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Soil Temperature, Organic-Carbon Storage, and Water-Holding Ability Should Be Accounted for the Empirical Soil Respiration Model Selection in Two Forest Ecosystems
Soil respiration (SR) is a main component of the carbon cycle in terrestrial ecosystems,
and being strongly affected by changes in the environment, it is a good indicator of the ecosystem’s
ability to cope with climate change. This research aims to find better empirical SR models using
25-year-long SR monitoring in two forest ecosystems formed on sandy Entic Podzol and loamy Haplic
Luvisol. The following parameters were considered in the examined models: the mean monthly soil
or air temperatures (Tsoil or Tair), the amount of precipitation during the current (P) and the previous
(PP) months, and the storage of soil organic carbon (SOC). The weighted non-linear regression was
used for model parameter estimations for the normal, wet, and dry years. To improve the model
resolutions by magnitude, we controlled the slope and intercept of the linear model comparison
between the measured and modeled data through the change in R0—SR at zero soil temperature.
The mean bias error (MBE), root-mean-square error (RMSE), and determination coefficient (R2) were
used for the estimation of the goodness of model performances. For the sandy Entic Podzol, it is
more appropriate to use the models dependent on SOC (TPPC). While for the loamy Haplic Luvisol,
the Raich–Hashimoto model (TPPrh) with the quadratic Tsoil or Tair dependency shows the better
results. An application of Tsoil for the model parameterization gives better results than Tair: the TPPC
model was able to adequately describe the cold-period SR (Tsoil 2 C); the TPPrh model was able to
avoid overestimations of the warm-period SR (Tsoil > 2 C). The TPPC model parameterized with
Tsoil can be used for the quality control of the cold-period SR measurements. Therefore, we showed
the importance of accounting for SOC and the water-holding ability when the optimal SR model is
chosen for the analysis.