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Predicting climate change effects on surface soil organic carbon of Louisiana, USA

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Abstract

This study aimed to assess the degree of potential temperature and precipitation change as predicted by the HadCM3 (Hadley Centre Coupled Model, version 3) climate model for Louisiana, and to investigate the effects of potential climate change on surface soil organic carbon (SOC) across Louisiana using the Rothamsted Carbon Model (RothC) and GIS techniques at the watershed scale. Climate data sets at a grid cell of 0.5° × 0.5° for the entire state of Louisiana were collected from the HadCM3 model output for three climate change scenarios: B2, A2, and A1F1, that represent low, higher, and even higher greenhouse gas emissions, respectively. Geo-referenced datasets including USDA-NRCS Soil Geographic Database (STATSGO), USGS Land Cover Dataset (NLCD), and the Louisiana watershed boundary data were gathered for SOC calculation at the watershed scale. A soil carbon turnover model, RothC, was used to simulate monthly changes in SOC from 2001 to 2100 under the projected temperature and precipitation changes. The simulated SOC changes in 253 watersheds from three time periods, 2001–2010, 2041–2050, and 2091–2100, were tested for the influence of the land covers and emissions scenarios using SAS PROC GLIMMIX and PDMIX800 macro to separate Tukey-Kramer (p < 0.01) adjusted means into letter comparisons. The study found that for most of the next 100 years in Louisiana, monthly mean temperature under all three emissions projections will increase; and monthly precipitation will, however, decrease. Under three emission scenarios, A1FI, A2, and B2, the mean SOC in the upper 30-cm depth of Louisiana forest soils will decrease from 33.0 t/ha in 2001 to 26.9, 28.4, and 29.2 t/ha in 2100, respectively; the mean SOC of Louisiana cropland soils will decrease from 44.4 t/ha in 2001 to 36.3, 38.4, and 39.6 t/ha in 2100, respectively; the mean SOC of Louisiana grassland soils will change from 30.7 t/ha in 2001 to 25.4, 26.6, and 27.0 t/ha in 2100, respectively. Annual SOC changes will be significantly different among the land cover classes including evergreen forest, mixed forest, deciduous forest, small grains, row crops, and pasture/hay (p < 0.0001), emissions scenarios (p < 0.0001), and their interactions (p < 0.0001).

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Acknowledgments

Special thanks go to the Center for Computation & Technology of the Louisiana State University for its support with the climate data computation. We would also like to thank Jo Smith and Pete Smith from University of Aberdeen, UK, for their invaluable advice during the RothC modeling. The Climate Research Unit (CRU) in the School of Environmental Sciences at the University of East Anglia, UK, provided climate data. This research was supported through a Louisiana Board of Regents grant, Contract No.: LEQSF (2004–2007)-RD-A-04.

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  1. JiangSu Key Laboratory of Public Project Audit, School of Technology, Nanjing Audit University, No. 86 West Yushan Road, Pukou District, Nanjing, JiangSu Province, 211815, People’s Republic of China

    Biao Zhong

  2. School of Renewable Natural Resources, Louisiana State University Agricultural Center, Baton Rouge, LA, 70803, USA

    Biao Zhong & Yi Jun Xu

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  1. Biao Zhong
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  2. Yi Jun Xu
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Zhong, B., Xu, Y.J. Predicting climate change effects on surface soil organic carbon of Louisiana, USA. Environ Monit Assess 186, 6169–6192 (2014). https://doi.org/10.1007/s10661-014-3847-x

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