scholarly journals Can current moisture responses predict soil CO<sub>2</sub> efflux under altered precipitation regimes? A synthesis of manipulation experiments

2014 ◽  
Vol 11 (11) ◽  
pp. 2991-3013 ◽  
Author(s):  
S. Vicca ◽  
M. Bahn ◽  
M. Estiarte ◽  
E. E. van Loon ◽  
R. Vargas ◽  
...  

Abstract. As a key component of the carbon cycle, soil CO2 efflux (SCE) is being increasingly studied to improve our mechanistic understanding of this important carbon flux. Predicting ecosystem responses to climate change often depends on extrapolation of current relationships between ecosystem processes and their climatic drivers to conditions not yet experienced by the ecosystem. This raises the question of to what extent these relationships remain unaltered beyond the current climatic window for which observations are available to constrain the relationships. Here, we evaluate whether current responses of SCE to fluctuations in soil temperature and soil water content can be used to predict SCE under altered rainfall patterns. Of the 58 experiments for which we gathered SCE data, 20 were discarded because either too few data were available or inconsistencies precluded their incorporation in the analyses. The 38 remaining experiments were used to test the hypothesis that a model parameterized with data from the control plots (using soil temperature and water content as predictor variables) could adequately predict SCE measured in the manipulated treatment. Only for 7 of these 38 experiments was this hypothesis rejected. Importantly, these were the experiments with the most reliable data sets, i.e., those providing high-frequency measurements of SCE. Regression tree analysis demonstrated that our hypothesis could be rejected only for experiments with measurement intervals of less than 11 days, and was not rejected for any of the 24 experiments with larger measurement intervals. This highlights the importance of high-frequency measurements when studying effects of altered precipitation on SCE, probably because infrequent measurement schemes have insufficient capacity to detect shifts in the climate dependencies of SCE. Hence, the most justified answer to the question of whether current moisture responses of SCE can be extrapolated to predict SCE under altered precipitation regimes is "no" – as based on the most reliable data sets available. We strongly recommend that future experiments focus more strongly on establishing response functions across a broader range of precipitation regimes and soil moisture conditions. Such experiments should make accurate measurements of water availability, should conduct high-frequency SCE measurements, and should consider both instantaneous responses and the potential legacy effects of climate extremes. This is important, because with the novel approach presented here, we demonstrated that, at least for some ecosystems, current moisture responses could not be extrapolated to predict SCE under altered rainfall conditions.

2014 ◽  
Vol 11 (1) ◽  
pp. 853-899 ◽  
Author(s):  
S. Vicca ◽  
M. Bahn ◽  
M. Estiarte ◽  
E. E. van Loon ◽  
R. Vargas ◽  
...  

Abstract. As a key component of the carbon cycle, soil CO2 efflux (SCE) is being increasingly studied to improve our mechanistic understanding of this important carbon flux. Predicting ecosystem responses to climate change often depends on extrapolation of current relationships between ecosystem processes and their climatic drivers to conditions not yet experienced by the ecosystem. This raises the question to what extent these relationships remain unaltered beyond the current climatic window for which observations are available to constrain the relationships. Here, we evaluate whether current responses of SCE to fluctuations in soil temperature and soil water content can be used to predict SCE under altered rainfall patterns. Of the 58 experiments for which we gathered SCE data, 20 were discarded because either too few data were available, or inconsistencies precluded their incorporation in the analyses. The 38 remaining experiments were used to test the hypothesis that a model parameterized with data from the control plots (using soil temperature and water content as predictor variables) could adequately predict SCE measured in the manipulated treatment. Only for seven of these 38 experiments, this hypothesis was rejected. Importantly, these were the experiments with the most reliable datasets, i.e., those providing high-frequency measurements of SCE. Accordingly, regression tree analysis demonstrated that measurement frequency was crucial; our hypothesis could be rejected only for experiments with measurement intervals of less than 11 days, and was not rejected for any of the 24 experiments with larger measurement intervals. This highlights the importance of high-frequency measurements when studying effects of altered precipitation on SCE, probably because infrequent measurement schemes have insufficient capacity to detect shifts in the climate-dependencies of SCE. We strongly recommend that future experiments focus more strongly on establishing response functions across a broader range of precipitation regimes and soil moisture conditions. Such experiments should make accurate measurements of water availability, they require high-frequency SCE measurements and they should consider both instantaneous responses and the potential legacy effects of climate extremes. This is important, because we demonstrated that at least for some ecosystems, current moisture responses cannot be extrapolated to predict SCE under altered rainfall.


