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Title: Land carbon sequestration within the conterminous United States: Regional- and state-level analyses: Regional U.S. land sinks and legacies

 [1];  [1];  [1];  [2];  [2]
  1. The Ecosystems Center, Marine Biological Laboratory, Woods Hole Massachusetts USA
  2. Joint Program on the Science and Policy of Global Change, Massachusetts Institute of Technology, Cambridge Massachusetts USA
Publication Date:
Sponsoring Org.:
OSTI Identifier:
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Resource Type:
Journal Article: Publisher's Accepted Manuscript
Journal Name:
Journal of Geophysical Research. Biogeosciences
Additional Journal Information:
Journal Volume: 120; Journal Issue: 2; Related Information: CHORUS Timestamp: 2017-10-23 18:11:16; Journal ID: ISSN 2169-8953
Wiley Blackwell (John Wiley & Sons)
Country of Publication:
United States

Citation Formats

Lu, Xiaoliang, Kicklighter, David W., Melillo, Jerry M., Reilly, John M., and Xu, Liyi. Land carbon sequestration within the conterminous United States: Regional- and state-level analyses: Regional U.S. land sinks and legacies. United States: N. p., 2015. Web. doi:10.1002/2014JG002818.
Lu, Xiaoliang, Kicklighter, David W., Melillo, Jerry M., Reilly, John M., & Xu, Liyi. Land carbon sequestration within the conterminous United States: Regional- and state-level analyses: Regional U.S. land sinks and legacies. United States. doi:10.1002/2014JG002818.
Lu, Xiaoliang, Kicklighter, David W., Melillo, Jerry M., Reilly, John M., and Xu, Liyi. 2015. "Land carbon sequestration within the conterminous United States: Regional- and state-level analyses: Regional U.S. land sinks and legacies". United States. doi:10.1002/2014JG002818.
title = {Land carbon sequestration within the conterminous United States: Regional- and state-level analyses: Regional U.S. land sinks and legacies},
author = {Lu, Xiaoliang and Kicklighter, David W. and Melillo, Jerry M. and Reilly, John M. and Xu, Liyi},
abstractNote = {},
doi = {10.1002/2014JG002818},
journal = {Journal of Geophysical Research. Biogeosciences},
number = 2,
volume = 120,
place = {United States},
year = 2015,
month = 2

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record at 10.1002/2014JG002818

Citation Metrics:
Cited by: 4works
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  • Protected areas (PAs) cover about 22% of the conterminous United States. Understanding their role on historical land use and land cover change (LULCC) and on the carbon cycle is essential to provide guidance for environmental policies. In this study, we compiled historical LULCC and PAs data to explore these interactions within the terrestrial ecosystem model (TEM). We found that intensive LULCC occurred in the conterminous United States from 1700 to 2005. More than 3 million km2 of forest, grassland and shrublands were converted into agricultural lands, which caused 10,607 Tg C release from land ecosystems to atmosphere. PAs had experiencedmore » little LULCC as they were generally established in the 20th century after most of the agricultural expansion had occurred. PAs initially acted as a carbon source due to land use legacies, but their accumulated carbon budget switched to a carbon sink in the 1960s, sequestering an estimated 1,642 Tg C over 1700–2005, or 13.4% of carbon losses in non-PAs. We also find that PAs maintain larger carbon stocks and continue sequestering carbon in recent years (2001–2005), but at a lower rate due to increased heterotrophic respiration as well as lower productivity associated to aging ecosystems. It is essential to continue efforts to maintain resilient, biodiverse ecosystems and avoid large-scale disturbances that would release large amounts of carbon in PAs.« less
  • Representing agricultural systems explicitly in Earth system models is important for understanding the water-energy-food nexus under climate change. In this study, we applied Version 4.5 of the Community Land Model (CLM) at a 0.125 degree resolution to provide the first county-scale validation of the model in simulating crop yields over the Conterminous United States (CONUS). We focused on corn and soybean that are both important grain crops and biofuel feedstocks (corn for bioethanol; soybean for biodiesel). We find that the default model substantially under- or over-estimate yields of corn and soybean as compared to the US Department of Agriculture (USDA)more » census data, with corresponding county-level root-mean square error (RMSE) of 45.3 Bu/acre and 12.9 Bu/acre, or 42% and 38% of the US mean yields for these crops, respectively. Based on the numerical experiments, the lack of proper representation of agricultural management practices, such as irrigation and fertilization, was identified as a major cause for the model's poor performance. After implementing an irrigation management scheme calibrated against county-level US Geological Survey (USGS) census data, the county-level RMSE for corn yields reduced to 42.6 Bu/acre. We then incorporated an optimized fertilizer scheme in rate and timing, which is achieved by the constraining annual total fertilizer amount against the USDA data, considering the dynamics between fertilizer demand and supply and adopting a calibrated fertilizer scheduling map. The proposed approach is shown to be effective in increasing the fertilizer use efficiency for corn yields, with county-level RMSE reduced to 23.8 Bu/acre (or 22% of the US mean yield). In regions with similar annual fertilizer applied as in the default, the improvements in corn yield simulations are mainly attributed to application of longer fertilization periods and consideration of the dynamics between fertilizer demand and supply. For soybean which is capable of fixing nitrogen to meet nitrogen demand, the reduced positive bias to 6.9 Bu/acre (or 21% of the country mean) was mainly attributed to consideration of the dynamic interactions between fertilizer demand and supply. Although large bias remains in terms of the spatial pattern (i.e. high county-level RMSE), mainly due to limited performance over the Western US, our results show that optimizing irrigation and fertilization can lead to promising improvement in crop and soybean yield simulations in terms of the mean and variability especially over the Mid-west corn belt, and subsequent evapotranspiration (ET) estimates. Finally, this study demonstrates the CLM4.5 capability for predicting crop yields and their interactions with climate, and highlights the value of continued model improvements and development to understand biogeophysical and biogeochemical impacts of land use and land cover change using an Earth system modeling framework.« less
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  • Previous studies on irrigation impacts on land surface fluxes/states were mainly conducted as sensitivity experiments, with limited analysis of uncertainties from the input data and model irrigation schemes used. In this study, we calibrated and evaluated the performance of irrigation water use simulated by the Community Land Model version 4 (CLM4) against observations from agriculture census. We investigated the impacts of irrigation on land surface fluxes and states over the conterminous United States (CONUS) and explored possible directions of improvement. Specifically, we found large uncertainty in the irrigation area data from two widely used sources and CLM4 tended to producemore » unrealistically large temporal variations of irrigation demand for applications at the water resources region scale over CONUS. At seasonal to interannual time scales, the effects of irrigation on surface energy partitioning appeared to be large and persistent, and more pronounced in dry than wet years. Even with model calibration to yield overall good agreement with the irrigation amounts from the National Agricultural Statistics Service (NASS), differences between the two irrigation area datasets still dominate the differences in the interannual variability of land surface response to irrigation. Our results suggest that irrigation amount simulated by CLM4 can be improved by (1) calibrating model parameter values to account for regional differences in irrigation demand and (2) accurate representation of the spatial distribution and intensity of irrigated areas.« less