Nitrogen supply modulates the effect of changes in drying-rewetting frequency on soil C and N cycling and greenhouse gas exchange

2015 ◽  
Vol 21 (10) ◽  
pp. 3854-3863 ◽  
Author(s):  
Lourdes Morillas ◽  
Jorge Durán ◽  
Alexandra Rodríguez ◽  
Javier Roales ◽  
Antonio Gallardo ◽  
...  
Keyword(s):  
Gas Exchange ◽  
Greenhouse Gas ◽  
Nitrogen Supply ◽  
N Cycling ◽  
Soil C ◽  
Soil C And N ◽  
C And N ◽  
C And N Cycling

The magnitude of temperature increase matters: how will soil organic mineralization respond to future climate warming?

2020 ◽  
Author(s):  
Jie Zhou ◽  
Yuan Wen ◽  
Lingling Shi ◽  
Michaela Dippold ◽  
Yakov Kuzyakov ◽  
...  
Keyword(s):  
Enzyme Activity ◽  
Climate Warming ◽  
Temperature Increase ◽  
Microbial Growth ◽  
N Cycling ◽  
Soil C ◽  
Soil Microbial ◽  
Soil C And N ◽  
C And N ◽  
C And N Cycling

<p>The Paris climate agreement is pursuing efforts to limit the increase in global temperature to below 2 °C above pre-industrial level. The overall consequence of relatively slight warming (~2 °C), on soil C and N stocks will be dependent on microorganisms decomposing organic matter through release of extracellular enzymes. Therefore, the capacity of soil microbial community to buffer climate warming in long-term and the self-regulatory mechanisms mediating soil C and N cycling through enzyme activity and microbial growth require a detailed comparative study. Here, microbial growth and the dynamics of enzyme activity (involved in C and N cycling) in response to 8 years warming (ambient, +1.6 °C, +3.2 °C) were investigated to identify shifts in soil and microbial functioning. A slight temperature increase (+1.6 °C) only altered microbial properties, but had no effect on either hydrolytic enzyme activity or basic soil properties. Stronger warming (+3.2 °C) increased the specific growth rate (μ<sub>m</sub>) of the microbial community, indicating an alteration in their ecological strategy, i.e. a shift towards fast-growing microorganisms and accelerated microbial turnover. Warming strongly changed microbial physiological state, as indicated by a 1.4-fold increase in the fraction of growing microorganisms (GMB) and 2 times decrease in lag-time with warming. This reduced total microbial biomass but increased specific enzyme activity to be ready to decompose increased rhizodeposition, as supported by the higher potential activitiy (V<sub>max</sub>) and lower affinity to substrates (higher K<sub>m</sub>) of enzymes hydrolyzing cellobiose and proteins cleavage in warmed soil. In other words, stronger warming magnitude (+3.2 °C) changed microbial communities, and was sufficient to benefit fast-growing microbial populations with enzyme functions that specific to degrade labile SOM. Combining with 48 literature observations, we confirmed that the slight magnitude of temperature increase (< 2 °C) only altered microbial properties, but further temperature increases (2-4 °C) was sufficient to change almost all soil, microbial, and enzyme properties and related processes. As a consequence, the revealed microbial regulatory mechanism of stability of soil C storage is strongly depended on the magnitude of future climate warming.</p>

Forest restoration treatments have subtle long-term effects on soil C and N cycling in mixed conifer forests

2016 ◽  
Vol 26 (5) ◽  
pp. 1503-1516 ◽  
Author(s):  
Peter W. Ganzlin ◽  
Michael J. Gundale ◽  
Rachel E. Becknell ◽  
Cory C. Cleveland
Keyword(s):  
Forest Restoration ◽  
N Cycling ◽  
Long Term Effects ◽  
Mixed Conifer ◽  
Soil C ◽  
Mixed Conifer Forests ◽  
Soil C And N ◽  
C And N ◽  
C And N Cycling

scholarly journals Parameter-induced uncertainty quantification of soil N<sub>2</sub>O, NO and CO<sub>2</sub> emission from Höglwald spruce forest (Germany) using the LandscapeDNDC model

