Articles | Volume 14, issue 7
https://doi.org/10.5194/essd-14-3471-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-14-3471-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
29 Jul 2022
Data description paper |
| 29 Jul 2022
| 29 Jul 2022
Aridec: an open database of litter mass loss from aridlands worldwide with recommendations on suitable model applications
Facultad de Agronomía, Universidad de Buenos Aires,
Buenos Aires, 1417, Argentina
Instituto de Investigaciones Fisiológicas y
Ecológicas Vinculadas a la Agricultura (IFEVA; CONICET-FAUBA), Buenos
Aires, 1417, Argentina
Ignacio Andrés Siebenhart
Facultad de Agronomía, Universidad de Buenos Aires,
Buenos Aires, 1417, Argentina
Instituto de Investigaciones Fisiológicas y
Ecológicas Vinculadas a la Agricultura (IFEVA; CONICET-FAUBA), Buenos
Aires, 1417, Argentina
Amy Theresa Austin
Facultad de Agronomía, Universidad de Buenos Aires,
Buenos Aires, 1417, Argentina
Instituto de Investigaciones Fisiológicas y
Ecológicas Vinculadas a la Agricultura (IFEVA; CONICET-FAUBA), Buenos
Aires, 1417, Argentina
Carlos A. Sierra
Max-Planck-Institut für Biogeochemie, Jena, 07745,
Germany
Swedish University of Agricultural Sciences, Uppsala, Sweden
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Agustín Sarquis and Carlos A. Sierra
Biogeosciences, 20, 1759–1771, https://doi.org/10.5194/bg-20-1759-2023, https://doi.org/10.5194/bg-20-1759-2023, 2023
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Although plant litter is chemically and physically heterogenous and undergoes multiple transformations, models that represent litter dynamics often ignore this complexity. We used a multi-model inference framework to include information content in litter decomposition datasets and studied the time it takes for litter to decompose as measured by the transit time. In arid lands, the median transit time of litter is about 3 years and has a negative correlation with mean annual temperature.
Maximiliano González-Sosa, Carlos A. Sierra, J. Andrés Quincke, Walter E. Baethgen, Susan Trumbore, and M. Virginia Pravia
SOIL, 10, 467–486, https://doi.org/10.5194/soil-10-467-2024, https://doi.org/10.5194/soil-10-467-2024, 2024
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Based on an approach that involved soil organic carbon (SOC) monitoring, radiocarbon measurement in bulk soil, and incubations from a long-term 60-year experiment, it was concluded that the avoidance of old carbon losses in the integrated crop–pasture systems is the main reason that explains their greater carbon storage capacities compared to continuous cropping. A better understanding of these processes is essential for making agronomic decisions to increase the carbon sequestration capacity.
Ingrid Chanca, Ingeborg Levin, Susan Trumbore, Kita Macario, Jost Lavric, Carlos Alberto Quesada, Alessandro Carioca de Araújo, Cléo Quaresma Dias Júnior, Hella van Asperen, Samuel Hammer, and Carlos Sierra
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Assessing the net carbon (C) budget of the Amazon entails considering the magnitude and timing of C absorption and losses through respiration (transit time of C). Radiocarbon-based estimates of the transit time of C in the Amazon Tall Tower Observatory (ATTO) suggest a doubling of the transit time from 6 ± 2 years and 18 ± 5 years (October 2019 and December 2021, respectively). This variability indicates that only a fraction of newly fixed C can be stored for decades or longer.
Andrés Tangarife-Escobar, Georg Guggenberger, Xiaojuan Feng, Guohua Dai, Carolina Urbina-Malo, Mina Azizi-Rad, and Carlos A. Sierra
Biogeosciences, 21, 1277–1299, https://doi.org/10.5194/bg-21-1277-2024, https://doi.org/10.5194/bg-21-1277-2024, 2024
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Soil organic matter stability depends on future temperature and precipitation scenarios. We used radiocarbon (14C) data and model predictions to understand how the transit time of carbon varies under environmental change in grasslands and peatlands. Soil moisture affected the Δ14C of peatlands, while temperature did not have any influence. Our models show the correspondence between Δ14C and transit time and could allow understanding future interactions between terrestrial and atmospheric carbon
Shane W. Stoner, Marion Schrumpf, Alison Hoyt, Carlos A. Sierra, Sebastian Doetterl, Valier Galy, and Susan Trumbore
Biogeosciences, 20, 3151–3163, https://doi.org/10.5194/bg-20-3151-2023, https://doi.org/10.5194/bg-20-3151-2023, 2023
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Soils store more carbon (C) than any other terrestrial C reservoir, but the processes that control how much C stays in soil, and for how long, are very complex. Here, we used a recent method that involves heating soil in the lab to measure the range of C ages in soil. We found that most C in soil is decades to centuries old, while some stays for much shorter times (days to months), and some is thousands of years old. Such detail helps us to estimate how soil C may react to changing climate.
Agustín Sarquis and Carlos A. Sierra
Biogeosciences, 20, 1759–1771, https://doi.org/10.5194/bg-20-1759-2023, https://doi.org/10.5194/bg-20-1759-2023, 2023
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Although plant litter is chemically and physically heterogenous and undergoes multiple transformations, models that represent litter dynamics often ignore this complexity. We used a multi-model inference framework to include information content in litter decomposition datasets and studied the time it takes for litter to decompose as measured by the transit time. In arid lands, the median transit time of litter is about 3 years and has a negative correlation with mean annual temperature.
Song Wang, Carlos Sierra, Yiqi Luo, Jinsong Wang, Weinan Chen, Yahai Zhang, Aizhong Ye, and Shuli Niu
Biogeosciences Discuss., https://doi.org/10.5194/bg-2023-33, https://doi.org/10.5194/bg-2023-33, 2023
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Nitrogen is important for plant growth and carbon uptake, which is uaually limited in nature and can constrain carbon storage and impact efforts to combat climate change. We developed a new method of combining data and models to determine if and how much an ecosystem is nitrogen limited. This new method can help determine if and to what extent an ecosystem is nitrogen-limited, providing insight into nutrient limitations on a global scale and guiding ecosystem management decisions.
Andrea Scheibe, Carlos A. Sierra, and Marie Spohn
Biogeosciences, 20, 827–838, https://doi.org/10.5194/bg-20-827-2023, https://doi.org/10.5194/bg-20-827-2023, 2023
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We explored carbon cycling in soils in three climate zones in Chile down to a depth of 6 m, using carbon isotopes. Our results show that microbial activity several meters below the soil surface is mostly fueled by recently fixed carbon and that strong decomposition of soil organic matter only occurs in the upper decimeters of the soils. The study shows that different layers of the critical zone are tightly connected and that processes in the deep soil depend on recently fixed carbon.
Carlos A. Sierra, Verónika Ceballos-Núñez, Henrik Hartmann, David Herrera-Ramírez, and Holger Metzler
Biogeosciences, 19, 3727–3738, https://doi.org/10.5194/bg-19-3727-2022, https://doi.org/10.5194/bg-19-3727-2022, 2022
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Empirical work that estimates the age of respired CO2 from vegetation tissue shows that it may take from years to decades to respire previously produced photosynthates. However, many ecosystem models represent respiration processes in a form that cannot reproduce these observations. In this contribution, we attempt to provide compelling evidence, based on recent research, with the aim to promote a change in the predominant paradigm implemented in ecosystem models.
