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Open AccessArticle

Hot-Moments of Soil CO2 Efflux in a Water-Limited Grassland

1
Department of Plant & Soil Sciences, University of Delaware, Newark, DE 19716, USA
2
Departamento de Física Aplicada, Universidad de Granada, 18071 Granada, Spain
3
Instituto Interuniversitario de Investigación del Sistema Tierra en Andalucía, Centro Andaluz del Medio Ambiente (IISTA-CEAMA), 18071 Granada, Spain
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Departamento de Ecología, Universidad de Granada, 18071 Granada, Spain
5
BC3—Basque Centre for Climate Change, Scientific Campus of the University of the Basque Country, 48940 Leioa, Spain
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IKERBASQUE—Basque Foundation for Science, Maria Diaz de Haro 3, 6 Solairua, 48013 Bilbao, Spain
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Departamento de Desertificación y Geo-ecología, Estación Experimental de Zonas Áridas (EEZA-CSIC), 04120 Almería, Spain
8
Departamento de Agronomía, Universidad de Almería, 04120 Almería, Spain
9
Centro Andaluz para la Evaluación y el Seguimiento del Cambio Global (CAESCG), 04120 Almería, Spain
*
Author to whom correspondence should be addressed.
Soil Syst. 2018, 2(3), 47; https://doi.org/10.3390/soilsystems2030047
Received: 8 June 2018 / Revised: 27 July 2018 / Accepted: 1 August 2018 / Published: 8 August 2018
(This article belongs to the Special Issue Formation and Fluxes of Soil Trace Gases)
The metabolic activity of water-limited ecosystems is strongly linked to the timing and magnitude of precipitation pulses that can trigger disproportionately high (i.e., hot-moments) ecosystem CO2 fluxes. We analyzed over 2-years of continuous measurements of soil CO2 efflux (Fs) under vegetation (Fsveg) and at bare soil (Fsbare) in a water-limited grassland. The continuous wavelet transform was used to: (a) describe the temporal variability of Fs; (b) test the performance of empirical models ranging in complexity; and (c) identify hot-moments of Fs. We used partial wavelet coherence (PWC) analysis to test the temporal correlation between Fs with temperature and soil moisture. The PWC analysis provided evidence that soil moisture overshadows the influence of soil temperature for Fs in this water limited ecosystem. Precipitation pulses triggered hot-moments that increased Fsveg (up to 9000%) and Fsbare (up to 17,000%) with respect to pre-pulse rates. Highly parameterized empirical models (using support vector machine (SVM) or an 8-day moving window) are good approaches for representing the daily temporal variability of Fs, but SVM is a promising approach to represent high temporal variability of Fs (i.e., hourly estimates). Our results have implications for the representation of hot-moments of ecosystem CO2 fluxes in these globally distributed ecosystems. View Full-Text
Keywords: arid grasslands; precipitation variability; machine learning; soil respiration; wavelet analysis; rain pulses arid grasslands; precipitation variability; machine learning; soil respiration; wavelet analysis; rain pulses
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Vargas, R.; Sánchez-Cañete P., E.; Serrano-Ortiz, P.; Curiel Yuste, J.; Domingo, F.; López-Ballesteros, A.; Oyonarte, C. Hot-Moments of Soil CO2 Efflux in a Water-Limited Grassland. Soil Syst. 2018, 2, 47.

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