Articles | Volume 16, issue 6
https://doi.org/10.5194/essd-16-3045-2024
© Author(s) 2024. 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-16-3045-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
28 Jun 2024
Data description article |
| 28 Jun 2024
| 28 Jun 2024
Physical, social, and biological attributes for improved understanding and prediction of wildfires: FPA FOD-Attributes dataset
Yavar Pourmohamad
Department of Civil Engineering, Boise State University, Boise, ID, USA
Department of Computer Science, Boise State University, Boise, ID, USA
John T. Abatzoglou
Management of Complex Systems Department, University of California, Merced, CA, USA
Erin J. Belval
USDA Forest Service, Rocky Mountain Research Station, Fort Collins, CO, USA
Erica Fleishman
College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis, OR, USA
Karen Short
USDA Forest Service, Rocky Mountain Research Station, Missoula, Montana, USA
Matthew C. Reeves
USDA Forest Service, Rocky Mountain Research Station, Missoula, Montana, USA
Nicholas Nauslar
Storm Prediction Center, National Weather Service, Boise, ID, USA
Philip E. Higuera
Department of Ecosystem and Conservation Sciences, University of Montana, Missoula, MT, USA
Eric Henderson
Department of Computer Science, Boise State University, Boise, ID, USA
Sawyer Ball
Department of Computer Science, Boise State University, Boise, ID, USA
Amir AghaKouchak
Department of Civil and Environmental Engineering, University of California, Irvine, CA, USA
Jeffrey P. Prestemon
USDA Forest Service, Southern Research Station, Research Triangle Park, NC, USA
Julia Olszewski
USDA Forest Service, Rocky Mountain Research Station, Missoula, Montana, USA
Mojtaba Sadegh
CORRESPONDING AUTHOR
Department of Civil Engineering, Boise State University, Boise, ID, USA
United Nations University Institute for Water, Environment and Health, United Nations University, Hamilton, ON, Canada
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Ali Reza Shahvaran, Roohollah Noori, Mohammed Basheer, Kaveh Madani, Chenxi Mi, Kun Shi, Yong Qiang Zhou, R. Iestyn Woolway, Yun Lin Zhang, Bo Qiang Qin, Amir AghaKouchak, and Michelle T. H. van Vliet
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-528, https://doi.org/10.5194/essd-2026-528, 2026
Preprint under review for ESSD
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We built the first global dataset that tracks daily water quality for more than five thousand lakes over twenty years. By turning complex satellite images into simple, ready-to-use tables, we make lake conditions easy to explore. Our new quality checks help users spot unusual or uncertain data. This resource opens the door for better research and smarter decisions about how lakes are changing in a warming world.
Chantelle Burton, Francesca Di Giuseppe, Matthew W. Jones, Douglas I. Kelley, Maria L. F. Barbosa, Joe McNorton, Robert Potts, Carlota Segura-Garcia, John T. Abatzoglou, Iulian Alin Rosu, Niels Andela, Liana O. Anderson, Sally Archibald, Esther Brambleby, Emilio Chuvieco, Andy Ciavarella, Hamish Clarke, Francisco de la Barrera, Carlos M. Di Bella, Enza Di Tomaso, Kebonye Dintwe, Tim S. Doherty, Victoria M. Donovan, Jonathan M. Eden, Paulo M. Fernandes, Lukas Fiedler, Louis Giglio, Olivia Haas, Douglas Hamilton, Stijn Hantson, Sarah Harris, Andrew J. Hartley, Theodore R. Keeping, Crystal A. Kolden, Jean-Luc Kouassi, Elena Kukavskaya, Seppe Lampe, Zhongwei Liu, Guilherme Mataveli, Sarah Meier, Duarte Oom, Mark Parrington, Yuquan Qu, Victor Resco de Dios, Julien Ruffault, Mojtaba Sadegh, Cristina Santín, Fernando Sedano, Jacquelyn K. Shuman, Patrícia S. Silva, Fiona R. Spuler, Carmen B. Steinmann, Veerachai Tanpipat, Miguel Ángel Torres-Vazquez, Marco Turco, Guido R. van der Werf, Dave van Wees, Sander Veraverbeke, Jakob B. Wessel, and Bo Zheng
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-587, https://doi.org/10.5194/essd-2026-587, 2026
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The third State of Wildfires report examines extreme wildfire events from 2025 to early 2026. It analyses key regional events in Midwestern Canadian Shield Forests, Northwest Iberia, and Chilean Temperate Forests and Matorral, assessing their impacts, drivers, and attributing them to climate change. Climate change greatly increased the likelihood of these fires, and without strong mitigation, such events will become more frequent.
