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

Soil Systems

Sánchez-Cañete P., Enrique¹  López-Ballesteros, Ana²  Domingo, Francisco⁴  Serrano-Ortiz, Penélope⁵  Vargas, Rodrigo⁶  Oyonarte, Cecilio⁷  Curiel Yuste, Jorge⁸ 

[1] Soil Sciences, University of Delaware, Newark, DE 19716, USA;BC3—Basque Centre for Climate Change, Scientific Campus of the University of the Basque Country, 48940 Leioa, Spain;Departamento de Desertificación y Geo-ecología, Estación Experimental de Zonas Áridas (EEZA-CSIC), 04120 Almería, Spain;Departamento de Ecología, Universidad de Granada, 18071 Granada, Spain;Departamento de Física Aplicada, Universidad de Granada, 18071 Granada, Spain;Department of Plant &IKERBASQUE—Basque Foundation for Science, Maria Diaz de Haro 3, 6 Solairua, 48013 Bilbao, Spain;Instituto Interuniversitario de Investigación del Sistema Tierra en Andalucía, Centro Andaluz del Medio Ambiente (IISTA-CEAMA), 18071 Granada, Spain

关键词: arid grassl;    s;    precipitation variability;    machine learning;    soil respiration;    wavelet analysis;    rain pulses;   

DOI  :  10.3390/soilsystems2030047

学科分类：土壤学

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY