bes-bibliography.bib

@article{ayresValidationRemotelySensed2024,
  title = {Validation of {{Remotely Sensed}} and {{Modeled Soil Moisture}} at {{Forested}} and {{Unforested NEON Sites}}},
  author = {Ayres, Edward and Reichle, Rolf H. and Colliander, Andreas and Cosh, Michael H. and Smith, Lucas},
  year = 2024,
  journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  volume = {17},
  pages = {14248--14264},
  issn = {2151-1535},
  doi = {10.1109/JSTARS.2024.3430928},
  urldate = {2025-10-31},
  abstract = {Soil moisture (SM) is an important driver for forest ecosystems, creating a need for globally extensive SM information that can only be achieved with satellite-based sensors and/or process-based model. However, the reliability of remotely sensed or modeled SM data in forests is poorly understood due to a lack of suitable validation sites and interference with remote sensing caused by vegetation water content. Here, we examine three multiyear SM products: remotely sensed surface (0--5 cm) SM from combined soil moisture active passive (SMAP) and Sentinel-1 observations (SMAP/Sentinel); the SMAP Level-4 surface (0--5 cm) and root-zone (0--1 m) SM data assimilation product (SMAP-L4); and simulated surface (0--10 cm) and root-zone (0--1 m) SM from the North American land data assimilation system (NLDAS). These estimates were compared with in situ measurements from 39 National Ecological Observatory Network sites throughout the U.S. At 21 unforested sites, the performance of the three products was similar for surface SM, and all three were able to track temporal changes in surface SM. The performance of the three products declined at 18 forested sites; however, while the performance declined modestly for SMAP-L4 and NLDAS, SMAP/Sentinel performance declined so much that it was largely unable to track changes in surface SM. The SMAP-L4 and NLDAS products also reliably captured temporal changes in root-zone SM at both forested and unforested sites. Our findings indicate that both SMAP-L4 and NLDAS can be used to track surface and root-zone SM changes in forests (unbiased root-mean-square deviation: 0.03--0.06 m3 m-3).},
  keywords = {Data assimilation,Forests,in situ validation,National Ecological Observatory network (NEON),North American land data assimilation system (NLDAS),Remote sensing,SMAP mission,Soil measurements,Soil moisture,soil moisture (SM),soil moisture active passive (SMAP),Vegetation mapping},
  file = {/Users/zobitz/Zotero/storage/L8IQ9CRE/Ayres et al. - 2024 - Validation of Remotely Sensed and Modeled Soil Moisture at Forested and Unforested NEON Sites.pdf}
}

@article{baldocchiMeasuringFluxesTrace2014,
  title = {Measuring Fluxes of Trace Gases and Energy between Ecosystems and the Atmosphere - the State and Future of the Eddy Covariance Method},
  author = {Baldocchi, Dennis},
  year = 2014,
  month = dec,
  journal = {Global Change Biology},
  volume = {20},
  number = {12},
  pages = {3600--3609},
  issn = {13541013},
  doi = {10.1111/gcb.12649},
  urldate = {2014-12-15},
  langid = {english},
  file = {/Users/zobitz/Zotero/storage/CD88K7ES/gcb12649.pdf}
}

@techreport{berenbaumReportNSFBIO2015,
  title = {Report from the {{NSF BIO Advisory Committee Subcommittee}} on {{NEON Scope Impacts}}},
  author = {Berenbaum, May R and Carpenter, Stephen R and Hampton, Stephanie E and Running, Steven W and Stanzione, Dan C},
  year = 2015,
  urldate = {2024-09-03},
  langid = {english},
  file = {/Users/zobitz/Zotero/storage/FPWFLSQQ/Berenbaum et al. - Report from the NSF BIO Advisory Committee Subcomm.pdf}
}

@article{bond-lambertyCOSORECommunityDatabase2020,
  title = {{{COSORE}}: {{A}} Community Database for Continuous Soil Respiration and Other Soil-Atmosphere Greenhouse Gas Flux Data},
  shorttitle = {{{COSORE}}},
  author = {{Bond-Lamberty}, Ben and Christianson, Danielle S. and Malhotra, Avni and Pennington, Stephanie C. and Sihi, Debjani and AghaKouchak, Amir and Anjileli, Hassan and Altaf Arain, M. and Armesto, Juan J. and Ashraf, Samaneh and Ataka, Mioko and Baldocchi, Dennis and Andrew Black, Thomas and Buchmann, Nina and Carbone, Mariah S. and Chang, Shih-Chieh and Crill, Patrick and Curtis, Peter S. and Davidson, Eric A. and Desai, Ankur R. and Drake, John E. and {El-Madany}, Tarek S. and Gavazzi, Michael and G{\"o}rres, Carolyn-Monika and Gough, Christopher M. and Goulden, Michael and Gregg, Jillian and {Guti{\'e}rrez del Arroyo}, Omar and He, Jin-Sheng and Hirano, Takashi and Hopple, Anya and Hughes, Holly and J{\"a}rveoja, J{\"a}rvi and Jassal, Rachhpal and Jian, Jinshi and Kan, Haiming and Kaye, Jason and Kominami, Yuji and Liang, Naishen and Lipson, David and Macdonald, Catriona A. and Maseyk, Kadmiel and Mathes, Kayla and Mauritz, Marguerite and Mayes, Melanie A. and McNulty, Steve and Miao, Guofang and Migliavacca, Mirco and Miller, Scott and Miniat, Chelcy F. and Nietz, Jennifer G. and Nilsson, Mats B. and Noormets, Asko and Norouzi, Hamidreza and O'Connell, Christine S. and Osborne, Bruce and Oyonarte, Cecilio and Pang, Zhuo and Peichl, Matthias and Pendall, Elise and {Perez-Quezada}, Jorge F. and Phillips, Claire L. and Phillips, Richard P. and Raich, James W. and Renchon, Alexandre A. and Ruehr, Nadine K. and {S{\'a}nchez-Ca{\~n}ete}, Enrique P. and Saunders, Matthew and Savage, Kathleen E. and Schrumpf, Marion and Scott, Russell L. and Seibt, Ulli and Silver, Whendee L. and Sun, Wu and Szutu, Daphne and Takagi, Kentaro and Takagi, Masahiro and Teramoto, Munemasa and Tjoelker, Mark G. and Trumbore, Susan and Ueyama, Masahito and Vargas, Rodrigo and Varner, Ruth K. and Verfaillie, Joseph and Vogel, Christoph and Wang, Jinsong and Winston, Greg and Wood, Tana E. and Wu, Juying and Wutzler, Thomas and Zeng, Jiye and Zha, Tianshan and Zhang, Quan and Zou, Junliang},
  year = 2020,
  journal = {Global Change Biology},
  volume = {26},
  number = {12},
  pages = {7268--7283},
  issn = {1365-2486},
  doi = {10.1111/gcb.15353},
  urldate = {2022-12-01},
  abstract = {Globally, soils store two to three times as much carbon as currently resides in the atmosphere, and it is critical to understand how soil greenhouse gas (GHG) emissions and uptake will respond to ongoing climate change. In particular, the soil-to-atmosphere CO2 flux, commonly though imprecisely termed soil respiration (RS), is one of the largest carbon fluxes in the Earth system. An increasing number of high-frequency RS measurements (typically, from an automated system with hourly sampling) have been made over the last two decades; an increasing number of methane measurements are being made with such systems as well. Such high frequency data are an invaluable resource for understanding GHG fluxes, but lack a central database or repository. Here we describe the lightweight, open-source COSORE (COntinuous SOil REspiration) database and software, that focuses on automated, continuous and long-term GHG flux datasets, and is intended to serve as a community resource for earth sciences, climate change syntheses and model evaluation. Contributed datasets are mapped to a single, consistent standard, with metadata on contributors, geographic location, measurement conditions and ancillary data. The design emphasizes the importance of reproducibility, scientific transparency and open access to data. While being oriented towards continuously measured RS, the database design accommodates other soil-atmosphere measurements (e.g. ecosystem respiration, chamber-measured net ecosystem exchange, methane fluxes) as well as experimental treatments (heterotrophic only, etc.). We give brief examples of the types of analyses possible using this new community resource and describe its accompanying R software package.},
  langid = {english},
  keywords = {carbon dioxide,greenhouse gases,methane,open data,open science,soil respiration},
  file = {/Users/zobitz/Zotero/storage/FV97HSEL/Bond-Lamberty et al. - 2020 - COSORE A community database for continuous soil r.pdf;/Users/zobitz/Zotero/storage/RZAWNANP/Bond-Lamberty et al. - 2020 - COSORE A community database for continuous soil r.pdf}
}

@article{bond-lambertyGlobalDatabaseSoil2010,
  title = {A Global Database of Soil Respiration Data},
  author = {{Bond-Lamberty}, B. and Thomson, A.},
  year = 2010,
  month = jun,
  journal = {Biogeosciences},
  volume = {7},
  number = {6},
  pages = {1915--1926},
  issn = {1726-4170},
  doi = {10.5194/bg-7-1915-2010},
  urldate = {2019-12-16},
  abstract = {{$<$}p{$><$}strong{$>$}Abstract.{$<$}/strong{$>$} Soil respiration -- \emph{R}\textsubscript{S}, the flux of CO\textsubscript{2} from the soil to the atmosphere -- is probably the least well constrained component of the terrestrial carbon cycle. Here we introduce the SRDB database, a near-universal compendium of published \emph{R}\textsubscript{S} data, and make it available to the scientific community both as a traditional static archive and as a dynamic community database that may be updated over time by interested users. The database encompasses all published studies that report one of the following data measured in the field (not laboratory): annual \emph{R}\textsubscript{S}, mean seasonal \emph{R}\textsubscript{S}, a seasonal or annual partitioning of \emph{R}\textsubscript{S} into its sources fluxes, \emph{R}\textsubscript{S} temperature response (Q\textsubscript{10}), or \emph{R}\textsubscript{S} at 10 {$^\circ$}C. Its orientation is thus to seasonal and annual fluxes, not shorter-term or chamber-specific measurements. To date, data from 818 studies have been entered into the database, constituting 3379 records. The data span the measurement years 1961--2007 and are dominated by temperate, well-drained forests. We briefly examine some aspects of the SRDB data -- its climate space coverage, mean annual \emph{R}\textsubscript{S} fluxes and their correlation with other carbon fluxes, \emph{R}\textsubscript{S} variability, temperature sensitivities, and the partitioning of \emph{R}\textsubscript{S} source flux -- and suggest some potential lines of research that could be explored using these data. The SRDB database is available online in a permanent archive as well as via a project-hosting repository; the latter source leverages open-source software technologies to encourage wider participation in the database's future development. Ultimately, we hope that the updating of, and corrections to, the SRDB will become a shared project, managed by the users of these data in the scientific community.{$<$}/p{$>$}},
  langid = {english},
  file = {/Users/zobitz/Zotero/storage/VQ9BMA4B/Bond-Lamberty and Thomson - 2010 - A global database of soil respiration data.pdf}
}

@article{bond-lambertyGlobalRelationshipHeterotrophic2004,
  title = {A Global Relationship between the Heterotrophic and Autotrophic Components of Soil Respiration?},
  author = {Bond-Lamberty, Ben and Wang, Chuankuan and Gower, Stith T.},
  year = 2004,
  month = oct,
  journal = {Global Change Biology},
  volume = {10},
  number = {10},
  pages = {1756--1766},
  issn = {1354-1013, 1365-2486},
  doi = {10.1111/j.1365-2486.2004.00816.x},
  urldate = {2024-01-22},
  abstract = {Soil surface CO2 flux (RS) is overwhelmingly the product of respiration by roots (autotrophic respiration, RA) and soil organisms (heterotrophic respiration, RH). Many studies have attempted to partition RS into these two components, with highly variable results. This study analyzes published data encompassing 54 forest sites and shows that RA and RH are each strongly (R240.8) correlated to annual RS across a wide range of forest ecosystems. Monte Carlo simulation showed that these correlations were significantly stronger than any correlation introduced as an artefact of measurement method. Biome type, measurement method, mean annual temperature, soil drainage, and leaf habit were not significant. For sites with available data, there was a significant (R2 5 0.56) correlation between total detritus input and RH, while RA was unrelated to net primary production. We discuss why RA and RH might be related to each other on large scales, as both ultimately depend on forest carbon balance and photosynthate supply. Limited data suggest that these or similar relationships have broad applicability in other ecosystem types. Site-specific measurements are always more desirable than the application of inferred broad relationships, but belowground measurements are difficult and expensive, while measuring RS is straightforward and commonly done. Thus the relationships presented here provide a useful method that can help constrain estimates of terrestrial carbon budgets.},
  langid = {english},
  file = {/Users/zobitz/Zotero/storage/HC8WXAIK/Bond‐Lamberty et al. - 2004 - A global relationship between the heterotrophic an.pdf}
}

@article{bond-lambertyNewTechniquesData2018,
  title = {New {{Techniques}} and {{Data}} for {{Understanding}} the {{Global Soil Respiration Flux}}},
  author = {Bond-Lamberty, Ben},
  year = 2018,
  journal = {Earth's Future},
  volume = {6},
  number = {9},
  pages = {1176--1180},
  issn = {2328-4277},
  doi = {10.1029/2018EF000866},
  urldate = {2019-11-12},
  abstract = {Soil respiration (Rs; the soil surface-to-atmosphere CO2 flux) has been measured in the field for decades, but only recently have we begun to assemble and leverage these small-scale but extensive data. Recently, Zhao et al. (2017, https://doi.org/10.1002/2016ef000480) applied a novel artificial neural network model to the problem of estimating the global Rs flux and understanding its variations between regions and biomes. Their results point to a convergence in estimates of global Rs, and the power of leveraging the long record of observed Rs in global ecosystems, but also to uncertainties about soils' response to climate change. It will take a combination of long-term studies, data syntheses, modeling intercomparisons, and probably a new generation of sampling networks and experiments to fully resolve these questions.},
  langid = {english},
  keywords = {carbon cycle,climate change,global change,soil respiration},
  file = {/Users/zobitz/Zotero/storage/GZ4SH9LY/Bond‐Lamberty - 2018 - New Techniques and Data for Understanding the Glob.pdf;/Users/zobitz/Zotero/storage/NVHEQ34S/Bond-Lamberty - 2018 - New Techniques and Data for Understanding the Glob.pdf}
}

@article{bond-lambertyTwentyYearsProgress2024,
  title = {Twenty {{Years}} of {{Progress}}, {{Challenges}}, and {{Opportunities}} in {{Measuring}} and {{Understanding Soil Respiration}}},
  author = {{Bond-Lamberty}, Ben and Ballantyne, Ashley and Berryman, Erin and {Fluet-Chouinard}, Etienne and Jian, Jinshi and Morris, Kendalynn A. and Rey, Ana and Vargas, Rodrigo},
  year = 2024,
  journal = {Journal of Geophysical Research: Biogeosciences},
  volume = {129},
  number = {2},
  pages = {e2023JG007637},
  issn = {2169-8961},
  doi = {10.1029/2023JG007637},
  urldate = {2024-08-28},
  abstract = {Soil respiration (Rs), the soil-to-atmosphere flux of CO2, is a dominant but uncertain part of the carbon cycle, even after decades of study. This review focuses on progress in understanding Rs from laboratory incubations to global estimates. We survey key developments of in situ ecosystem-scale Rs observations and manipulations, synthesize Rs meta-analyses and global flux estimates, and discuss the most compelling challenges and opportunities for the future. Increasingly sophisticated lab experiments have yielded insights into the interaction among heterotrophic respiration, substrate supply, and enzymatic kinetics, and extended incubation-based analyses across space and time. Observational and manipulative field-based experiments have used improved measurement approaches to deepen our understanding of the integrated effects of environmental change and disturbance on Rs. Freely-available observational databases have enabled meta-analyses and studies probing the magnitude of, and constraints on, the global Rs flux. Key challenges for the field include expanding Rs measurements, experiments, and opportunities to under-represented communities and ecosystems; reconciling independent estimates of global respiration fluxes and trends; testing and leveraging the power of machine learning and process-based models, both independently and in conjunction with each other; and continuing the field's tradition of using novel experiments to explore diverse mechanisms and ecosystems.},
  copyright = {\copyright{} 2024 Battelle Memorial Institute and The Authors. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.},
  langid = {english},
  keywords = {carbon cycle,climate change,machine learning,review,soil respiration},
  file = {/Users/zobitz/Zotero/storage/8BYECEIC/Bond-Lamberty et al. - 2024 - Twenty Years of Progress, Challenges, and Opportun.pdf;/Users/zobitz/Zotero/storage/TZTIZ2FN/Bond-Lamberty et al. - 2024 - Twenty Years of Progress, Challenges, and Opportunities in Measuring and Understanding Soil Respirat.pdf;/Users/zobitz/Zotero/storage/NT4G8FSS/2023JG007637.html}
}