2012 ◽  
Vol 9 (10) ◽  
pp. 3983-3998 ◽  
Author(s):  
K.-H. Rahn ◽  
C. Werner ◽  
R. Kiese ◽  
E. Haas ◽  
K. Butterbach-Bahl
Keyword(s):  
Greenhouse Gas ◽  
Trace Gas ◽  
Parameter Distribution ◽  
Model Parameters ◽  
Gas Emissions ◽  
Soil C ◽  
Trace Gas Emissions ◽  
Soil Atmosphere ◽  
Soil C And N ◽  
C And N

Abstract. Assessing the uncertainties of simulation results of ecological models is becoming increasingly important, specifically if these models are used to estimate greenhouse gas emissions on site to regional/national levels. Four general sources of uncertainty effect the outcome of process-based models: (i) uncertainty of information used to initialise and drive the model, (ii) uncertainty of model parameters describing specific ecosystem processes, (iii) uncertainty of the model structure, and (iv) accurateness of measurements (e.g., soil-atmosphere greenhouse gas exchange) which are used for model testing and development. The aim of our study was to assess the simulation uncertainty of the process-based biogeochemical model LandscapeDNDC. For this we set up a Bayesian framework using a Markov Chain Monte Carlo (MCMC) method, to estimate the joint model parameter distribution. Data for model testing, parameter estimation and uncertainty assessment were taken from observations of soil fluxes of nitrous oxide (N2O), nitric oxide (NO) and carbon dioxide (CO2) as observed over a 10 yr period at the spruce site of the Höglwald Forest, Germany. By running four independent Markov Chains in parallel with identical properties (except for the parameter start values), an objective criteria for chain convergence developed by Gelman et al. (2003) could be used. Our approach shows that by means of the joint parameter distribution, we were able not only to limit the parameter space and specify the probability of parameter values, but also to assess the complex dependencies among model parameters used for simulating soil C and N trace gas emissions. This helped to improve the understanding of the behaviour of the complex LandscapeDNDC model while simulating soil C and N turnover processes and associated C and N soil-atmosphere exchange. In a final step the parameter distribution of the most sensitive parameters determining soil-atmosphere C and N exchange were used to obtain the parameter-induced uncertainty of simulated N2O, NO and CO2 emissions. These were compared to observational data of an calibration set (6 yr) and an independent validation set of 4 yr. The comparison showed that most of the annual observed trace gas emissions were in the range of simulated values and were predicted with a high certainty (Root-mean-squared error (RMSE) NO: 2.4 to 18.95 g N ha−1 d−1, N2O: 0.14 to 21.12 g N ha−1 d−1, CO2: 5.4 to 11.9 kg C ha−1 d−1). However, LandscapeDNDC simulations were sometimes still limited to accurately predict observed seasonal variations in fluxes.

scholarly journals Modeling the effects of tree species and incubation temperature on soil's extracellular enzyme activity in 78-year-old tree plantations

2017 ◽  
Vol 14 (23) ◽  
pp. 5393-5402 ◽  
Author(s):  
Xiaoqi Zhou ◽  
Shen S. J. Wang ◽  
Chengrong Chen
Keyword(s):  
Tree Species ◽  
Incubation Temperature ◽  
N Cycling ◽  
Residual Soil ◽  
Coniferous Tree ◽  
Soil C ◽  
Hoop Pine ◽  
Soil C And N ◽  
C And N ◽  
Coniferous Tree Species

