Articles | Volume 15, issue 13
https://doi.org/10.5194/gmd-15-5045-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-5045-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
04 Jul 2022
Model description paper |
| 04 Jul 2022
| 04 Jul 2022
SnowClim v1.0: high-resolution snow model and data for the western United States
Abby C. Lute
CORRESPONDING AUTHOR
Water Resources Program, University of Idaho, Moscow, ID 83844, USA
now at: Woodwell Climate Research Center, Falmouth, MA 02540, USA
John Abatzoglou
Management of Complex Systems, University of California, Merced, CA 95343, USA
Timothy Link
Department of Forest, Rangeland, and Fire Sciences, University of
Idaho, Moscow, ID 83844, USA
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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, Gao Cong, Carlos M. Di Bella, Kebonye Dintwe, Victoria M. Donovan, Sarah Harris, Elena A. Kukavskaya, Brigitte N’Dri, Cristina Santín, Galia Selaya, Johan Sjöström, John 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 Discuss., https://doi.org/10.5194/essd-2025-483, https://doi.org/10.5194/essd-2025-483, 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, 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.
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
EGUsphere, https://doi.org/10.5194/egusphere-2025-2934, https://doi.org/10.5194/egusphere-2025-2934, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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The new WULFFSS is a monthly gridded forest-fire model to simulate forest fires across the western United States in response to vegetation, topographic, anthropogenic, and climate factors. This effort is motivated by the ten-fold increase in western U.S. annual forest area burned over the past 40 years. The WULFFSS 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.
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.
Yavar Pourmohamad, John T. Abatzoglou, Erin J. Belval, Erica Fleishman, Karen Short, Matthew C. Reeves, Nicholas Nauslar, Philip E. Higuera, Eric Henderson, Sawyer Ball, Amir AghaKouchak, Jeffrey P. Prestemon, Julia Olszewski, and Mojtaba Sadegh
Earth Syst. Sci. Data, 16, 3045–3060, https://doi.org/10.5194/essd-16-3045-2024, https://doi.org/10.5194/essd-16-3045-2024, 2024
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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.
Jianning Ren, Jennifer C. Adam, Jeffrey A. Hicke, Erin J. Hanan, Christina L. Tague, Mingliang Liu, Crystal A. Kolden, and John T. Abatzoglou
Hydrol. Earth Syst. Sci., 25, 4681–4699, https://doi.org/10.5194/hess-25-4681-2021, https://doi.org/10.5194/hess-25-4681-2021, 2021
Short summary
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Mountain pine beetle outbreaks have caused widespread tree mortality. While some research shows that water yield increases after trees are killed, many others document no change or a decrease. The climatic and environmental mechanisms driving hydrologic response to tree mortality are not well understood. We demonstrated that the direction of hydrologic response is a function of multiple factors, so previous studies do not necessarily conflict with each other; they represent different conditions.
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Short summary
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.
We developed a snow model that can be used to quantify snowpack over large areas with a high...