A. Park Williams, John T. Abatzoglou, Gavin D. Madakumbura, Haibo Liu, Stefan Rahimi, and Christopher Daly
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-391, https://doi.org/10.5194/essd-2026-391, 2026
Preprint under review for ESSD
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Here we introduce ParallelClim-WestUS-Daily, a new dataset of daily gridded climate data for the western US from 1951–2025 as well as a parallel, counterfactual realization of the observed climate but without human-caused climate change. This dataset will enable assessments of how climate change has affected complex hydroclimatic processes such as hydrology, vegetation dynamics, and wildfire.
Soheil Radfar, Hamed Moftakhari, David F. Muñoz, Avantika Gori, Ferdinand Diermanse, Ning Lin, and Amir AghaKouchak
Hydrol. Earth Syst. Sci., 30, 1397–1420, https://doi.org/10.5194/hess-30-1397-2026, https://doi.org/10.5194/hess-30-1397-2026, 2026
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Flooding in coastal areas often occurs when several mechanisms act together, causing compound flooding. Researchers increasingly use hybrid models that combine numerical models with statistical tools to study these events. Yet, the term “hybrid model” has been used inconsistently. This paper provides a clear definition and classification system, along with examples and technical challenges.
A. Park Williams, Winslow D. Hansen, Caroline S. Juang, John T. Abatzoglou, Volker C. Radeloff, Bowen Wang, Jazlynn Hall, Jatan Buch, and Gavin D. Madakumbura
Geosci. Model Dev., 19, 1157–1191, https://doi.org/10.5194/gmd-19-1157-2026, https://doi.org/10.5194/gmd-19-1157-2026, 2026
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The new Western United States Large Forest Fire Stochastic Simulator (WULFFSS) is a monthly gridded model to simulate forest fires across the western United States in response to vegetation, topographic, anthropogenic, and climate factors. The model is highly skillful, accounting for over 80 % of the observed variability in annual forest-fire area and capturing observed spatial, intra-annual variations, and trends.
A. Park Williams, Caroline S. Juang, and Karen C. Short
Earth Syst. Sci. Data, 17, 7359–7372, https://doi.org/10.5194/essd-17-7359-2025, https://doi.org/10.5194/essd-17-7359-2025, 2025
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The Western United States MTBS-Interagency Database of Large Wildfires, 1984–2024 (WUMI2024a) represents more than 22000 large (≥1 km2) wildfires in the western United States from 1984 through 2024, including maps of fire perimeters and areas burned. It was compiled from seven government datasets and quality controlled. This dataset will aid research on the causes and effects of wildfire in a changing world.
Douglas I. Kelley, Chantelle Burton, Francesca Di Giuseppe, Matthew W. Jones, Maria L. F. Barbosa, Esther Brambleby, Joe R. McNorton, Zhongwei Liu, Anna S. I. Bradley, Katie Blackford, Eleanor Burke, Andrew Ciavarella, Enza Di Tomaso, Jonathan Eden, Igor José M. Ferreira, Lukas Fiedler, Andrew J. Hartley, Theodore R. Keeping, Seppe Lampe, Anna Lombardi, Guilherme Mataveli, Yuquan Qu, Patrícia S. Silva, Fiona R. Spuler, Carmen B. Steinmann, Miguel Ángel Torres-Vázquez, Renata Veiga, Dave van Wees, Jakob B. Wessel, Emily Wright, Bibiana Bilbao, Mathieu Bourbonnais, Cong Gao, Carlos M. Di Bella, Kebonye Dintwe, Victoria M. Donovan, Sarah Harris, Elena A. Kukavskaya, Aya Brigitte N'Dri, Cristina Santín, Galia Selaya, Johan Sjöström, John T. Abatzoglou, Niels Andela, Rachel Carmenta, Emilio Chuvieco, Louis Giglio, Douglas S. Hamilton, Stijn Hantson, Sarah Meier, Mark Parrington, Mojtaba Sadegh, Jesus San-Miguel-Ayanz, Fernando Sedano, Marco Turco, Guido R. van der Werf, Sander Veraverbeke, Liana O. Anderson, Hamish Clarke, Paulo M. Fernandes, and Crystal A. Kolden
Earth Syst. Sci. Data, 17, 5377–5488, https://doi.org/10.5194/essd-17-5377-2025, https://doi.org/10.5194/essd-17-5377-2025, 2025
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The second State of Wildfires report examines extreme wildfire events from 2024 to early 2025. It analyses key regional events in Southern California, Northeast Amazonia, Pantanal–Chiquitano, and the Congo Basin, assessing their drivers and predictability and attributing them to climate change and land use. Seasonal outlooks and decadal projections are provided. Climate change greatly increased the likelihood of these fires, and without strong mitigation, such events will become more frequent.
