TITLE:
Temperature Sensitivity of Soil Respiration Probed by Numerical Analysis of Field-Observed Data Sets
AUTHORS:
Ippei Iiyama
KEYWORDS:
Air-Filled Porosity, Inverse Analysis, Mass Balance, Potentially Maximum CO2 Production Rate, Soil Gas Diffusion, Water Content
JOURNAL NAME:
Journal of Geoscience and Environment Protection,
Vol.11 No.8,
August
25,
2023
ABSTRACT: Temperature
sensitivity of soil respiration is essential to predict possible changes in
terrestrial carbon budget on various scenarios about atmospheric and soil
climates. Although it is often evaluated by using respiratory quotient “Q10”,
Q10 values of soil respiration seem to vary depending on methods or
scales of evaluation. Aiming at probing how Q10 values of soil
respiration are evaluated differently for a field, this study used a model of
soil respiration rate, and numerically evaluated soil respiration rates along
depth by fitting the model to depth distributions of CO2 concentration measured in a field. And temperature sensitivity of soil
respiration rate was evaluated by comparing the determined soil respiration
rates with atmospheric and soil temperatures measured in the field. The results
showed that the relation between surface CO2 emission rates and
atmospheric temperatures was represented by lower Q10 values than
that between soil respiration rates and soil temperatures, presumably because
the top soil layers had acclimatized in more extent to the existing thermal
regime than the underlying deeper layers. Thus, for evaluating effects of
long-term rise in atmospheric temperature on soil respiration, it is necessary
to precisely predict the long-term change in depth distribution of soil
temperature as well as to quantify temperature sensitivity of soil respiration
along depth. The evaluated sensitivity of surface CO2 emission rate
to atmospheric temperature showed hysteresis, implying the needs for more
knowledge about temperature sensitivity of soil respiration evaluated in both
warming and cooling processes for better understandings and predictions about
terrestrial carbon cycling.