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Lorenz Curve and Gini Coefficient Reveal Hot Spots and Hot Moments for Nitrous Oxide Emissions
Author(s) -
Saha Debasish,
Kemanian Armen R.,
Montes Felipe,
Gall Heather,
Adler Paul R.,
Rau Benjamin M.
Publication year - 2018
Publication title -
journal of geophysical research: biogeosciences
Language(s) - English
Resource type - Journals
eISSN - 2169-8961
pISSN - 2169-8953
DOI - 10.1002/2017jg004041
Subject(s) - gini coefficient , skewness , lorenz curve , greenhouse gas , atmospheric sciences , environmental science , coefficient of variation , spatial variability , nitrous oxide , spatial distribution , mathematics , inequality , statistics , chemistry , physics , geology , economic inequality , oceanography , mathematical analysis , organic chemistry
Identifying hot spots and hot moments of nitrous oxide (N 2 O) emissions in the landscape is critical for monitoring and mitigating the emission of this potent greenhouse gas. We propose a novel use of the Lorenz curve and Gini coefficient (G) to improve the estimation of the mean as well as the spatial and temporal variation of N 2 O emissions from a bioenergy landscape. The analyses indicate that the G was better correlated ( R 2 = 0.72, P < 0.001) with daily N 2 O emissions than the coefficient of variation and skewness. A hot moment for N 2 O emissions occurred after a storm event, with a heterogeneous spatial distribution of N 2 O emissions (G = 0.65); in contrast, CO 2 emissions remained spatially uniform throughout the same period (G = 0.36). Volumetric soil air content below 0.03 m 3 m −3 occurred more frequently in the wetter footslope positions and created N 2 O hot spots, with a high temporal inequality during the growing season (G = 0.75). In contrast, well‐drained shoulder positions were cold spots, with uniformly distributed and low N 2 O emissions (G = 0.44). The spatial N 2 O inequality mirrored the landscape wetness generated by rain events, while biogeochemical equality prevailed in the landscape. The Lorenz curve and G are tools to standardize the spatial and temporal variation of N 2 O emissions across diverse landscapes and management scenarios. These two inequality indicators, in association with spatial maps, can help delineate the critical spatial mosaics and temporal windows of N 2 O emissions and guide landscape‐scale monitoring and mitigation strategies to reduce N 2 O emissions.