Hossein Abbasizadeh, Petr Maca, Martin Hanel, Mads Troldborg, and Amir AghaKouchak
Hydrol. Earth Syst. Sci., 29, 4761–4790, https://doi.org/10.5194/hess-29-4761-2025, https://doi.org/10.5194/hess-29-4761-2025, 2025
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Here, we represented catchments as networks of variables connected by cause-and-effect relationships. By comparing the performance of statistical and machine learning methods with and without incorporating causal information to predict runoff properties, we showed that causal information can enhance models' robustness by reducing the accuracy drop between the training and testing phases, improving the model's interpretability, and mitigating overfitting issues, especially with small training samples.
Bhupinderjeet Singh, Mingliang Liu, John Abatzoglou, Jennifer Adam, and Kirti Rajagopalan
EGUsphere, https://doi.org/10.5194/egusphere-2024-2284, https://doi.org/10.5194/egusphere-2024-2284, 2024
Preprint archived
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Hydrology models rely on simplistic static approaches to precipitation phase partitioning. We evaluate model skill changes for a suite of snow metrics by transitioning to a more accurate dynamic partitioning. We found that the transition resulted in a better match between modeled and observed metrics, with a 50 % reduction in model bias, emphasizing the need for the hydrological modeling community to adopt dynamic partitioning.
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.
Heidi Kreibich, Kai Schröter, Giuliano Di Baldassarre, Anne F. Van Loon, Maurizio Mazzoleni, Guta Wakbulcho Abeshu, Svetlana Agafonova, Amir AghaKouchak, Hafzullah Aksoy, Camila Alvarez-Garreton, Blanca Aznar, Laila Balkhi, Marlies H. Barendrecht, Sylvain Biancamaria, Liduin Bos-Burgering, Chris Bradley, Yus Budiyono, Wouter Buytaert, Lucinda Capewell, Hayley Carlson, Yonca Cavus, Anaïs Couasnon, Gemma Coxon, Ioannis Daliakopoulos, Marleen C. de Ruiter, Claire Delus, Mathilde Erfurt, Giuseppe Esposito, Didier François, Frédéric Frappart, Jim Freer, Natalia Frolova, Animesh K. Gain, Manolis Grillakis, Jordi Oriol Grima, Diego A. Guzmán, Laurie S. Huning, Monica Ionita, Maxim Kharlamov, Dao Nguyen Khoi, Natalie Kieboom, Maria Kireeva, Aristeidis Koutroulis, Waldo Lavado-Casimiro, Hong-Yi Li, Maria Carmen LLasat, David Macdonald, Johanna Mård, Hannah Mathew-Richards, Andrew McKenzie, Alfonso Mejia, Eduardo Mario Mendiondo, Marjolein Mens, Shifteh Mobini, Guilherme Samprogna Mohor, Viorica Nagavciuc, Thanh Ngo-Duc, Huynh Thi Thao Nguyen, Pham Thi Thao Nhi, Olga Petrucci, Nguyen Hong Quan, Pere Quintana-Seguí, Saman Razavi, Elena Ridolfi, Jannik Riegel, Md Shibly Sadik, Nivedita Sairam, Elisa Savelli, Alexey Sazonov, Sanjib Sharma, Johanna Sörensen, Felipe Augusto Arguello Souza, Kerstin Stahl, Max Steinhausen, Michael Stoelzle, Wiwiana Szalińska, Qiuhong Tang, Fuqiang Tian, Tamara Tokarczyk, Carolina Tovar, Thi Van Thu Tran, Marjolein H. J. van Huijgevoort, Michelle T. H. van Vliet, Sergiy Vorogushyn, Thorsten Wagener, Yueling Wang, Doris E. Wendt, Elliot Wickham, Long Yang, Mauricio Zambrano-Bigiarini, and Philip J. Ward
Earth Syst. Sci. Data, 15, 2009–2023, https://doi.org/10.5194/essd-15-2009-2023, https://doi.org/10.5194/essd-15-2009-2023, 2023
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As the adverse impacts of hydrological extremes increase in many regions of the world, a better understanding of the drivers of changes in risk and impacts is essential for effective flood and drought risk management. We present a dataset containing data of paired events, i.e. two floods or two droughts that occurred in the same area. The dataset enables comparative analyses and allows detailed context-specific assessments. Additionally, it supports the testing of socio-hydrological models.
Nadine Borduas-Dedekind, Karen C. Short, and Samuel P. Carlson
Earth Syst. Sci. Data, 15, 1437–1440, https://doi.org/10.5194/essd-15-1437-2023, https://doi.org/10.5194/essd-15-1437-2023, 2023
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This article describes the use of the open-discussion manuscript review process as an educational exercise for early career scientists.
Abby C. Lute, John Abatzoglou, and Timothy Link
Geosci. Model Dev., 15, 5045–5071, https://doi.org/10.5194/gmd-15-5045-2022, https://doi.org/10.5194/gmd-15-5045-2022, 2022
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We developed a snow model that can be used to quantify snowpack over large areas with a high degree of spatial detail. We ran the model over the western United States, creating a snow and climate dataset for three time periods. Compared to observations of snowpack, the model captured the key aspects of snow across time and space. The model and dataset will be useful in understanding historical and future changes in snowpack, with relevance to water resources, agriculture, and ecosystems.
Sofia Hallerbäck, Laurie S. Huning, Charlotte Love, Magnus Persson, Katarina Stensen, David Gustafsson, and Amir AghaKouchak
The Cryosphere, 16, 2493–2503, https://doi.org/10.5194/tc-16-2493-2022, https://doi.org/10.5194/tc-16-2493-2022, 2022
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Using unique data, some dating back to the 18th century, we show a significant trend in shorter ice duration, later freeze, and earlier break-up dates across Sweden. In recent observations, the mean ice durations have decreased by 11–28 d and the chance of years with an extremely short ice cover duration (less than 50 d) have increased by 800 %. Results show that even a 1 °C increase in air temperatures can result in a decrease in ice duration in Sweden of around 8–23 d.
Cited articles
Abatzoglou, J. T.: Development of gridded surface meteorological data for ecological applications and modelling, Int. J. Climatol., 33, 121–131, 2013.
Abt, K. L., Butry, D. T., Prestemon, J. P., and Scranton, S.: Effect of fire prevention programs on accidental and incendiary wildfires on tribal lands in the United States, Int. J. Wildl. Fire, 24, 749–762, 2015.
Aldersley, A., Murray, S. J., and Cornell, S. E.: Global and regional analysis of climate and human drivers of wildfire, Sci. Total Environ., 409, 3472–3481, 2011.
Alizadeh, M. R., Abatzoglou, J. T., Luce, C. H., Adamowski, J. F., Farid, A., and Sadegh, M.: Warming enabled upslope advance in western US forest fires, P. Natl. Acad. Sci. USA, 118, e2009717118, https://doi.org/10.1073/pnas.2009717118, 2021.
Alizadeh, M. R., Abatzoglou, J. T., Adamowski, J., Modaresi Rad, A., AghaKouchak, A., Pausata, F. S. R., and Sadegh, M.: Elevation-dependent intensification of fire danger in the western United States, Nat. Commun., 14, 1773, https://doi.org/10.1038/s41467-023-37311-4, 2023.
Balch, J. K., Bradley, B. A., Abatzoglou, J. T., Chelsea Nagy, R., Fusco, E. J., and Mahood, A. L.: Human-started wildfires expand the fire niche across the United States, P. Natl. Acad. Sci. USA, 114, 2946–2951, https://doi.org/10.1073/pnas.1617394114, 2017.
Barros, A. M. G., Day, M. A., Preisler, H. K., Abatzoglou, J. T., Krawchuk, M. A., Houtman, R., and Ager, A. A.: Contrasting the role of human-and lightning-caused wildfires on future fire regimes on a Central Oregon landscape, Environ. Res. Lett., 16, 64081, https://doi.org/10.1088/1748-9326/ac03da, 2021.
Boise State University: FPA FOD-Attributes, Boise State University [data set], https://fpafod.boisestate.edu (last access: 20 June 2023), 2024.
Bowman, D. M. J. S., Balch, J. K., Artaxo, P., Bond, W. J., Carlson, J. M., Cochrane, M. A., D'Antonio, C. M., DeFries, R. S., Doyle, J. C., and Harrison, S. P.: Fire in the Earth system, Science, 324, 481–484, 2009.
Bowman, D. M. J. S., Balch, J., Artaxo, P., Bond, W. J., Cochrane, M. A., D'antonio, C. M., DeFries, R., Johnston, F. H., Keeley, J. E., and Krawchuk, M. A.: The human dimension of fire regimes on Earth, J. Biogeogr., 38, 2223–2236, 2011.
Butler, W. H., Monroe, A., and McCaffrey, S.: Collaborative implementation for ecological restoration on US public lands: implications for legal context, accountability, and adaptive management, Environ. Manage., 55, 564–577, 2015.
Chen, B. and Jin, Y.: Spatial patterns and drivers for wildfire ignitions in California, Environ. Res. Lett., 17, 055004, https://doi.org/10.1088/1748-9326/ac60da, 2022.
Chuvieco, E., Aguado, I., Salas, J., García, M., Yebra, M., and Oliva, P.: Satellite remote sensing contributions to wildland fire science and management, Curr. For. Reports, 6, 81–96, 2020.
Climate and Economic Justice Screening Tool: https://screeningtool.geoplatform.gov/, last access: 10 December 2023.
Cohen, J. D. and Deeming, J. E.: The National Fire Danger Rating System: Basic Equations (General Technical Report PSW-GTR-82), US Dep. Agric. For. Serv. Berkeley, CA, USA, 1985.
Colavito, M. M.: The role of science in the collaborative forest landscape restoration program, J. For., 115, 34–42, 2017.
Colorado Encyclopedia: East Troublesome Fire: https://coloradoencyclopedia.org/article/east-troublesome-fire, last access: 10 December 2023.
Dahal, D., Pastick, N. J., Boyte, S. P., Parajuli, S., Oimoen, M. J., and Megard, L. J.: Multi-Species Inference of Exotic Annual and Native Perennial Grasses in Rangelands of the Western United States Using Harmonized Landsat and Sentinel-2 Data, Remote Sens., 14, 807, https://doi.org/10.3390/rs14040807, 2022 (data available at: https://data.usgs.gov/datacatalog/data/USGS:61716970d34ea36449a77130, last access: 16 October 2023).
Dennison, P. E., Brewer, S. C., Arnold, J. D., and Moritz, M. A.: Large wildfire trends in the western United States, 1984–2011, Geophys. Res. Lett., 41, 2928–2933, 2014.
Dewitz, J.: National land cover database (NLCD) 2016 products, US Geol. Surv. data release, 10, P96HHBIE, https://doi.org/10.1016/j.rse.2021.112357, 2019.
Didan, K.: MODIS/Terra Vegetation Indices 16-Day L3 Global 1km SIN Grid V061. NASA EOSDIS Land Processes Distributed Active Archive Center [data set], https://doi.org/10.5067/MODIS/MOD13A2.061, 2021.
Dissing, D. and Verbyla, D. L.: Spatial patterns of lightning strikes in interior Alaska and their relations to elevation and vegetation, Can. J. For. Res., 33, 770–782, 2003.
Douglas, J., Mills, T. J., Artly, D., Ashe, D., Bartuska, A., Black, R. L., Coloff, S., Cruz, J., Edrington, M., and Edwardson, J.: Review and update of the 1995 federal wildland fire management policy, US Dept. of the Interior; US Dept. of Agriculture, 2001.
Eidenshink, J., Schwind, B., Brewer, K., Zhu, Z.-L., Quayle, B., and Howard, S.: A project for monitoring trends in burn severity, Fire Ecol., 3, 3–21, 2007.
Finney, M. A., McHugh, C. W., Grenfell, I. C., Riley, K. L., and Short, K. C.: A simulation of probabilistic wildfire risk components for the continental United States, Stoch. Environ. Res. Risk Assess., 25, 973–1000, 2011.
Flanagan, B. E., Hallisey, E. J., Adams, E., and Lavery, A.: Measuring community vulnerability to natural and anthropogenic hazards: the Centers for Disease Control and Prevention's Social Vulnerability Index, J. Environ. Health, 80, 34–36, 2018 (data available at: https://www.atsdr.cdc.gov/placeandhealth/svi/data_documentation_download.html, last access: 16 October 2023).
Flannigan, M. D. and Wotton, B. M.: Lightning-ignited forest fires in northwestern Ontario, Can. J. For. Res., 21, 277–287, 1991.
Fowler, M., Rad, A. M., Utych, S., Adams, A., Alamian, S., Pierce, J., Dennison, P., Abatzoglou, J. T., AghaKouchak, A., and Montrose, L.: A dataset on human perception of and response to wildfire smoke, Sci. Data, 6, 1–10, 2019.
Fuquay, D. M., Baughman, R. G., Latham, D. J.: A model for predicting lightning fire ignition in wildland fuels, Intermountain Forest and Range Experiment Station, Forest Service, US, Wildfires, Paper 5, https://digitalcommons.usu.edu/govdocs_wfires/5 (last access: 16 October 2023), 1979.
Hély, C., Flannigan, M., Bergeron, Y., and McRae, D.: Role of vegetation and weather on fire behavior in the Canadian mixedwood boreal forest using two fire behavior prediction systems, Can. J. For. Res., 31, 430–441, 2001.
Hessilt, T. D., Abatzoglou, J. T., Chen, Y., Randerson, J. T., Scholten, R. C., Van Der Werf, G., and Veraverbeke, S.: Future increases in lightning ignition efficiency and wildfire occurrence expected from drier fuels in boreal forest ecosystems of western North America, Environ. Res. Lett., 17, 054008, https://doi.org/10.1088/1748-9326/ac6311, 2022.
Home Land Security: Fire Stations, https://opendata.arcgis.com/api/v3/datasets/0ccaf0c53b794eb8ac3d3de6afdb3286_0/downloads/data?format=shp&spatialRefId=3857&where=1%3D1, last access: 13 March 2023.
Houtman, R. M., Montgomery, C. A., Gagnon, A. R., Calkin, D. E., Dietterich, T. G., McGregor, S., and Crowley, M.: Allowing a wildfire to burn: estimating the effect on future fire suppression costs, Int. J. Wildl. Fire, 22, 871–882, 2013.
Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., and Ferreira, L. G.: Overview of the radiometric and biophysical performance of the MODIS vegetation indices, Remote Sens. Environ., 83, 195–213, 2002.
Iglesias, V., Balch, J. K., and Travis, W. R.: US fires became larger, more frequent, and more widespread in the 2000s, Sci. Adv., 8, eabc0020, https://doi.org/10.1126/sciadv.abc0020, 2022.
Kennedy, C. M., Oakleaf, J. R., Theobald, D. M., Baruch-Mordo, S., and Kiesecker, J.: Managing the middle: A shift in conservation priorities based on the global human modification gradient, Glob. Chang. Biol., 25, 811–826, 2019.
Khorshidi, M. S., Dennison, P. E., Nikoo, M. R., AghaKouchak, A., Luce, C. H., and Sadegh, M.: Increasing concurrence of wildfire drivers tripled megafire critical danger days in Southern California between 1982 and 2018, Environ. Res. Lett., 15, 104002, https://doi.org/10.1088/1748-9326/abae9e, 2020.
Kummu, M., Taka, M., and Guillaume, J. H. A.: Gridded global datasets for gross domestic product and Human Development Index over 1990–2015, Sci. Data, 5, 1–15, https://doi.org/10.5061/dryad.dk1j0, 2018.
Latham, D. and Williams, E.: Lightning and forest fires, in: Forest Fires, Elsevier, 375–418, 2001.
Maranghides, A., Link, E., Hawks, S., Wilson, M., Brewer, W., Brown, C., Vihnaneck, B., and Walton, W. D.: A Case Study of the Camp Fire–Fire Progression Timeline Appendix C, Community WUI Fire Hazard Evaluation Framework, 2021.
Mazzeo, G., De Santis, F., Falconieri, A., Filizzola, C., Lacava, T., Lanorte, A., Marchese, F., Nolè, G., Pergola, N., and Pietrapertosa, C.: Integrated Satellite System for Fire Detection and Prioritization, Remote Sens., 14, 335, https://doi.org/10.3390/rs14020335, 2022.
McGee, T., McFarlane, B., and Tymstra, C.: Wildfire: a Canadian perspective, in: Wildfire hazards, risks and disasters, Elsevier, 35–58, 2015.
Meisner, B. N., Chase, R. A., McCutchan, M. H., Mees, R., Benoit, J. W., Ly, B., Albright, D., Strauss, D., and Ferryman, T.: A lightning fire ignition assessment model, in: 12th Conference on Fire and Forest Meteorology'. Jekyll Island, GA, 172–178, 1993.
Miller, C. and Ager, A. A.: A review of recent advances in risk analysis for wildfire management, Int. J. Wildl. fire, 22, 1–14, 2012.
Modaresi Rad, A., Abatzoglou, J. T., Kreitler, J., Alizadeh, M. R., AghaKouchak, A., Hudyma, N., Nauslar, N. J., and Sadegh, M.: Human and infrastructure exposure to large wildfires in the United States, Nat. Sustain., 6, 1–9, 2023.
Nguyan, D., Belval, E. J., Wei, Y., Short, K. C., and Calkin, D. E.: Dataset of United States Incident Management Situation Reports, 2007–2021, Sci. Data, 11, https://doi.org/10.1038/s41597-023-02876-8, 2023.
Noonan-Wright, E. K., Opperman, T. S., Finney, M. A., Zimmerman, G. T., Seli, R. C., Elenz, L. M., Calkin, D. E., and Fiedler, J. R.: Developing the US wildland fire decision support system, J. Combust., 2011, 1–14, https://doi.org/10.1155/2011/168473, 2011.
Omernik, J. M.: Ecoregions of the conterminous United States, Annals of the Association of American Geographers 77:118-125, https://gaftp.epa.gov/EPADataCommons/ORD/Ecoregions/us/us_eco_l4_state_boundaries.zip (last access: 10 December 2023), 1987.
Pineda, N., Altube, P., Alcasena, F. J., Casellas, E., Segundo, H. S., and Montanyà, J.: Characterising the holdover phase of lightning-ignited wildfires in Catalonia, Agric. For. Meteorol., 324, 1–15, https://doi.org/10.1016/j.agrformet.2022.109111, 2022.
Pourmohamad, Y., Abatzoglou, J., Belval, E., Short, K., Fleishman, E., Reeves, M., Nauslar, N., Higuera, P., Henderson, E., Ball, S., AghaKouchak, A., Prestemon, J., Olszewski, J., and Sadegh, M. Physical, Social, and Biological Attributes for Improved Understanding and Prediction of Wildfires: FPA FOD-Attributes Dataset (1.0), Zenodo [data set], https://doi.org/10.5281/zenodo.8381129, 2023.
Prestemon, J. P. and Butry, D. T.: Time to burn: modeling wildland arson as an autoregressive crime function, Am. J. Agric. Econ., 87, 756–770, 2005.
Rad, A. M., Abatzoglou, J., Fleishman, E., Mockrin, M. H., Radeloff, V. C., Pourmohamad, Y., Cattau, M. E., Johnson, J. M., Higuera, P. E., Nauslar, N., and Sadegh, M.: Social vulnerability of the people exposed to wildfires, Sci. Adv., 9, eadh461, https://doi.org/10.1126/sciadv.adh4615, 2023.
Reeves, M. and Frid, L.: The Rangeland Vegetation Simulator: A user-driven system for quantifying production, succession, disturbance and fuels in non-forest environments, in: 10th International Rangeland Congress, 1062, 2016.
Reeves, M. C., Hanberry, B. B., Wilmer, H., Kaplan, N. E., and Lauenroth, W. K.: An assessment of production trends on the Great Plains from 1984 to 2017, Rangel. Ecol. Manag., 78, 165–179, 2021.
Riley, S. J., DeGloria, S. D., and Elliot, R.: Index that quantifies topographic heterogeneity, Intermt. J. Sci., 5, 23–27, 1999.
Roberto Barbosa, M., Carlos Sícoli Seoane, J., Guimarães Buratto, M., Santana de Oliveira Dias, L., Paulo Carvalho Raivel, J., and Lobos Martins, F.: Forest Fire Alert System: a Geo Web GIS prioritization model considering land susceptibility and hotspots – a case study in the Carajás National Forest, Brazilian Amazon, Int. J. Geogr. Inf. Sci., 24, 873–901, 2010.
Ronchi, E., Gwynne, S. M. V, Rein, G., Wadhwani, R., Intini, P., and Bergstedt, A.: e-Sanctuary: Open multi-physics framework for modelling wildfire urban evacuation, Fire Protection Research Foundation Quincy, 2017.
Rothberg, D.: “It's gone, it's gone:” Nation's largest wildfire in Nevada devastates ranches, sage grouse, Nevada Indep., 12 July 2018.
Rouse, J. W., Haas, R. H., Schell, J. A., and Deering, D. W.: Monitoring vegetation systems in the Great Plains with ERTS, NASA Spec. Publ, 351, 309, https://doi.org/10.4095/330629, 1974.
Santos, F., Bailey, J. K., and Schweitzer, J. A.: The eco-evolutionary role of fire in shaping terrestrial ecosystems, Funct. Ecol., 37, 2090–2095, 2023.
Scott, J., Helmbrecht, D., Thompson, M. P., Calkin, D. E., and Marcille, K.: Probabilistic assessment of wildfire hazard and municipal watershed exposure, Nat. Hazards, 64, 707–728, 2012.
Short, K. C.: A spatial database of wildfires in the United States, 1992–2011, Earth Syst. Sci. Data, 6, 1–27, https://doi.org/10.5194/essd-6-1-2014, 2014.
Short, K. C.: Spatial wildfire occurrence data for the United States, 1992–2018 [FPA_FOD_20210617], 5th Edition. Fort Collins, CO: Forest Service Research Data Archive, https://doi.org/10.2737/RDS-2013-0009.5, 2021.
Short, K. C.: Spatial wildfire occurrence data for the United States, 1992–2020 [FPA_FOD_20221014], 6th Edition. Fort Collins, CO: Forest Service Research Data Archive, https://doi.org/10.2737/RDS-2013-0009.6, 2022.
Short, K. C., Grenfell, I. C., Riley, K. L., and Vogler, K. C.: Pyromes of the conterminous United States, Forest Service Research Data Archive [data set], https://doi.org/10.2737/RDS-2020-0020, 2020.
Show, S. B. and Kotok, E. I.: Forest fires in California, 1911–1920: an analytical study, U.S. Dept. of Agriculture, Washington, D.C., 1923.
Shuman, J. K., Balch, J. K., Barnes, R. T., Higuera, P. E., Roos, C. I., Schwilk, D. W., Stavros, E. N., Banerjee, T., Bela, M. M., and Bendix, J.: Reimagine fire science for the anthropocene, P. Natl. Acad. Sci. USA Nexus, 1, pgac115, https://doi.org/10.1093/pnasnexus/pgac115, 2022.
Silva, F. R., Martínez, J. R. M., and González-Cabán, A.: A methodology for determining operational priorities for prevention and suppression of wildland fires, Int. J. Wildl. fire, 23, 544–554, 2014.
Silva, F. R. Y., O'Connor, C. D., Thompson, M. P., Martinez, J. R. M., and Calkin, D. E.: Modelling suppression difficulty: current and future applications, Int. J. Wildl. FIRE, 29, 752, https://doi.org/10.1071/wf19042, 2020.
St. Denis, L. A., Short, K. C., McConnell, K., Cook, M. C., Mietkiewicz, N. P., Buckland, M., and Balch, J. K.: all-hazards dataset mined from the US National Incident Management System 1999–2020, Sci. data, 10, 112, https://doi.org/10.1038/s41597-023-01955-0, 2023.
Stephens, S. L., Agee, J. K., Fule, P. Z., North, M. P., Romme, W. H., Swetnam, T. W., and Turner, M. G.: Managing forests and fire in changing climates, Science, 342, 41–42, 2013.
Tatem, A. J.: WorldPop, open data for spatial demography, Sci. Data, 4, 1–4, 2017.
TIGER: US Census Roads: https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.2022.html#list-tab-790442341, last access: 16 October 2023.
U.S. Department of Interior, Geological Survey, and U.S. Department of Agriculture: LANDFIRE Existing Vegetation Type layer, http://landfire.cr.usgs.gov/viewer/ (last access: 16 October 2023), last update: May 2022a.
U.S. Department of Interior, Geological Survey, and U.S. Department of Agriculture: LANDFIRE Fire Regime Groups Type layer, http://landfire.cr.usgs.gov/viewer/ (last access: 16 October 2023), last update: May 2022b.
U.S. Department of Interior, Geological Survey, and U.S. Department of Agriculture: LANDFIRE Topography Type layer, http://landfire.cr.usgs.gov/viewer/ (last access: 16 October 2023), last update: May 2022c.
U.S. Geological Survey (USGS): Gap Analysis Project (GAP): Protected Areas Database of the United States (PAD-US) 3.0: U.S. Geological Survey data release, https://doi.org/10.5066/P9Q9LQ4B, 2022.
Vermote, E.: NOAA Climate Data Record (CDR) of AVHRR normalized difference vegetation index (NDVI), version 5, NOAA National Centers for Environmental Information, https://doi.org/10.7289/V5ZG6QH9, 2019.
Viegas, D. X., Viegas, M., and Ferreira, A. D.: Moisture content of fine forest fuels and fire occurrence in central Portugal, Int. J. Wildl. Fire, 2, 69–86, 1992.
Weaver, J. F., Lindsey, D., Bikos, D., Schmidt, C. C., and Prins, E.: Fire detection using GOES rapid scan imagery, Weather Forecast., 19, 496–510, 2004.
Weiss, A.: Topographic position and landforms analysis, in: Poster presentation, ESRI user conference, 9–13 July 2001, San Diego, CA, poster no. 200, 2001.
Westerling, A. L.: Increasing western US forest wildfire activity: sensitivity to changes in the timing of spring, Philos. T. Roy. Soc. B, 371, 20150178, https://doi.org/10.1098/rstb.2015.0178, 2016.
Wierzchowski, J., Heathcott, M., and Flannigan, M. D.: Lightning and lightning fire, central cordillera, Canada, Int. J. Wildl. Fire, 11, 41–51, 2002.
Wildland Fire Executive Council: The National Cohesive Wildland Fire Management Strategy: Phase III Western Regional Action Plan, 99 pp., https://www.frames.gov/catalog/14351 (last access: 5 January 2024), 2013.
Wildland fire perimeters full history: https://data-nifc.opendata.arcgis.com/datasets/nifc::wfigs-wildland-fire-perimeters-full-history/explore, last access: 26 February 2023.
WorldPop: Global high resolution population denominators project, Funded by Bill Melinda Gates Found. Sch. Geogr. Environ. Sci. Univ. Southampton; Dep. Geogr. Geosci. Univ. Louisville; Dep. Geogr. Univ. Namur) Cent. Int. Earth Sci. Inf. Netw.(CIESIN), Columbia Univ, 2018.
Short summary
The FPA FOD-Attributes dataset provides > 300 biological, physical, social, and administrative attributes associated with > 2.3×106 wildfire incidents across the US from 1992 to 2020. The dataset can be used to (1) answer numerous questions about the covariates associated with human- and lightning-caused wildfires and (2) support descriptive, diagnostic, predictive, and prescriptive wildfire analytics, including the development of machine learning models.
The FPA FOD-Attributes dataset provides > 300 biological, physical, social, and administrative...
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