Abstract. Forest plantations have been widely used as an effective measure for increasing soil carbon (C), and nitrogen (N) stocks and soil enzyme activities play a key role in soil C and N losses during decomposition of soil organic matter. However, few studies have been carried out to elucidate the mechanisms behind the differences in soil C and N cycling by different tree species in response to climate warming. Here, we measured the responses of soil's extracellular enzyme activity (EEA) to a gradient of temperatures using incubation methods in 78-year-old forest plantations with different tree species. Based on a soil enzyme kinetics model, we established a new statistical model to investigate the effects of temperature and tree species on soil EEA. In addition, we established a tree species–enzyme–C∕N model to investigate how temperature and tree species influence soil C∕N contents over time without considering plant C inputs. These extracellular enzymes included C acquisition enzymes (β-glucosidase, BG), N acquisition enzymes (N-acetylglucosaminidase, NAG; leucine aminopeptidase, LAP) and phosphorus acquisition enzymes (acid phosphatases). The results showed that incubation temperature and tree species significantly influenced all soil EEA and Eucalyptus had 1.01–2.86 times higher soil EEA than coniferous tree species. Modeling showed that Eucalyptus had larger soil C losses but had 0.99–2.38 times longer soil C residence time than the coniferous tree species over time. The differences in the residual soil C and N contents between Eucalyptus and coniferous tree species, as well as between slash pine (Pinus elliottii Engelm. var. elliottii) and hoop pine (Araucaria cunninghamii Ait.), increase with time. On the other hand, the modeling results help explain why exotic slash pine can grow faster, as it has 1.22–1.38 times longer residual soil N residence time for LAP, which mediate soil N cycling in the long term, than native coniferous tree species like hoop pine and kauri pine (Agathis robusta C. Moore). Our results will be helpful for understanding the mechanisms of soil C and N cycling by different tree species, which will have implications for forest management.

scholarly journals Parameter-induced uncertainty quantification of soil N<sub>2</sub>O, NO and CO<sub>2</sub> emission from Höglwald spruce forest (Germany) using the LandscapeDNDC model

2012 ◽  
Vol 9 (4) ◽  
pp. 5249-5286 ◽  
Author(s):  
K.-H. Rahn ◽  
C. Werner ◽  
R. Kiese ◽  
E. Haas ◽  
K. Butterbach-Bahl
Keyword(s):  
Greenhouse Gas ◽  
Trace Gas ◽  
Parameter Distribution ◽  
Model Parameters ◽  
Gas Emissions ◽  
Soil C ◽  
Trace Gas Emissions ◽  
Soil Atmosphere ◽  
Soil C And N ◽  
C And N

Abstract. Assessing the uncertainties of simulation results of ecological models is becoming of increasing importance, specifically if these models are used to estimate greenhouse gas emissions at site to regional/national levels. Four general sources of uncertainty effect the outcome of process-based models: (i) uncertainty of information used to initialise and drive the model, (ii) uncertainty of model parameters describing specific ecosystem processes, (iii) uncertainty of the model structure and (iv) accurateness of measurements (e.g. soil-atmosphere greenhouse gas exchange) which are used for model testing and development. The aim of our study was to assess the simulation uncertainty of the process-based biogeochemical model LandscapeDNDC. For this we set up a Bayesian framework using a Markov Chain Monte Carlo (MCMC) method, to estimate the joint model parameter distribution. Data for model testing, parameter estimation and uncertainty assessment were taken from observations of soil fluxes of nitrous oxide (N2O), nitric oxide (NO), and carbon dioxide (CO2) as observed over a 10 yr period at the spruce site of the Höglwald Forest, Germany. By running four independent Markov Chains in parallel with identical properties (except for the parameter start values), an objective criteria for chain convergence developed by Gelman et al. (2003) could be used. Our approach showed that by means of the joined parameter distribution, we were able not only to limit the parameter space and specify the probability of parameter values, but also to assess the complex dependencies among model parameters used for simulating soil C and N trace gas emissions. This helped to improve the understanding of the behaviour of the complex LandscapeDNDC model while simulating soil C and N turnover processes and associated C and N soil-atmosphere exchange. In a final step the parameter distribution of the most sensitive parameters determining soil-atmosphere C and N exchange were used to obtain the parameter-induced uncertainty of simulated N2O, NO and CO2 emissions. These were compared to observational data of the calibration set (6 yr) and an independent validation set of 4 yr. The comparison showed that most of the annual observed trace gas emissions were in the range of simulated values and were predicted with a high certainty (Residual mean squared error (RMSE) NO: 2.5 to 21.3 g N ha−1 d−1, N2O: 0.2 to 21.4 g N ha−1 d−1, CO2: 5.8 to 12.6 kg C ha−1 d−1). However, LandscapeDNDC simulations were sometimes limited to accurately predict observed seasonal variations in fluxes.

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