Carlos A. Sierra, Susan E. Crow, Martin Heimann, Holger Metzler, and Ernst-Detlef Schulze
Biogeosciences, 18, 1029–1048, https://doi.org/10.5194/bg-18-1029-2021, https://doi.org/10.5194/bg-18-1029-2021, 2021
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The climate benefit of carbon sequestration (CBS) is a metric developed to quantify avoided warming by two separate processes: the amount of carbon drawdown from the atmosphere and the time this carbon is stored in a reservoir. This metric can be useful for quantifying the role of forests and soils for climate change mitigation and to better quantify the benefits of carbon removals by sinks.
Christina Schädel, Jeffrey Beem-Miller, Mina Aziz Rad, Susan E. Crow, Caitlin E. Hicks Pries, Jessica Ernakovich, Alison M. Hoyt, Alain Plante, Shane Stoner, Claire C. Treat, and Carlos A. Sierra
Earth Syst. Sci. Data, 12, 1511–1524, https://doi.org/10.5194/essd-12-1511-2020, https://doi.org/10.5194/essd-12-1511-2020, 2020
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Carbon loss to the atmosphere via microbial decomposition is often assessed by laboratory soil incubation studies that measure greenhouse gases released from soils under controlled conditions. Here, we introduce the Soil Incubation Database (SIDb) version 1.0, a compilation of time series data from incubations, structured into a new, publicly available, open-access database of carbon dioxide and methane flux. We also provide guidance for database entry and the required variables.
Nora Linscheid, Lina M. Estupinan-Suarez, Alexander Brenning, Nuno Carvalhais, Felix Cremer, Fabian Gans, Anja Rammig, Markus Reichstein, Carlos A. Sierra, and Miguel D. Mahecha
Biogeosciences, 17, 945–962, https://doi.org/10.5194/bg-17-945-2020, https://doi.org/10.5194/bg-17-945-2020, 2020
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Vegetation typically responds to variation in temperature and rainfall within days. Yet seasonal changes in meteorological conditions, as well as decadal climate variability, additionally shape the state of ecosystems. It remains unclear how vegetation responds to climate variability on these different timescales. We find that the vegetation response to climate variability depends on the timescale considered. This scale dependency should be considered for modeling land–atmosphere interactions.
Corey R. Lawrence, Jeffrey Beem-Miller, Alison M. Hoyt, Grey Monroe, Carlos A. Sierra, Shane Stoner, Katherine Heckman, Joseph C. Blankinship, Susan E. Crow, Gavin McNicol, Susan Trumbore, Paul A. Levine, Olga Vindušková, Katherine Todd-Brown, Craig Rasmussen, Caitlin E. Hicks Pries, Christina Schädel, Karis McFarlane, Sebastian Doetterl, Christine Hatté, Yujie He, Claire Treat, Jennifer W. Harden, Margaret S. Torn, Cristian Estop-Aragonés, Asmeret Asefaw Berhe, Marco Keiluweit, Ágatha Della Rosa Kuhnen, Erika Marin-Spiotta, Alain F. Plante, Aaron Thompson, Zheng Shi, Joshua P. Schimel, Lydia J. S. Vaughn, Sophie F. von Fromm, and Rota Wagai
Earth Syst. Sci. Data, 12, 61–76, https://doi.org/10.5194/essd-12-61-2020, https://doi.org/10.5194/essd-12-61-2020, 2020
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The International Soil Radiocarbon Database (ISRaD) is an an open-source archive of soil data focused on datasets including radiocarbon measurements. ISRaD includes data from bulk or
whole soils, distinct soil carbon pools isolated in the laboratory by a variety of soil fractionation methods, samples of soil gas or water collected interstitially from within an intact soil profile, CO2 gas isolated from laboratory soil incubations, and fluxes collected in situ from a soil surface.
Verónika Ceballos-Núñez, Andrew D. Richardson, and Carlos A. Sierra
Biogeosciences, 15, 1607–1625, https://doi.org/10.5194/bg-15-1607-2018, https://doi.org/10.5194/bg-15-1607-2018, 2018
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Will the terrestrial biosphere be a carbon source or sink in the future? Different model simulations cannot reach a consensus, so we need to diagnose the performance of these models. We implemented three models differing in their carbon allocation strategies and assessed their performance using three metrics. The most sensible metric was the distribution of carbon age and transit times. Thus, empirical measurements of these distributions could be key to reduce the model uncertainty.
Carlos A. Sierra, Saadatullah Malghani, and Henry W. Loescher
Biogeosciences, 14, 703–710, https://doi.org/10.5194/bg-14-703-2017, https://doi.org/10.5194/bg-14-703-2017, 2017
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Temperature, moisture, and oxygen are interacting variables that control the rates of soil organic matter decomposition. With a well-replicated experiment, the authors show that decomposition rates in a boreal forest soil are not limited at high temperatures in the presence of enough water and oxygen. Similarly, at high humidity, oxygen is the main limiting factor for decomposition. The authors conclude that interactions among the three variables are the main determinants of decomposition rates.
C. A. Sierra, M. Müller, and S. E. Trumbore
Geosci. Model Dev., 7, 1919–1931, https://doi.org/10.5194/gmd-7-1919-2014, https://doi.org/10.5194/gmd-7-1919-2014, 2014
C. A. Sierra, E. M. Jiménez, B. Reu, M. C. Peñuela, A. Thuille, and C. A. Quesada
Biogeosciences, 10, 3455–3464, https://doi.org/10.5194/bg-10-3455-2013, https://doi.org/10.5194/bg-10-3455-2013, 2013
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This study generated a high-precision dataset, locating forest harvested carbon and quantifying post-harvest wood emissions for various uses. It enhances our understanding of forest harvesting and post-harvest carbon dynamics in China, providing essential data for estimating the forest ecosystem carbon budget and emphasizing wood utilization's impact on carbon emissions.
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Earth Syst. Sci. Data, 16, 1771–1810, https://doi.org/10.5194/essd-16-1771-2024, https://doi.org/10.5194/essd-16-1771-2024, 2024
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This study generated a spatially continuous plant functional trait dataset (~1 km) in China in combination with field observations, environmental variables and vegetation indices using machine learning methods. Results showed that wood density, leaf P concentration and specific leaf area showed good accuracy with an average R2 of higher than 0.45. This dataset could provide data support for development of Earth system models to predict vegetation distribution and ecosystem functions.
Kai Yan, Jingrui Wang, Rui Peng, Kai Yang, Xiuzhi Chen, Gaofei Yin, Jinwei Dong, Marie Weiss, Jiabin Pu, and Ranga B. Myneni
Earth Syst. Sci. Data, 16, 1601–1622, https://doi.org/10.5194/essd-16-1601-2024, https://doi.org/10.5194/essd-16-1601-2024, 2024
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Variations in observational conditions have led to poor spatiotemporal consistency in leaf area index (LAI) time series. Using prior knowledge, we leveraged high-quality observations and spatiotemporal correlation to reprocess MODIS LAI, thereby generating HiQ-LAI, a product that exhibits fewer abnormal fluctuations in time series. Reprocessing was done on Google Earth Engine, providing users with convenient access to this value-added data and facilitating large-scale research and applications.
