Multi-scale geospatial agroecosystem modeling: A case study on the influence of soil data resolution on carbon budget estimates

Xuesong Zhang; Ritvik Sahajpal; David H. Manowitz; Kaiguang Zhao; Stephen D. LeDuc; Min Xu; Wei Xiong; Aiping Zhang; Roberto C. Izaurralde; Allison M. Thomson; Tristram O. West; Wilfred M. Post

doi:10.1016/j.scitotenv.2014.01.099

Multi-scale geospatial agroecosystem modeling: A case study on the influence of soil data resolution on carbon budget estimates

Xuesong Zhang, Ritvik Sahajpal, David H. Manowitz, Kaiguang Zhao, Stephen D. LeDuc, Min Xu, Wei Xiong, Aiping Zhang, Roberto C. Izaurralde, Allison M. Thomson, Tristram O. West, Wilfred M. Post

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24 Scopus citations

Abstract

The development of effective measures to stabilize atmospheric CO₂ concentration and mitigate negative impacts of climate change requires accurate quantification of the spatial variation and magnitude of the terrestrial carbon (C) flux. However, the spatial pattern and strength of terrestrial C sinks and sources remain uncertain. In this study, we designed a spatially-explicit agroecosystem modeling system by integrating the Environmental Policy Integrated Climate (EPIC) model with multiple sources of geospatial and surveyed datasets (including crop type map, elevation, climate forcing, fertilizer application, tillage type and distribution, and crop planting and harvesting date), and applied it to examine the sensitivity of cropland C flux simulations to two widely used soil databases (i.e. State Soil Geographic-STATSGO of a scale of 1:250,000 and Soil Survey Geographic-SSURGO of a scale of 1:24,000) in Iowa, USA. To efficiently execute numerous EPIC runs resulting from the use of high resolution spatial data (56m), we developed a parallelized version of EPIC. Both STATSGO and SSURGO led to similar simulations of crop yields and Net Ecosystem Production (NEP) estimates at the State level. However, substantial differences were observed at the county and sub-county (grid) levels. In general, the fine resolution SSURGO data outperformed the coarse resolution STATSGO data for county-scale crop-yield simulation, and within STATSGO, the area-weighted approach provided more accurate results. Further analysis showed that spatial distribution and magnitude of simulated NEP were more sensitive to the resolution difference between SSURGO and STATSGO at the county or grid scale. For over 60% of the cropland areas in Iowa, the deviations between STATSGO- and SSURGO-derived NEP were larger than 1MgCha^-1yr^-1, or about half of the average cropland NEP, highlighting the significant uncertainty in spatial distribution and magnitude of simulated C fluxes resulting from differences in soil data resolution.

Original language	English
Pages (from-to)	138-150
Number of pages	13
Journal	Science of the Total Environment
Volume	479-480
Issue number	1
DOIs	https://doi.org/10.1016/j.scitotenv.2014.01.099
State	Published - May 1 2014
Externally published	Yes

Funding

We sincerely appreciate the valuable comments provided by the anonymous reviewers, which greatly improved the quality of this paper. This work was partially funded by the DOE Great Lakes Bioenergy Research Center ( DOE BER Office of Science DE-FC02-07ER64494 , DOE BER Office of Science KP1601050 , DOE EERE OBP 20469-19145 ), NASA ( NNH08ZDA001N and NNH12AU03I ), and USDA ( CSREES-2009-34263-19774 (G-1449-1) and NIFA-2010-34263-21075 (G-1470-3) ). The views expressed here are those of the authors and do not necessarily represent the views or policies of the U.S. Environmental Protection Agency.

Keywords

Climate change
EPIC
Net Ecosystem Production
Parallel computing
SSURGO
STATSGO
Spatial resolution

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10.1016/j.scitotenv.2014.01.099

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Zhang, X., Sahajpal, R., Manowitz, D. H., Zhao, K., LeDuc, S. D., Xu, M., Xiong, W., Zhang, A., Izaurralde, R. C., Thomson, A. M., West, T. O., & Post, W. M. (2014). Multi-scale geospatial agroecosystem modeling: A case study on the influence of soil data resolution on carbon budget estimates. Science of the Total Environment, 479-480(1), 138-150. https://doi.org/10.1016/j.scitotenv.2014.01.099

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title = "Multi-scale geospatial agroecosystem modeling: A case study on the influence of soil data resolution on carbon budget estimates",

abstract = "The development of effective measures to stabilize atmospheric CO2 concentration and mitigate negative impacts of climate change requires accurate quantification of the spatial variation and magnitude of the terrestrial carbon (C) flux. However, the spatial pattern and strength of terrestrial C sinks and sources remain uncertain. In this study, we designed a spatially-explicit agroecosystem modeling system by integrating the Environmental Policy Integrated Climate (EPIC) model with multiple sources of geospatial and surveyed datasets (including crop type map, elevation, climate forcing, fertilizer application, tillage type and distribution, and crop planting and harvesting date), and applied it to examine the sensitivity of cropland C flux simulations to two widely used soil databases (i.e. State Soil Geographic-STATSGO of a scale of 1:250,000 and Soil Survey Geographic-SSURGO of a scale of 1:24,000) in Iowa, USA. To efficiently execute numerous EPIC runs resulting from the use of high resolution spatial data (56m), we developed a parallelized version of EPIC. Both STATSGO and SSURGO led to similar simulations of crop yields and Net Ecosystem Production (NEP) estimates at the State level. However, substantial differences were observed at the county and sub-county (grid) levels. In general, the fine resolution SSURGO data outperformed the coarse resolution STATSGO data for county-scale crop-yield simulation, and within STATSGO, the area-weighted approach provided more accurate results. Further analysis showed that spatial distribution and magnitude of simulated NEP were more sensitive to the resolution difference between SSURGO and STATSGO at the county or grid scale. For over 60% of the cropland areas in Iowa, the deviations between STATSGO- and SSURGO-derived NEP were larger than 1MgCha-1yr-1, or about half of the average cropland NEP, highlighting the significant uncertainty in spatial distribution and magnitude of simulated C fluxes resulting from differences in soil data resolution.",

keywords = "Climate change, EPIC, Net Ecosystem Production, Parallel computing, SSURGO, STATSGO, Spatial resolution",

author = "Xuesong Zhang and Ritvik Sahajpal and Manowitz, {David H.} and Kaiguang Zhao and LeDuc, {Stephen D.} and Min Xu and Wei Xiong and Aiping Zhang and Izaurralde, {Roberto C.} and Thomson, {Allison M.} and West, {Tristram O.} and Post, {Wilfred M.}",

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Zhang, X, Sahajpal, R, Manowitz, DH, Zhao, K, LeDuc, SD, Xu, M, Xiong, W, Zhang, A, Izaurralde, RC, Thomson, AM, West, TO & Post, WM 2014, 'Multi-scale geospatial agroecosystem modeling: A case study on the influence of soil data resolution on carbon budget estimates', Science of the Total Environment, vol. 479-480, no. 1, pp. 138-150. https://doi.org/10.1016/j.scitotenv.2014.01.099

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AU - Sahajpal, Ritvik

AU - Manowitz, David H.

AU - Zhao, Kaiguang

AU - LeDuc, Stephen D.

AU - Xu, Min

AU - Xiong, Wei

AU - Zhang, Aiping

AU - Izaurralde, Roberto C.

AU - Thomson, Allison M.

AU - West, Tristram O.

AU - Post, Wilfred M.

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Multi-scale geospatial agroecosystem modeling: A case study on the influence of soil data resolution on carbon budget estimates

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