the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Validating laboratory insights into the drivers of soil rewetting respiration pulses with field measurements
Abstract. Improved understanding of the mechanisms driving heterotrophic CO2 emissions after rewetting of a dry soil may improve projections of future soil carbon fate. While drying and rewetting (DRW) under laboratory conditions has demonstrated that heterotrophic CO2 emissions depend on DRW features and soil and environmental conditions, these laboratory insights have not been validated in field conditions. To this aim, we collated mean respiration rates over 48 hours after rewetting from two data sources: 37 laboratory studies reporting data for more than three DRW cycles (laboratory respiration, LR), and six field datasets recording hourly heterotrophic respiration and soil moisture (field respiration, FR). LR and FR were explained by six predictors using random forest algorithms and partial dependence plots. Results indicated that the most important driver of LR and FR were SOC and temperature, respectively. Both LR and FR increased with increasing SOC and temperature. LR increased with soil dryness before rewetting, but this trend was less clear in FR. LR decreased with soil moisture increments at rewetting, while FR increased with soil moisture increments. LR was higher in soils from humid climates than from arid climates, but this effect was not observed in FR. We concluded that laboratory insights could be partly validated with current datasets. Caution should be taken when extending laboratory insights to predicting fluxes in ecosystem.
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CC1: 'Comment on egusphere-2024-3324', Oliver Dilly, 18 Nov 2024
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Dear authors,
with interest I read your paper. Some points may be added from earlier studies.
Not only organic matter and temperature seems key for respiration during rewetting but also vegetation pattern, soil microbiota chacterisitics and also abiotic factors like salt/aridity.Sincerely Oliver Dilly
Some related own observations:
Dilly O., Mogge B., Kutsch W. L., Kappen L., Munch J.C. (1997) Aspects of carbon and nitrogen cycling in soils of the Bornhöved Lake district. I. Microbial biomass content, microbial activities and in situ emissions of carbon dioxide as well as nitrous oxide of arable and grassland soils. Biogeochemistry 39, 189-205
Mamilov A. Sh., Dilly O. (2002) Soil microbial eco-physiology as affected by short-term variations in environmental conditions. Soil Biology and Biochemistry 34, 1283-1290
Mamilov A., Dilly O.M., Mamilov S., Inubushi K. (2004) Microbial eco-physiology of degrading Aral Sea wetlands. Consequences for C-cycling. Soil Science and Plant Nutrition 50, 839-842
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CC2: 'Reply on CC1', Xiankun Li, 19 Nov 2024
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Thank you for these references.
When we get a chance to revise this preprint, we will most likely cite them in the Introduction and Uncertainties.
Citation: https://doi.org/10.5194/egusphere-2024-3324-CC2
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CC2: 'Reply on CC1', Xiankun Li, 19 Nov 2024
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RC1: 'Comment on egusphere-2024-3324', Romain Barnard, 29 Nov 2024
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Dear authors,
General comments
I enjoyed reading this paper, thank you. Comparing field vs lab soil rewetting responses is challenging, since the hierarchy of the drivers of the multiple processes involved is expected to change, as well as the scale at which they can affect these processes, not to mention the analysis snags that arise when aggregating different datasets. The study is timely, proposes a solid analysis, is concise and well written.
My major concern is the absence of plants in the study, which echoes the importance of SOC in the results. Plant C inputs to the soil through rhizodeposition are a major driver of soil microbial activity, thereby of soil biogeochemical cycling (see section “Importance of plants” in Barnard et al. 2020). I expect C limitation (or even starvation) to strongly impact soil processes, and to push SOC forward in the hierarchy of drivers. As a consequence, the absence of plants strongly limits the generalization of the study. This tends to be forgotten as the discussion develops, and appears to be totally ignored in the conclusion for example. This is reinforced by the semantic nature of “field site”. The field data originates from trenched plots, which the authors specify ensures only heterotrophic respiration (L100), yet this also ensures no labile C inputs to the soil, although it is not specified. There are few field sites, sensu natural condition sites, that are devoid of plant labile C input at some point in time (of course some periods preclude plant activity, eg extreme heat, drought or cold, which however bring many biological and biogeochemical processes to a halt or close to it), hence the term “field site”, albeit correct here, can be somewhat misleading and contribute to the trend towards an over-generalization of the results that climaxes in the conclusion.
Soil drainage is commonly observed to differ greatly between lab and field soil experiments, and could contribute to explaining the results in the “Drier soils…” and “The effects of rewetting…” sections of the discussion. Lab conditions, even using undisturbed soil cores, result in preferential flow paths on the sides of the cores for example. Since the soil cores (or samples) in the lab are typically from the soil top layers, upon rewetting, water will more easily reach the bottom of the soil column and ultimately rewet the soil from both top and bottom, resulting in a more homogeneous and efficient rewetting that in the field. In the field, water tends to drain down the soil column and the rewetting effect is not concentrated in the top soil layers. Monitoring soil water content is sometimes not enough to take this effect into account, since it is often performed on a whole-sample weight basis in the lab (or even based in the amount of water added) as opposed to probes or soil sampling in the field, that cannot capture the soil water content of the entire column of soil.
The rationale for the 48h timeframe choice for mean respiration rate is extensively justified (L82). Did the authors poke around the dataset using a different timeframe to see what the results look like, even on a subset of the data? This approach would be somewhat similar to a sensitivity analysis to gauge the effects of set parameters on the results, and could be useful to ascertain that the choices made are robust, in a similar way to what is presented for Dthetarewet (L182).
Specific comments
L129 Could you please better explain why Dthetarewet and Dthetatolerance were set at 25% and 12.5% of soil moisture, respectively?
L298 Please replace “samples” by “cores” or “monoliths”.
Technical corrections
The list of lab studies is duplicated.
Citation: https://doi.org/10.5194/egusphere-2024-3324-RC1
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