@article{boonDeterminationGasDiffusion2013,
  title = {Determination of the Gas Diffusion Coefficient of a Peat Grassland Soil},
  author = {Boon, A. and Robinson, J. S. and Nightingale, P. D. and Cardenas, L. and Chadwick, D. R. and Verhoef, A.},
  year = 2013,
  journal = {European Journal of Soil Science},
  volume = {64},
  number = {5},
  pages = {681--687},
  issn = {1365-2389},
  doi = {10.1111/ejss.12056},
  urldate = {2024-11-20},
  abstract = {Peatland habitats are important carbon stocks that also have the potential to be significant sources of greenhouse gases, particularly when subject to changes such as artificial drainage and application of fertilizer. Models aiming to estimate greenhouse gas release from peatlands require an accurate estimate of the diffusion coefficient of gas transport through soil (Ds). The availability of specific measurements for peatland soils is currently limited. This study measured Ds for a peat soil with an overlying clay horizon and compared values with those from widely available models. The Ds value of a sandy loam reference soil was measured for comparison. Using the Currie (1960) method, Ds was measured between an air-filled porosity ({$\epsilon$}) range of 0 and 0.5 cm3 cm-3. Values of Ds for the peat cores ranged between 3.2 \texttimes{} 10-4 and 4.4 \texttimes{} 10-3 m2 hour-1, for loamy clay cores between 0 and 4.7 \texttimes{} 10-3 m2 hour-1 and for the sandy reference soil they were between 5.4 \texttimes{} 10-4 and 3.4 \texttimes{} 10-3 m2 hour-1. The agreement of measured and modelled values of relative diffusivity (Ds/D0, with D0 the diffusion coefficient through free air) varied with soil type; however, the Campbell (1985) model provided the best replication of measured values for all soils. This research therefore suggests that the use of the Campbell model in the absence of accurately measured Ds and porosity values for a study soil would be appropriate. Future research into methods to reduce shrinkage of peat during measurement and therefore allow measurement of Ds for a greater range of {$\epsilon$} would be beneficial.},
  copyright = {\copyright{} 2013 The Authors. European Journal of Soil Science published by John Wiley \& Sons Ltd on behalf of British Society of Soil Science},
  langid = {english},
  file = {/Users/zobitz/Zotero/storage/52AHNLX2/Boon et al. - 2013 - Determination of the gas diffusion coefficient of a peat grassland soil.pdf;/Users/zobitz/Zotero/storage/APSKD4A6/ejss.html}
}

@article{boumaAssessmentRootSoil2000,
  title = {On the Assessment of Root and Soil Respiration for Soils of Different Textures: Interactions with Soil Moisture Contents and Soil {{CO2}} Concentrations},
  shorttitle = {On the Assessment of Root and Soil Respiration for Soils of Different Textures},
  author = {Bouma, Tjeerd J. and Bryla, David R.},
  year = 2000,
  month = dec,
  journal = {Plant and Soil},
  volume = {227},
  number = {1},
  pages = {215--221},
  issn = {1573-5036},
  doi = {10.1023/A:1026502414977},
  urldate = {2024-10-28},
  abstract = {Estimates of root and soil respiration are becoming increasingly important in agricultural and ecological research, but there is little understanding how soil texture and water content may affect these estimates. We examined the effects of soil texture on (i) estimated rates of root and soil respiration and (ii) soil CO2 concentrations, during cycles of soil wetting and drying in the citrus rootstock, Volkamer lemon (Citrus volkameriana Tan. and Pasq.). Plants were grown in soil columns filled with three different soil mixtures varying in their sand, silt and clay content. Root and soil respiration rates, soil water content, plant water uptake and soil CO2 concentrations were measured and dynamic relationships among these variables were developed for each soil texture treatment. We found that although the different soil textures differed in their plant-soil water relations characteristics, plant growth was only slightly affected. Root and soil respiration rates were similar under most soil moisture conditions for soils varying widely in percentages of sand, silt and clay. Only following irrigation did CO2 efflux from the soil surface vary among soils. That is, efflux of CO2 from the soil surface was much more restricted after watering (therefore rendering any respiration measurements inaccurate) in finer textured soils than in sandy soils because of reduced porosity in the finer textured soils. Accordingly, CO2 reached and maintained the highest concentrations in finer textured soils ({$>$} 40 mmol CO2 mol-1). This study revealed that changes in soil moisture can affect interpretations of root and soil measurements based on CO2 efflux, particularly in fine textured soils. The implications of the present findings for field soil CO2 flux measurements are discussed.},
  langid = {english},
  keywords = {citrus,Citrus volkameriana,root respiration,soil CO2 concentration,soil CO2 efflux,soil water relations,Volkamer lemon}
}

@article{chenDoesGeneralTemperatureDependent2005,
  title = {Does a {{General Temperature-Dependent Q10 Model}} of {{Soil Respiration Exist}} at {{Biome}} and {{Global Scale}}?},
  author = {Chen, Hua and Tian, Han-Qin},
  year = 2005,
  month = nov,
  journal = {Journal of Integrative Plant Biology},
  volume = {47},
  number = {11},
  pages = {1288--1302},
  issn = {1744-7909},
  doi = {10.1111/j.1744-7909.2005.00211.x},
  urldate = {2016-07-19},
  abstract = {Abstract:  Soil respiration (SR) is commonly modeled by a Q10 (an indicator of temperature sensitivity) function in ecosystem models. Q10 is usually treated as a constant of 2 in these models, although Q10 value of SR often decreases with increasing temperatures. It remains unclear whether a general temperature-dependent Q10 model of SR exists at biome and global scale. In this paper, we have compiled the long-term Q10 data of 38 SR studies ranging from the Boreal, Temperate, to Tropical/Subtropical biome on four continents. Our analysis indicated that the general temperature-dependent biome Q10 models of SR existed, especially in the Boreal and Temperate biomes. A single-exponential model was better than a simple linear model in fitting the average Q10 values at the biome scale. Average soil temperature is a better predictor of Q10 value than average air temperature in these models, especially in the Boreal biome. Soil temperature alone could explain about 50\% of the Q10 variations in both the Boreal and Temperate biome single-exponential Q10 model. Q10 value of SR decreased with increasing soil temperature but at quite different rates among the three biome Q10 models. The k values (Q10 decay rate constants) were 0.09, 0.07, and 0.02/oC in the Boreal, Temperate, and Tropical/Subtropical biome, respectively, suggesting that Q10 value is the most sensitive to soil temperature change in the Boreal biome, the second in the Temperate biome, and the least sensitive in the Tropical/Subtropical biome. This also indirectly confirms that acclimation of SR in many soil warming experiments probably occurs. The k value in the ``global'' single-exponential Q10 model which combined both the Boreal and Temperate biome data set was 0.08/{$^\circ$}C. However, the global general temperature-dependent Q10 model developed using the data sets of the three biomes is not adequate for predicting Q10 values of SR globally. The existence of the general temperature-dependent Q10 models of SR in the Boreal and Temperate biome has important implications for modeling SR, especially in the Boreal biome. More detail model runs are needed to exactly evaluate the impact of using a fixed Q10 vs a temperature-dependent Q10 on SR estimate in ecosystem models (e.g., TEM, Biome-BGC, and PnET). (Managing editor: Ya-Qin HAN)},
  langid = {english},
  keywords = {air temperature,biome Q10 model,global Q10 model,simple linear model,single-exponential model,soil respiration (SR),soil temperature,Temperature sensitivity (Q10)},
  file = {/Users/zobitz/Zotero/storage/6TKWPHZY/CHEN_et_al-2005-Journal_of_Integrative_Plant_Biology.pdf}
}

@article{davidsonVariabilityRespirationTerrestrial2006,
  title = {On the Variability of Respiration in Terrestrial Ecosystems: Moving beyond {{Q10}}},
  author = {Davidson, E A and Janssens, I A and Luo, Y.},
  year = 2006,
  journal = {Global Change Biology},
  volume = {12},
  pages = {154--164},
  doi = {10.1111/j.1365-2486.2005.01065.x},
  file = {/Users/zobitz/Zotero/storage/KT46JW6K/DAVIDSON_et_al-2006-Global_Change_Biology.pdf}
}

@article{dejongCalculationSoilRespiration1972,
  title = {Calculation of {{Soil Respiration}} and {{Activity}} from {{CO2 Profiles}} in the {{Soil}}},
  author = {{de Jong}, E. and Schappert, H. J. V.},
  year = 1972,
  journal = {Soil Science},
  volume = {113},
  number = {5},
  pages = {328--333},
  urldate = {2024-06-04},
  file = {/Users/zobitz/Zotero/storage/7PNMRD98/calculation_of_soil_respiration_and_activity_from.6.pdf;/Users/zobitz/Zotero/storage/J2PVFSPJ/calculation_of_soil_respiration_and_activity_from.6.html}
}

@article{desaiDriversDecadalCarbon2022,
  title = {Drivers of {{Decadal Carbon Fluxes Across Temperate Ecosystems}}},
  author = {Desai, Ankur R. and Murphy, Bailey A. and Wiesner, Susanne and Thom, Jonathan and Butterworth, Brian J. and {Koupaei-Abyazani}, Nikaan and Muttaqin, Andi and Paleri, Sreenath and Talib, Ammara and Turner, Jess and Mineau, James and Merrelli, Aronne and Stoy, Paul and Davis, Ken},
  year = 2022,
  journal = {Journal of Geophysical Research: Biogeosciences},
  volume = {127},
  number = {12},
  pages = {e2022JG007014},
  issn = {2169-8961},
  doi = {10.1029/2022JG007014},
  urldate = {2024-04-15},
  abstract = {Long-running eddy covariance flux towers provide insights into how the terrestrial carbon cycle operates over multiple timescales. Here, we evaluated variation in net ecosystem exchange (NEE) of carbon dioxide (CO2) across the Chequamegon Ecosystem-Atmosphere Study AmeriFlux core site cluster in the upper Great Lakes region of the USA from 1997 to 2020. The tower network included two mature hardwood forests with differing management regimes (US-WCr and US-Syv), two fen wetlands with varying levels of canopy sheltering and vegetation (US-Los and US-ALQ), and a very tall (400 m) landscape-level tower (US-PFa). Together, they provided over 70 site-years of observations. The 19-tower Chequamegon Heterogenous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors 2019 campaign centered around US-PFa provided additional information on the spatial variation of NEE. Decadal variability was present in all long-term sites, but cross-site coherence in interannual NEE in the earlier part of the record became weaker with time as non-climatic factors such as local disturbances likely dominated flux time series. Average decadal NEE at the tall tower transitioned from carbon source to sink to near neutral over 24 years. Respiration had a greater effect than photosynthesis on driving variations in NEE at all sites. Declining snowfall offset potential increases in assimilation from warmer springs, as less-insulated soils delayed start of spring green-up. Higher CO2 increased maximum net assimilation parameters but not total gross primary productivity. Stand-scale sites were larger net sinks than the landscape tower. Clustered, long-term carbon flux observations provide value for understanding the diverse links between carbon and climate and the challenges of upscaling these responses across space.},
  copyright = {\copyright{} 2022. The Authors.},
  langid = {english},
  keywords = {AmeriFlux,carbon fluxes,CHEESEHEAD19,eddy covariance,forests,wetlands},
  file = {/Users/zobitz/Zotero/storage/6EH38TEI/Desai et al. - 2022 - Drivers of Decadal Carbon Fluxes Across Temperate .pdf;/Users/zobitz/Zotero/storage/KFS5WR54/2022JG007014.html}
}

@book{efronIntroductionBootstrap1994,
  title = {An {{Introduction}} to the {{Bootstrap}}},
  author = {Efron, Bradley and Tibshirani, R. J.},
  year = 1994,
  month = may,
  publisher = {{Chapman and Hall/CRC}},
  address = {New York},
  doi = {10.1201/9780429246593},
  abstract = {An Introduction to the Bootstrap arms scientists and engineers as well as statisticians with the computational techniques they need to analyze and understand complicated data sets. The bootstrap is a computer-based method of statistical inference that answers statistical questions without formulas and gives a direct appreciation of variance, bias, coverage, and other probabilistic phenomena. This book presents an overview of the bootstrap and related methods for assessing statistical accuracy, concentrating on the ideas rather than their mathematical justification. Not just for beginners, the presentation starts off slowly, but builds in both scope and depth to ideas that are quite sophisticated.},
  isbn = {978-0-429-24659-3}
}

@article{elshallRelativeModelScore2018,
  title = {Relative Model Score: A Scoring Rule for Evaluating Ensemble Simulations with Application to Microbial Soil Respiration Modeling},
  shorttitle = {Relative Model Score},
  author = {Elshall, Ahmed S. and Ye, Ming and Pei, Yongzhen and Zhang, Fan and Niu, Guo-Yue and {Barron-Gafford}, Greg A.},
  year = 2018,
  month = oct,
  journal = {Stochastic Environmental Research and Risk Assessment},
  volume = {32},
  number = {10},
  pages = {2809--2819},
  issn = {1436-3259},
  doi = {10.1007/s00477-018-1592-3},
  urldate = {2024-10-21},
  abstract = {This paper defines a new scoring rule, namely relative model score (RMS), for evaluating ensemble simulations of environmental models. RMS implicitly incorporates the measures of ensemble mean accuracy, prediction interval precision, and prediction interval reliability for evaluating the overall model predictive performance. RMS is numerically evaluated from the probability density functions of ensemble simulations given by individual models or several models via model averaging. We demonstrate the advantages of using RMS through an example of soil respiration modeling. The example considers two alternative models with different fidelity, and for each model Bayesian inverse modeling is conducted using two different likelihood functions. This gives four single-model ensembles of model simulations. For each likelihood function, Bayesian model averaging is applied to the ensemble simulations of the two models, resulting in two multi-model prediction ensembles. Predictive performance for these ensembles is evaluated using various scoring rules. Results show that RMS outperforms the commonly used scoring rules of log-score, pseudo Bayes factor based on Bayesian model evidence (BME), and continuous ranked probability score (CRPS). RMS avoids the problem of rounding error specific to log-score. Being applicable to any likelihood functions, RMS has broader applicability than BME that is only applicable to the same likelihood function~of multiple models. By directly considering the relative score of candidate models at each cross-validation datum, RMS results in more plausible model ranking than CRPS. Therefore, RMS is considered as a robust scoring rule for evaluating predictive performance of single-model and multi-model prediction ensembles.},
  langid = {english},
  keywords = {Bayes factor,Continuous ranked probability score,Dispersion,Log-score,Reliability,Scoring rule}
}

