rates of Recent United States Land-Cover Changes. Photogramm Eng Rem S 68: 1091-1099.
[28]	Blankenship K, Smith J, Swaty R, et al. (2012) Modeling on the Grand Scale: LANDFIRE Lessons Learned. In: Kerns, BK, Shlisky, AJ, Daniel, CJ, editors. Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14-16, 2011. Gen Tech Rep PNW-GTR-869. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station: 43-56.
[29]	Creutzburg MK, Halofsky JS, Hemstrom MA (2012) Using state-and-transition models to project cheatgrass and juniper invasion in Southeastern Oregon sagebrush steppe. In: Kerns, BK, Shlisky, AJ, Daniel, CJ, editors. Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14-16, 2011. Gen Tech Rep PNW-GTR-869. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station: 73-84.
[30]	Duncan JA, Burcsu T (2012) Landscape development and mule deer habitat in Central Oregon. In: Kerns, BK, Shlisky, AJ, Daniel, CJ, editors. Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14-16, 2011. Gen Tech Rep PNW-GTR-869. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station: 85-102.
[31]	Daniel CJ, Frid L (2012) Predicting landscape vegetation dynamics using state-and-transition simulation models. In: Kerns, BK, Shlisky, AJ, Daniel, CJ, editors. Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14-16, 2011. Gen Tech Rep PNW-GTR-869. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station: 5-22.
[32]	Shlisky AJ, Vandendriesche D (2012) Use of state-and-transition simulation modeling in National Forest planning in the Pacific Northwest, U.S.A. In: Kerns, BK, Shlisky, AJ, Daniel, CJ, editors. Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14-16, 2011. Gen Tech Rep PNW-GTR-869. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station: 23-42.
[33]	Kerns BK, Hemstrom MA, Conklin D, et al. (2012) Approaches to incorporating climate change effects in state and transition simulation models of vegetation. In: Kerns, BK, Shlisky, AJ, Daniel, CJ, editors. Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14-16, 2011. Gen Tech Rep PNW-GTR-869. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station: 161-172.
[34]	Hemstrom MA, Halofsky JE, Conklin DR, et al. (2014) Chapter 7: Developing climate-informed state-and-transition models. In: Halofsky JE, Creutzburg MK, Hemstrom MA, editors. Integrating social, economic, and ecological values across large landscapes. Gen Tech Rep PNW-GTR-896. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station: 175-202.
[35]	Apex Resource Management Solutions Ltd., ST-Sim: State-and-Transition Simulation Model Framework. 2014. Available from: http://www.apexrms.com/.
[36]	Sleeter RR, Acevedo C, Soulard CE, et al. (2015) Methods used to parameterize the spatially-explicit components of a state-and-transition simulation model. AIMS Environ Sci [accepted].
[37]	38. Huang B, Zhang L, Wu B (2009) Spatiotemporal analysis of rural-urban land conversion. Int J Geogr Inf Sci 23: -398. doi: 10.1080/13658810802119685
[38]	Jin S, Sader SA (2006) Effects of forest ownership and change on forest harvest rates, types and trends in northern Maine. Forest Ecol Manag 228: 177-186. doi: 10.1016/j.foreco.2006.03.009
[39]	Giri C, Zhu Z, Reed B (2005) A comparative analysis of the Global Land Cover 2000 and MODIS land cover data sets. Remote Sens Environ 94: 123-132. doi: 10.1016/j.rse.2004.09.005
[40]	Lambin EF, Turner BL, Geist HJ, et al. (2001) The causes of land-use and land-cover change: moving beyond the myths. Global Environ Chang 11: 261-269. doi: 10.1016/S0959-3780(01)00007-3
[41]	Goldewijk KK (2001) Estimating global land use change over the past 300 years: the HYDE database. Global Biogeochem Cy 15: 417-433. doi: 10.1029/1999GB001232

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