Document Type

Article

Publication Date

2-2021

Publisher

American Geophysical Union

Source Publication

Journal of Geophysical Research Biogeosciences

Source ISSN

2169-8953

Abstract

The sensitivity of soil carbon dynamics to climate change is a major uncertainty in carbon cycle models. Of particular interest is the response of soil biogeochemical cycles to variability in hydroclimatic states and the related quantification of soil memory. Toward this goal, the power spectra of soil hydrologic and biogeochemical states were analyzed using measurements of soil temperature, moisture, oxygen, and carbon dioxide at two sites. Power spectra indicated multiscale power law scaling across subhourly to annual timescales. Precipitation fluctuations were most strongly expressed in the soil biogeochemical signals at monthly to annual timescales. Soil moisture and temperature fluctuations were comparable in strength at one site, while temperature was dominant at the other. The effect of soil hydrologic, thermal, and biogeochemical processes on gas concentration variability was evidenced by low spectral entropy relative to the white noise character of precipitation. A full mass balance model was unable to capture high-frequency soil temperature influence, indicating a gap in commonly used model assumptions. A linearized model was shown to capture the main features of the observed and modeled gas concentration spectra and demonstrated how the means and variances of soil moisture and temperature interact to produce the gas concentration spectra. Breakpoints in the spectra corresponded to the mean rate of gas efflux, providing a first-order estimate of the soil biogeochemical integral timescale (~1 min). These methods can be used to identify biogeochemical system dynamics to develop robust, process-based soil biogeochemistry models that capture variability in addition to long-term mean values.

Plain Language Summary

The ability to describe how climate change impacts soil carbon and nutrient cycles with models is a necessary tool for ecosystem management and sustainability. One difficulty in developing these predictive models is the so-called “legacy effect”—for example, one wet summer may alter the ecosystem for many years afterward. Soil data and models are used here to quantify the relative strength of short- and long-term variability of soil biogeochemical systems and how it responds to rainfall, soil moisture, and soil temperature. We found that variability in soil biogeochemistry is concentrated at longer timescales of several weeks to months and this is because the soil stores water and heat, retaining a “memory” of past rainfall and temperature. Further, this analysis offered a new perspective on the equations used in current models—models driven by soil moisture and temperature are able to capture the legacy in soil biogeochemical data.

Comments

Published version. Journal of Geophysical Research Biogeosciences, Vol. 126, No. 2 (February 2021): e2020JG005865. DOI. © 2021 American Geophysical Union. Used with permission.

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