Fabio Oriani, Gregoire Mariethoz, and Manuel Chevalier
Earth Syst. Sci. Data, 16, 731–742, https://doi.org/10.5194/essd-16-731-2024, https://doi.org/10.5194/essd-16-731-2024, 2024
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Modern and fossil pollen data contain precious information for reconstructing the climate and environment of the past. However, these data are only achieved for single locations with no continuity in space. We present here a systematic atlas of 194 digital maps containing the spatial estimation of contemporary pollen presence over Europe. This dataset constitutes a free and ready-to-use tool to study climate, biodiversity, and environment in time and space.
João Paulo Darela-Filho, Anja Rammig, Katrin Fleischer, Tatiana Reichert, Laynara Figueiredo Lugli, Carlos Alberto Quesada, Luis Carlos Colocho Hurtarte, Mateus Dantas de Paula, and David M. Lapola
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Phosphorus (P) is crucial for plant growth, and scientists have created models to study how it interacts with carbon cycle in ecosystems. To apply these models, it is important to know the distribution of phosphorus in soil. In this study we estimated the distribution of phosphorus in the Amazon region. The results showed a clear gradient of soil development and P content. These maps can help improve ecosystem models and generate new hypotheses about phosphorus availability in the Amazon.
Mengyao Zhu, Junhu Dai, Huanjiong Wang, Juha M. Alatalo, Wei Liu, Yulong Hao, and Quansheng Ge
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This study utilized 24,552 in situ phenology observation records from the Chinese Phenology Observation Network to model and map 24 woody plant species phenology and ground forest phenology over China from 1951 to 2020. These phenology maps are the first gridded, independent and reliable phenology data sources for China, offering a high spatial resolution of 0.1° and an average deviation of about 10 days. It contributes to more comprehensive research on plant phenology and climate change.
Jiabin Pu, Kai Yan, Samapriya Roy, Zaichun Zhu, Miina Rautiainen, Yuri Knyazikhin, and Ranga B. Myneni
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Earth Syst. Sci. Data, 15, 5093–5103, https://doi.org/10.5194/essd-15-5093-2023, https://doi.org/10.5194/essd-15-5093-2023, 2023
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We present a dataset from the decade-long monitoring of Lake Żabińskie, a hardwater and eutrophic lake in northeast Poland. The lake contains varved sediments, which form a unique archive of past environmental variability. The monitoring program was designed to capture a pattern of relationships between meteorological conditions, limnological processes, and modern sedimentation and to verify if meteorological and limnological phenomena can be precisely tracked with varves.
Sen Cao, Muyi Li, Zaichun Zhu, Zhe Wang, Junjun Zha, Weiqing Zhao, Zeyu Duanmu, Jiana Chen, Yaoyao Zheng, Yue Chen, Ranga B. Myneni, and Shilong Piao
Earth Syst. Sci. Data, 15, 4877–4899, https://doi.org/10.5194/essd-15-4877-2023, https://doi.org/10.5194/essd-15-4877-2023, 2023
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The long-term global leaf area index (LAI) products are critical for characterizing vegetation dynamics under environmental changes. This study presents an updated GIMMS LAI product (GIMMS LAI4g; 1982−2020) based on PKU GIMMS NDVI and massive Landsat LAI samples. With higher accuracy than other LAI products, GIMMS LAI4g removes the effects of orbital drift and sensor degradation in AVHRR data. It has better temporal consistency before and after 2000 and a more reasonable global vegetation trend.
Muyi Li, Sen Cao, Zaichun Zhu, Zhe Wang, Ranga B. Myneni, and Shilong Piao
Earth Syst. Sci. Data, 15, 4181–4203, https://doi.org/10.5194/essd-15-4181-2023, https://doi.org/10.5194/essd-15-4181-2023, 2023
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Long-term global Normalized Difference Vegetation Index (NDVI) products support the understanding of changes in vegetation under environmental changes. This study generates a consistent global NDVI product (PKU GIMMS NDVI) from 1982–2022 that eliminates the issue of orbital drift and sensor degradation in Advanced Very High Resolution Radiometer (AVHRR) data. More accurate than its predecessor (GIMMS NDVI3g), it shows high temporal consistency with MODIS NDVI in describing vegetation trends.
Parisa Sarzaeim, Francisco Muñoz-Arriola, Diego Jarquin, Hasnat Aslam, and Natalia De Leon Gatti
Earth Syst. Sci. Data, 15, 3963–3990, https://doi.org/10.5194/essd-15-3963-2023, https://doi.org/10.5194/essd-15-3963-2023, 2023
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A genomic, phenomic, and climate database for maize phenotype predictability in the US and Canada is introduced. The database encompasses climate from multiple sources and OMICS from the Genomes to Fields initiative (G2F) data from 2014 to 2021, including codes for input data quality and consistency controls. Earth system modelers and breeders can use CLIM4OMICS since it interconnects the climate and biological system sciences. CLIM4OMICS is designed to foster phenotype predictability.
Elisabeth Mauclet, Maëlle Villani, Arthur Monhonval, Catherine Hirst, Edward A. G. Schuur, and Sophie Opfergelt
Earth Syst. Sci. Data, 15, 3891–3904, https://doi.org/10.5194/essd-15-3891-2023, https://doi.org/10.5194/essd-15-3891-2023, 2023
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Permafrost ecosystems are limited in nutrients for vegetation development and constrain the biological activity to the active layer. Upon Arctic warming, permafrost degradation exposes organic and mineral soil material that may directly influence the capacity of the soil to retain key nutrients for vegetation growth and development. Here, we demonstrate that the average total exchangeable nutrient density (Ca, K, Mg, and Na) is more than 2 times higher in the permafrost than in the active layer.
Anna G. Boegehold, Ashley M. Burtner, Andrew C. Camilleri, Glenn Carter, Paul DenUyl, David Fanslow, Deanna Fyffe Semenyuk, Casey M. Godwin, Duane Gossiaux, Thomas H. Johengen, Holly Kelchner, Christine Kitchens, Lacey A. Mason, Kelly McCabe, Danna Palladino, Dack Stuart, Henry Vanderploeg, and Reagan Errera
Earth Syst. Sci. Data, 15, 3853–3868, https://doi.org/10.5194/essd-15-3853-2023, https://doi.org/10.5194/essd-15-3853-2023, 2023
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Western Lake Erie suffers from cyanobacterial harmful algal blooms (HABs) despite decades of international management efforts. In response, the US National Oceanic and Atmospheric Administration (NOAA) Great Lakes Environmental Research Laboratory (GLERL) and the Cooperative Institute for Great Lakes Research (CIGLR) created an annual sampling program to detect, monitor, assess, and predict HABs. Here we describe the data collected from this monitoring program from 2012 to 2021.
Akli Benali, Nuno Guiomar, Hugo Gonçalves, Bernardo Mota, Fábio Silva, Paulo M. Fernandes, Carlos Mota, Alexandre Penha, João Santos, José M. C. Pereira, and Ana C. L. Sá
Earth Syst. Sci. Data, 15, 3791–3818, https://doi.org/10.5194/essd-15-3791-2023, https://doi.org/10.5194/essd-15-3791-2023, 2023
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We reconstructed the spread of 80 large wildfires that burned recently in Portugal and calculated metrics that describe how wildfires behave, such as rate of spread, growth rate, and energy released. We describe the fire behaviour distribution using six percentile intervals that can be easily communicated to both research and management communities. The database will help improve our current knowledge on wildfire behaviour and support better decision making.
