Articles | Volume 15, issue 8
https://doi.org/10.5194/essd-15-3791-2023
© Author(s) 2023. 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-15-3791-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
23 Aug 2023
Data description paper |
| 23 Aug 2023
| 23 Aug 2023
The Portuguese Large Wildfire Spread database (PT-FireSprd)
Akli Benali
CORRESPONDING AUTHOR
Centro de Estudos Florestais e Laboratório Associado TERRA, Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, 1349-017 Lisboa, Portuga
Nuno Guiomar
MED – Mediterranean Institute for Agriculture, Environment and Development & CHANGE – Global Change and Sustainability, University of Évora-PM, Apartado 94, 7006-554 Évora, Portugal
EaRSLab – Earth Remote Sensing Laboratory, University of Évora-CLV, Rua Romão Ramalho, 59, 7000-671 Évora, Portugal
IIFA – Institute for Advanced Studies and Research, University of Évora-PV, Largo Marquês de Marialva, Apartado 94, 7002-554 Évora, Portugal
Hugo Gonçalves
Força Especial de Proteção Civil, 2080-221 Almeirim,
Portugal
Autoridade Nacional de Emergência e Proteção Civil,
2799-51 Carnaxide, Portugal
Bernardo Mota
National Physical Laboratory (NPL), Climate Earth Observation (CEO),
Hampton Rd. Teddington, TW11 0LW, UK
Fábio Silva
Força Especial de Proteção Civil, 2080-221 Almeirim,
Portugal
Autoridade Nacional de Emergência e Proteção Civil,
2799-51 Carnaxide, Portugal
Paulo M. Fernandes
CITAB – Centro de Investigação e de Tecnologias
Agro-Ambientais e Biológicas, Universidade de Trás-os-Montes e Alto
Douro, 5001-801 Vila Real, Portugal
Carlos Mota
Força Especial de Proteção Civil, 2080-221 Almeirim,
Portugal
Autoridade Nacional de Emergência e Proteção Civil,
2799-51 Carnaxide, Portugal
Alexandre Penha
Autoridade Nacional de Emergência e Proteção Civil,
2799-51 Carnaxide, Portugal
João Santos
Força Especial de Proteção Civil, 2080-221 Almeirim,
Portugal
Autoridade Nacional de Emergência e Proteção Civil,
2799-51 Carnaxide, Portugal
José M. C. Pereira
Centro de Estudos Florestais e Laboratório Associado TERRA, Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, 1349-017 Lisboa, Portuga
Ana C. L. Sá
Centro de Estudos Florestais e Laboratório Associado TERRA, Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, 1349-017 Lisboa, Portuga
Related authors
Dina Jahanianfard, Joana Parente, Oscar Gonzalez-Pelayo, and Akli Benali
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-305, https://doi.org/10.5194/essd-2024-305, 2024
Preprint under review for ESSD
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This data description paper provides details on the development of the first Portuguese burn severity atlas in Portugal from 1984 to 2022 derived from satellite imagery via Google Earth Engine platform. Moreover, a semi-automated code was also developed, which can be used to create burn severity atlas of any other region in the world. The maps of this atlas can be used not only in fields related to fire ecology and management, but also within research areas related to air, water, and soil.
Tomás Calheiros, Akli Benali, Mário Pereira, João Silva, and João Nunes
Nat. Hazards Earth Syst. Sci., 22, 4019–4037, https://doi.org/10.5194/nhess-22-4019-2022, https://doi.org/10.5194/nhess-22-4019-2022, 2022
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Fire weather indices are used to assess the effect of weather on wildfires. Fire weather risk was computed and combined with large wildfires in Portugal. Results revealed the influence of vegetation cover: municipalities with a prevalence of shrublands, located in eastern parts, burnt under less extreme conditions than those with higher forested areas, situated in coastal regions. These findings are a novelty for fire science in Portugal and should be considered for fire management.
Ana C. L. Sá, Bruno Aparicio, Akli Benali, Chiara Bruni, Michele Salis, Fábio Silva, Martinho Marta-Almeida, Susana Pereira, Alfredo Rocha, and José Pereira
Nat. Hazards Earth Syst. Sci., 22, 3917–3938, https://doi.org/10.5194/nhess-22-3917-2022, https://doi.org/10.5194/nhess-22-3917-2022, 2022
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Assessing landscape wildfire connectivity supported by wildfire spread simulations can improve fire hazard assessment and fuel management plans. Weather severity determines the degree of fuel patch connectivity and thus the potential to spread large and intense wildfires. Mapping highly connected patches in the landscape highlights patch candidates for prior fuel treatments, which ultimately will contribute to creating fire-resilient Mediterranean landscapes.
Matthew W. Jones, Douglas I. Kelley, Chantelle A. Burton, Francesca Di Giuseppe, Maria Lucia F. Barbosa, Esther Brambleby, Andrew J. Hartley, Anna Lombardi, Guilherme Mataveli, Joe R. McNorton, Fiona R. Spuler, Jakob B. Wessel, John T. Abatzoglou, Liana O. Anderson, Niels Andela, Sally Archibald, Dolors Armenteras, Eleanor Burke, Rachel Carmenta, Emilio Chuvieco, Hamish Clarke, Stefan H. Doerr, Paulo M. Fernandes, Louis Giglio, Douglas S. Hamilton, Stijn Hantson, Sarah Harris, Piyush Jain, Crystal A. Kolden, Tiina Kurvits, Seppe Lampe, Sarah Meier, Stacey New, Mark Parrington, Morgane M. G. Perron, Yuquan Qu, Natasha S. Ribeiro, Bambang H. Saharjo, Jesus San-Miguel-Ayanz, Jacquelyn K. Shuman, Veerachai Tanpipat, Guido R. van der Werf, Sander Veraverbeke, and Gavriil Xanthopoulos
Earth Syst. Sci. Data, 16, 3601–3685, https://doi.org/10.5194/essd-16-3601-2024, https://doi.org/10.5194/essd-16-3601-2024, 2024
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This inaugural State of Wildfires report catalogues extreme fires of the 2023–2024 fire season. For key events, we analyse their predictability and drivers and attribute them to climate change and land use. We provide a seasonal outlook and decadal projections. Key anomalies occurred in Canada, Greece, and western Amazonia, with other high-impact events catalogued worldwide. Climate change significantly increased the likelihood of extreme fires, and mitigation is required to lessen future risk.
Dina Jahanianfard, Joana Parente, Oscar Gonzalez-Pelayo, and Akli Benali
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-305, https://doi.org/10.5194/essd-2024-305, 2024
Preprint under review for ESSD
Short summary
Short summary
This data description paper provides details on the development of the first Portuguese burn severity atlas in Portugal from 1984 to 2022 derived from satellite imagery via Google Earth Engine platform. Moreover, a semi-automated code was also developed, which can be used to create burn severity atlas of any other region in the world. The maps of this atlas can be used not only in fields related to fire ecology and management, but also within research areas related to air, water, and soil.
Tomás Calheiros, Akli Benali, Mário Pereira, João Silva, and João Nunes
Nat. Hazards Earth Syst. Sci., 22, 4019–4037, https://doi.org/10.5194/nhess-22-4019-2022, https://doi.org/10.5194/nhess-22-4019-2022, 2022
Short summary
Short summary
Fire weather indices are used to assess the effect of weather on wildfires. Fire weather risk was computed and combined with large wildfires in Portugal. Results revealed the influence of vegetation cover: municipalities with a prevalence of shrublands, located in eastern parts, burnt under less extreme conditions than those with higher forested areas, situated in coastal regions. These findings are a novelty for fire science in Portugal and should be considered for fire management.
Ana C. L. Sá, Bruno Aparicio, Akli Benali, Chiara Bruni, Michele Salis, Fábio Silva, Martinho Marta-Almeida, Susana Pereira, Alfredo Rocha, and José Pereira
Nat. Hazards Earth Syst. Sci., 22, 3917–3938, https://doi.org/10.5194/nhess-22-3917-2022, https://doi.org/10.5194/nhess-22-3917-2022, 2022
Short summary
Short summary
Assessing landscape wildfire connectivity supported by wildfire spread simulations can improve fire hazard assessment and fuel management plans. Weather severity determines the degree of fuel patch connectivity and thus the potential to spread large and intense wildfires. Mapping highly connected patches in the landscape highlights patch candidates for prior fuel treatments, which ultimately will contribute to creating fire-resilient Mediterranean landscapes.
Sergey Venevsky, Yannick Le Page, José M. C. Pereira, and Chao Wu
Geosci. Model Dev., 12, 89–110, https://doi.org/10.5194/gmd-12-89-2019, https://doi.org/10.5194/gmd-12-89-2019, 2019
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We present SEVER-FIRE (v1.0), incorporated into the SEVER DGVM. One of the major focuses of SEVER-FIRE is an implementation of the pyrogenic behavior of humans (timing of their activities and their willingness and necessity to ignite or suppress fire), related to socioeconomic and demographic conditions in a geographical domain of the model application. Unlike other DGVM- and ESM-based global fire models, we do not use any satellite-derived assumptions in equations of fire model development.
Yannick Le Page, Douglas Morton, Corinne Hartin, Ben Bond-Lamberty, José Miguel Cardoso Pereira, George Hurtt, and Ghassem Asrar
Earth Syst. Dynam., 8, 1237–1246, https://doi.org/10.5194/esd-8-1237-2017, https://doi.org/10.5194/esd-8-1237-2017, 2017
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Fires damage large areas of eastern Amazon forests when ignitions from human activity coincide with droughts, while more humid central and western regions are less affected. Here, we use a fire model to estimate that fire activity could increase by an order of magnitude without climate mitigation. Our results show that avoiding further agricultural expansion can limit fire ignitions but that tackling climate change is essential to insulate the interior Amazon through the 21st century.
Y. Le Page, D. Morton, B. Bond-Lamberty, J. M. C. Pereira, and G. Hurtt
Biogeosciences, 12, 887–903, https://doi.org/10.5194/bg-12-887-2015, https://doi.org/10.5194/bg-12-887-2015, 2015
G. López-Saldaña, I. Bistinas, and J. M. C. Pereira
Biogeosciences, 12, 557–565, https://doi.org/10.5194/bg-12-557-2015, https://doi.org/10.5194/bg-12-557-2015, 2015
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Land surface albedo is a key parameter to derive Earth’s surface energy balance. Any changes in the albedo have repercussions in the amount of energy that is retained by the Earth. Fire modifies albedo because it removes vegetation from the land surface; therefore, investigating these changes on a global scale can help to understand the role of fire within the Earth system.
I. Bistinas, S. P. Harrison, I. C. Prentice, and J. M. C. Pereira
Biogeosciences, 11, 5087–5101, https://doi.org/10.5194/bg-11-5087-2014, https://doi.org/10.5194/bg-11-5087-2014, 2014
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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.
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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
Earth Syst. Sci. Data, 16, 715–729, https://doi.org/10.5194/essd-16-715-2024, https://doi.org/10.5194/essd-16-715-2024, 2024
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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
Earth Syst. Sci. Data, 16, 277–293, https://doi.org/10.5194/essd-16-277-2024, https://doi.org/10.5194/essd-16-277-2024, 2024
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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
Earth Syst. Sci. Data, 16, 15–34, https://doi.org/10.5194/essd-16-15-2024, https://doi.org/10.5194/essd-16-15-2024, 2024
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Long-term global LAI/FPAR products provide the fundamental dataset for accessing vegetation dynamics and studying climate change. This study develops a sensor-independent LAI/FPAR climate data record based on the integration of Terra-MODIS/Aqua-MODIS/VIIRS LAI/FPAR standard products and applies advanced gap-filling techniques. The SI LAI/FPAR CDR provides a valuable resource for researchers studying vegetation dynamics and their relationship to climate change in the 21st century.
Wojciech Tylmann, Alicja Bonk, Dariusz Borowiak, Paulina Głowacka, Kamil Nowiński, Joanna Piłczyńska, Agnieszka Szczerba, and Maurycy Żarczyński
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.
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.
Agustín Sarquis, Ignacio Andrés Siebenhart, Amy Theresa Austin, and Carlos A. Sierra
Earth Syst. Sci. Data, 14, 3471–3488, https://doi.org/10.5194/essd-14-3471-2022, https://doi.org/10.5194/essd-14-3471-2022, 2022
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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.
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
Albini, F. A.: Wildland Fires: Predicting the behavior of wildland
fires – among nature's most potent forces – can save lives, money, and
natural resources, Am. Sci., 72, 590–597, 1984.
Alcasena, F., Ager, A., Le Page, Y., Bessa, P., Loureiro, C., and Oliveira,
T.: Assessing wildfire exposure to communities and protected areas in
Portugal, Fire, 4, 82, https://doi.org/10.3390/fire404008, 2021.
Alexander, M. and Cruz, M. G.: Are the applications of wildland fire
behaviour models getting ahead of their evaluation again?, Environ. Model.
Softw., 41, 65–71, https://doi.org/10.1016/j.envsoft.2012.11.001, 2013.
Alexander, M. E. and Cruz, M. G.: Evaluating a model for predicting active
crown fire rate of spread using wildfire observations, Can. J. Forest Res., 36,
3015–3028, https://doi.org/10.1139/x06-174, 2006.
Alexander, M. E. and Lanoville, R. A.: Wildfires as a source of fire
behavior data: a case study from Northwest Territories, Canada. 9th Conf.
Fire and Forest Meteorology, 21–24 April, San Diego, CA, American
Meteorological Society, Boston, Mass, 86–93, 1987.
Alexander, M. E. and Thomas, D. A.: Wildland fire behavior case studies and
analyses: Other examples, methods, reporting standards, and some practical
advice, Fire Manag. Today, 63, 4–12, 2003.
Andela, N., Morton, D. C., Giglio, L., Paugam, R., Chen, Y., Hantson, S., van der Werf, G. R., and Randerson, J. T.: The Global Fire Atlas of individual fire size, duration, speed and direction, Earth Syst. Sci. Data, 11, 529–552, https://doi.org/10.5194/essd-11-529-2019, 2019.
Anderson, W. R., Cruz, M. G., Fernandes, P. M., McCaw, L., Vega, J. A.,
Bradstock, R. A., Fogarty, L .G., Gould, J. B., McCarthy, G. H.,
Marsden-Smedley, J. B., Matthews, S., Mattingley, G., Pearce, H. G., and van
Wilgen, B. W.: A generic, empirical-based model for predicting rate of fire
spread in shrublands, Int. J. Wildland Fire, 24, 443–460,
https://doi.org/10.1071/WF14130, 2015.
Artés, T., Oom, D., De Rigo, D., Durrant, T. H., Maianti, P.,
Libertà, G., and San-Miguel-Ayanz, J.: A global wildfire dataset for the
analysis of fire regimes and fire behaviour, Sci. Data, 6, 1–11,
https://doi.org/10.1038/s41597-019-0312-2, 2019.
Benali, A., Guiomar, N., Gonçalves, H., Mota, B., Silva, F., Fernandes,
P. M., Mota, C., Penha, A., Santos, J., Pereira, J. M. C., and Sá, A. C. L:
The Portuguese Large Wildfire Spread Database (PT-FireSprd), Zenodo [data set],
https://doi.org/10.5281/zenodo.7495506, 2022.
Briones-Herrera, C. I., Vega-Nieva, D. J., Monjarás-Vega, N. A.,
Briseño-Reyes, J., López-Serrano, P. M., Corral-Rivas, J. J.,
Alvarado-Celestino, E., Arellano-Pérez, S., Álvarez-González,
J. G., Ruiz-González, A. D., Jolly, W. M., and Parks, S. A.: Near
real-time automated early mapping of the perimeter of large forest fires
from the aggregation of VIIRS and MODIS active fires in Mexico, Remote
Sens., 12, 2061, https://doi.org/10.3390/rs12122061, 2020.
Butler, B. W. and Reynolds, T. D.: Wildfire case study: Butte City,
southeastern Utah, 1 July 1994, USDA For. Serv., Intermt. Res. Stn., Ogden,
UT. Gen. Tech. Rep. INT-GTR-351, https://doi.org/10.2737/INT-GTR-351, 1997.
Catchpole, W. R., Catchpole, E. A., Butler, B. W., Rothermel, R. C., Morris,
G. A., and Latham, D. J.: Rate of spread of free-burning fires in woody
fuels in a wind tunnel, Combust. Sci. Technol., 131, 1–37,
https://doi.org/10.1080/00102209808935753, 1998.
Chen, Y., Hantson, S., Andela, N., Coffield, S. R., Graff, C. A., Morton, D.
C., Ott, L.E., Foufoula-Georgiou, E., Smyth, P., Goulden, M. L., and
Randerson, J. T.: California wildfire spread derived using VIIRS satellite
observations and an object-based tracking system, Sci. Data, 9, 1–15,
https://doi.org/10.1038/s41597-022-01343-0, 2022.
Cheney, N. P.: Fire behaviour during the Pickering Brook wildfire, January
2005 (Perth Hills Fires 71-80), Conserv. Sci. West. Aust., 7, 451–468,
2010.
Cheney, N. P., Gould, J. S., McCaw, W. L., and Anderson, W. R.: Predicting
fire behaviour in dry eucalypt forest in southern Australia, Forest Ecol.
Manag., 280, 120–131, https://doi.org/10.1016/j.foreco.2012.06.012, 2012.
Coen, J. L. and Riggan, P. J.: Simulation and thermal imaging of the 2006
Esperanza Wildfire in southern California: application of a coupled
weather–wildland fire model, Int. J. Wildland Fire, 23, 755–770,
https://doi.org/10.1071/WF12194, 2014.
Collins, B. M., Miller. J. D., Thode, A. E., Kelly, M., van Wagtendonk, J.
W., and Stephens, S. L.: Interactions among wildland fires in a long-
established Sierra Nevada natural fire area, Ecosystems 12, 114–128,
https://doi.org/10.1007/s10021-008-9211-7, 2019.
Countryman, C. M.: The fire environment concept, USDA Forest Service,
Pacific Southwest Range and Experiment Station, Berkeley, California, USA,
1972.
Crowley, M. A., Cardille, J. A., White, J. C., and Wulder, M. A.: Generating
intra-year metrics of wildfire progression using multiple open-access
satellite data streams, Remote Sens. Environ., 232, 111295,
https://doi.org/10.1016/j.rse.2019.111295, 2019.
Cruz, M. G.: Monte Carlo-based ensemble method for prediction of grassland
fire spread, Int. J. Wildland Fire, 19, 521–530, https://doi.org/10.1071/WF08195,
2010.
Cruz, M. G. and Alexander, M. E.: Uncertainty associated with model
predictions of surface and crown fire rates of spread, Environ. Modell.
Softw., 47, 16–28, https://doi.org/10.1016/j.envsoft.2013.04.004, 2013.
Cruz, M. G. and Alexander, M. E.: The 10 % wind speed rule of thumb for
estimating a wildfire's forward rate of spread in forests and shrublands,
Ann. Forest Sci., 76, 1–11, https://doi.org/10.1007/s13595-019-0829-8, 2019.
Cruz, M. G., Gould, J. S., Alexander, M. E., Sullivan, A. L., McCaw, W. L.,
and Matthews, S.: Empirical-based models for predicting head-fire rate of
spread in Australian fuel types, Aust. Forestry, 78, 118–158,
https://doi.org/10.1080/00049158.2015.1055063, 2015.
Cruz, M. G., Alexander, M. E., Sullivan, A. L., Gould, J. S., and Kilinc,
M.: Assessing improvements in models used to operationally predict wildland
fire rate of spread, Environ. Modell. Softw., 105, 54–63,
https://doi.org/10.1016/j.envsoft.2018.03.027, 2018.
Cruz, M. G., Alexander, M. E., and Kilinc, M.: Wildfire rates of spread in
grasslands under critical burning conditions, Fire, 5, 55,
https://doi.org/10.3390/fire5020055, 2022.
Cruz, M. G., Cheney, N. P., Gould, J. S., McCaw, W. L., Kilinc, M., and
Sullivan, A. L.: An empirical-based model for predicting the forward spread
rate of wildfires in eucalypt forests, Int. J. Wildland Fire, 31, 81–95,
https://doi.org/10.1071/WF21068, 2021.
Dale, M. R. T. and Fortin, M. J.: From graphs to spatial graphs, Annu. Rev.
Ecol. Evol. Systs., 41, 21–38, https://doi.org/10.1146/annurev-ecolsys-102209-144718, 2010.
Duff, T. J., Chong, D. M., and Tolhurst, K. G.: Quantifying spatio-temporal
differences between fire shapes: Estimating fire travel paths for the
improvement of dynamic spread models, Environ. Modell. Softw, 46, 33–43,
https://doi.org/10.1016/j.envsoft.2013.02.005, 2013.
Fernandes, P. M., Botelho, H. S., Rego, F. C., and Loureiro, C.: Empirical
modelling of surface fire behaviour in maritime pine stands, Int. J.
Wildland Fire, 18, 698–710, https://doi.org/10.1071/WF08023, 2009.
Fernandes, P. M., Barros, A. M., Pinto, A., and Santos, J. A.:
Characteristics and controls of extremely large wildfires in the western
Mediterranean Basin, J. Geophys. Res.-Biogeo., 121, 2141–2157,
https://doi.org/10.1002/2016JG003389, 2016.
Fernandes, P. M., Sil, A., Ascoli, D., Cruz, M. G., Alexander, M. E., Rossa,
C. G., Baeza, J., Burrows, N., Davies, G. M., Fidelis, A., Gould, J. S.,
Govender, N., Kilinc, M., and McCaw, L.: Drivers of wildland fire behaviour
variation across the Earth, in: Advances in Forest Fire
Research, Chapter 7 – Short contributions, edited by: Viegas, D. X., ADAI/CEIF, University of Coimbra, 1267–1270,
https://doi.org/10.14195/978-989-26-16-506_154, 2018.
Fernandes, P. M., Sil, A., Ascoli, D., Cruz, M. G., Rossa, C. G., and
Alexander, M. E.: Characterizing fire behavior across the globe, in: Proceedings of the Fire
Continuum-Preparing for the future of wildland fire, edited by: Hood,
S. M., Drury, S., Steelman, T., and Steffens, R., 21–24 May 2018,
Missoula, MT, Proceedings RMRS-P-78, Fort Collins, CO, US Department of
Agriculture, Forest Service, Rocky Mountain Research Station, 258–263,
2020.
Finney, M. A., McAllister, S. S., Forthofer, J. M., and Grumstrup, T. P.:
Wildland Fire Behaviour: Dynamics, Principles and Processes, CSIRO Pub., ISBN 978148309108,
2021.
Forestry Canada Fire Danger Group: Development and structure of the Canadian
Forest Fire Behavior Prediction System. For. Can., Ottawa, Ont. Inf. Rep.
ST-X-3, ISBN 0662198123, 1992.
Frantz, D., Stellmes, M., Röder, A., and Hill, J.: Fire spread from
MODIS burned area data: Obtaining fire dynamics information for every single
fire, Int. J. Wildland Fire, 25, 1228–1237, https://doi.org/10.1071/WF16003, 2016.
Giglio, L., Descloitres, J., Justice, C. O., and Kaufman, Y. J.: An enhanced
contextual fire de- tection algorithm for MODIS, Remote Sens. Environ., 87,
273–282, https://doi.org/10.1016/S0034-4257(03)00184-6, 2003.
Giglio, L., Schroeder, W., and Justice, C. O.: The collection 6 MODIS active
fire detection algorithm and fire productsm Remote Sens. Environ., 178,
31–41, https://doi.org/10.1016/j.rse.2016.02.054, 2016.
Gollner, M., Trouve, A., Altintas, I., Block, J., de Callafon, R., Clements,
C., Cortes, A., Ellicott, E., Filippi, J. B., Finney, M., Ide, K., Jenkins,
M. A., Jimenez, D., Lautenberger, C., Mandel, J., Rochoux, M., and Simeoni,
A.: Towards data-driven operational wildfire spread modeling, in: Report of the NSF-Funded Wildfire Workshop, College Park, MD, USA, University of Maryland, 2015.
Guerreiro, J., Fonseca, C., Salgueiro, A., Fernandes, P., Iglésias, E.
L., Neufville, R., Mateus, P., Castellnou, M., Silva, J. S., Moura, J. M.,
Rego, F. C., and Caldeira, D.: Análise e apuramento dos factos relativos
aos incêndios que ocorreram em Pedrogão Grande, Castanheira de
Pêra, Ansião, Alvaiázere, Figueiró dos Vinhos, Arganil,
Góis, Penela, Pampilhosa da Serra, Oleiros e Sertã, entre 17 e 24 de
junho de 2017, Comissão Técnica Independente, Assembleia da
República, Lisboa,
https://www.parlamento.pt/Documents/2017/Outubro/RelatórioCTI_VF.pdf (last access: December 2022), 2017.
Guerreiro, J., Fonseca, C., Salgueiro, A., Fernandes, P., Iglésias, E.
L., Neufville, R., Mateus, P., Castellnou, M., Silva, J. S., Moura, J. M.,
Rego, F. C., and Caldeira, D.: Avaliação dos Incêndios ocorridos
entre 14 e 16 de outubro de 2017 em Portugal Continental, Comissão
Técnica Independente, Assembleia da República, Lisboa,
https://www.parlamento.pt/Documents/2018/Marco/RelatorioCTI190318N.pdf (last access: December 2022),
2018.
Humber, M., Zubkova, M., and Giglio, L.: A remote sensing-based approach to
estimating the fire spread rate parameter for individual burn patch
extraction, Int. J. Remote Sens., 43, 649–673,
https://doi.org/10.1080/01431161.2022.2027544, 2022.
Khanmohammadi, S., Arashpour, M., Golafshani, E. M., Cruz, M. G.,
Rajabifard, A., and Bai, Y.: Prediction of wildfire rate of spread in
grasslands using machine learning methods, Environ. Modell. Softw., 156,
105507, https://doi.org/10.1016/j.envsoft.2022.105507, 2022.
Kilinc, M., Anderson, W., and Price, B.: The Applicability of Bushfire
Behaviour Models in Australia, Victorian Government, Department of
Sustainability and Environment, DSE Schedule 5: Fire Severity Rating
Project, Melbourne, VIC, Technical Report 1, 2012.
McCaw, W. L., Gould, J. S., Cheney, N. P., Ellis, P. F. M., and Anderson, W.
R.: Changes in behaviour of fire in dry eucalypt forest as fuel increases
with age, Forest Ecol. Manag., 271, 170–181, https://doi.org/10.1016/j.foreco.2012.02.003,
2012.
Niro, F., Goryl, P., Dransfeld, S., Boccia, V., Gascon, F., Adams, J.,
Themann, B., Scifoni, S. and Doxani, G.: European Space Agency (ESA)
Calibration/Validation Strategy for Optical Land-Imaging Satellites and
Pathway towards Interoperability, Remote Sens., 13, 3003,
https://doi.org/10.3390/rs13153003, 2021.
Oom, D., Silva, P. C., Bistinas, I., and Pereira, J. M. C.: Highlighting
biome-specific sensitivity of fire size distributions to time-gap parameter
using a new algorithm for fire event individuation, Remote Sens., 8, 663,
https://doi.org/10.3390/rs8080663, 2016.
Palaiologou, P., Kalabokidis, K., Ager, A. A., and Day, M. A.: Development
of Comprehensive Fuel Management Strategies for Reducing Wildfire Risk in
Greece, Forests, 11, 789, https://doi.org/10.3390/f11080789, 2020.
Palheiro, P. M., Fernandes, P. M., and Cruz, M. G.: A fire behaviour-based fire
danger classification for maritime pine stands: comparison of two
approaches, Forest Ecol. Manag., 234, p. S54, https://doi.org/10.1016/j.foreco.2006.08.075,
2006.
Parisien, M. A., Parks, S. A., Miller, C., Krawchuk, M. A., Heathcott, M., and
Moritz, M. A.: Contributions of ignitions, fuels, and weather to the burn
probability of a boreal landscape, Ecosystems, 14, 1141–1155,
https://doi.org/10.1007/s10021-011-9474-2, 2011.
Parks, S. A.: Mapping day-of-burning with coarse-resolution satellite
fire-detection data, Int. J. Wildland Fire, 23, 215–223,
https://doi.org/10.1071/WF13138, 2014.
Pereira, J. M., Oom, D., Silva, P. C., and Benali, A.: Wild, tamed, and
domesticated: Three fire macroregimes for global pyrogeography in the
Anthropocene, Ecol. Appl., 32, e2588, https://doi.org/10.1002/eap.2588, 2022.
Pereira, M. G., Malamud, B. D., Trigo, R. M., and Alves, P. I.: The history and characteristics of the 1980–2005 Portuguese rural fire database, Nat. Hazards Earth Syst. Sci., 11, 3343–3358, https://doi.org/10.5194/nhess-11-3343-2011, 2011.
Pinto, M. M., DaCamara, C. C., Trigo, I. F., Trigo, R. M., and Turkman, K. F.: Fire danger rating over Mediterranean Europe based on fire radiative power derived from Meteosat, Nat. Hazards Earth Syst. Sci., 18, 515–529, https://doi.org/10.5194/nhess-18-515-2018, 2018.
Rodríguez y Silva, F. and Molina-Martínez, J. R.: Modeling
Mediterranean forest fuels by integrating field data and mapping tools, Eur.
J. For. Res., 131, 571–582, https://doi.org/10.1007/s10342-011-0532-2, 2012.
Rothermel, R. C.: A mathematical model for predicting fire spread in wildland
fuels, Res. Pap. INT-115. Ogden, UT, U.S. Department of Agriculture,
Intermountain Forest and Range Experiment Station, 1972.
Sá, A. C.,
Benali, A., Fernandes, P. M., Pinto, R. M., Trigo, R. M., Salis, M., Russo,
A., Jerez, S., Soares, P. M. M., Schroeder, W., and Pereira, J. M. C.:
Evaluating fire growth simulations using satellite active fire data, Remote
Sens. Environ., 190, 302–317, https://doi.org/10.1016/j.rse.2016.12.023, 2017.
Salis, M., Del Giudice, L., Arca, B., Ager, A. A., Alcasena-Urdiroz, F.,
Lozano, O., Bacciu, V., Spano, D., and Duce, P.: Modeling the effects of
different fuel treatment mosaics on wildfire spread and behavior in a
Mediterranean agro-pastoral area, J. Environ. Manage., 212, 490–505,
https://doi.org/10.1016/j.jenvman.2018.02.020, 2018.
Santoni, P.-A., Filippi, J.-B., Balbi, J.-H., and Bosseur, F.: Wildland fire
behaviour case studies and fuel models for landscape-scale fire modeling, J.
Combust., 2011, 613424, https://doi.org/10.1155/2011/613424, 2011.
Schag, G. M., Stow, D. A., Riggan, P. J., Tissell, R. G., and Coen, J. L.:
Examining landscape-scale fuel and terrain controls of wildfire spread rates
using repetitive airborne thermal infrared (ATIR) imagery, Fire, 4, 6,
https://doi.org/10.3390/fire4010006, 2021.
Schroeder, W., Oliva, P., Giglio, L., and Csiszar, I. A.: The New VIIRS 375 m active fire detection data product: Algorithm description and initial
assessment, Remote Sens. Environ., 143, 85–96,
https://doi.org/10.1016/j.rse.2013.12.008, 2014.
Scott, J. H. and Reinhardt, E. D.: Assessing Crown Fire Potential by
Linking Models of Surface and Crown Fire Behavior, US Department of
Agriculture, Forest Service, Rocky Mountain Research Station, Fort Collins,
CO, Research Paper RMRS-RP-29, https://doi.org/10.2737/RMRS-RP-29, 2001.
Sharples, J. J., McRae, R. H., and Wilkes, S. R.: Wind–terrain effects on
the propagation of wildfires in rugged terrain: fire channelling, Int. J.
Wildland Fire, 21, 282–296, https://doi.org/10.1071/WF10055, 2012.
Sifakis, N. I., Iossifidis, C., Kontoes, C., and Keramitsoglou, I.: Wildfire
detection and tracking over Greece using MSG-SEVIRI satellite data, Remote
Sens., 3, 524–538, https://doi.org/10.3390/rs3030524, 2011.
Stocks, B. J., Alexander, M. E., Wotton, B. M., Stefner, C. N., Flannigan,
M. D., Taylor, S. W., Lavoie, N., Mason, J. A., Hartley, G. R., Maffey, M.
E., Dalrymple, G. N., Blake, T. W., and Cruz, M. G., and Lanoville, R. A.: Crown
fire behaviour in a northern jack pine black spruce forest, Can. J. For.
Res., 34, 1548–1560, https://doi.org/10.1139/x04-054, 2004.
Storey, M. A., Price, O. F., Sharples, J. J., and Bradstock, R. A.: Drivers
of long-distance spotting during wildfires in south-eastern Australia, Int.
J. Wildland Fire, 29, 459–472, https://doi.org/10.1071/WF19124, 2020.
Storey, M. A., Bedward, M., Price, O. F., Bradstock, R. A., and Sharples, J.
J.: Derivation of a Bayesian fire spread model using large-scale wildfire
observations, Environ. Model. Softw., 144, 105127,
https://doi.org/10.1016/j.envsoft.2021.105127, 2021.
Stow, D. A., Riggan, P. J., Storey, E. A., and Coulter, L. L.: Measuring fire
spread rates from repeat pass airborne thermal infrared imagery, Remote
Sens. Lett., 5, 803–812, https://doi.org/10.1080/2150704X.2014.967882, 2014.
Vaillant, N. M., Ewell, C. M., and Fites-Kaufman, J. A.: Capturing crown fire
behavior on wildland fires - the Fire Behavior Assessment Team in action,
Fire Manag. Today, 73, 41–45, 2014.
Valero, M. M., Rios, O., Pastor, E., and Planas, E.: Automated location of
active fire perimeters in aerial infrared imaging using unsupervised edge
detectors, Int. J. Wildland Fire, 27, 241–256, https://doi.org/10.1071/WF17093, 2018.
Veraverbeke, S., Sedano, F., Hook, S. J., Randerson, J. T., Jin, Y., and
Rogers, B. M.: Mapping the daily progression of large wildland fires using
MODIS active fire data, Int. J. Wildland Fire, 23, 655–667,
https://doi.org/10.1071/WF13015, 2014.
Viegas, D. X., Almeida, M. F., Ribeiro, L. M., Raposo, J., Viegas, M. T.,
Oliveira, R., Alves, D., Pinto, C., Rodrigues, A., Ribeiro, C., Lopes, S.,
Jorge, H., and Viegas, C. X.: Análise dos Incêndios Florestais
Ocorridos a 15 de outubro de 2017, Centro de Estudos sobre Incêndios
Florestais (CEIF/ADAI/LAETA), 2019.
Wade, D. D. and Ward, D. E.: An analysis of the Air Force Bomb Range Fire,
Res. Pap. SE–105, Asheville, NC, USDA Forest Service, Southeastern Forest
Experiment Station, 1973.
Wolfe, R. E., Roy, D. P., and Vermote, E.: MODIS land data storage,
gridding, and compositing methodology: level 2 grid, IEEE T. Geosci.
Remote, 36, 1324–1338, https://doi.org/10.1109/36.701082, 1998.
Wooster, M. J., Roberts, G., Freeborn, P. H., Xu, W., Govaerts, Y., Beeby, R., He, J., Lattanzio, A., Fisher, D., and Mullen, R.: LSA SAF Meteosat FRP products – Part 1: Algorithms, product contents, and analysis, Atmos. Chem. Phys., 15, 13217–13239, https://doi.org/10.5194/acp-15-13217-2015, 2015.
Wotton, B. M.: Interpreting and using outputs from the Canadian Forest Fire
Danger Rating System in research applications, Environ. Ecol. Stat., 16,
107–131, https://doi.org/10.1007/s10651-007-0084-2, 2009.
Short summary
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.
We reconstructed the spread of 80 large wildfires that burned recently in Portugal and...
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