Inverse determination of heterotrophic soil respiration response to temperature and water content under field conditions
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Heterotrophic soil respiration is an important flux within the global carbon cycle. Exact knowledge of the response functions for soil temperature and soil water content is crucial for a reliable prediction of soil carbon turnover. The classical statistical approach for the in situ determination of the temperature response (Q10 or activation energy) of field soil respiration has been criticised for neglecting confounding factors, such as spatial and temporal changes in soil water content and soil organic matter. The aim of this paper is to evaluate an alternative method to estimate the temperature and soil water content response of heterotrophic soil respiration. The new method relies on inverse parameter estimation using a 1-dimensional CO2 transport and carbon turnover model. Inversion results showed that different formulations of the temperature response function resulted in estimated response factors that hardly deviated over the entire range of soil water content and for temperature below 25°C. For higher temperatures, the temperature response was highly uncertain due to the infrequent occurrence of soil temperatures above 25°C. The temperature sensitivity obtained using inverse modelling was within the range of temperature sensitivities estimated from statistical processing of the data. It was concluded that inverse parameter estimation is a promising tool for the determination of the temperature and soil water content response of soil respiration. Future synthetic model studies should investigate to what extent the inverse modelling approach can disentangle confounding factors that typically affect statistical estimates of the sensitivity of soil respiration to temperature and soil water content.
KeywordsHeterotrophic soil respiration Temperature sensitivity Soil water content sensitivity Inverse parameter estimation SOILCO2/RothC SCE algorithm AIC
This research was supported by the German Research Foundation DFG (Transregional Collaborative Research Centre 32—Patterns in Soil-Vegetation-Atmosphere systems: monitoring, modelling and data assimilation), TERENO (Terrestrial Environmental Observatories) of the Helmholtz Gemeinschaft and by the Hessian initiative for the development of scientific and economic excellence (LOEWE) at the Biodiversity and Climate Research Centre (BiK-F), Frankfurt/Main. We thank Axel Knaps and Rainer Harms for providing the climate data. The organic carbon content of the soil was analysed by the Central Division of Analytical Chemistry at the Forschungszentrum Jülich GmbH. We would like to thank Claudia Walraf and Stefan Masjoshustmann for the physical fractionation of the soil samples and Ludger Bornemann (Institute of Crop Science and Resource Conservation—Division of Soil Science, University of Bonn) for the analysis of black carbon. We are grateful to Horst Hardelauf for modifications of the model source code. Furthermore, we thank three anonymous reviewers for their helpful advices.
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