International Center for Climate and Global Change Research CHESS Cluster Leading Center

Increased global nitrous oxide emissions from streams and rivers in the Anthropocene

Yuanzhi Yao, Hanqin Tian, Hao Shi, Shufen Pan, Rongting Xu, Naiqing Pan, Josep G Canadell
Nature Climate Change
Emissions of nitrous oxide (N 2 O) from the world’s river networks constitute a poorly constrained term in the global N 2 O budget 1, 2. This N 2 O component was previously estimated as indirect emissions from agricultural soils 3 with large uncertainties 4, 5, 6, 7, 8, 9, 10. Here, we present an improved model representation of nitrogen and N 2 O processes of the land–ocean aquatic continuum 11 constrained with an ensemble of 11 data products. The model–data framework provides a quantification for how changes in nitrogen inputs (fertilizer, deposition and manure), climate and atmospheric CO 2 concentration, and terrestrial processes have affected the N 2 O emissions from the world’s streams and rivers during 1900–2016. The results show a fourfold increase of global riverine N 2 O emissions from 70.4±15.4 Gg N 2 ON yr− 1 in 1900 to 291.3±58.6 Gg N 2 ON yr− 1 in 2016, although the N 2 O emissions started …

Global soil nitrous oxide emissions since the preindustrial era estimated by an ensemble of terrestrial biosphere models: Magnitude, attribution, and uncertainty

Hanqin Tian, Jia Yang, Rongting Xu, Chaoqun Lu, Josep G Canadell, Eric A Davidson, Robert B Jackson, Almut Arneth, Jinfeng Chang, Philippe Ciais, Stefan Gerber, Akihiko Ito, Fortunat Joos, Sebastian Lienert, Palmira Messina, Stefan Olin, Shufen Pan, Changhui Peng, Eri Saikawa, Rona L Thompson, Nicolas Vuichard, Wilfried Winiwarter, Sönke Zaehle, Bowen Zhang
Global change biology
Our understanding and quantification of global soil nitrous oxide (N2O) emissions and the underlying processes remain largely uncertain. Here, we assessed the effects of multiple anthropogenic and natural factors, including nitrogen fertilizer (N) application, atmospheric N deposition, manure N application, land cover change, climate change, and rising atmospheric CO2 concentration, on global soil N2O emissions for the period 1861–2016 using a standard simulation protocol with seven process‐based terrestrial biosphere models. Results suggest global soil N2O emissions have increased from 6.3 ± 1.1 Tg N2O‐N/year in the preindustrial period (the 1860s) to 10.0 ± 2.0 Tg N2O‐N/year in the recent decade (2007–2016). Cropland soil emissions increased from 0.3 Tg N2O‐N/year to 3.3 Tg N2O‐N/year over the same period, accounting for 82% of the total increase. Regionally, China, South Asia, and Southeast …

Impacts of tillage practices on soil carbon stocks in the US corn-soybean cropping system during 1998 to 2016

Authors: Zhen Yu, Chaoqun Lu, David A Hennessy, Hongli Feng, Hanqin Tian

Journal: Environmental Research Letters

Description: Tillage alters the thermal and wetness conditions in soil, which facilitates soil organic matter oxidation and water transportation, leading to rapid depletion of soil carbon (C). Little is known about tillage intensity change (TIC) and its impacts in the US corn-soybean rotation system over the past two decades. Using time-series tillage maps developed from a private survey and a process-based land ecosystem model, here we examined how tillage intensity has changed across the nation and affected soil organic carbon (SOC) storage from 1998 to 2016. Results derived from the combination of tillage survey data and cropland distribution maps show that total corn-soybean area consistently increased from 62.3 Mha in 1998 to 66.8 Mha in 2008 and to 73.1 Mha in 2016, among which the acreage under no-till system increased from 16.9 Mha in 1998 to 28.9 Mha in 2008, while conservation and conventional tillage …

