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1. Multiscale Legacy Responses of Soil Gas Concentrations to Soil Moisture and Temperature Fluctuations

   https://doi.org/10.1029/2020JG005865  

   Parolari, Anthony J.; Sizemore, Joseph; Katul, Gabriel G. (February 2021, Journal of Geophysical Research: Biogeosciences)

   Abstract The sensitivity of soil carbon dynamics to climate change is a major uncertainty in carbon cycle models. Of particular interest is the response of soil biogeochemical cycles to variability in hydroclimatic states and the related quantification of soil memory. Toward this goal, the power spectra of soil hydrologic and biogeochemical states were analyzed using measurements of soil temperature, moisture, oxygen, and carbon dioxide at two sites. Power spectra indicated multiscale power law scaling across subhourly to annual timescales. Precipitation fluctuations were most strongly expressed in the soil biogeochemical signals at monthly to annual timescales. Soil moisture and temperature fluctuations were comparable in strength at one site, while temperature was dominant at the other. The effect of soil hydrologic, thermal, and biogeochemical processes on gas concentration variability was evidenced by low spectral entropy relative to the white noise character of precipitation. A full mass balance model was unable to capture high‐frequency soil temperature influence, indicating a gap in commonly used model assumptions. A linearized model was shown to capture the main features of the observed and modeled gas concentration spectra and demonstrated how the means and variances of soil moisture and temperature interact to produce the gas concentration spectra. Breakpoints in the spectra corresponded to the mean rate of gas efflux, providing a first‐order estimate of the soil biogeochemical integral timescale (∼1 min). These methods can be used to identify biogeochemical system dynamics to develop robust, process‐based soil biogeochemistry models that capture variability in addition to long‐term mean values. 

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2. Projecting Permafrost Thaw of Sub‐Arctic Tundra With a Thermodynamic Model Calibrated to Site Measurements

   https://doi.org/10.1029/2020JG006218  

   Garnello, A.; Marchenko, S.; Nicolsky, D.; Romanovsky, V.; Ledman, J.; Celis, G.; Schädel, C.; Luo, Y.; Schuur, E. A. G. (June 2021, Journal of Geophysical Research: Biogeosciences)

   Abstract Northern circumpolar permafrost thaw affects global carbon cycling, as large amounts of stored soil carbon becomes accessible to microbial breakdown under a warming climate. The magnitude of carbon release is linked to the extent of permafrost thaw, which is locally variable and controlled by soil thermodynamics. Soil thermodynamic properties, such as thermal diffusivity, govern the reactivity of the soil‐atmosphere thermal gradient, and are controlled by soil composition and drainage. In order to project permafrost thaw for an Alaskan tundra experimental site, we used seven years of site data to calibrate a soil thermodynamic model using a data assimilation technique. The model reproduced seasonal and interannual temperature dynamics for shallow (5–40 cm) and deep soil layers (2–4 m), and simulations of seasonal thaw depth closely matched observed data. The model was then used to project permafrost thaw at the site to the year 2100 using climate forcing data for three future climate scenarios (RCP 4.5, 6.0, and 8.5). Minimal permafrost thawing occurred until mean annual air temperatures rose above the freezing point, after which we measured over a 1 m increase in thaw depth for every 1 °C rise in mean annual air temperature. Under no projected warming scenario was permafrost remaining in the upper 3 m of soil by 2100. We demonstrated an effective data assimilation method that optimizes parameterization of a soil thermodynamic model. The sensitivity of local permafrost to climate warming illustrates the vulnerability of sub‐Arctic tundra ecosystems to significant and rapid soil thawing. 

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3. Diagnosis toward predicting mean annual runoff in ungauged basins

   https://doi.org/10.5194/hess-25-945-2021  

   Gao, Yuan; Yao, Lili; Chang, Ni-Bin; Wang, Dingbao (January 2021, Hydrology and Earth System Sciences)

   Abstract. Prediction of mean annual runoff is of great interest but still poses achallenge in ungauged basins. The present work diagnoses the prediction inmean annual runoff affected by the uncertainty in estimated distribution ofsoil water storage capacity. Based on a distribution function, a waterbalance model for estimating mean annual runoff is developed, in which theeffects of climate variability and the distribution of soil water storagecapacity are explicitly represented. As such, the two parameters in themodel have explicit physical meanings, and relationships between theparameters and controlling factors on mean annual runoff are established.The estimated parameters from the existing data of watershed characteristicsare applied to 35 watersheds. The results showed that the model couldcapture 88.2 % of the actual mean annual runoff on average across thestudy watersheds, indicating that the proposed new water balance model ispromising for estimating mean annual runoff in ungauged watersheds. Theunderestimation of mean annual runoff is mainly caused by theunderestimation of the area percentage of low soil water storage capacitydue to neglecting the effect of land surface and bedrock topography. Higherspatial variability of soil water storage capacity estimated through theheight above the nearest drainage (HAND) and topographic wetness index (TWI)indicated that topography plays a crucial role in determining the actualsoil water storage capacity. The performance of mean annual runoffprediction in ungauged basins can be improved by employing better estimationof soil water storage capacity including the effects of soil, topography,and bedrock. It leads to better diagnosis of the data requirement forpredicting mean annual runoff in ungauged basins based on a newly developedprocess-based model finally. 