Yuelong Xiao, Qunming Wang, Xiaohua Tong, and Peter M. Atkinson
Earth Syst. Sci. Data, 15, 3365–3386, https://doi.org/10.5194/essd-15-3365-2023, https://doi.org/10.5194/essd-15-3365-2023, 2023
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Forest age is closely related to forest production, carbon cycles, and other ecosystem services. Existing stand age products in China derived from remote-sensing images are of a coarse spatial resolution and are not suitable for applications at the regional scale. Here, we mapped young forest ages across China at an unprecedented fine spatial resolution of 30 m. The overall accuracy (OA) of the generated map of young forest stand ages across China was 90.28 %.
Emily H. Stanley, Luke C. Loken, Nora J. Casson, Samantha K. Oliver, Ryan A. Sponseller, Marcus B. Wallin, Liwei Zhang, and Gerard Rocher-Ros
Earth Syst. Sci. Data, 15, 2879–2926, https://doi.org/10.5194/essd-15-2879-2023, https://doi.org/10.5194/essd-15-2879-2023, 2023
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The Global River Methane Database (GRiMeDB) presents CH4 concentrations and fluxes for flowing waters and concurrent measures of CO2, N2O, and several physicochemical variables, plus information about sample locations and methods used to measure gas fluxes. GRiMeDB is intended to increase opportunities to understand variation in fluvial CH4, test hypotheses related to greenhouse gas dynamics, and reduce uncertainty in future estimates of gas emissions from world streams and rivers.
Xueqin Yang, Xiuzhi Chen, Jiashun Ren, Wenping Yuan, Liyang Liu, Juxiu Liu, Dexiang Chen, Yihua Xiao, Qinghai Song, Yanjun Du, Shengbiao Wu, Lei Fan, Xiaoai Dai, Yunpeng Wang, and Yongxian Su
Earth Syst. Sci. Data, 15, 2601–2622, https://doi.org/10.5194/essd-15-2601-2023, https://doi.org/10.5194/essd-15-2601-2023, 2023
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We developed the first time-mapped, continental-scale gridded dataset of monthly leaf area index (LAI) in three leaf age cohorts (i.e., young, mature, and old) from 2001–2018 data (referred to as Lad-LAI). The seasonality of three LAI cohorts from the new Lad-LAI product agrees well at eight sites with very fine-scale collections of monthly LAI. The proposed satellite-based approaches can provide references for mapping finer spatiotemporal-resolution LAI products with different leaf age cohorts.
Yann Quilcaille, Fulden Batibeniz, Andreia F. S. Ribeiro, Ryan S. Padrón, and Sonia I. Seneviratne
Earth Syst. Sci. Data, 15, 2153–2177, https://doi.org/10.5194/essd-15-2153-2023, https://doi.org/10.5194/essd-15-2153-2023, 2023
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We present a new database of four annual fire weather indicators over 1850–2100 and over all land areas. In a 3°C warmer world with respect to preindustrial times, the mean fire weather would increase on average by at least 66% in both intensity and duration and even triple for 1-in-10-year events. The dataset is a freely available resource for fire danger studies and beyond, highlighting that the best course of action would require limiting global warming as much as possible.
Beatriz P. Cazorla, Javier Cabello, Andrés Reyes, Emilio Guirado, Julio Peñas, Antonio J. Pérez-Luque, and Domingo Alcaraz-Segura
Earth Syst. Sci. Data, 15, 1871–1887, https://doi.org/10.5194/essd-15-1871-2023, https://doi.org/10.5194/essd-15-1871-2023, 2023
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This dataset provides scientists, environmental managers, and the public in general with valuable information on the first characterization of ecosystem functional diversity based on primary production developed in the Sierra Nevada (Spain), a biodiversity hotspot in the Mediterranean basin and an exceptional natural laboratory for ecological research within the Long-Term Social-Ecological Research (LTSER) network.
Shengli Tao, Zurui Ao, Jean-Pierre Wigneron, Sassan Saatchi, Philippe Ciais, Jérôme Chave, Thuy Le Toan, Pierre-Louis Frison, Xiaomei Hu, Chi Chen, Lei Fan, Mengjia Wang, Jiangling Zhu, Xia Zhao, Xiaojun Li, Xiangzhuo Liu, Yanjun Su, Tianyu Hu, Qinghua Guo, Zhiheng Wang, Zhiyao Tang, Yi Y. Liu, and Jingyun Fang
Earth Syst. Sci. Data, 15, 1577–1596, https://doi.org/10.5194/essd-15-1577-2023, https://doi.org/10.5194/essd-15-1577-2023, 2023
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We provide the first long-term (since 1992), high-resolution (8.9 km) satellite radar backscatter data set (LHScat) with a C-band (5.3 GHz) signal dynamic for global lands. LHScat was created by fusing signals from ERS (1992–2001; C-band), QSCAT (1999–2009; Ku-band), and ASCAT (since 2007; C-band). LHScat has been validated against independent ERS-2 signals. It could be used in a variety of studies, such as vegetation monitoring and hydrological modelling.
Jose V. Moris, Pedro Álvarez-Álvarez, Marco Conedera, Annalie Dorph, Thomas D. Hessilt, Hugh G. P. Hunt, Renata Libonati, Lucas S. Menezes, Mortimer M. Müller, Francisco J. Pérez-Invernón, Gianni B. Pezzatti, Nicolau Pineda, Rebecca C. Scholten, Sander Veraverbeke, B. Mike Wotton, and Davide Ascoli
Earth Syst. Sci. Data, 15, 1151–1163, https://doi.org/10.5194/essd-15-1151-2023, https://doi.org/10.5194/essd-15-1151-2023, 2023
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This work describes a database on holdover times of lightning-ignited wildfires (LIWs). Holdover time is defined as the time between lightning-induced fire ignition and fire detection. The database contains 42 datasets built with data on more than 152 375 LIWs from 13 countries in five continents from 1921 to 2020. This database is the first freely-available, harmonized and ready-to-use global source of holdover time data, which may be used to investigate LIWs and model the holdover phenomenon.
Brendan Byrne, David F. Baker, Sourish Basu, Michael Bertolacci, Kevin W. Bowman, Dustin Carroll, Abhishek Chatterjee, Frédéric Chevallier, Philippe Ciais, Noel Cressie, David Crisp, Sean Crowell, Feng Deng, Zhu Deng, Nicholas M. Deutscher, Manvendra K. Dubey, Sha Feng, Omaira E. García, David W. T. Griffith, Benedikt Herkommer, Lei Hu, Andrew R. Jacobson, Rajesh Janardanan, Sujong Jeong, Matthew S. Johnson, Dylan B. A. Jones, Rigel Kivi, Junjie Liu, Zhiqiang Liu, Shamil Maksyutov, John B. Miller, Scot M. Miller, Isamu Morino, Justus Notholt, Tomohiro Oda, Christopher W. O'Dell, Young-Suk Oh, Hirofumi Ohyama, Prabir K. Patra, Hélène Peiro, Christof Petri, Sajeev Philip, David F. Pollard, Benjamin Poulter, Marine Remaud, Andrew Schuh, Mahesh K. Sha, Kei Shiomi, Kimberly Strong, Colm Sweeney, Yao Té, Hanqin Tian, Voltaire A. Velazco, Mihalis Vrekoussis, Thorsten Warneke, John R. Worden, Debra Wunch, Yuanzhi Yao, Jeongmin Yun, Andrew Zammit-Mangion, and Ning Zeng
Earth Syst. Sci. Data, 15, 963–1004, https://doi.org/10.5194/essd-15-963-2023, https://doi.org/10.5194/essd-15-963-2023, 2023
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Changes in the carbon stocks of terrestrial ecosystems result in emissions and removals of CO2. These can be driven by anthropogenic activities (e.g., deforestation), natural processes (e.g., fires) or in response to rising CO2 (e.g., CO2 fertilization). This paper describes a dataset of CO2 emissions and removals derived from atmospheric CO2 observations. This pilot dataset informs current capabilities and future developments towards top-down monitoring and verification systems.
Nicholas A. Beresford, Sergii Gashchak, Michael D. Wood, and Catherine L. Barnett
Earth Syst. Sci. Data, 15, 911–920, https://doi.org/10.5194/essd-15-911-2023, https://doi.org/10.5194/essd-15-911-2023, 2023
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Camera traps were established in a highly contaminated area of the Chornobyl Exclusion Zone (CEZ) to capture images of mammals. Over 1 year, 14 mammal species were recorded. The number of species observed did not vary with estimated radiation exposure. The data will be of value from the perspectives of effects of radiation on wildlife and also rewilding in this large, abandoned area. They may also have value in future studies investigating impacts of recent Russian military action in the CEZ.
Yongzhe Chen, Xiaoming Feng, Bojie Fu, Haozhi Ma, Constantin M. Zohner, Thomas W. Crowther, Yuanyuan Huang, Xutong Wu, and Fangli Wei
Earth Syst. Sci. Data, 15, 897–910, https://doi.org/10.5194/essd-15-897-2023, https://doi.org/10.5194/essd-15-897-2023, 2023
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This study presented a long-term (2002–2021) above- and belowground biomass dataset for woody vegetation in China at 1 km resolution. It was produced by combining various types of remote sensing observations with adequate plot measurements. Over 2002–2021, China’s woody biomass increased at a high rate, especially in the central and southern parts. This dataset can be applied to evaluate forest carbon sinks across China and the efficiency of ecological restoration programs in China.
Ricardo Dalagnol, Lênio Soares Galvão, Fabien Hubert Wagner, Yhasmin Mendes de Moura, Nathan Gonçalves, Yujie Wang, Alexei Lyapustin, Yan Yang, Sassan Saatchi, and Luiz Eduardo Oliveira Cruz Aragão
Earth Syst. Sci. Data, 15, 345–358, https://doi.org/10.5194/essd-15-345-2023, https://doi.org/10.5194/essd-15-345-2023, 2023
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The AnisoVeg dataset brings 22 years of monthly satellite data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor for South America at 1 km resolution aimed at vegetation applications. It has nadir-normalized data, which is the most traditional approach to correct satellite data but also unique anisotropy data with strong biophysical meaning, explaining 55 % of Amazon forest height. We expect this dataset to help large-scale estimates of vegetation biomass and carbon.
Yili Jin, Haoyan Wang, Jie Xia, Jian Ni, Kai Li, Ying Hou, Jing Hu, Linfeng Wei, Kai Wu, Haojun Xia, and Borui Zhou
Earth Syst. Sci. Data, 15, 25–39, https://doi.org/10.5194/essd-15-25-2023, https://doi.org/10.5194/essd-15-25-2023, 2023
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The TiP-Leaf dataset was compiled from direct field measurements and included 11 leaf traits from 468 species of 1692 individuals, covering a great proportion of species and vegetation types on the highest plateau in the world. This work is the first plant trait dataset that represents all of the alpine vegetation on the TP, which is not only an update of the Chinese plant trait database, but also a great contribution to the global trait database.
Timon Miesner, Ulrike Herzschuh, Luidmila A. Pestryakova, Mareike Wieczorek, Evgenii S. Zakharov, Alexei I. Kolmogorov, Paraskovya V. Davydova, and Stefan Kruse
Earth Syst. Sci. Data, 14, 5695–5716, https://doi.org/10.5194/essd-14-5695-2022, https://doi.org/10.5194/essd-14-5695-2022, 2022
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We present data which were collected on expeditions to the northeast of the Russian Federation. One table describes the 226 locations we visited during those expeditions, and the other describes 40 289 trees which we recorded at these locations. We found out that important information on the forest cannot be predicted precisely from satellites. Thus, for anyone interested in distant forests, it is important to go to there and take measurements or use data (as presented here).
Philipp Brun, Niklaus E. Zimmermann, Chantal Hari, Loïc Pellissier, and Dirk Nikolaus Karger
Earth Syst. Sci. Data, 14, 5573–5603, https://doi.org/10.5194/essd-14-5573-2022, https://doi.org/10.5194/essd-14-5573-2022, 2022
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Using mechanistic downscaling, we developed CHELSA-BIOCLIM+, a set of 15 biologically relevant, climate-related variables at unprecedented resolution, as a basis for environmental analyses. It includes monthly time series for 38+ years and 30-year averages for three future periods and three emission scenarios. Estimates matched well with station measurements, but few biases existed. The data allow for detailed assessments of climate-change impact on ecosystems and their services to societies.
Shaoyang He, Yongqiang Zhang, Ning Ma, Jing Tian, Dongdong Kong, and Changming Liu
Earth Syst. Sci. Data, 14, 5463–5488, https://doi.org/10.5194/essd-14-5463-2022, https://doi.org/10.5194/essd-14-5463-2022, 2022
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This study developed a daily, 500 m evapotranspiration and gross primary production product (PML-V2(China)) using a locally calibrated water–carbon coupled model, PML-V2, which was well calibrated against observations at 26 flux sites across nine land cover types. PML-V2 (China) performs satisfactorily in the plot- and basin-scale evaluations compared with other mainstream products. It improved intra-annual ET and GPP dynamics, particularly in the cropland ecosystem.
Han Ma, Shunlin Liang, Changhao Xiong, Qian Wang, Aolin Jia, and Bing Li
Earth Syst. Sci. Data, 14, 5333–5347, https://doi.org/10.5194/essd-14-5333-2022, https://doi.org/10.5194/essd-14-5333-2022, 2022
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The fraction of absorbed photosynthetically active radiation (FAPAR) is one of the essential climate variables. This study generated a global land surface FAPAR product with a 250 m resolution based on a deep learning model that takes advantage of the existing FAPAR products and MODIS time series of observation information. Direct validation and intercomparison revealed that our product better meets user requirements and has a greater spatiotemporal continuity than other existing products.
Hannah Adams, Jane Ye, Bhaleka D. Persaud, Stephanie Slowinski, Homa Kheyrollah Pour, and Philippe Van Cappellen
Earth Syst. Sci. Data, 14, 5139–5156, https://doi.org/10.5194/essd-14-5139-2022, https://doi.org/10.5194/essd-14-5139-2022, 2022
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Climate warming and land-use changes are altering the environmental factors that control the algal
productivityin lakes. To predict how environmental factors like nutrient concentrations, ice cover, and water temperature will continue to influence lake productivity in this changing climate, we created a dataset of chlorophyll-a concentrations (a compound found in algae), associated water quality parameters, and solar radiation that can be used to for a wide range of research questions.