@article{falgeGapFillingStrategies2001,
  title = {Gap Filling Strategies for Defensible Annual Sums of Net Ecosystem Exchange},
  author = {Falge, Eva and Baldocchi, Dennis and Olson, Richard and Anthoni, Peter and Aubinet, Marc and Bernhofer, Christian and Burba, George and Ceulemans, Reinhart and Clement, Robert and Dolman, Han and Granier, Andr{\'e} and Gross, Patrick and Gr{\"u}nwald, Thomas and Hollinger, David and Jensen, Niels-Otto and Katul, Gabriel and Keronen, Petri and Kowalski, Andrew and Lai, Chun Ta and Law, Beverley E. and Meyers, Tilden and Moncrieff, John and Moors, Eddy and Munger, J. William and Pilegaard, Kim and Rannik, {\"U}llar and Rebmann, Corinna and Suyker, Andrew and Tenhunen, John and Tu, Kevin and Verma, Shashi and Vesala, Timo and Wilson, Kell and Wofsy, Steve},
  year = 2001,
  month = mar,
  journal = {Agricultural and Forest Meteorology},
  volume = {107},
  number = {1},
  pages = {43--69},
  issn = {0168-1923},
  doi = {10.1016/S0168-1923(00)00225-2},
  urldate = {2024-10-18},
  abstract = {Heightened awareness of global change issues within both science and political communities has increased interest in using the global network of eddy covariance flux towers to more fully understand the impacts of natural and anthropogenic phenomena on the global carbon balance. Comparisons of net ecosystem exchange (FNEE) responses are being made among biome types, phenology patterns, and stress conditions. The comparisons are usually performed on annual sums of FNEE; however, the average data coverage during a year is only 65\%. Therefore, robust and consistent gap filling methods are required. We review several methods of gap filling and apply them to data sets available from the EUROFLUX and AmeriFlux databases. The methods are based on mean diurnal variation (MDV), look-up tables (LookUp), and nonlinear regressions (Regr.), and the impact of different gap filling methods on the annual sum of FNEE is investigated. The difference between annual FNEE filled by MDV compared to FNEE filled by Regr. ranged from -45 to +200gCm-2 per year (MDV-Regr.). Comparing LookUp and Regr. methods resulted in a difference (LookUp-Regr.) ranging from -30 to +150gCm-2 per year. We also investigated the impact of replacing measurements at night, when turbulent mixing is insufficient. The nighttime correction for low friction velocities (u{$\ast$}) shifted annual FNEE on average by +77gCm-2 per year, but in certain cases as much as +185gCm-2 per year. Our results emphasize the need to standardize gap filling-methods for improving the comparability of flux data products from regional and global flux networks.},
  keywords = {AmeriFlux,Data filling,Eddy covariance,EUROFLUX,FLUXNET,Interpolation techniques},
  file = {/Users/zobitz/Zotero/storage/XJVJ96D5/Falge et al. - 2001 - Gap filling strategies for defensible annual sums .pdf;/Users/zobitz/Zotero/storage/2YZ46B2H/S0168192300002252.html}
}

@article{farranceUncertaintyMeasurementReview2012,
  title = {Uncertainty of {{Measurement}}: {{A Review}} of the {{Rules}} for {{Calculating Uncertainty Components}} through {{Functional Relationships}}},
  shorttitle = {Uncertainty of {{Measurement}}},
  author = {Farrance, Ian and Frenkel, Robert},
  year = 2012,
  month = may,
  journal = {The Clinical Biochemist Reviews},
  volume = {33},
  number = {2},
  pages = {49--75},
  issn = {0159-8090},
  urldate = {2024-05-09},
  abstract = {The Evaluation of Measurement Data - Guide to the Expression of Uncertainty in Measurement (usually referred to as the GUM) provides general rules for evaluating and expressing uncertainty in measurement. When a measurand, y, is calculated from other measurements through a functional relationship, uncertainties in the input variables will propagate through the calculation to an uncertainty in the output y. The manner in which such uncertainties are propagated through a functional relationship provides much of the mathematical challenge to fully understanding the GUM., The aim of this review is to provide a general overview of the GUM and to show how the calculation of uncertainty in the measurand may be achieved through a functional relationship. That is, starting with the general equation for combining uncertainty components as outlined in the GUM, we show how this general equation can be applied to various functional relationships in order to derive a combined standard uncertainty for the output value of the particular function (the measurand). The GUM equation may be applied to any mathematical form or functional relationship (the starting point for laboratory calculations) and describes the propagation of uncertainty from the input variable(s) to the output value of the function (the end point or outcome of the laboratory calculation). A rule-based approach is suggested with a number of the more common rules tabulated for the routine calculation of measurement uncertainty.},
  pmcid = {PMC3387884},
  pmid = {22896744},
  file = {/Users/zobitz/Zotero/storage/VIBET8G9/Farrance and Frenkel - 2012 - Uncertainty of Measurement A Review of the Rules .pdf}
}

@article{friedlingsteinGlobalCarbonBudget2023,
  title = {Global {{Carbon Budget}} 2023},
  author = {Friedlingstein, Pierre and O'Sullivan, Michael and Jones, Matthew W. and Andrew, Robbie M. and Bakker, Dorothee C. E. and Hauck, Judith and Landsch{\"u}tzer, Peter and Le Qu{\'e}r{\'e}, Corinne and Luijkx, Ingrid T. and Peters, Glen P. and Peters, Wouter and Pongratz, Julia and Schwingshackl, Clemens and Sitch, Stephen and Canadell, Josep G. and Ciais, Philippe and Jackson, Robert B. and Alin, Simone R. and Anthoni, Peter and Barbero, Leticia and Bates, Nicholas R. and Becker, Meike and Bellouin, Nicolas and Decharme, Bertrand and Bopp, Laurent and Brasika, Ida Bagus Mandhara and Cadule, Patricia and Chamberlain, Matthew A. and Chandra, Naveen and Chau, Thi-Tuyet-Trang and Chevallier, Fr{\'e}d{\'e}ric and Chini, Louise P. and Cronin, Margot and Dou, Xinyu and Enyo, Kazutaka and Evans, Wiley and Falk, Stefanie and Feely, Richard A. and Feng, Liang and Ford, Daniel J. and Gasser, Thomas and Ghattas, Josefine and Gkritzalis, Thanos and Grassi, Giacomo and Gregor, Luke and Gruber, Nicolas and G{\"u}rses, {\"O}zg{\"u}r and Harris, Ian and Hefner, Matthew and Heinke, Jens and Houghton, Richard A. and Hurtt, George C. and Iida, Yosuke and Ilyina, Tatiana and Jacobson, Andrew R. and Jain, Atul and Jarn{\'i}kov{\'a}, Tereza and Jersild, Annika and Jiang, Fei and Jin, Zhe and Joos, Fortunat and Kato, Etsushi and Keeling, Ralph F. and Kennedy, Daniel and Klein Goldewijk, Kees and Knauer, J{\"u}rgen and Korsbakken, Jan Ivar and K{\"o}rtzinger, Arne and Lan, Xin and Lef{\`e}vre, Nathalie and Li, Hongmei and Liu, Junjie and Liu, Zhiqiang and Ma, Lei and Marland, Greg and Mayot, Nicolas and McGuire, Patrick C. and McKinley, Galen A. and Meyer, Gesa and Morgan, Eric J. and Munro, David R. and Nakaoka, Shin-Ichiro and Niwa, Yosuke and O'Brien, Kevin M. and Olsen, Are and Omar, Abdirahman M. and Ono, Tsuneo and Paulsen, Melf and Pierrot, Denis and Pocock, Katie and Poulter, Benjamin and Powis, Carter M. and Rehder, Gregor and Resplandy, Laure and Robertson, Eddy and R{\"o}denbeck, Christian and Rosan, Thais M. and Schwinger, J{\"o}rg and S{\'e}f{\'e}rian, Roland and Smallman, T. Luke and Smith, Stephen M. and {Sospedra-Alfonso}, Reinel and Sun, Qing and Sutton, Adrienne J. and Sweeney, Colm and Takao, Shintaro and Tans, Pieter P. and Tian, Hanqin and Tilbrook, Bronte and Tsujino, Hiroyuki and Tubiello, Francesco and {van der Werf}, Guido R. and {van Ooijen}, Erik and Wanninkhof, Rik and Watanabe, Michio and {Wimart-Rousseau}, Cathy and Yang, Dongxu and Yang, Xiaojuan and Yuan, Wenping and Yue, Xu and Zaehle, S{\"o}nke and Zeng, Jiye and Zheng, Bo},
  year = 2023,
  month = dec,
  journal = {Earth System Science Data},
  volume = {15},
  number = {12},
  pages = {5301--5369},
  publisher = {Copernicus GmbH},
  issn = {1866-3508},
  doi = {10.5194/essd-15-5301-2023},
  urldate = {2024-01-14},
  abstract = {Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFOS) are based on energy statistics and cement production data, while emissions from land-use change (ELUC), mainly deforestation, are based on land-use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly, and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) is estimated with global ocean biogeochemistry models and observation-based fCO2 products. The terrestrial CO2 sink (SLAND) is estimated with dynamic global vegetation models. Additional lines of evidence on land and ocean sinks are provided by atmospheric inversions, atmospheric oxygen measurements, and Earth system models. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and incomplete understanding of the contemporary carbon cycle. All uncertainties are reported as \textpm 1{$\sigma$}.  For the year 2022, EFOS increased by 0.9 \% relative to 2021, with fossil emissions at 9.9\textpm 0.5 Gt C yr-1 (10.2\textpm 0.5 Gt C yr-1 when the cement carbonation sink is not included), and ELUC was 1.2\textpm 0.7 Gt C yr-1, for a total anthropogenic CO2 emission (including the cement carbonation sink) of 11.1\textpm 0.8 Gt C yr-1 (40.7\textpm 3.2 Gt CO2 yr-1). Also, for 2022, GATM was 4.6\textpm 0.2 Gt C yr-1 (2.18\textpm 0.1 ppm yr-1; ppm denotes parts per million), SOCEAN was 2.8\textpm 0.4 Gt C yr-1, and SLAND was 3.8\textpm 0.8 Gt C yr-1, with a BIM of -0.1 Gt C yr-1 (i.e. total estimated sources marginally too low or sinks marginally too high). The global atmospheric CO2 concentration averaged over 2022 reached 417.1\textpm 0.1 ppm. Preliminary data for 2023 suggest an increase in EFOS relative to 2022 of +1.1 \% (0.0 \% to 2.1 \%) globally and atmospheric CO2 concentration reaching 419.3 ppm, 51 \% above the pre-industrial level (around 278 ppm in 1750). Overall, the mean of and trend in the components of the global carbon budget are consistently estimated over the period 1959--2022, with a near-zero overall budget imbalance, although discrepancies of up to around 1 Gt C yr-1 persist for the representation of annual to semi-decadal variability in CO2 fluxes. Comparison of estimates from multiple approaches and observations shows the following: (1) a persistent large uncertainty in the estimate of land-use changes emissions, (2) a low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) a discrepancy between the different methods on the strength of the ocean sink over the last decade.  This living-data update documents changes in methods and data sets applied to this most recent global carbon budget as well as evolving community understanding of the global carbon cycle. The data presented in this work are available at https://doi.org/10.18160/GCP-2023 (Friedlingstein et al., 2023).},
  langid = {english},
  file = {/Users/zobitz/Zotero/storage/2AABQ5EA/Friedlingstein et al. - 2023 - Global Carbon Budget 2023.pdf}
}

@article{friedlingsteinGlobalCarbonBudget2025,
  title = {Global {{Carbon Budget}} 2024},
  author = {Friedlingstein, Pierre and O'Sullivan, Michael and Jones, Matthew W. and Andrew, Robbie M. and Hauck, Judith and Landsch{\"u}tzer, Peter and Le Qu{\'e}r{\'e}, Corinne and Li, Hongmei and Luijkx, Ingrid T. and Olsen, Are and Peters, Glen P. and Peters, Wouter and Pongratz, Julia and Schwingshackl, Clemens and Sitch, Stephen and Canadell, Josep G. and Ciais, Philippe and Jackson, Robert B. and Alin, Simone R. and Arneth, Almut and Arora, Vivek and Bates, Nicholas R. and Becker, Meike and Bellouin, Nicolas and Berghoff, Carla F. and Bittig, Henry C. and Bopp, Laurent and Cadule, Patricia and Campbell, Katie and Chamberlain, Matthew A. and Chandra, Naveen and Chevallier, Fr{\'e}d{\'e}ric and Chini, Louise P. and Colligan, Thomas and Decayeux, Jeanne and Djeutchouang, Laique M. and Dou, Xinyu and Duran Rojas, Carolina and Enyo, Kazutaka and Evans, Wiley and Fay, Amanda R. and Feely, Richard A. and Ford, Daniel J. and Foster, Adrianna and Gasser, Thomas and Gehlen, Marion and Gkritzalis, Thanos and Grassi, Giacomo and Gregor, Luke and Gruber, Nicolas and G{\"u}rses, {\"O}zg{\"u}r and Harris, Ian and Hefner, Matthew and Heinke, Jens and Hurtt, George C. and Iida, Yosuke and Ilyina, Tatiana and Jacobson, Andrew R. and Jain, Atul K. and Jarn{\'i}kov{\'a}, Tereza and Jersild, Annika and Jiang, Fei and Jin, Zhe and Kato, Etsushi and Keeling, Ralph F. and Klein Goldewijk, Kees and Knauer, J{\"u}rgen and Korsbakken, Jan Ivar and Lan, Xin and Lauvset, Siv K. and Lef{\`e}vre, Nathalie and Liu, Zhu and Liu, Junjie and Ma, Lei and Maksyutov, Shamil and Marland, Gregg and Mayot, Nicolas and McGuire, Patrick C. and Metzl, Nicolas and Monacci, Natalie M. and Morgan, Eric J. and Nakaoka, Shin-Ichiro and Neill, Craig and Niwa, Yosuke and N{\"u}tzel, Tobias and Olivier, Lea and Ono, Tsuneo and Palmer, Paul I. and Pierrot, Denis and Qin, Zhangcai and Resplandy, Laure and Roobaert, Aliz{\'e}e and Rosan, Thais M. and R{\"o}denbeck, Christian and Schwinger, J{\"o}rg and Smallman, T. Luke and Smith, Stephen M. and {Sospedra-Alfonso}, Reinel and Steinhoff, Tobias and Sun, Qing and Sutton, Adrienne J. and S{\'e}f{\'e}rian, Roland and Takao, Shintaro and Tatebe, Hiroaki and Tian, Hanqin and Tilbrook, Bronte and Torres, Olivier and Tourigny, Etienne and Tsujino, Hiroyuki and Tubiello, Francesco and {van der Werf}, Guido and Wanninkhof, Rik and Wang, Xuhui and Yang, Dongxu and Yang, Xiaojuan and Yu, Zhen and Yuan, Wenping and Yue, Xu and Zaehle, S{\"o}nke and Zeng, Ning and Zeng, Jiye},
  year = 2025,
  month = mar,
  journal = {Earth System Science Data},
  volume = {17},
  number = {3},
  pages = {965--1039},
  publisher = {Copernicus GmbH},
  issn = {1866-3508},
  doi = {10.5194/essd-17-965-2025},
  urldate = {2025-06-23},
  abstract = {Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize datasets and methodologies to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFOS) are based on energy statistics and cement production data, while emissions from land-use change (ELUC) are based on land-use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly, and its growth rate (GATM) is computed from the annual changes in concentration. The global net uptake of CO2 by the ocean (SOCEAN, called the ocean sink) is estimated with global ocean biogeochemistry models and observation-based fCO2 products (fCO2 is the fugacity of CO2). The global net uptake of CO2 by the land (SLAND, called the land sink) is estimated with dynamic global vegetation models. Additional lines of evidence on land and ocean sinks are provided by atmospheric inversions, atmospheric oxygen measurements, and Earth system models. The sum of all sources and sinks results in the carbon budget imbalance (BIM), a measure of imperfect data and incomplete understanding of the contemporary carbon cycle. All uncertainties are reported as \textpm 1{$\sigma$}.  For the year 2023, EFOS increased by 1.3 \% relative to 2022, with fossil emissions at 10.1 \textpm{} 0.5 GtC yr-1 (10.3 \textpm{} 0.5 GtC yr-1 when the cement carbonation sink is not included), and ELUC was 1.0 \textpm{} 0.7 GtC yr-1, for a total anthropogenic CO2 emission (including the cement carbonation sink) of 11.1 \textpm{} 0.9 GtC yr-1 (40.6 \textpm{} 3.2 GtCO2 yr-1). Also, for 2023, GATM was 5.9 \textpm{} 0.2 GtC yr-1 (2.79 \textpm{} 0.1 ppm yr-1; ppm denotes parts per million), SOCEAN was 2.9 \textpm{} 0.4 GtC yr-1, and SLAND was 2.3 \textpm{} 1.0 GtC yr-1, with a near-zero BIM (-0.02 GtC yr-1). The global atmospheric CO2 concentration averaged over 2023 reached 419.31 \textpm{} 0.1 ppm. Preliminary data for 2024 suggest an increase in EFOS relative to 2023 of +0.8 \% (-0.2 \% to 1.7 \%) globally and an atmospheric CO2 concentration increase by 2.87 ppm, reaching 422.45 ppm, 52 \% above the pre-industrial level (around 278 ppm in 1750). Overall, the mean of and trend in the components of the global carbon budget are consistently estimated over the period 1959--2023, with a near-zero overall budget imbalance, although discrepancies of up to around 1 GtC yr-1 persist for the representation of annual to semi-decadal variability in CO2 fluxes. Comparison of estimates from multiple approaches and observations shows the following: (1) a persistent large uncertainty in the estimate of land-use change emissions, (2) low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) a discrepancy between the different methods on the mean ocean sink.  This living-data update documents changes in methods and datasets applied to this most recent global carbon budget as well as evolving community understanding of the global carbon cycle. The data presented in this work are available at https://doi.org/10.18160/GCP-2024 (Friedlingstein et al., 2024).},
  langid = {english},
  file = {/Users/zobitz/Zotero/storage/TYPBZQHW/Friedlingstein et al. - 2025 - Global Carbon Budget 2024.pdf}
}

@article{hamdiSynthesisAnalysisTemperature2013,
  title = {Synthesis Analysis of the Temperature Sensitivity of Soil Respiration from Laboratory Studies in Relation to Incubation Methods and Soil Conditions},
  author = {Hamdi, Salwa and Moyano, Fernando and Sall, Saidou and Bernoux, Martial and Chevallier, Tiphaine},
  year = 2013,
  month = mar,
  journal = {Soil Biology and Biochemistry},
  volume = {58},
  pages = {115--126},
  issn = {0038-0717},
  doi = {10.1016/j.soilbio.2012.11.012},
  urldate = {2019-11-12},
  abstract = {The temperature sensitivity of soil respiration is a main factor determining the response of global terrestrial soil carbon to global warming and, consequently, its feedback on atmospheric CO2 concentrations. A data synthesis was performed to summarize information available in the literature on the temperature sensitivity of soil respiration obtained in laboratory soil incubations and expressed as Q10. The influence of common experimental variables and methods, i.e. range of incubation temperatures, length of incubation, calculation methods, and amounts of soil organic carbon, was analyzed. We found a small but significant difference between the Q10 values calculated with different experimental methods as well as time-related trends showing an initial decrease followed by stable values. Q10 values ranged from 0.5 to over 300 and were negatively correlated with temperature, but only at the range of temperatures below 25~{$^\circ$}C. A similar dependence of the activation energy (derived from the Arrhenius equation) with temperature was observed. A negative relationship with total organic carbon content of soils was found in forest and grassland ecosystems, with an average decrease in Q10 of 0.02~mgC~g-1 soil, explaining their slightly lower mean Q10s compared to cultivated soils. Because most of the observed variability remained unexplained, we emphasize the need for new approaches in future studies to the problem of understanding the temperature sensitivity of soil organic matter decomposition.},
  langid = {english},
  keywords = {Heterotrophic soil respiration,Laboratory incubation,Meta-analysis,Soil organic carbon,Temperature sensitivity ()},
  file = {/Users/zobitz/Zotero/storage/TNET7JG6/Hamdi et al. - 2013 - Synthesis analysis of the temperature sensitivity .pdf}
}

@article{hiranoLongtermHalfhourlyMeasurement2003,
  title = {Long-Term Half-Hourly Measurement of Soil {{CO2}} Concentration and Soil Respiration in a Temperate Deciduous Forest},
  author = {Hirano, Takashi and Kim, Honghyun and Tanaka, Yumiko},
  year = 2003,
  journal = {Journal of Geophysical Research: Atmospheres},
  volume = {108},
  number = {D20},
  issn = {2156-2202},
  doi = {10.1029/2003JD003766},
  urldate = {2019-12-17},
  abstract = {We conducted a field experiment in a cool-temperate deciduous forest to investigate the dynamic behavior of soil CO2 and the vertical distribution of soil respiration. Soil CO2 concentration (C) was measured half-hourly at four depths for 6 months in 2000 with infrared gas analyzers installed below ground. Using C profiles, soil surface CO2 efflux (F0), CO2 production rates of the topsoil (PA), and CO2 flux from the subsoil to topsoil (FCA) were evaluated half-hourly by applying Fick's first law. Some remarkable short-term and long-term variations were found in C, F0, PA, FCA, and the contribution of topsoil respiration to total soil respiration (PA/F0), which include (1) rapid increase in C and decrease in F0 and PA due to rainwater infiltration, (2) diurnal variation in C coupled with that of the atmosphere, (3) diurnal variation in F0 and PA similar to that of topsoil temperature, (4) decrease in C, F0, and PA following soil drying in August, (5) linearly increasing FCA between late May and mid-September, and (6) decrease in PA/F0 from around 0.9 during summer to 0.3 in November. The variation of PA was mainly controlled by soil temperature at -0.07 m between 7{$^\circ$} and 17{$^\circ$}C, although PA did not respond well to soil temperature above and below this temperature range. Above 17{$^\circ$}C, PA increased linearly with soil moisture, and moisture variation accounted for the PA decrease in August. Neither temperature nor moisture explained the PA behavior below 7{$^\circ$}C. Subsoil respiration (FCA) showed an exponential relationship with soil temperature at -1 m.},
  copyright = {Copyright 2003 by the American Geophysical Union.},
  langid = {english},
  keywords = {soil CO2,soil respiration,temperate forest},
  file = {/Users/zobitz/Zotero/storage/89RK7X7M/Hirano et al. - 2003 - Long-term half-hourly measurement of soil CO2 conc.pdf}
}

@article{jacksonEcologySoilCarbon2017,
  title = {The {{Ecology}} of {{Soil Carbon}}: {{Pools}}, {{Vulnerabilities}}, and {{Biotic}} and {{Abiotic Controls}}},
  shorttitle = {The {{Ecology}} of {{Soil Carbon}}},
  author = {Jackson, Robert B. and Lajtha, Kate and Crow, Susan E. and Hugelius, Gustaf and Kramer, Marc G. and Pi{\~n}eiro, Gervasio},
  year = 2017,
  month = nov,
  journal = {Annual Review of Ecology, Evolution and Systematics},
  volume = {48},
  number = {Volume 48, 2017},
  pages = {419--445},
  publisher = {Annual Reviews},
  issn = {1543-592X, 1545-2069},
  doi = {10.1146/annurev-ecolsys-112414-054234},
  urldate = {2024-04-15},
  abstract = {Soil organic matter (SOM) anchors global terrestrial productivity and food and fiber supply. SOM retains water and soil nutrients and stores more global carbon than do plants and the atmosphere combined. SOM is also decomposed by microbes, returning CO2, a greenhouse gas, to the atmosphere. Unfortunately, soil carbon stocks have been widely lost or degraded through land use changes and unsustainable forest and agricultural practices. To understand its structure and function and to maintain and restore SOM, we need a better appreciation of soil organic carbon (SOC) saturation capacity and the retention of above- and belowground inputs in SOM. Our analysis suggests root inputs are approximately five times more likely than an equivalent mass of aboveground litter to be stabilized as SOM. Microbes, particularly fungi and bacteria, and soil faunal food webs strongly influence SOM decomposition at shallower depths, whereas mineral associations drive stabilization at depths greater than {$\sim$}30 cm. Global uncertainties in the amounts and locations of SOM include the extent of wetland, peatland, and permafrost systems and factors that constrain soil depths, such as shallow bedrock. In consideration of these uncertainties, we estimate global SOC stocks at depths of 2 and 3 m to be between 2,270 and 2,770 Pg, respectively, but could be as much as 700 Pg smaller. Sedimentary deposits deeper than 3 m likely contain \&gt;500 Pg of additional SOC. Soils hold the largest biogeochemically active terrestrial carbon pool on Earth and are critical for stabilizing atmospheric CO2 concentrations. Nonetheless, global pressures on soils continue from changes in land management, including the need for increasing bioenergy and food production.},
  langid = {english},
  file = {/Users/zobitz/Zotero/storage/NIIQXZ5W/Jackson et al. - 2017 - The Ecology of Soil Carbon Pools, Vulnerabilities.pdf;/Users/zobitz/Zotero/storage/PMBECSIM/annurev-ecolsys-112414-054234.html}
}

@article{jianCollarPropertiesMeasurement2020,
  title = {Collar {{Properties}} and {{Measurement Time Confer Minimal Bias Overall}} on {{Annual Soil Respiration Estimates}} in a {{Global Database}}},
  author = {Jian, Jinshi and Gough, Christopher and Sihi, Debjani and Hopple, Anya M. and {Bond-Lamberty}, Ben},
  year = 2020,
  journal = {Journal of Geophysical Research: Biogeosciences},
  volume = {125},
  number = {12},
  pages = {e2020JG006066},
  issn = {2169-8961},
  doi = {10.1029/2020JG006066},
  urldate = {2024-08-28},
  abstract = {Measuring the soil-to-atmosphere carbon dioxide (CO2) flux (soil respiration, RS) is important to understanding terrestrial carbon balance and to forecasting climate change. Such measurements are frequently made using measurement collars permanently inserted into the soil surface. However, differences in measurement duration and frequency, as well as collar properties, may lead to biases in the estimation of annual RS. Using a newly updated global RS database (SRDB-V5), we investigated the annual RS bias associated with five methodological factors: collar height, collar coverage area, collar insertion depth, measurement duration, and measurement frequency. We found that annual RS was negatively correlated with collar insertion depth, consistent with the idea that collar insertion cuts roots and thus reduces RS. Annual RS was also negatively related with collar height and collar coverage area, perhaps because uniform head-space mixing is difficult to achieve in larger volume chambers; however, these effects were quantitatively small (bias of 2\% to 10\% of mean RS). We found no correlation of measurement duration or measurement frequency with annual RS. These findings suggest that variation in RS methodology generally introduces minimal bias overall. Therefore, compilations of minimally adjusted annual RS measurements provide a reliable resource for synthesis studies, global annual RS modeling, and investigation of how soil carbon responds to climate change.},
  copyright = {\copyright 2020. American Geophysical Union. All Rights Reserved.},
  langid = {english},
  keywords = {collar,database,Soil respiration},
  file = {/Users/zobitz/Zotero/storage/K2SNTKSN/Jian et al. - 2020 - Collar Properties and Measurement Time Confer Minimal Bias Overall on Annual Soil Respiration Estima.pdf;/Users/zobitz/Zotero/storage/KQTMALSA/2020JG006066.html}
}

@article{jiangGlobalSoilRespiration2024,
  title = {Global Soil Respiration Predictions with Associated Uncertainties from Different Spatio-Temporal Data Subsets},
  author = {Jiang, Junjie and Feng, Lingxia and Hu, Junguo and Liu, Haoqi and Zhu, Chao and Chen, Baitong and Chen, Taolue},
  year = 2024,
  month = sep,
  journal = {Ecological Informatics},
  volume = {82},
  pages = {102777},
  issn = {1574-9541},
  doi = {10.1016/j.ecoinf.2024.102777},
  urldate = {2024-08-28},
  abstract = {Soil respiration (Rs), the second-largest flux in the global carbon cycle, is a crucial but uncertain component. To improve the understanding of global Rs, we constructed single global models, and specific models classified by climate type, land cover type, year of the data record, and elevation range using the random forest algorithm to predict global Rs values and explore the associated uncertainty in the models. The results showed a similar overall predictive performance for the models, with an R-squared value greater than 0.63; however, significant differences were observed compared to the global Rs estimate (23 Pg C). All the models estimated larger values of Rs than the single global model, mainly owing to imbalances in the sample data on which the prediction models were based. One exception to this result is the land cover model, which estimates a smaller global Rs for 2020 (95.1 Pg C). Overall, the single global model estimates were closer to those obtained for temperate zones owing to differences in the training data distribution, which resulted in smaller global estimates than those of other classification-specific models. Prediction models using observations before 2000 tend to underestimate the global Rs. However, the use of classification-specific Rs models proved helpful in addressing the persistent temporal and spatial imbalances in Rs sampling. Expanding the coverage of Rs records both temporally and spatially and updating the global Rs database promptly would improve the estimation accuracy of global Rs prediction models while enhancing the understanding of the overall global carbon budget and the feedback of soil carbon with regard to climate warming.},
  keywords = {Feature selection,Random forest algorithm,Single global model,Soil carbon cycle,Soil respiration},
  file = {/Users/zobitz/Zotero/storage/GHSLQDD5/Jiang et al. - 2024 - Global soil respiration predictions with associated uncertainties from different spatio-temporal dat.pdf;/Users/zobitz/Zotero/storage/HPMZZ7ZW/S1574954124003194.html}
}

@article{jianHistoricallyInconsistentProductivity2022,
  title = {Historically Inconsistent Productivity and Respiration Fluxes in the Global Terrestrial Carbon Cycle},
  author = {Jian, Jinshi and Bailey, Vanessa and Dorheim, Kalyn and Konings, Alexandra G. and Hao, Dalei and Shiklomanov, Alexey N. and Snyder, Abigail and Steele, Meredith and Teramoto, Munemasa and Vargas, Rodrigo and {Bond-Lamberty}, Ben},
  year = 2022,
  month = apr,
  journal = {Nature Communications},
  volume = {13},
  number = {1},
  pages = {1733},
  publisher = {Nature Publishing Group},
  issn = {2041-1723},
  doi = {10.1038/s41467-022-29391-5},
  urldate = {2022-04-01},
  abstract = {The terrestrial carbon cycle is a major source of uncertainty in climate projections. Its dominant fluxes, gross primary productivity (GPP), and respiration (in particular soil respiration, RS), are typically estimated from independent satellite-driven models and upscaled in situ measurements, respectively. We combine carbon-cycle flux estimates and partitioning coefficients to show that historical estimates of global GPP and RS are irreconcilable. When we estimate GPP based on RS measurements and some assumptions about RS:GPP ratios, we found the resulted global GPP values (bootstrap mean \$\$\textbraceleft 149\textbraceright\_\textbraceleft -23\textbraceright\textasciicircum\textbraceleft +29\textbraceright\$\$Pg C yr-1) are significantly higher than most GPP estimates reported in the literature (\$\$\textbraceleft 113\textbraceright\_\textbraceleft -18\textbraceright\textasciicircum\textbraceleft +18\textbraceright\$\$Pg C yr-1). Similarly, historical GPP estimates imply a soil respiration flux (RsGPP, bootstrap mean of \$\$\textbraceleft 68\textbraceright\_\textbraceleft -8\textbraceright\textasciicircum\textbraceleft +10\textbraceright\$\$Pg C yr-1) statistically inconsistent with most published RS values (\$\$\textbraceleft 87\textbraceright\_\textbraceleft -8\textbraceright\textasciicircum\textbraceleft +9\textbraceright\$\$Pg C yr-1), although recent, higher, GPP estimates are narrowing this gap. Furthermore, global RS:GPP ratios are inconsistent with spatial averages of this ratio calculated from individual sites as well as CMIP6 model results. This discrepancy has implications for our understanding of carbon turnover times and the terrestrial sensitivity to climate change. Future efforts should reconcile the discrepancies associated with calculations for GPP and Rs to improve estimates of the global carbon budget.},
  copyright = {2022 The Author(s)},
  langid = {english},
  keywords = {Biogeochemistry,Carbon cycle,Climate change},
  file = {/Users/zobitz/Zotero/storage/T2RVTRK3/Jian et al. - 2022 - Historically inconsistent productivity and respira.pdf}
}

@article{jianRestructuredUpdatedGlobal2021,
  title = {A Restructured and Updated Global Soil Respiration Database ({{SRDB-V5}})},
  author = {Jian, Jinshi and Vargas, Rodrigo and {Anderson-Teixeira}, Kristina and Stell, Emma and Herrmann, Valentine and Horn, Mercedes and Kholod, Nazar and Manzon, Jason and Marchesi, Rebecca and Paredes, Darlin and {Bond-Lamberty}, Ben},
  year = 2021,
  month = feb,
  journal = {Earth System Science Data},
  volume = {13},
  number = {2},
  pages = {255--267},
  publisher = {Copernicus GmbH},
  issn = {1866-3508},
  doi = {10.5194/essd-13-255-2021},
  urldate = {2021-07-27},
  abstract = {{$<$}p{$><$}strong class="journal-contentHeaderColor"{$>$}Abstract.{$<$}/strong{$>$} Field-measured soil respiration (\emph{R}\textsubscript{S}, the soil-to-atmosphere CO\textsubscript{2} flux) observations were compiled into a global soil respiration database (SRDB) a decade ago, a resource that has been widely used by the biogeochemistry community to advance our understanding of \emph{R}\textsubscript{S} dynamics. Novel carbon cycle science questions require updated and augmented global information with better interoperability among datasets. Here, we restructured and updated the global \emph{R}\textsubscript{S} database to version SRDB-V5. The updated version has all previous fields revised for consistency and simplicity, and it has several new fields to include ancillary information (e.g., \emph{R}\textsubscript{S} measurement time, collar insertion depth, collar area). The new SRDB-V5 includes published papers through 2017 (800 independent studies), where total observations increased from 6633 in SRDB-V4 to 10 366 in SRDB-V5. The SRDB-V5 features more \emph{R}\textsubscript{S} data published in the Russian and Chinese scientific literature and has an improved global spatio-temporal coverage and improved global climate space representation. We also restructured the database so that it has stronger interoperability with other datasets related to carbon cycle science. For instance, linking SRDB-V5 with an hourly timescale global soil respiration database (HGRsD) and a community database for continuous soil respiration (COSORE) enables researchers to explore new questions. The updated SRDB-V5 aims to be a data framework for the scientific community to share seasonal to annual field \emph{R}\textsubscript{S} measurements, and it provides opportunities for the biogeochemistry community to better understand the spatial and temporal variability in \emph{R}\textsubscript{S}, its components, and the overall carbon cycle.{$<$}/p{$>$} {$<$}p{$>$}The database can be downloaded at https://github.com/bpbond/srdb and will be made available in the Oak Ridge National Laboratory's Distributed Active Archive Center (ORNL DAAC).{$<$}/p{$>$} {$<$}p{$>$}All data and code to reproduce the results in this study can be found at {$<$}a href="https://doi.org/10.5281/zenodo.3876443"{$>$}https://doi.org/10.5281/zenodo.3876443{$<$}/a{$>$} (Jian and Bond-Lamberty, 2020).{$<$}/p{$>$}},
  langid = {english},
  file = {/Users/zobitz/Zotero/storage/WSCDRNRE/Jian et al. - 2021 - A restructured and updated global soil respiration.pdf}
}

@article{jobbagyVerticalDistributionSoil2000,
  title = {The {{Vertical Distribution}} of {{Soil Organic Carbon}} and Its {{Relation}} to {{Climate}} and {{Vegetation}}},
  author = {Jobb{\'a}gy, Esteban G. and Jackson, Robert B.},
  year = 2000,
  month = apr,
  journal = {Ecological Applications},
  volume = {10},
  number = {2},
  pages = {423--436},
  issn = {1051-0761},
  doi = {10.1890/1051-0761(2000)010[0423:TVDOSO]2.0.CO;2},
  urldate = {2021-01-21},
  abstract = {As the largest pool of terrestrial organic carbon, soils interact strongly with atmospheric composition, climate, and land cover change. Our capacity to predict and ameliorate the consequences of global change depends in part on a better understanding of the distributions and controls of soil organic carbon (SOC) and how vegetation change may affect SOC distributions with depth. The goals of this paper are (1) to examine the association of SOC content with climate and soil texture at different soil depths; (2) to test the hypothesis that vegetation type, through patterns of allocation, is a dominant control on the vertical distribution of SOC; and (3) to estimate global SOC storage to 3 m, including an analysis of the potential effects of vegetation change on soil carbon storage. We based our analysis on Ͼ2700 soil profiles in three global databases supplemented with data for climate, vegetation, and land use. The analysis focused on mineral soil layers.},
  langid = {english},
  file = {/Users/zobitz/Zotero/storage/C8ASP6TH/Jobbágy and Jackson - 2000 - THE VERTICAL DISTRIBUTION OF SOIL ORGANIC CARBON A.pdf}
}

@misc{jurasinskiFluxFluxRate2022,
  title = {Flux: {{Flux Rate Calculation}} from {{Dynamic Closed Chamber Measurements}}},
  author = {Jurasinski, Gerald and Koebsch, Franziska and Guenther, Anke and Beetz, Sascha},
  year = 2022,
  month = jun,
  doi = {10.32614/CRAN.package.flux},
  urldate = {2025-10-31},
  abstract = {Several functions for the estimation of greenhouse gas (GHG) flux rates using closed chamber concentration measurements. The package follows a modular concept: Fluxes can be calculated in just two simple steps or in several steps if more control is wanted. Functions for further analyses (GPP and Reco model fitting and prediction for budgets including error terms) are also available.},
  copyright = {GPL-2,  GPL-2}
}

@article{liuRobustGapfillingApproach2023,
  title = {A Robust Gap-Filling Approach for {{European Space Agency Climate Change Initiative}} ({{ESA CCI}}) Soil Moisture Integrating Satellite Observations, Model-Driven Knowledge, and Spatiotemporal Machine Learning},
  author = {Liu, Kai and Li, Xueke and Wang, Shudong and Zhang, Hongyan},
  year = 2023,
  month = jan,
  journal = {Hydrology and Earth System Sciences},
  volume = {27},
  number = {2},
  pages = {577--598},
  publisher = {Copernicus GmbH},
  issn = {1027-5606},
  doi = {10.5194/hess-27-577-2023},
  urldate = {2024-10-18},
  abstract = {Spatiotemporally continuous soil moisture (SM) data are increasingly in demand for ecological and hydrological research. Satellite remote sensing has potential for mapping SM, but the continuity of satellite-derived SM is hampered by data gaps resulting from inadequate satellite coverage, snow cover, frozen soil, radio-frequency interference, and so on. Therefore, we propose a new gap-filling approach to reconstruct daily SM time series using the European Space Agency Climate Change Initiative (ESA CCI). The developed approach integrates satellite observations, model-driven knowledge, and a machine learning algorithm that leverages both spatial and temporal domains. Taking SM in China as an example, the reconstructed SM showed high accuracy when validated against multiple sets of in situ measurements, with a root mean square error (RMSE) and a mean absolute error (MAE) of 0.09--0.14 and 0.07--0.13 cm3 cm-3, respectively. Further evaluation with a 10-fold cross-validation revealed median values of the coefficient of determination (R2), RMSE, and MAE of 0.56, 0.025, and 0.019 cm3 cm-3, respectively. The reconstructive performance was noticeably reduced both when excluding one explanatory variable and keeping the other variables unchanged and when removing the spatiotemporal domain strategy or the residual calibration procedure. In comparison with gap-filled SM data based on a satellite-derived diurnal temperature range (DTR), the gap-filled SM data from bias-corrected model-derived DTRs exhibited relatively lower accuracy but higher spatial coverage. Application of our gap-filling approach to long-term SM datasets (2005--2015) produced a promising result (R2=0.72). A more accurate trend was achieved relative to that of the original CCI SM when assessed with in situ measurements (i.e., 0.49 versus 0.28, respectively, in terms of R2). Our findings indicate the feasibility of integrating satellite observations, model-driven knowledge, and spatiotemporal machine learning to fill gaps in short- and long-term SM time series, thereby providing a potential avenue for applications to similar studies.},
  langid = {english},
  file = {/Users/zobitz/Zotero/storage/G3ECM2NQ/Liu et al. - 2023 - A robust gap-filling approach for European Space A.pdf}
}

@misc{lunchNeonUtilitiesUtilitiesWorking2025,
  title = {{{neonUtilities}}: {{Utilities}} for {{Working}} with {{NEON Data}}},
  shorttitle = {{{neonUtilities}}},
  author = {Lunch, Claire and Laney, Christine and Mietkiewicz, Nathan and Sokol, Eric and Cawley, Kaelin and Network), NEON (National Ecological Observatory},
  year = 2025,
  month = jul,
  doi = {10.32614/CRAN.package.neonUtilities},
  urldate = {2025-08-15},
  abstract = {NEON data packages can be accessed through the NEON Data Portal {$<$}https://www.neonscience.org{$>$} or through the NEON Data API (see {$<$}https://data.neonscience.org/data-api{$>$} for documentation). Data delivered from the Data Portal are provided as monthly zip files packaged within a parent zip file, while individual files can be accessed from the API. This package provides tools that aid in discovering, downloading, and reformatting data prior to use in analyses. This includes downloading data via the API, merging data tables by type, and converting formats. For more information, see the readme file at {$<$}https://github.com/NEONScience/NEON-utilities{$>$}.},
  copyright = {AGPL-3}
}

@article{luoEcologicalForecastingData2011,
  title = {Ecological Forecasting and Data Assimilation in a Data-Rich Era},
  author = {Luo, Yiqi and Ogle, Kiona and Tucker, Colin and Fei, Shenfeng and Gao, Chao and LaDeau, Shannon and Clark, James S. and Schimel, David S.},
  year = 2011,
  month = jul,
  journal = {Ecological Applications},
  volume = {21},
  number = {5},
  pages = {1429--1442},
  issn = {1939-5582},
  doi = {10.1890/09-1275.1},
  urldate = {2016-11-23},
  abstract = {Several forces are converging to transform ecological research and increase its emphasis on quantitative forecasting. These forces include (1) dramatically increased volumes of data from observational and experimental networks, (2) increases in computational power, (3) advances in ecological models and related statistical and optimization methodologies, and most importantly, (4) societal needs to develop better strategies for natural resource management in a world of ongoing global change. Traditionally, ecological forecasting has been based on process-oriented models, informed by data in largely ad hoc ways. Although most ecological models incorporate some representation of mechanistic processes, today's models are generally not adequate to quantify real-world dynamics and provide reliable forecasts with accompanying estimates of uncertainty. A key tool to improve ecological forecasting and estimates of uncertainty is data assimilation (DA), which uses data to inform initial conditions and model parameters, thereby constraining a model during simulation to yield results that approximate reality as closely as possible. This paper discusses the meaning and history of DA in ecological research and highlights its role in refining inference and generating forecasts. DA can advance ecological forecasting by (1) improving estimates of model parameters and state variables, (2) facilitating selection of alternative model structures, and (3) quantifying uncertainties arising from observations, models, and their interactions. However, DA may not improve forecasts when ecological processes are not well understood or never observed. Overall, we suggest that DA is a key technique for converting raw data into ecologically meaningful products, which is especially important in this era of dramatically increased availability of data from observational and experimental networks.},
  langid = {english},
  keywords = {Data assimilation,data-model fusion,ecological forecasting,inverse analysis,optimization,predictions,prognosis,projections},
  file = {/Users/zobitz/Zotero/storage/FNJTR5JH/09-1275.1.pdf}
}

@article{maierUsingGradientMethod2014,
  title = {Using the Gradient Method to Determine Soil Gas Flux: {{A}} Review},
  shorttitle = {Using the Gradient Method to Determine Soil Gas Flux},
  author = {Maier, M. and {Schack-Kirchner}, H.},
  year = 2014,
  month = jul,
  journal = {Agricultural and Forest Meteorology},
  volume = {192--193},
  pages = {78--95},
  issn = {01681923},
  doi = {10.1016/j.agrformet.2014.03.006},
  urldate = {2019-11-07},
  abstract = {Gas exchange between soil and atmosphere represents a major component of global greenhouse gas fluxes. Chamber methods and micro-meteorological methods are well-established techniques to measure gas fluxes. The gradient method is not as widely used, but it has gained increased attention during the last decade. In this review we provide an overview of the gradient method, from the concept over different aspects of the application to the limitations and challenges of the method.},
  langid = {english},
  file = {/Users/zobitz/Zotero/storage/4D8SN434/Maier and Schack-Kirchner - 2014 - Using the gradient method to determine soil gas fl.pdf}
}

@article{mariethozFeaturepreservingInterpolationFiltering2015,
  title = {Feature-Preserving Interpolation and Filtering of Environmental Time Series},
  author = {Mariethoz, Gregoire and Linde, Niklas and Jougnot, Damien and Rezaee, Hassan},
  year = 2015,
  month = oct,
  journal = {Environmental Modelling \& Software},
  volume = {72},
  pages = {71--76},
  issn = {1364-8152},
  doi = {10.1016/j.envsoft.2015.07.001},
  urldate = {2024-10-18},
  abstract = {We propose a method for filling gaps and removing interferences in time series for applications involving continuous monitoring of environmental variables. The approach is non-parametric and based on an iterative pattern-matching between the affected and the valid parts of the time series. It considers several variables jointly in the pattern matching process and allows preserving linear or non-linear dependences between variables. The uncertainty in the reconstructed time series is quantified through multiple realizations. The method is tested on self-potential data that are affected by strong interferences as well as data gaps, and the results show that our approach allows reproducing the spectral features of the original signal. Even in the presence of intense signal perturbations, it significantly improves the signal and corrects bias introduced by asymmetrical interferences. Potential applications are wide-ranging, including geophysics, meteorology and hydrology.},
  keywords = {Gap-filling,Geophysics,Geostatistics,Interferences,Multiple-point,Uncertainty},
  file = {/Users/zobitz/Zotero/storage/U96QCTNY/Mariethoz et al. - 2015 - Feature-preserving interpolation and filtering of .pdf;/Users/zobitz/Zotero/storage/62UGQCEY/S1364815215300062.html}
}

@article{marshallDiffusionGasesPorous1959,
  title = {The {{Diffusion}} of {{Gases Through Porous Media}}},
  author = {Marshall, T. J.},
  year = 1959,
  journal = {Journal of Soil Science},
  volume = {10},
  number = {1},
  pages = {79--82},
  issn = {1365-2389},
  doi = {10.1111/j.1365-2389.1959.tb00667.x},
  urldate = {2024-11-12},
  abstract = {The effect of the obstruction of solid on the permeability of porous media is examined. From this it is concluded that, in Penman's equation for diffusion of gases through porous media, the constant 0.66 should be replaced by at where a is the porosity.},
  langid = {english},
  file = {/Users/zobitz/Zotero/storage/8BYK8RLP/j.1365-2389.1959.tb00667.html}
}

@article{millingtonDiffusionAggregatedPorous1971,
  title = {Diffusion in Aggregated Porous Media},
  author = {Millington, R. J. and Shearer, R. C.},
  year = 1971,
  journal = {Soil science},
  volume = {111},
  number = {6},
  pages = {372--378},
  issn = {0038-075X},
  urldate = {2020-01-07},
  langid = {english},
  file = {/Users/zobitz/Zotero/storage/S63NW7IH/Millington and Shearer - 1971 - Diffusion in aggregated porous media.pdf}
}

@article{moffatComprehensiveComparisonGapfilling2007,
  title = {Comprehensive Comparison of Gap-Filling Techniques for Eddy Covariance Net Carbon Fluxes},
  author = {Moffat, Antje M. and Papale, Dario and Reichstein, Markus and Hollinger, David Y. and Richardson, Andrew D. and Barr, Alan G. and Beckstein, Clemens and Braswell, Bobby H. and Churkina, Galina and Desai, Ankur R. and Falge, Eva and Gove, Jeffrey H. and Heimann, Martin and Hui, Dafeng and Jarvis, Andrew J. and Kattge, Jens and Noormets, Asko and Stauch, Vanessa J.},
  year = 2007,
  month = dec,
  journal = {Agricultural and Forest Meteorology},
  volume = {147},
  number = {3},
  pages = {209--232},
  issn = {0168-1923},
  doi = {10.1016/j.agrformet.2007.08.011},
  urldate = {2024-10-18},
  abstract = {We review 15 techniques for estimating missing values of net ecosystem CO2 exchange (NEE) in eddy covariance time series and evaluate their performance for different artificial gap scenarios based on a set of 10 benchmark datasets from six forested sites in Europe. The goal of gap filling is the reproduction of the NEE time series and hence this present work focuses on estimating missing NEE values, not on editing or the removal of suspect values in these time series due to systematic errors in the measurements (e.g., nighttime flux, advection). The gap filling was examined by generating 50 secondary datasets with artificial gaps (ranging in length from single half-hours to 12 consecutive days) for each benchmark dataset and evaluating the performance with a variety of statistical metrics. The performance of the gap filling varied among sites and depended on the level of aggregation (native half-hourly time step versus daily), long gaps were more difficult to fill than short gaps, and differences among the techniques were more pronounced during the day than at night. The non-linear regression techniques (NLRs), the look-up table (LUT), marginal distribution sampling (MDS), and the semi-parametric model (SPM) generally showed good overall performance. The artificial neural network based techniques (ANNs) were generally, if only slightly, superior to the other techniques. The simple interpolation technique of mean diurnal variation (MDV) showed a moderate but consistent performance. Several sophisticated techniques, the dual unscented Kalman filter (UKF), the multiple imputation method (MIM), the terrestrial biosphere model (BETHY), but also one of the ANNs and one of the NLRs showed high biases which resulted in a low reliability of the annual sums, indicating that additional development might be needed. An uncertainty analysis comparing the estimated random error in the 10 benchmark datasets with the artificial gap residuals suggested that the techniques are already at or very close to the noise limit of the measurements. Based on the techniques and site data examined here, the effect of gap filling on the annual sums of NEE is modest, with most techniques falling within a range of \textpm 25gCm-2year-1.},
  keywords = {Carbon flux,Eddy covariance,FLUXNET,Gap-filling comparison,Net ecosystem exchange (NEE),Review of gap-filling techniques},
  file = {/Users/zobitz/Zotero/storage/34DCNH5W/S016819230700216X.html}
}

@article{moldrupModelingDiffusionReaction1999,
  title = {Modeling Diffusion and Reaction in Soils: 9. {{The Buckingham-Burdine-Campbell}} Equation for Gas Diffusivity in Undisturbed Soil},
  shorttitle = {{{MODELING DIFFUSION AND REACTION IN SOILS}}},
  author = {Moldrup, P. and Olesen, T. and Yamaguchi, T. and Schj{\o}nning, P. and Rolston, D. E.},
  year = 1999,
  month = feb,
  journal = {Soil Science},
  volume = {164},
  number = {2},
  pages = {75},
  issn = {0038-075X},
  urldate = {2019-11-16},
  abstract = {Variations of gas diffusivity (ratio of gas diffusion coefficients in soil and free air, DS/D0) with air-filled porosity ({$\varepsilon$}) influence the transport, degradation, and volatilization of reactive gasses in soil systems. We show that the prediction accuracy of the Penman-Millington-Quirk (PMQ) diffusivity model (introduced in Part VII of this series) is often improved significantly by including as a reference point a measured value of the gas diffusion coefficient (Dfc) at a single soil-water potential, {$\psi$}, between -100 and -500 cm H20. As a result, the root mean square error of prediction was reduced by 45\% (based on individual DS/D0 measurements) and by {$\geq$} 65\% (based on mean values of 6 to 9 closely-spaced DS/D0 measurements) for undisturbed soil samples from six differently textured surface soils. Gas permeability is measured more easily and more rapidly than gas diffusivity, and we suggest that a measured value of gas permeability (kfc) at a single soil-water potential, combined with a tortuous tube permeability model and the PMQ diffusivity model, can also be used to improve DS/D0 predictions. For practical use, a relation between the equivalent tube radius (rfc) at {$\psi$} = -100 cm H20 and clay content, taken to represent the soil structure-forming ability, is proposed for surface soils. Gas diffusive transport simulations using the DARC numerical model (Part I of this series) verified that the inclusion of a single (Dfc or kfc) measurement in the DS/D0({$\varepsilon$}) predictions can improve simulation accuracy significantly. Dfc- and kfc-based diffusivity models require limited measurement effort and seem promising for site-specific simulations of gas diffusion and reaction.},
  langid = {american}
}

@misc{neonBarometricPressure,
  title = {Barometric Pressure ({{DP1}}.00004.001)},
  author = {{NEON}},
  year = 2024,
  publisher = {National Ecological Observatory Network (NEON)},
  doi = {10.48443/RT4V-KZ04},
  copyright = {Creative Commons Zero v1.0 Universal},
  langid = {english},
  keywords = {aquatic,atmospheric pressure,barometric pressure,metabolism,meteorological station,tower}
}

@misc{neonSoilCO2,
  title = {Soil {{CO2}} Concentration ({{DP1}}.00095.001)},
  author = {{NEON}},
  year = 2024,
  publisher = {National Ecological Observatory Network (NEON)},
  doi = {10.48443/E7GR-6G94},
  copyright = {Creative Commons Zero v1.0 Universal},
  langid = {english},
  keywords = {carbon cycle,carbon dioxide (CO2),carbon exchange,efflux,flux,respiration,soil,soil biological activity,soil CO2 concentration,terrestrial}
}

@misc{neonSoilProperties,
  title = {Soil Physical and Chemical Properties, {{Megapit}} ({{DP1}}.00096.001)},
  author = {{NEON}},
  year = 2024,
  publisher = {National Ecological Observatory Network (NEON)},
  doi = {10.48443/S6ND-Q840},
  copyright = {Creative Commons Zero v1.0 Universal},
  langid = {english},
  keywords = {bulk density,clay,coarse fragments,megapit,MGP,rocks,sand,silt,soil cores,soil horizons,soil order,soil pits,soil properties,soil series,soil taxonomy,soil texture}
}

@misc{neonSoilTemp,
  title = {Soil Temperature ({{DP1}}.00041.001)},
  author = {{NEON}},
  year = 2024,
  publisher = {National Ecological Observatory Network (NEON)},
  doi = {10.48443/Q24X-PW21},
  copyright = {Creative Commons Zero v1.0 Universal},
  langid = {english},
  keywords = {heat,profile,soil,soil temperature,temperature}
}

@misc{neonSoilWater,
  title = {Soil Water Content and Water Salinity ({{DP1}}.00094.001)},
  author = {{NEON}},
  year = 2024,
  publisher = {National Ecological Observatory Network (NEON)},
  doi = {10.48443/A8VY-Y813},
  copyright = {Creative Commons Zero v1.0 Universal},
  langid = {english},
  keywords = {drought,flooding,H2O,hydrology,salinity,soil,soil moisture,soil water content,water content}
}

@article{nissanGlobalWarmingAccelerates2023,
  title = {Global Warming Accelerates Soil Heterotrophic Respiration},
  author = {Nissan, Alon and Alcolombri, Uria and Peleg, Nadav and Galili, Nir and {Jimenez-Martinez}, Joaquin and Molnar, Peter and Holzner, Markus},
  year = 2023,
  month = jun,
  journal = {Nature Communications},
  volume = {14},
  number = {1},
  pages = {3452},
  publisher = {Nature Publishing Group},
  issn = {2041-1723},
  doi = {10.1038/s41467-023-38981-w},
  urldate = {2024-04-15},
  abstract = {Carbon efflux from soils is the largest terrestrial carbon source to the atmosphere, yet it is still one of the most uncertain fluxes in the Earth's carbon budget. A dominant component of this flux is heterotrophic respiration, influenced by several environmental factors, most notably soil temperature and moisture. Here, we develop a mechanistic model from micro to global scale to explore how changes in soil water content and temperature affect soil heterotrophic respiration. Simulations, laboratory measurements, and field observations validate the new approach. Estimates from the model show that heterotrophic respiration has been increasing since the 1980s at a rate of about 2\% per decade globally. Using future projections of surface temperature and soil moisture, the model predicts a global increase of about 40\% in heterotrophic respiration by the end of the century under the worst-case emission scenario, where the Arctic region is expected to experience a more than two-fold increase, driven primarily by declining soil moisture rather than temperature increase.},
  copyright = {2023 The Author(s)},
  langid = {english},
  keywords = {Carbon cycle,Climate change},
  file = {/Users/zobitz/Zotero/storage/5GQER6EI/Nissan et al. - 2023 - Global warming accelerates soil heterotrophic resp.pdf}
}

@article{normanComparisonSixMethods1997,
  title = {A Comparison of Six Methods for Measuring Soil-surface Carbon Dioxide Fluxes},
  author = {Norman, J. M. and Kucharik, C. J. and Gower, S. T. and Baldocchi, D. D. and Crill, P. M. and Rayment, M. and Savage, K. and Striegl, R. G.},
  year = 1997,
  month = dec,
  journal = {Journal of Geophysical Research: Atmospheres},
  volume = {102},
  number = {D24},
  pages = {28771--28777},
  issn = {0148-0227},
  doi = {10.1029/97JD01440},
  urldate = {2024-11-02},
  abstract = {Measurements of soil-surface CO               2               fluxes are important for characterizing the carbon budget of boreal forests because these fluxes can be the second largest component of the budget. Several methods for measuring soil-surface CO               2               fluxes are available: (1) closed-dynamic-chamber systems, (2) closed-static-chamber systems, (3) open-chamber systems, and (4) eddy covariance systems. This paper presents a field comparison of six individual systems for measuring soil-surface CO               2               fluxes with each of the four basic system types represented. A single system is used as a reference and compared to each of the other systems individually in black spruce (Picea mariana), jack pine (Pinus banksiana), or aspen (Populus tremuloides) forests. Fluxes vary from 1 to 10 {$\mu$}mol CO               2               m               -2               s               -1               . Adjustment factors to bring all of the systems into agreement vary from 0.93 to 1.45 with an uncertainty of about 10--15\%.},
  copyright = {http://onlinelibrary.wiley.com/termsAndConditions\#vor},
  langid = {english}
}

@misc{pedersenHMRFluxEstimation2024,
  title = {{{HMR}}: {{Flux Estimation}} with {{Static Chamber Data}}},
  author = {Pedersen, Asger R},
  year = 2024,
  month = jul,
  doi = {10.32614/CRAN.package.HMR},
  urldate = {2025-10-31},
  abstract = {HMR reads static chamber data from a semicolon/comma separated ASCII text file and analyzes the selected data series by either user selected (with decision support) models or by user configured automatically selected models. Results are exported to a semicolon/comma separated ASCII text file.},
  copyright = {GPL-2 \textbar{} GPL-3 [expanded from: GPL ({$\geq$} 2)]}
}

@article{phillipsValueSoilRespiration2017,
  title = {The Value of Soil Respiration Measurements for Interpreting and Modeling Terrestrial Carbon Cycling},
  author = {Phillips, Claire L. and {Bond-Lamberty}, Ben and Desai, Ankur R. and Lavoie, Martin and Risk, Dave and Tang, Jianwu and {Todd-Brown}, Katherine and Vargas, Rodrigo},
  year = 2017,
  month = apr,
  journal = {Plant and Soil},
  volume = {413},
  number = {1},
  pages = {1--25},
  issn = {1573-5036},
  doi = {10.1007/s11104-016-3084-x},
  urldate = {2024-01-31},
  abstract = {An acceleration of model-data synthesis activities has leveraged many terrestrial carbon datasets, but utilization of soil respiration (RS) data has not kept pace.},
  langid = {english},
  keywords = {Carbon,CO2,Data-model fusion,Soil respiration},
  file = {/Users/zobitz/Zotero/storage/2AUFEN48/Phillips et al. - 2017 - The value of soil respiration measurements for int.pdf}
}

@article{rafteryUsingBayesianModel2005,
  title = {Using {{Bayesian Model Averaging}} to {{Calibrate Forecast Ensembles}}},
  author = {Raftery, Adrian E. and Gneiting, Tilmann and Balabdaoui, Fadoua and Polakowski, Michael},
  year = 2005,
  month = may,
  doi = {10.1175/MWR2906.1},
  urldate = {2024-10-21},
  abstract = {Ensembles used for probabilistic weather forecasting often exhibit a spread-error correlation, but they tend to be underdispersive. This paper proposes a statistical method for postprocessing ensembles based on Bayesian model averaging (BMA), which is a standard method for combining predictive distributions from different sources. The BMA predictive probability density function (PDF) of any quantity of interest is a weighted average of PDFs centered on the individual bias-corrected forecasts, where the weights are equal to posterior probabilities of the models generating the forecasts and reflect the models' relative contributions to predictive skill over the training period. The BMA weights can be used to assess the usefulness of ensemble members, and this can be used as a basis for selecting ensemble members; this can be useful given the cost of running large ensembles. The BMA PDF can be represented as an unweighted ensemble of any desired size, by simulating from the BMA predictive distribution. The BMA predictive variance can be decomposed into two components, one corresponding to the between-forecast variability, and the second to the within-forecast variability. Predictive PDFs or intervals based solely on the ensemble spread incorporate the first component but not the second. Thus BMA provides a theoretical explanation of the tendency of ensembles to exhibit a spread-error correlation but yet be underdispersive. The method was applied to 48-h forecasts of surface temperature in the Pacific Northwest in January--June 2000 using the University of Washington fifth-generation Pennsylvania State University--NCAR Mesoscale Model (MM5) ensemble. The predictive PDFs were much better calibrated than the raw ensemble, and the BMA forecasts were sharp in that 90\% BMA prediction intervals were 66\% shorter on average than those produced by sample climatology. As a by-product, BMA yields a deterministic point forecast, and this had root-mean-square errors 7\% lower than the best of the ensemble members and 8\% lower than the ensemble mean. Similar results were obtained for forecasts of sea level pressure. Simulation experiments show that BMA performs reasonably well when the underlying ensemble is calibrated, or even overdispersed.},
  chapter = {Monthly Weather Review},
  langid = {english},
  file = {/Users/zobitz/Zotero/storage/FHY2FUAU/Raftery et al. - 2005 - Using Bayesian Model Averaging to Calibrate Foreca.pdf}
}

@article{rheaultGoFluxUserfriendlyWay2024,
  title = {{{goFlux}}: {{A}} User-Friendly Way to Calculate {{GHG}} Fluxes Yourself, Regardless of User Experience},
  shorttitle = {{{goFlux}}},
  author = {Rheault, Karelle and Christiansen, Jesper Riis and Larsen, Klaus Steenberg},
  year = 2024,
  month = apr,
  journal = {Journal of Open Source Software},
  volume = {9},
  number = {96},
  pages = {6393},
  issn = {2475-9066},
  doi = {10.21105/joss.06393},
  urldate = {2025-10-31},
  abstract = {Rheault et al., (2024). goFlux: A user-friendly way to calculate GHG fluxes yourself, regardless of user experience. Journal of Open Source Software, 9(96), 6393, https://doi.org/10.21105/joss.06393},
  langid = {english},
  file = {/Users/zobitz/Zotero/storage/QN3ECNJJ/Rheault et al. - 2024 - goFlux A user-friendly way to calculate GHG fluxes yourself, regardless of user experience.pdf}
}

@article{sallamMeasurementGasDiffusion1984,
  title = {Measurement of {{Gas Diffusion Coefficient}} under {{Relatively Low Air}}-filled {{Porosity}}},
  author = {Sallam, A. and Jury, W. A. and Letey, J.},
  year = 1984,
  month = jan,
  journal = {Soil Science Society of America Journal},
  volume = {48},
  number = {1},
  pages = {3--6},
  issn = {0361-5995, 1435-0661},
  doi = {10.2136/sssaj1984.03615995004800010001x},
  urldate = {2024-11-02},
  abstract = {Knowledge of the diffusion coefficient is necessary for modeling gas movement through soil. Various equations have been proposed relating apparent gas diffusion to soil parameters. A laboratory study was conductedto measure gas diffusion throughsoil at low air-filled porosity (O.OS, 0.10, and 0.15) using an inert gas (Freon: CC12F2). The Penman model DSIDA = 0.66a, where DA and Ds are gas diffusion coefficients in air and soil, respectively, and a is the volumetric soil air content, greatly overestimated the measured Ds at all porosities. The Millington-Quirk model DSIDA = a3-33/\textasciicircum 2, where 4{$>$} is the soil porosity, had the same curve shape but slightly underestimated the measured Ds. Much better agreement between calculated and measured Ds was obtained by reducing the exponent 3.33 in the Millington-Quirk model to 3.10.},
  copyright = {http://onlinelibrary.wiley.com/termsAndConditions\#vor},
  langid = {english},
  file = {/Users/zobitz/Zotero/storage/TQICGDGJ/Sallam et al. - 1984 - Measurement of Gas Diffusion Coefficient under Rel.pdf}
}

@article{sanchez-caneteImprovingAccuracyGradient2017,
  title = {Improving the Accuracy of the Gradient Method for Determining Soil Carbon Dioxide Efflux},
  author = {{S{\'a}nchez-Ca{\~n}ete}, Enrique P. and Scott, Russell L. and {van Haren}, Joost and {Barron-Gafford}, Greg A.},
  year = 2017,
  journal = {Journal of Geophysical Research: Biogeosciences},
  volume = {122},
  number = {1},
  pages = {50--64},
  issn = {2169-8961},
  doi = {10.1002/2016JG003530},
  urldate = {2024-08-28},
  abstract = {Soil CO2 efflux (Fsoil) represents a significant source of ecosystem CO2 emissions that is rarely quantified with high-temporal-resolution data in carbon flux studies. Fsoil estimates can be obtained by the low-cost gradient method (GM), but the utility of the method is hindered by uncertainties in the application of published models for the diffusion coefficient. Therefore, to address and resolve these uncertainties, we compared Fsoil measured by 2 soil CO2 efflux chambers and Fsoil estimated by 16 gas transport models using the GM across 1 year. We used 14 published empirical gas diffusion models and 2 in situ models: (1) a gas transfer model called ``Chamber model'' obtained using a calibration between the chamber and the gradient method and (2) a diffusion model called ``SF6 model'' obtained through an interwell conservative tracer experiment. Most of the published models using the GM underestimated cumulative annual Fsoil by 55\% to 361\%, while the Chamber model closely approximated cumulative Fsoil (0.6\% error). Surprisingly, the SF6 model combined with the GM underestimated Fsoil by 32\%. Differences between in situ models could stem from the Chamber model implicitly accounting for production of soil CO2, while the conservative tracer model does not. Therefore, we recommend using the GM only after calibration with chamber measurements to generate reliable long-term ecosystem Fsoil measurements. Accurate estimates of Fsoil will improve our understanding of soil respiration's contribution to ecosystem fluxes.},
  copyright = {\copyright 2016. American Geophysical Union. All Rights Reserved.},
  langid = {english},
  keywords = {carbon emissions,CO2 sensors,conservative tracers,diffusion coefficient,soil CO2 flux,soil respiration},
  file = {/Users/zobitz/Zotero/storage/PLNUZP85/Sánchez-Cañete et al. - 2017 - Improving the accuracy of the gradient method for determining soil carbon dioxide efflux.pdf;/Users/zobitz/Zotero/storage/7TB2VRYC/2016JG003530.html}
}

@article{shaoBioticClimaticControls2015,
  title = {Biotic and Climatic Controls on Interannual Variability in Carbon Fluxes across Terrestrial Ecosystems},
  author = {Shao, Junjiong and Zhou, Xuhui and Luo, Yiqi and Li, Bo and Aurela, Mika and Billesbach, David and Blanken, Peter D. and Bracho, Rosvel and Chen, Jiquan and Fischer, Marc and Fu, Yuling and Gu, Lianhong and Han, Shijie and He, Yongtao and Kolb, Thomas and Li, Yingnian and Nagy, Zoltan and Niu, Shuli and Oechel, Walter C. and Pinter, Krisztina and Shi, Peili and Suyker, Andrew and Torn, Margaret and Varlagin, Andrej and Wang, Huimin and Yan, Junhua and Yu, Guirui and Zhang, Junhui},
  year = 2015,
  month = jun,
  journal = {Agricultural and Forest Meteorology},
  volume = {205},
  pages = {11--22},
  issn = {0168-1923},
  doi = {10.1016/j.agrformet.2015.02.007},
  urldate = {2019-05-22},
  abstract = {Interannual variability (IAV, represented by standard deviation) in net ecosystem exchange of CO2 (NEE) is mainly driven by climatic drivers and biotic variations (i.e., the changes in photosynthetic and respiratory responses to climate), the effects of which are referred to as climatic (CE) and biotic effects (BE), respectively. Evaluating the relative contributions of CE and BE to the IAV in carbon (C) fluxes and understanding their controlling mechanisms are critical in projecting ecosystem changes in the future climate. In this study, we applied statistical methods with flux data from 65 sites located in the Northern Hemisphere to address this issue. Our results showed that the relative contribution of BE (CnBE) and CE (CnCE) to the IAV in NEE was 57\%\textpm 14\% and 43\%\textpm 14\%, respectively. The discrepancy in the CnBE among sites could be largely explained by water balance index (WBI). Across water-stressed ecosystems, the CnBE decreased with increasing aridity (slope=0.18\%mm-1). In addition, the CnBE tended to increase and the uncertainty reduced as timespan of available data increased from 5 to 15 years. Inter-site variation of the IAV in NEE mainly resulted from the IAV in BE (72\%) compared to that in CE (37\%). Interestingly, positive correlations between BE and CE occurred in grasslands and dry ecosystems (r{$>$}0.45, P{$<$}0.05) but not in other ecosystems. These results highlighted the importance of BE in determining the IAV in NEE and the ability of ecosystems to regulate C fluxes under climate change might decline when the ecosystems experience more severe water stress in the future.},
  keywords = {Biotic effect,Climatic effect,Climatic stress,Interannual variability,Net ecosystem exchange,Relative importance},
  file = {/Users/zobitz/Zotero/storage/BXK4MN8I/Shao et al. - 2015 - Biotic and climatic controls on interannual variab.pdf}
}

@article{shaoSoilMicrobialRespiration2013,
  title = {Soil Microbial Respiration from Observations and {{Earth System Models}}},
  author = {Shao, Pu and Zeng, Xubin and Moore, David J. P. and Zeng, Xiaodong},
  year = 2013,
  month = sep,
  journal = {Environmental Research Letters},
  volume = {8},
  number = {3},
  pages = {034034},
  issn = {1748-9326},
  doi = {10.1088/1748-9326/8/3/034034},
  urldate = {2013-10-03},
  abstract = {Soil microbial respiration (Rh) is a large but uncertain component of the terrestrial carbon cycle. Carbon--climate feedbacks associated with changes to Rh are likely, but Rh parameterization in Earth System Models (ESMs) has not been rigorously evaluated largely due to a lack of appropriate measurements. Here we assess, for the first time, Rh estimates from eight ESMs and their environmental drivers across several biomes against a comprehensive soil respiration database (SRDB-V2). Climatic, vegetation, and edaphic factors exert strong controls on annual Rh in ESMs, but these simple controls are not as apparent in the observations. This raises questions regarding the robustness of ESM projections of Rh in response to future climate change. Since there are many more soil respiration (Rs) observations than Rh data, two `reality checks' for ESMs are also created using the Rs data. Guidance is also provided on the Rh improvement in ESMs.},
  langid = {english},
  file = {/Users/zobitz/Zotero/storage/GTPYWF7M/Shao et al 2013 Env Res Letters Soil Microbial respiration from observations and ESMs.pdf;/Users/zobitz/Zotero/storage/QTJ2YJUM/Shao et al. - 2013 - Soil microbial respiration from observations and E.pdf}
}

@article{sihiComparingModelsMicrobial2016,
  title = {Comparing Models of Microbial--Substrate Interactions and Their Response to Warming},
  author = {Sihi, Debjani and Gerber, Stefan and Inglett, Patrick W. and Inglett, Kanika Sharma},
  year = 2016,
  month = mar,
  journal = {Biogeosciences},
  volume = {13},
  number = {6},
  pages = {1733--1752},
  issn = {1726-4189},
  doi = {10.5194/bg-13-1733-2016},
  urldate = {2019-10-10},
  abstract = {Recent developments in modelling soil organic carbon decomposition include the explicit incorporation of enzyme and microbial dynamics. A characteristic of these models is a positive feedback between substrate and consumers, which is absent in traditional first-order decay models. With sufficiently large substrate, this feedback allows an unconstrained growth of microbial biomass. We explore mechanisms that curb unrestricted microbial growth by including finite potential sites where enzymes can bind and by allowing microbial scavenging for enzymes. We further developed a model where enzyme synthesis is not scaled to microbial biomass but associated with a respiratory cost and microbial population adjusts enzyme production in order to optimise their growth. We then tested short- and long-term responses of these models to a step increase in temperature and find that these models differ in the long-term when shortterm responses are harmonised. We show that several mechanisms, including substrate limitation, variable production of microbial enzymes, and microbes feeding on extracellular enzymes eliminate oscillations arising from a positive feedback between microbial biomass and depolymerisation. The model where enzyme production is optimised to yield maximum microbial growth shows the strongest reduction in soil organic carbon in response to warming, and the trajectory of soil carbon largely follows that of a first-order decomposition model. Modifications to separate growth and maintenance respiration generally yield short-term differences, but results converge over time because microbial biomass approaches a quasi-equilibrium with the new conditions of carbon supply and temperature.},
  langid = {english},
  file = {/Users/zobitz/Zotero/storage/EE589K6J/Sihi et al. - 2016 - Comparing models of microbial–substrate interactio.pdf}
}

@article{tangAssessingSoilCO22003,
  title = {Assessing Soil {{CO2}} Efflux Using Continuous Measurements of {{CO2}} Profiles in Soils with Small Solid-State Sensors},
  author = {Tang, Jianwu and Baldocchi, Dennis D and Qi, Ye and Xu, Liukang},
  year = 2003,
  month = sep,
  journal = {Agricultural and Forest Meteorology},
  volume = {118},
  number = {3},
  pages = {207--220},
  issn = {0168-1923},
  doi = {10.1016/S0168-1923(03)00112-6},
  urldate = {2019-11-12},
  abstract = {This paper describes a new method to monitor continuously soil CO2 profiles using small solid-state CO2 sensors buried at different depths of the soil. Based on the measurement of soil CO2 profile and a gaseous diffusivity model, we estimated soil CO2 efflux, which was mainly from heterotrophic respiration, and its temporal variation in a dry season in a Mediterranean savanna ecosystem in California. The daily mean values of CO2 concentrations in soils had small variation, but the diurnal variation was significant and correlated well with soil temperature. The daily mean CO2 concentration remained steady at 396{$\mu$}molmol-1 at 2cm depth during the dry summer from days 200 to 235 in 2002. Over the same period, CO2 concentration decreased from 721 to 611{$\mu$}molmol-1 at 8cm depth, and from 1044 to 871{$\mu$}molmol-1 at 16cm. The vertical soil CO2 concentrations changed almost linearly with depth up to 16cm, but the gradient varied over time. Based on the soil CO2 gradient and the diffusion coefficient estimated from the Millington--Quirk model, continuous soil CO2 efflux was calculated. The daily mean values of CO2 efflux slightly decreased from 0.43 to 0.33{$\mu$}molm-2s-1 with a mean of 0.37{$\mu$}molm-2s-1. The mean diurnal range of CO2 efflux was greater than the range of daily mean CO2 efflux within the study period. The diurnal variation of soil CO2 efflux ranged from 0.32 to 0.45{$\mu$}molm-2s-1 with the peak value reached between 14:30 and 16:30h. This pattern corresponded well with the increase in soil temperatures during this time. By plotting CO2 efflux vs. soil temperature, we found that CO2 efflux correlated exponentially with soil temperature at the depth of 8cm, with R2 of 0.86 and Q10 of 1.27 in the summer dry season. The Q10 value increased with the depth of soil temperature measurements. The high correlation between CO2 efflux and temperature explains the diurnal pattern of CO2 efflux, but moisture may become another factor driving the seasonal pattern when moisture changes over seasons. The estimated CO2 efflux using this method was very close to chamber measurements, suggesting that this method can be used for long-term continuous measurements of soil CO2 efflux.},
  langid = {english},
  keywords = {CO efflux,CO profile,CO sensor,Diffusion,Soil CO concentration,Soil respiration},
  file = {/Users/zobitz/Zotero/storage/AZ7GPLYX/Tang et al. - 2003 - Assessing soil CO2 efflux using continuous measure.pdf}
}

@article{tangContinuousMeasurementsSoil2005,
  title = {Continuous Measurements of Soil Respiration with and without Roots in a Ponderosa Pine Plantation in the {{Sierra Nevada Mountains}}},
  author = {Tang, Jianwu and Misson, Laurent and Gershenson, Alexander and Cheng, Weixin and Goldstein, Allen H.},
  year = 2005,
  month = oct,
  journal = {Agricultural and Forest Meteorology},
  volume = {132},
  number = {3},
  pages = {212--227},
  issn = {0168-1923},
  doi = {10.1016/j.agrformet.2005.07.011},
  urldate = {2019-11-12},
  abstract = {Continuous measurements of soil respiration and its components help us understand diurnal and seasonal variations in soil respiration and its mechanism. We continuously measured CO2 concentration at various depths in the soil and calculated surface CO2 efflux based on CO2 gradients and diffusivity in a young ponderosa pine plantation in the Sierra Nevada Mountains of California. We determined soil respiration both in a control plot that included roots and in a trenched plot that had no roots. The difference between these plots was used to partition soil respiration into root respiration and heterotrophic respiration. We found that both CO2 concentration in the soil and surface CO2 efflux in the control plot were higher than in the trenched plot. The diurnal range of soil respiration in the trenched plot was larger than in the control. We observed dramatic pulses of soil respiration in response to rain events in summer and fall during the dry season. We modeled the seasonal variation in soil respiration without the pulses using soil temperature and moisture as driving variables and simulated soil respiration pulses using an exponential decay function in response to the volume of rain. Daily mean soil respiration peaked at 5.0{$\mu$}molm-2s-1 in the control and at 2.7{$\mu$}molm-2s-1 in the trenched plot in June before the rain pulses. Soil respiration increased from 4.9 to 8.2 and to 12.1{$\mu$}molm-2s-1 after the first and second rain events in the control, and increased from 2.2 to 4.1 and to 6.6{$\mu$}molm-2s-1 in the trenched plot. After incorporating the pulse effect, the model simulated measured data well. Annual soil respiration in 2003 was estimated as 1184gCm-2y-1. The average ratio of root over total respiration was 0.56 during the growing season and 0.16 during the non-growing season with an annual average of 0.44.},
  langid = {english},
  keywords = {CO concentration,CO efflux,CO gradient,CO2 concentration,CO2 efflux,CO2 gradient,Heterotrophic respiration,Pulse,Root respiration},
  file = {/Users/zobitz/Zotero/storage/Q3IMZPWQ/Tang et al. - 2005 - Continuous measurements of soil respiration with a.pdf}
}

@book{taylorIntroductionErrorAnalysis2022,
  title = {An {{Introduction}} to {{Error Analysis}}: {{The Study}} of {{Uncertainties}} in {{Physical Measurements}}, {{Third Edition}}},
  shorttitle = {An {{Introduction}} to {{Error Analysis}}},
  author = {Taylor, John R.},
  year = 2022,
  edition = {3rd},
  publisher = {University Science Press},
  address = {Melville, NY},
  urldate = {2024-11-12},
  langid = {american},
  file = {/Users/zobitz/Zotero/storage/2DPIP4SA/an-introduction-to-error-analysis-the-study-of-uncertainties-in-physical-measurements-third-edi.html}
}

@article{turcuContinuousSoilCarbon2005,
  title = {Continuous {{Soil Carbon Dioxide}} and {{Oxygen Measurements}} and {{Estimation}} of {{Gradient-Based Gaseous Flux}}},
  author = {Turcu, Vasile E. and Jones, Scott B. and Or, Dani},
  year = 2005,
  journal = {Vadose Zone Journal},
  volume = {4},
  number = {4},
  pages = {1161--1169},
  issn = {1539-1663},
  doi = {10.2136/vzj2004.0164},
  urldate = {2022-06-23},
  abstract = {The magnitude and dynamics of soil gaseous fluxes play critical roles in the global gas balance; yet, these processes are not captured at sufficient temporal resolution by standard methods based on periodic sampling and surface chamber measurements. A novel method for continuous measurement of soil surface gas fluxes based on subsurface CO2 and O2 concentration gradient measurements was developed. We tested the gradient-based method under steady- and transient-state soil water content and temperature conditions and compared results with a state-of-the-art surface chamber CO2 flux system. The new aspects of the method include fast-response sensors installed in the soil profile providing continuous record of concentration gradients coupled with concurrent estimates of water content-dependent gaseous diffusion coefficient enabling calculation of surface gaseous fluxes. Low-cost infrared sensors were used for CO2 concentration measurements, and galvanic cells for O2 measurements. An imposed CO2 concentration gradient in a dry soil column resulted in a quasilinear CO2 concentration profile and surface CO2 flux in agreement with chamber-measured fluxes. A series of continuous concentration measurements under variable water content conditions and wetting events showed agreement with surface chamber measurements. Within several days of surface wetting, soil CO2 concentrations attained 10 mL L-1, one order of magnitude higher than ambient concentrations, whereas O2 concentrations decreased. The gradient-based approach minimizes soil surface perturbations and provides insights into subsurface soil CO2 and O2 dynamics and the distribution and magnitude of soil respiration processes as related to soil environmental factors. The subsurface gradient-based measurement system represents an order-of-magnitude reduction in cost compared with research-grade surface chamber devices.},
  langid = {english},
  file = {/Users/zobitz/Zotero/storage/MJGWAACK/Turcu et al. - 2005 - Continuous Soil Carbon Dioxide and Oxygen Measurem.pdf}
}

@article{varneyEvaluationSoilCarbon2022,
  title = {Evaluation of Soil Carbon Simulation in {{CMIP6 Earth}} System Models},
  author = {Varney, Rebecca M. and Chadburn, Sarah E. and Burke, Eleanor J. and Cox, Peter M.},
  year = 2022,
  month = oct,
  journal = {Biogeosciences},
  volume = {19},
  number = {19},
  pages = {4671--4704},
  publisher = {Copernicus GmbH},
  issn = {1726-4170},
  doi = {10.5194/bg-19-4671-2022},
  urldate = {2024-04-15},
  abstract = {The response of soil carbon represents one of the key uncertainties in future climate change. The ability of Earth system models (ESMs) to simulate present-day soil carbon is therefore vital for reliably estimating global carbon budgets required for Paris Agreement targets. In this study CMIP6 ESMs are evaluated against empirical datasets to assess the ability of each model to simulate soil carbon and related controls: net primary productivity (NPP) and soil carbon turnover time ({$\tau$}s). Comparing CMIP6 with the previous generation of models (CMIP5), a lack of consistency in modelled soil carbon remains, particularly the underestimation of northern high-latitude soil carbon stocks. There is a robust improvement in the simulation of NPP in CMIP6 compared with CMIP5; however, an unrealistically high correlation with soil carbon stocks remains, suggesting the potential for an overestimation of the long-term terrestrial carbon sink. Additionally, the same improvements are not seen in the simulation of {$\tau$}s. These results suggest that much of the uncertainty associated with modelled soil carbon stocks can be attributed to the simulation of below-ground processes, and greater emphasis is required on improving the representation of below-ground soil processes in future developments of models. These improvements would help to reduce the uncertainty in projected carbon release from global soils under climate change and to increase confidence in the carbon budgets associated with different levels of global warming.},
  langid = {english},
  file = {/Users/zobitz/Zotero/storage/VM7DSD4S/Varney et al. - 2022 - Evaluation of soil carbon simulation in CMIP6 Eart.pdf}
}

@article{warnerSpatialPredictionsAssociated2019,
  title = {Spatial {{Predictions}} and {{Associated Uncertainty}} of {{Annual Soil Respiration}} at the {{Global Scale}}},
  author = {Warner, D. L. and {Bond-Lamberty}, B. and Jian, J. and Stell, E. and Vargas, R.},
  year = 2019,
  journal = {Global Biogeochemical Cycles},
  volume = {33},
  number = {12},
  pages = {1733--1745},
  issn = {1944-9224},
  doi = {10.1029/2019GB006264},
  urldate = {2024-08-28},
  abstract = {Soil respiration (Rs), the soil-to-atmosphere CO2 flux produced by microbes and plant roots, is a critical but uncertain component of the global carbon cycle. Our current understanding of the variability and dynamics is limited by the coarse spatial resolution of existing estimates. We predicted annual Rs and associated uncertainty across the world at 1-km resolution using a quantile regression forest algorithm trained with observations from the global Soil Respiration Database spanning from 1961 to 2011. This model yielded a global annual Rs estimate of 87.9 Pg C/year with an associated global uncertainty of 18.6 (mean absolute error) and 40.4 (root mean square error) Pg C/year. The estimated annual heterotrophic respiration (Rh), derived from empirical relationships with Rs, was 49.7 Pg C/year over the same period. Predicted Rs rates and associated uncertainty varied widely across vegetation types, with the greatest predicted rates of Rs in evergreen broadleaf forests (accounting for 20.9\% of global Rs). The greatest prediction uncertainties were in northern latitudes and arid to semiarid ecosystems, suggesting that these areas should be targeted in future measurement campaigns. This study provides predictions of Rs (and associated prediction uncertainty) at unprecedentedly high spatial resolution across the globe that could help constrain local-to-global process-based models. Furthermore, it provides insights into the large variability of Rs and Rh across vegetation classes and identifies regions and vegetation types with poor model performance that should be prioritized for future data collection.},
  copyright = {\copyright 2019. American Geophysical Union. All Rights Reserved.},
  langid = {english},
  keywords = {carbon cycle,global,Machine learning,soil CO2 efflux,soil respiration},
  file = {/Users/zobitz/Zotero/storage/K5IF8TPJ/Warner et al. - 2019 - Spatial Predictions and Associated Uncertainty of Annual Soil Respiration at the Global Scale.pdf;/Users/zobitz/Zotero/storage/G9D3YQW2/2019GB006264.html}
}

@article{wilsonFluxfinderPackageReproducible2024,
  title = {Fluxfinder: {{An R Package}} for {{Reproducible Calculation}} and {{Initial Processing}} of {{Greenhouse Gas Fluxes From Static Chamber Measurements}}},
  shorttitle = {Fluxfinder},
  author = {Wilson, Stephanie J. and {Bond-Lamberty}, Ben and Noyce, Genevieve and Bittencourt Peixoto, Roberta and Megonigal, J. Patrick},
  year = 2024,
  journal = {Journal of Geophysical Research: Biogeosciences},
  volume = {129},
  number = {11},
  pages = {e2024JG008208},
  issn = {2169-8961},
  doi = {10.1029/2024JG008208},
  urldate = {2025-10-31},
  abstract = {Fluxes of greenhouse gases are a critical component of the earth's natural climate, but anthropogenic emissions have created an imbalance and resulted in global climate change. Quantifying the emission of these gases is vital to our understanding of their sources and sinks, both natural and anthropogenic. The static chamber method, in which a system of interest is enclosed, and gas concentrations are measured over time, is widely used to estimate fluxes of greenhouse gases. With the development of instruments such as infrared gas analyzers (IRGAs) supporting high-frequency concentration data, there is a growing need for open-source workflows to calculate fluxes. Here we present fluxfinder, an R package designed to support reproducible calculations and processing of greenhouse gas fluxes measured with the static chamber method. The package includes raw data file parsing from widely used IRGAs, metadata matching, unit conversion, flux estimations, and initial quality assurance/quality control (QA/QC). Diagnostic graphical plots provide a transparent way to differentiate between measurement issues and nonlinear behavior. The package is also designed to be easily integrated with the gasfluxes package for further fitting of nonlinear concentration-time models, allowing alternative or additional flux QA/QC. The fluxfinder package offers a flexible workflow that is easily adaptable to promote open and reproducible greenhouse gas flux estimations.},
  copyright = {\copyright{} 2024 American Geophysical Union. All Rights Reserved.},
  langid = {english},
  keywords = {calculation,fluxes,greenhouse gases,static chambers},
  file = {/Users/zobitz/Zotero/storage/E9PHNA8K/2024JG008208.html}
}

@article{yanMoistureFunctionSoil2018,
  title = {A Moisture Function of Soil Heterotrophic Respiration That Incorporates Microscale Processes},
  author = {Yan, Zhifeng and {Bond-Lamberty}, Ben and {Todd-Brown}, Katherine E. and Bailey, Vanessa L. and Li, SiLiang and Liu, CongQiang and Liu, Chongxuan},
  year = 2018,
  month = jul,
  journal = {Nature Communications},
  volume = {9},
  number = {1},
  pages = {2562},
  issn = {2041-1723},
  doi = {10.1038/s41467-018-04971-6},
  urldate = {2024-10-28},
  abstract = {Soil heterotrophic respiration (HR) is an important source of soil-to-atmosphere CO2 flux, but its response to changes in soil water content (\texttheta ) is poorly understood. Earth system models commonly use empirical moisture functions to describe the HR--\texttheta{} relationship, introducing significant uncertainty in predicting CO2 flux from soils. Generalized, mechanistic models that address this uncertainty are thus urgently needed. Here we derive, test, and calibrate a novel moisture function, fm, that encapsulates primary physicochemical and biological processes controlling soil HR. We validated fm using simulation results and published experimental data, and established the quantitative relationships between parameters of fm and measurable soil properties, which enables fm to predict the HR--\texttheta{} relationships for different soils across spatial scales. The fm function predicted comparable HR--\texttheta{} relationships with laboratory and field measurements, and may reduce the uncertainty in predicting the response of soil organic carbon stocks to climate change compared with the empirical moisture functions currently used in Earth system models.},
  langid = {english},
  file = {/Users/zobitz/Zotero/storage/72EIDYQD/Yan et al. - 2018 - A moisture function of soil heterotrophic respirat.pdf}
}

@article{yanPorescaleInvestigationResponse2016,
  title = {Pore-Scale Investigation on the Response of Heterotrophic Respiration to Moisture Conditions in Heterogeneous Soils},
  author = {Yan, Zhifeng and Liu, Chongxuan and {Todd-Brown}, Katherine E. and Liu, Yuanyuan and {Bond-Lamberty}, Ben and Bailey, Vanessa L.},
  year = 2016,
  month = dec,
  journal = {Biogeochemistry},
  volume = {131},
  number = {1},
  pages = {121--134},
  issn = {1573-515X},
  doi = {10.1007/s10533-016-0270-0},
  urldate = {2024-10-28},
  abstract = {The relationship between microbial respiration rate and soil moisture content is an important property for understanding and predicting soil organic carbon degradation, CO2 production and emission, and their subsequent effects on climate change. This paper reports a pore-scale modeling study to investigate the response of heterotrophic respiration to moisture conditions in soils and to evaluate various factors that affect this response. X-ray computed tomography was used to derive soil pore structures, which were then used for pore-scale model investigation. The pore-scale results were then averaged to calculate the effective respiration rates as a function of water content in soils. The calculated effective respiration rate first increases and then decreases with increasing soil water content, showing a maximum respiration rate at water saturation degree of 0.75, which is consistent with field and laboratory observations. The relationship between the respiration rate and moisture content is affected by various factors, including pore-scale organic carbon bioavailability, the rate of oxygen delivery, soil pore structure and physical heterogeneity, soil clay content, and microbial drought resistivity. Overall, this study provides mechanistic insights into the soil respiration response to the change in moisture conditions, and reveals a complex relationship between heterotrophic microbial respiration rate and moisture content in soils that is affected by various hydrological, geophysical, and biochemical factors.},
  langid = {english},
  keywords = {Clay content,Environmental Chemistry,Heterotrophic respiration,Moisture,Pore-scale,Process model,Soil structure},
  file = {/Users/zobitz/Zotero/storage/PFUHNGRV/Yan et al. - 2016 - Pore-scale investigation on the response of hetero.pdf}
}

@article{zhangTemporalGapFilling12Hourly2023,
  title = {Temporal {{Gap-Filling}} of 12-{{Hourly SMAP Soil Moisture Over}} the {{CONUS Using Water Balance Budgeting}}},
  author = {Zhang, Runze and Kim, Seokhyeon and Kim, Hyunglok and Fang, Bin and Sharma, Ashish and Lakshmi, Venkataraman},
  year = 2023,
  journal = {Water Resources Research},
  volume = {59},
  number = {12},
  pages = {e2023WR034457},
  issn = {1944-7973},
  doi = {10.1029/2023WR034457},
  urldate = {2024-10-18},
  abstract = {Temporal gaps in satellite-based soil moisture (SM) products are a persistent issue. This study presents an entirely observation-based method to derive volumetric SM content for filling gaps in Soil Moisture Active Passive (SMAP) retrievals. Using a water balance equation, 12-hr topsoil water amount variations are determined based on observed precipitation from the Global Precipitation Measurement Mission (inflow) and a hydrologic loss function (outflow) built on SMAP dry-downs. A temporally seamless SM product, composed of SMAP dry-downs and precipitation-driven moisture approximations, was generated as a secondary outcome in determining optimal water balance parameters. This data set maintains the original SMAP SM dynamics with a median Pearson correlation (R) of 0.69 and an unbiased root-mean-square error (ubRMSE) of 0.05 m3/m3. Using these parameters and available SMAP observations, a 12-hourly SM product was produced over the conterminous United States. Validated against in situ measurements, this 12-hourly SM product exhibits good performance with a median R of 0.63 and captures most SM peaks induced by heavy rainfall. A time series examination revealed the produced 12-hourly SM product closely corresponds to in situ SM variations and outperforms two other SMAP-based 12-hourly SM products gap-filled using temporal linear interpolation and a three-dimensional smoothing approach, especially during sparse SMAP data periods. The proposed scheme's validity is further verified by the comparable performance of the exclusive filled-on SM estimates. Utilizing the 12-hourly SM data set and its paired hydrologic losses could enhance the quantification connections among the hydrologic components and benefit the understanding of land-surface hydrology.},
  copyright = {\copyright{} 2023 The Authors.},
  langid = {english},
  keywords = {gap-filling,remote sensing,SMAP,soil moisture,water balance},
  file = {/Users/zobitz/Zotero/storage/KEF8HPZU/Zhang et al. - 2023 - Temporal Gap-Filling of 12-Hourly SMAP Soil Moistu.pdf}
}

@article{zhaoFluxCalRPackageCalculating2019,
  title = {{{FluxCalR}}: A {{R}} Package for Calculating {{CO2}} and {{CH4}} Fluxes from Static Chambers},
  shorttitle = {{{FluxCalR}}},
  author = {Zhao, Junbin},
  year = 2019,
  month = nov,
  journal = {Journal of Open Source Software},
  volume = {4},
  number = {43},
  pages = {1751},
  issn = {2475-9066},
  doi = {10.21105/joss.01751},
  urldate = {2025-10-31},
  abstract = {Zhao, (2019). FluxCalR: a R package for calculating CO\textasciitilde 2\textasciitilde{} and CH\textasciitilde 4\textasciitilde{} fluxes from static chambers. Journal of Open Source Software, 4(43), 1751, https://doi.org/10.21105/joss.01751},
  langid = {english},
  file = {/Users/zobitz/Zotero/storage/4RQC3XL3/Zhao - 2019 - FluxCalR a R package for calculating CO~2~ and CH~4~ fluxes from static chambers.pdf}
}

@misc{zobitzNeonSoilFluxComputeSoil2024,
  title = {{{neonSoilFlux}}: {{Compute Soil Carbon Fluxes}} for the {{National Ecological Observatory Network Sites}}},
  shorttitle = {{{neonSoilFlux}}},
  author = {Zobitz, John and Ayres, Edward and O'Rourke, Katie and Werbin, Zoey and Lee, Lajntxiag and Abdi, Ridwan and Mehmeti, Dijon{\"e} and Xiong, Ly},
  year = 2024,
  month = may,
  doi = {10.32614/CRAN.package.neonSoilFlux},
  urldate = {2025-08-15},
  abstract = {Acquires and synthesizes soil carbon fluxes at sites located in the National Ecological Observatory Network (NEON). Provides flux estimates and associated uncertainty as well as key environmental measurements (soil water, temperature, CO2 concentration) that are used to compute soil fluxes.},
  copyright = {GPL ({$\geq$} 3)}
}

@misc{zobitzNeonSoilFluxV2902025,
  title = {{{neonSoilFlux}} v2.9.0},
  shorttitle = {Jmzobitz/{{neonSoilFlux}}},
  author = {Zobitz, John and Ayers, Edward and O'Rourke, Katie and Werbin, Zoey and Lee, Lajntxiag and Abdi, Ridwan and Mehmeti, Dijon{\"e} and Xiong, Ly},
  year = 2025,
  month = nov,
  doi = {10.5281/zenodo.17624494},
  urldate = {2025-11-17},
  abstract = {Release of code used to generate final publication ready results for BES-manuscript.},
  howpublished = {Zenodo},
  file = {/Users/zobitz/Zotero/storage/SLFMB87W/17624494.html}
}

@misc{zobitzSupportingCodeData2025,
  title = {Supporting {{Code}} and {{Data}} for {{neonSoilFlux}}: {{An R Package}} for {{Continuous Sensor-Based Estimation}} of {{Soil CO2 Fluxes}}},
  shorttitle = {Jmzobitz/{{BES-manuscript}}},
  author = {Zobitz, John and Zimmerman, Naupaka},
  year = 2025,
  month = nov,
  doi = {10.5281/zenodo.17516320},
  urldate = {2025-11-03},
  abstract = {No description provided.},
  howpublished = {Zenodo},
  file = {/Users/zobitz/Zotero/storage/KM8CEEWV/17516320.html}
}