1999 ◽  
Vol 56 (3) ◽  
pp. 221-226 ◽  
Author(s):  
Daniel Epron ◽  
Lætitia Farque ◽  
Éric Lucot ◽  
Pierre-Marie Badot

Geoderma ◽  
2022 ◽  
Vol 405 ◽  
pp. 115404
Author(s):  
Sonia Chamizo ◽  
Emilio Rodríguez-Caballero ◽  
Enrique P. Sánchez-Cañete ◽  
Francisco Domingo ◽  
Yolanda Cantón

2016 ◽  
Author(s):  
Luitgard Schwendenmann ◽  
Cate Macinnis-Ng

Abstract. Total soil CO2 efflux and its component fluxes, autotrophic and heterotrophic respiration, were measured in a native forest in northern Aotearoa-New Zealand. The forest is dominated by Agathis australis (kauri) and is on an acidic, clay rich soil. Soil CO2 efflux, volumentric soil water content and soil temperature were measured bi-weekly to monthly at 42 locations over 18 months. Trenching and regression analysis was used to partition the total soil CO2 efflux. The effect of tree structure was investigated by calculating an index of local contribution (Ic, based on tree size and distance to the measurement location) followed by correlation analysis between Ic and soil CO2 efflux, root biomass, litterfall and soil characteristics. The mean total soil CO2 efflux was 3.47 μmol m−2 s−1. Using uni- and bivariate models showed that soil temperature (< 40 %) and volumetric soil water content (< 20 %) were poor predictors of the temporal variation in total soil CO2 efflux. In contrast, a stronger temperature sensitivity (around 57 %) was found for heterotrophic respiration. Autotrophic respiration accounted for 25 (trenching) or 28 % (regression analysis) of total soil CO2 efflux. We found significant positive relationships between kauri tree size distribution (Ic) and soil CO2 efflux, root biomass and mineral soil CN ratio within 5–6 m of the measurement points. Using multiple regression analysis revealed that 97 % of the spatial variability in soil CO2 efflux in this kauri dominated stand was explained by root biomass and soil temperature. Our findings highlight the need to consider tree species effects and spatial patterns in soil carbon related studies.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xiliang Song ◽  
Yihao Zhu ◽  
Weifeng Chen

AbstractThe soil carbon (C) pools in coastal wetlands are known as “blue C” and have been damaged extensively owing to climate change and land reclamation. Because soil respiration (RS) is the primary mechanism through which soil carbon is released into the atmosphere at a global scale, investigating the dynamic characteristics of the soil respiration rate in reclaimed coastal wetlands is necessary to understand its important role in maintaining the global C cycle. In the present study, seasonal and diurnal changes in soil respiration were monitored in one bare wetland (CK) and two reclaimed wetlands (CT, a cotton monoculture pattern, and WM, a wheat–maize continuous cropping pattern) in the Yellow River Delta. At the diurnal scale, the RS at the three study sites displayed single-peak curves, with the lowest values occurring at midnight (00:00 a.m.) and the highest values occurring at midday (12:00 a.m.). At the seasonal scale, the mean diurnal RS of the CK, CT and WM in April was 0.24, 0.26 and 0.79 μmol CO2 m−2 s−1, and it increased to a peak in August for these areas. Bare wetland conversion to croplands significantly elevated the soil organic carbon (SOC) pool. The magnitude of the RS was significantly different at the three sites, and the yearly total amounts of CO2 efflux were 375, 513 and 944 g CO2·m−2 for the CK, CT and WM, respectively. At the three study sites, the surface soil temperature had a significant and positive relationship to the RS at both the diurnal and seasonal scales, and it accounted for 20–52% of the seasonal variation in the daytime RS. The soil water content showed a significant but negative relationship to the RS on diurnal scale only at the CK site, while it significantly increased with the RS on seasonal scale at all study sites. Although the RS showed a noticeable relationship to the combination of soil temperature and water content, the synergic effects of these two environment factors were not much higher than the individual effects. In addition, the correlation analysis showed that the RS was also influenced by the soil physico-chemical properties and that the soil total nitrogen had a closer positive relationship to the RS than the other nutrients, indicating that the soil nitrogen content plays a more important role in promoting carbon loss.


Author(s):  
Fabio Sigrist

AbstractWe introduce a novel boosting algorithm called ‘KTBoost’ which combines kernel boosting and tree boosting. In each boosting iteration, the algorithm adds either a regression tree or reproducing kernel Hilbert space (RKHS) regression function to the ensemble of base learners. Intuitively, the idea is that discontinuous trees and continuous RKHS regression functions complement each other, and that this combination allows for better learning of functions that have parts with varying degrees of regularity such as discontinuities and smooth parts. We empirically show that KTBoost significantly outperforms both tree and kernel boosting in terms of predictive accuracy in a comparison on a wide array of data sets.


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