Mathew Lipson, Sue Grimmond, Martin Best, Winston T. L. Chow, Andreas Christen, Nektarios Chrysoulakis, Andrew Coutts, Ben Crawford, Stevan Earl, Jonathan Evans, Krzysztof Fortuniak, Bert G. Heusinkveld, Je-Woo Hong, Jinkyu Hong, Leena Järvi, Sungsoo Jo, Yeon-Hee Kim, Simone Kotthaus, Keunmin Lee, Valéry Masson, Joseph P. McFadden, Oliver Michels, Wlodzimierz Pawlak, Matthias Roth, Hirofumi Sugawara, Nigel Tapper, Erik Velasco, and Helen Claire Ward
Earth Syst. Sci. Data, 14, 5157–5178, https://doi.org/10.5194/essd-14-5157-2022, https://doi.org/10.5194/essd-14-5157-2022, 2022
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We describe a new openly accessible collection of atmospheric observations from 20 cities around the world, capturing 50 site years. The observations capture local meteorology (temperature, humidity, wind, etc.) and the energy fluxes between the land and atmosphere (e.g. radiation and sensible and latent heat fluxes). These observations can be used to improve our understanding of urban climate processes and to test the accuracy of urban climate models.
Keyang He, Houyuan Lu, Jianping Zhang, and Can Wang
Earth Syst. Sci. Data, 14, 4777–4791, https://doi.org/10.5194/essd-14-4777-2022, https://doi.org/10.5194/essd-14-4777-2022, 2022
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Here we presented the first quantitative spatiotemporal cropping patterns spanning the Neolithic and Bronze ages in northern China. Temporally, millet agriculture underwent a dramatic transition from low-yield broomcorn to high-yield foxtail millet around 6000 cal. a BP under the influence of climate and population. Spatially, millet agriculture spread westward and northward from the mid-lower Yellow River (MLY) to the agro-pastoral ecotone (APE) around 6000 cal. a BP and diversified afterwards.
Kailiang Yu, Johan van den Hoogen, Zhiqiang Wang, Colin Averill, Devin Routh, Gabriel Reuben Smith, Rebecca E. Drenovsky, Kate M. Scow, Fei Mo, Mark P. Waldrop, Yuanhe Yang, Weize Tang, Franciska T. De Vries, Richard D. Bardgett, Peter Manning, Felipe Bastida, Sara G. Baer, Elizabeth M. Bach, Carlos García, Qingkui Wang, Linna Ma, Baodong Chen, Xianjing He, Sven Teurlincx, Amber Heijboer, James A. Bradley, and Thomas W. Crowther
Earth Syst. Sci. Data, 14, 4339–4350, https://doi.org/10.5194/essd-14-4339-2022, https://doi.org/10.5194/essd-14-4339-2022, 2022
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We used a global-scale dataset for the surface topsoil (>3000 distinct observations of abundance of soil fungi versus bacteria) to generate the first quantitative map of soil fungal proportion across terrestrial ecosystems. We reveal striking latitudinal trends. Fungi dominated in regions with low mean annual temperature (MAT) and net primary productivity (NPP) and bacteria dominated in regions with high MAT and NPP.
Juha Lemmetyinen, Juval Cohen, Anna Kontu, Juho Vehviläinen, Henna-Reetta Hannula, Ioanna Merkouriadi, Stefan Scheiblauer, Helmut Rott, Thomas Nagler, Elisabeth Ripper, Kelly Elder, Hans-Peter Marshall, Reinhard Fromm, Marc Adams, Chris Derksen, Joshua King, Adriano Meta, Alex Coccia, Nick Rutter, Melody Sandells, Giovanni Macelloni, Emanuele Santi, Marion Leduc-Leballeur, Richard Essery, Cecile Menard, and Michael Kern
Earth Syst. Sci. Data, 14, 3915–3945, https://doi.org/10.5194/essd-14-3915-2022, https://doi.org/10.5194/essd-14-3915-2022, 2022
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The manuscript describes airborne, dual-polarised X and Ku band synthetic aperture radar (SAR) data collected over several campaigns over snow-covered terrain in Finland, Austria and Canada. Colocated snow and meteorological observations are also presented. The data are meant for science users interested in investigating X/Ku band radar signatures from natural environments in winter conditions.
Alejandro Miranda, Rayén Mentler, Ítalo Moletto-Lobos, Gabriela Alfaro, Leonardo Aliaga, Dana Balbontín, Maximiliano Barraza, Susanne Baumbach, Patricio Calderón, Fernando Cárdenas, Iván Castillo, Gonzalo Contreras, Felipe de la Barra, Mauricio Galleguillos, Mauro E. González, Carlos Hormazábal, Antonio Lara, Ian Mancilla, Francisca Muñoz, Cristian Oyarce, Francisca Pantoja, Rocío Ramírez, and Vicente Urrutia
Earth Syst. Sci. Data, 14, 3599–3613, https://doi.org/10.5194/essd-14-3599-2022, https://doi.org/10.5194/essd-14-3599-2022, 2022
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Achieving a local understanding of fire regimes requires high-resolution, systematic and dynamic data. High-quality information can help to transform evidence into decision-making. Taking advantage of big-data and remote sensing technics we developed a flexible workflow to reconstruct burned area and fire severity data for more than 8000 individual fires in Chile. The framework developed for the database can be applied anywhere in the world with minimal adaptation.
Ulrike Herzschuh, Chenzhi Li, Thomas Böhmer, Alexander K. Postl, Birgit Heim, Andrei A. Andreev, Xianyong Cao, Mareike Wieczorek, and Jian Ni
Earth Syst. Sci. Data, 14, 3213–3227, https://doi.org/10.5194/essd-14-3213-2022, https://doi.org/10.5194/essd-14-3213-2022, 2022
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Pollen preserved in environmental archives such as lake sediments and bogs are extensively used for reconstructions of past vegetation and climate. Here we present LegacyPollen 1.0, a dataset of 2831 fossil pollen records from all over the globe that were collected from publicly available databases. We harmonized the names of the pollen taxa so that all datasets can be jointly investigated. LegacyPollen 1.0 is available as an open-access dataset.
Cited articles
Adair, E. C., Parton, W. J., Del Grosso, S. J., Silver, W. L., Harmon, M.
E., Hall, S. A., Burke, I. C., and Hart, S. C.: Simple three-pool model
accurately describes patterns of long-term litter decomposition in diverse
climates, Glob. Chang. Biol., 14, 2636–2660,
https://doi.org/10.1111/j.1365-2486.2008.01674.x, 2008.
Adair, E. C., Parton, W. J., King, J. Y., Brandt, L. A., and Lin, Y.:
Accounting for photodegradation dramatically improves prediction of carbon
losses in dryland systems, Ecosphere, 8, e01892, https://doi.org/10.1002/ecs2.1892,
2017.
Ahlström, A., Raupach, M. R., Schurgers, G., Smith, B., Arneth, A.,
Jung, M., Reichstein, M., Canadell, J. G., Friedlingstein, P., Jain, A. K.,
Kato, E., Poulter, B., Sitch, S., Stocker, B. D., Viovy, N., Wang, Y. P.,
Wiltshire, A., Zaehle, S., and Zeng, N.: The dominant role of semi-arid
ecosystems in the trend and variability of the land CO2 sink, Science, 348, 895–899, https://doi.org/10.1126/science.aaa1668, 2015.
Anderson, D. R. and Burnham, K. P.: Model selection and multi-model
inference, 2nd edn., edited by: Burnham, K. P. and Anderson, D. R., Springer-Verlag, NY, https://doi.org/10.1007/b97636, 2004.