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Global manure nitrogen production and application in cropland during 1860–2014

Global manure nitrogen production and application in cropland during 1860–2014: a 5 arcmin gridded global dataset for Earth system modeling


Bowen Zhang, Hanqin Tian, Chaoqun Lu, Shree R. S. Dangal, Jia Yang, and Shufen Pan


Abstract. Given the important role of nitrogen input from livestock systems in terrestrial nutrient cycles and the atmospheric chemical composition, it is vital to have a robust estimation of the magnitude and spatiotemporal variation in manure nitrogen production and its application to cropland across the globe. In this study, we used the dataset from the Global Livestock Impact Mapping System (GLIMS) in conjunction with country-specific annual livestock populations to reconstruct the manure nitrogen production during 1860–2014. The estimated manure nitrogen production increased from 21.4 Tg N yr−1 in 1860 to 131.0 Tg N yr−1 in 2014 with a significant annual increasing trend (0.7 Tg N yr−1p < 0.01). Changes in manure nitrogen production exhibited high spatial variability and concentrated in several hotspots (e.g., Western Europe, India, northeastern China, and southeastern Australia) across the globe over the study period. In the 1860s, the northern midlatitude region was the largest manure producer, accounting for ∼ 52 % of the global total, while low-latitude regions became the largest share (∼ 48 %) in the most recent 5 years (2010–2014). Among all the continents, Asia accounted for over one-fourth of the global manure production during 1860–2014. Cattle dominated the manure nitrogen production and contributed ∼ 44 % of the total manure nitrogen production in 2014, followed by goats, sheep, swine, and chickens. The manure nitrogen application to cropland accounts for less than one-fifth of the total manure nitrogen production over the study period. The 5 arcmin gridded global dataset of manure nitrogen production generated from this study could be used as an input for global or regional land surface and ecosystem models to evaluate the impacts of manure nitrogen on key biogeochemical processes and water quality. To ensure food security and environmental sustainability, it is necessary to implement proper manure management practices on cropland across the globe. Datasets are available at (Zhang et al., 2017).

Methane emissions from global wetlands

Methane emissions from global wetlands: An assessment of the uncertainty associated with various wetland extent data sets
Bowen Zhang, Hanqin Tian, Chaoqun Lu, Guangsheng Chen, Shufen Pan, Christopher Anderson, Benjamin Poulter

A wide range of estimates on global wetland methane (CH4) fluxes has been reported during the recent two decades. This gives rise to urgent needs to clarify and identify the uncertainty sources, and conclude a reconciled estimate for global CH4 fluxes from wetlands. Most estimates by using bottom-up approach rely on wetland data sets, but these data sets show largely inconsistent in term of both wetland extent and spatiotemporal distribution. A quantitative assessment of uncertainties associated with these discrepancies among wetland data sets has not been well investigated yet. By comparing the five widely used global wetland data sets (GISS, GLWD, Kaplan, GIEMS and SWAMPS-GLWD), in this study, we found large differences in the wetland extent, ranging from 5.3 to 10.2 million km2, as well as their spatial and temporal distributions among the five data sets. These discrepancies in wetland data sets resulted in large bias in model-estimated global wetland CH4 emissions as simulated by using the Dynamic Land Ecosystem Model (DLEM). The model simulations indicated that the mean global wetland CH4 emissions during 2000-2007 were 177.2 ± 49.7 Tg CH4 yr-1, based on the five different data sets. The tropical regions contributed the largest portion of estimated CH4 emissions from global wetlands, but also have the largest discrepancy. Among six continents, the largest uncertainty was found in South America. Thus, the improved estimates of wetland extent and CH4 emissions in the tropical regions and South America would be a critical step toward an accurate estimate of global CH4 emissions. This uncertainty analysis also reveals an important need for our scientific community to generate a global scale wetland data set with higher spatial resolution and shorter time interval, by integrating multiple sources of field and satellite data with modeling approaches, for cross-scale extrapolation.