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4. Distinct, direct and climate‐mediated environmental controls on global particulate and mineral‐associated organic carbon storage

   https://doi.org/10.1111/gcb.17080  

   Hansen, Paige M; Even, Rebecca; King, Alison E; Lavallee, Jocelyn; Schipanski, Meagan; Cotrufo, M Francesca (January 2024, Global Change Biology)

   Abstract Identifying controls on soil organic carbon (SOC) storage, and where SOC is most vulnerable to loss, are essential to managing soils for both climate change mitigation and global food security. However, we currently lack a comprehensive understanding of the global drivers of SOC storage, especially with regards to particulate (POC) and mineral‐associated organic carbon (MAOC). To better understand hierarchical controls on POC and MAOC, we applied path analyses to SOC fractions, climate (i.e., mean annual temperature [MAT] and mean annual precipitation minus potential evapotranspiration [MAP‐PET]), carbon (C) input (i.e., net primary production [NPP]), and soil property data synthesized from 72 published studies, along with data we generated from the National Ecological Observatory Network soil pits (n = 901 total observations). To assess the utility of investigating POC and MAOC separately in understanding SOC storage controls, we then compared these results with another path analysis predicting bulk SOC storage. We found that POC storage is negatively related to MAT and soil pH, while MAOC storage is positively related to NPP and MAP‐PET, but negatively related to soil % sand. Our path analysis predicting bulk SOC revealed similar trends but explained less variation in C storage than our POC and MAOC analyses. Given that temperature and pH impose constraints on microbial decomposition, this indicates that POC is primarily controlled by SOC loss processes. In contrast, strong relationships with variables related to plant productivity constraints, moisture, and mineral surface availability for sorption indicate that MAOC is primarily controlled by climate‐driven variations in C inputs to the soil, as well as C stabilization mechanisms. Altogether, these results demonstrate that global POC and MAOC storage are controlled by separate environmental variables, further justifying the need to quantify and model these C fractions separately to assess and forecast the responses of SOC storage to global change. 

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5. Impact of biochar amendment on soil microbial biomass carbon enhancement under field experiments: a meta-analysis

   https://doi.org/10.1007/s42773-024-00391-6  

   Kumar, Yogesh; Ren, Wei; Tao, Haiying; Tao, Bo; Lindsey, Laura_E (January 2025, Biochar)

   Abstract Biochar is well-accepted as a viable climate mitigation strategy to promote agricultural and environmental benefits such as soil carbon sequestration and crop productivity while reducing greenhouse gas emissions. However, its effects on soil microbial biomass carbon (SMBC) in field experiments have not yet been thoroughly explored. In this study, we collected 539 paired globally published observations to study the impacts of biochar on SMBC under field experiments. Our results suggested an overall positive impact of biochar (21.31%) on SMBC, varying widely with different climate conditions, soil types, biochar properties, and management practices. Biochar application exhibits significant impacts under climates with mean annual temperature (MAT) < 15 °C and mean annual precipitation (MAP) between 500 and 1000 mm. Soils of coarse and fine texture, alkaline pH (SPH), soil total organic carbon (STC) content up to 10 g/kg, soil total nitrogen (STN) content up to 1.5 g/kg, and low soil cation exchange capacity (SCEC) content of < 5 cmol/kg received higher positive effects of biochar application on SMBC. Biochar produced from crop residue, specifically from cotton and maize residue, at pyrolysis temperature (BTM) of < 400 °C, with a pH (BPH) between 8 and 9, low application rate (BAP) of < 10 t/ha, and high ash content (BASH) > 400 g/kg resulted in an increase in SMBC. Low biochar total carbon (BTC) and high total nitrogen (BTN) positively affect the SMBC. Repeated application significantly increased the SMBC by 50.11%, and fresh biochar in the soil (≤ 6 months) enhanced SMBC compared to the single application and aged biochar. Biochar applied with nitrogen fertilizer (up to 300 kg/ha) and manure/compost showed significant improvements in SMBC, but co-application with straw resulted in a slight negative impact on the SMBC. The best-fit gradient boosting machines model, which had the lowest root mean square error, demonstrated the relative importance of various factors on biochar effectiveness: biochar, soil, climate, and nitrogen applications at 46.2%, 38.1%, 8.3%, and 7.4%, respectively. Soil clay proportion, BAP, nitrogen application, and MAT were the most critical variables for biochar impacts on SMBC. The results showed that biochar efficiency varies significantly in different climatic conditions, soil environments, field management practices, biochar properties, and feedstock types. Our meta-analysis of field experiments provides the first quantitative review of biochar impacts on SMBC, demonstrating its potential for rehabilitating nutrient-deprived soils and promoting sustainable land management. To improve the efficiency of biochar amendment, we call for long-term field experiments to measure SMBC across diverse agroecosystems. Graphical Abstract 

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