Austin, A. T.: Has water limited our imagination for aridland
biogeochemistry, Trends Ecol. Evol., 26, 229–235,
https://doi.org/10.1016/j.tree.2011.02.003, 2011.
Austin, A. T., Méndez, M. S. and Ballaré, C. L.: Photodegradation
alleviates the lignin bottleneck for carbon turnover in terrestrial
ecosystems, P. Natl. Acad. Sci. USA, 113, 4392–4397,
https://doi.org/10.1073/pnas.1516157113, 2016.
Bona, K. A., Hilger, A., Burgess, M., Wozney, N., and Shaw, C.: A peatland
productivity and decomposition parameter database, Ecology, 99,
2406–2406, https://doi.org/10.1002/ecy.2462, 2018.
Bonan, G. B. and Doney, S. C.: Climate, ecosystems, and planetary futures:
The challenge to predict life in Earth system models, Science, 359, eaam8328,
https://doi.org/10.1126/science.aam8328, 2018.
Brun, R., Reichert, P., and Künsch, H. R.: Practical identifiability
analysis of large environmental simulation models, Water Resour. Res.,
37, 1015–1030, https://doi.org/10.1029/2000WR900350, 2001.
Cepeda-Pizarro, J. G.: Litter decomposition in deserts: an overview with an
example from coastal arid Chile, Rev. Chil. Hist. Nat., 66, 323–336, 1993.
Chapin, F. S., Matson, P. A., and Vitousek, P. M. (Eds.): Principles of Terrestrial
Ecosystem Ecology, Springer New York, New York, NY, https://doi.org/10.1007/978-1-4419-9504-9, 2011.
Chen, M., Parton, W. J., Adair, E. C., Asao, S., Hartman, M. D., and Gao, W.:
Simulation of the effects of photodecay on long-term litter decay using
DayCent, Ecosphere, 7, e01631, https://doi.org/10.1002/ecs2.1631, 2016.
Cotrufo, M. F., Wallenstein, M. D., Boot, C. M., Denef, K., and Paul, E.: The
Microbial Efficiency-Matrix Stabilization (MEMS) framework integrates plant
litter decomposition with soil organic matter stabilization: Do labile plant
inputs form stable soil organic matter?, Glob. Chang. Biol., 19,
988–995, https://doi.org/10.1111/gcb.12113, 2013.
Davidson, E. A. and Janssens, I. A.: Temperature sensitivity of soil carbon
decomposition and feedbacks to climate change, Nature, 440, 165–173,
https://doi.org/10.1038/nature04514, 2006.
Day, T. A., Bliss, M. S., Tomes, A. R., Ruhland, C. T., and Guénon, R.:
Desert leaf litter decay: Coupling of microbial respiration, water-soluble
fractions and photodegradation, Glob. Chang. Biol., 24, 5454–5470,
https://doi.org/10.1111/gcb.14438, 2018.
Feng, S. and Fu, Q.: Expansion of global drylands under a warming climate, Atmos. Chem. Phys., 13, 10081–10094, https://doi.org/10.5194/acp-13-10081-2013, 2013.
Fick, S. E. and Hijmans, R. J.: WorldClim 2: new 1-km spatial resolution
climate surfaces for global land areas, Int. J. Climatol., 37,
4302–4315, https://doi.org/10.1002/joc.5086, 2017.
Foereid, B., Rivero, M. J., Primo, O., and Ortiz, I.: Modelling
photodegradation in the global carbon cycle, Soil Biol. Biochem., 43,
1383–1386, https://doi.org/10.1016/j.soilbio.2011.03.004, 2011.
GBIF.org: GBIF main site, https://www.gbif.org (last access: 18 July 2022),
2022.
GitHub: Homepage, https://github.com/ (last access: 18 July 2022), 2022.
Google LLC: Google Earth Pro, 2020.
Hampton, S. E., Anderson, S. S., Bagby, S. C., Gries, C., Han, X., Hart, E.
M., Jones, M. B., Lenhardt, W. C., MacDonald, A., Michener, W. K., Mudge,
J., Pourmokhtarian, A., Schildhauer, M. P., Woo, K. H., and Zimmerman, N.:
The Tao of open science for ecology, Ecosphere, 6, art120,
https://doi.org/10.1890/ES14-00402.1, 2015.
Harmon, M. E., Nadelhoffer, K. J., and Blair, J. M.: Measuring decomposition,
nutrient turnover, and stores in plant litter, in: Standard Soil Methods for
Long-Term Ecological Research, edited by: Robertson, G., Coleman, D.,
Bledsoe, C., and Sollins, P., 202–240, Oxford University Press, Oxford, ISBN-10 0195120833,
ISBN-13 978-0195120837,
1999.
Huang, J., Ma, J., Guan, X., Li, Y., and He, Y.: Progress in Semi-arid
Climate Change Studies in China, Adv. Atmos. Sci., 36, 922–937,
https://doi.org/10.1007/s00376-018-8200-9, 2019.
Hughes, P., McBratney, A. B., Huang, J., Minasny, B., Micheli, E., and
Hempel, J.: Comparisons between USDA Soil Taxonomy and the Australian Soil
Classification System I: Data harmonization, calculation of taxonomic
distance and inter-taxa variation, Geoderma, 307, 198–209,
https://doi.org/10.1016/j.geoderma.2017.08.009, 2017.
Jafari, M., Tavili, A., Panahi, F., Esfahan, E. Z., and Ghorbani, M.:
Reclamation of Arid Lands, Springer, edited by: Förstner, U.,
Rulkens, W. H., and
Salomons, W.,
https://doi.org/10.1007/978-3-319-54828-9, 2018.
Kattge, J., Bönisch, G., Díaz, S., et al.: TRY plant trait database – enhanced coverage and open access, Glob.
Chang. Biol., 26, 119–188, https://doi.org/10.1111/gcb.14904, 2020.
Luo, Y., Ahlström, A., Allison, S. D., Batjes, N. H., Brovkin, V.,
Carvalhais, N., Chappell, A., Ciais, P., Davidson, E. A., Finzi, A.,
Georgiou, K., Guenet, B., Hararuk, O., Harden, J. W., He, Y., Hopkins, F.,
Jiang, L., Koven, C., Jackson, R. B., Jones, C. D., Lara, M. J., Liang, J.,
McGuire, A. D., Parton, W., Peng, C., Randerson, J. T., Salazar, A., Sierra,
C. A., Smith, M. J., Tian, H., Todd-Brown, K. E. O., Torn, M., van
Groenigen, K. J., Wang, Y. P., West, T. O., Wei, Y., Wieder, W. R., Xia, J.,
Xu, X., Xu, X., and Zhou, T.: Toward more realistic projections of soil
carbon dynamics by Earth system models, Global Biogeochem. Cycles, 30,
40–56, https://doi.org/10.1002/2015GB005239, 2016.
NASA Langley Research Center (LaRC): NASA Prediction of Worldwide Energy
Resources (POWER) Project, https://power.larc.nasa.gov/, last access: 3 December 2021.
Noy-Meir, I.: Desert Ecosystems: Environment and Producers, Annu. Rev. Ecol.
Syst., 4, 25–51, https://doi.org/10.1146/annurev.es.04.110173.000325, 1973.
Pfeiffer, M., Padarian, J., Osorio, R., Bustamante, N., Olmedo, G. F., Guevara, M., Aburto, F., Albornoz, F., Antilén, M., Araya, E., Arellano, E., Barret, M., Barrera, J., Boeckx, P., Briceño, M., Bunning, S., Cabrol, L., Casanova, M., Cornejo, P., Corradini, F., Curaqueo, G., Doetterl, S., Duran, P., Escudey, M., Espinoza, A., Francke, S., Fuentes, J. P., Fuentes, M., Gajardo, G., García, R., Gallaud, A., Galleguillos, M., Gomez, A., Hidalgo, M., Ivelic-Sáez, J., Mashalaba, L., Matus, F., Meza, F., Mora, M. D. L. L., Mora, J., Muñoz, C., Norambuena, P., Olivera, C., Ovalle, C., Panichini, M., Pauchard, A., Pérez-Quezada, J. F., Radic, S., Ramirez, J., Riveras, N., Ruiz, G., Salazar, O., Salgado, I., Seguel, O., Sepúlveda, M., Sierra, C., Tapia, Y., Tapia, F., Toledo, B., Torrico, J. M., Valle, S., Vargas, R., Wolff, M., and Zagal, E.: CHLSOC: the Chilean Soil Organic Carbon database, a multi-institutional collaborative effort, Earth Syst. Sci. Data, 12, 457–468, https://doi.org/10.5194/essd-12-457-2020, 2020.
Poulter, B., Frank, D., Ciais, P., Myneni, R. B., Andela, N., Bi, J.,
Broquet, G., Canadell, J. G., Chevallier, F., Liu, Y. Y., Running, S. W.,
Sitch, S., and Van Der Werf, G. R.: Contribution of semi-arid ecosystems to
interannual variability of the global carbon cycle, Nature, 509,
600–603, https://doi.org/10.1038/nature13376, 2014.
Prescott, C. E. and Vesterdal, L.: Decomposition and transformations along
the continuum from litter to soil organic matter in forest soils, For. Ecol.
Manage., 498, 119522, https://doi.org/10.1016/j.foreco.2021.119522, 2021.
QGIS Development Team: QGIS Geographic Information System, http://www.qgis.org (last access: 18 July 2022), 2021.
R Core Team: R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org/ (last access: 19 July 2022), 2020.
Reynolds, J. F., Smith, D. M. S., Lambin, E. F., Turner, B. L., Mortimore,
M., Batterbury, S. P. J., Downing, T. E., Dowlatabadi, H., Fernandez, R. J.,
Herrick, J. E., Huber-Sannwald, E., Jiang, H., Leemans, R., Lynam, T.,
Maestre, F. T., Ayarza, M., and Walker, B.: Global Desertification: Building
a Science for Dryland Development, Science, 316, 847–851,
https://doi.org/10.1126/science.1131634, 2007.
Rohatgi, A.: WebPlotDigitizer,
Version 4.3,
Pacifica, CA, USA, https://automeris.io/WebPlotDigitizer,
last access: July 2020.
Safriel, U. and Adeel, Z.: Dryland Systems, in: Ecosystems and Human
Well-being: Current State and Trends, vol, 1, edited by: Hassan, R.,
Scholes, R., and Ash, N., 623–662, Island Press, Washington, ISSN 00184888, 2005.
Sarquis, A., Siebenhart, I. A., Austin, A. T., and Sierra, C. A.: aridec, Zenodo [data set],
https://doi.org/10.5281/zenodo.6600345, 2022.
Schädel, C., Beem-Miller, J., Aziz Rad, M., Crow, S. E., Hicks Pries, C. E., Ernakovich, J., Hoyt, A. M., Plante, A., Stoner, S., Treat, C. C., and Sierra, C. A.: Decomposability of soil organic matter over time: the Soil Incubation Database (SIDb, version 1.0) and guidance for incubation procedures, Earth Syst. Sci. Data, 12, 1511–1524, https://doi.org/10.5194/essd-12-1511-2020, 2020.
Shumway, R. H. and Stoffer, D. S.: Time Series Analysis and Its
Applications, 4th edn., edited by: DeVeaux, R., Fienberg, S. E., and Olkin, I.,
Springer International Publishing, Cham, https://doi.org/10.1007/978-3-319-52452-8, 2017.
Sierra, C. A. and Müller, M.: A general mathematical framework for
representing soil organic matter dynamics, Ecol. Monogr., 85, 505–524,
https://doi.org/10.1890/15-0361.1, 2015.
Sierra, C. A., Müller, M., and Trumbore, S. E.: Models of soil organic matter decomposition: the SoilR package, version 1.0, Geosci. Model Dev., 5, 1045–1060, https://doi.org/10.5194/gmd-5-1045-2012, 2012.
Sierra, C. A., Malghani, S., and Müller, M.: Model structure and
parameter identification of soil organic matter models, Soil Biol. Biochem.,
90, 197–203, https://doi.org/10.1016/j.soilbio.2015.08.012, 2015.
Soetaert, K. and Petzoldt, T.: Inverse Modelling, Sensitivity and Monte
Carlo Analysis in R Using Package FME, J. Stat. Softw., 33, 1–28,
https://doi.org/10.18637/jss.v033.i03, 2010.
Tedersoo, L., Laanisto, L., Rahimlou, S., Toussaint, A., Hallikma, T., and
Pärtel, M.: Global database of plants with root-symbiotic nitrogen
fixation: NodDB, edited by: Michalet, R., J. Veg. Sci., 29, 560–568,
https://doi.org/10.1111/jvs.12627, 2018.
Trabucco, A. and Zomer, R.: Global Aridity Index and Potential
Evapotranspiration (ET0) Climate Database v2, CGIAR Consort. Spat. Inf., https://doi.org/10.6084/m9.figshare.7504448.v3,
2019.
United Nations Environment Programme: World atlas of desertification, 2nd
edn., edited by: Middleton, N. and Thomas, D. S. G., Arnold, London, Great
Britain, ISBN 0340691662, 1997.
Verbist, K., Santibañez, F., Gabriels, D., and Soto, G.: Atlas de Zonas
Áridas de América Latina y el Caribe, United Nations Educational,
Scientific and Cultural Organization, edited by: Verbist, K., Santibáñez, Soto,
F.
G.,
Donoso, M. C., and Gabriels,
D., ISBN 9789290891642, 2010.
White, R. P. and Nackoney, J.: Drylands, people and ecosystem goods and
services: a web-based geospatial analysis, http://pdf.wri.org/drylands.pdf (last access: 18 July 2022), 2003.
Yao, J., Liu, H., Huang, J., Gao, Z., Wang, G., Li, D., Yu, H., and Chen, X.:
Accelerated dryland expansion regulates future variability in dryland gross
primary production, Nat. Commun., 11, 1665,
https://doi.org/10.1038/s41467-020-15515-2, 2020.
Zhang, X. and Wang, W.: Control of climate and litter quality on leaf litter
decomposition in different climatic zones, J. Plant Res., 128, 791–802,
https://doi.org/10.1007/s10265-015-0743-6, 2015.
Short summary
Plant litter breakdown in aridlands is driven by processes different from those in more humid ecosystems. A better understanding of these processes will allow us to make better predictions of future carbon cycling. We have compiled aridec, a database of plant litter decomposition studies in aridlands and tested some modeling applications for potential users. Aridec is open for use and collaboration, and we hope it will help answer newer and more important questions as the database develops.
Plant litter breakdown in aridlands is driven by processes different from those in more humid...
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