This Master of Environmental Data Science (MEDS) Capstone Project Presentation features a machine learning algorithm designed to interpret RHESSys output and extract meaningful insights into the possible impacts of climate change on forest health, and visualize findings in an interactive manner that is accessible to forest managers, students, and the general public.
Thursday, May 26 2022, 1:10-1:30
Bren Hall 1414 + Livestream
Student group: Alex Clippinger, Wylie Hampson, Shale Hunter, Peter Menzies
Faculty Advisor: Christina (Naomi) Tague
Naomi discusses how we are using RHESSys to explore fire in this short presentation.
In this new publication, RHESSys-WMFire is used to address the overarching question: How does vegetation modulate the effects of climate change on fire regimes in a semiarid watershed? More specifically – what are the relative and opposing roles of key exogenous drivers (climate change/CO2) and key endogenous drivers (fuel load/fuel aridity) in driving fire regimes.
Ren, J., Hanan, E.J., Abatzoglou, J.T., Kolden, C.A., Tague, C.(N.)L., Kennedy, M.C., et al. (2022) Projecting future fire regimes in a semiarid watershed of the inland northwestern United States: Interactions among climate change, vegetation productivity, and fuel dynamics, Earth’s Future 10(3), e2021EF002518. doi: 10.1029/2021EF002518
Congratulations to lab friend and collaborator Makki Khorchani (Instituto Pirenaico de Ecología (IPE-CSIC)) on his PhD defense last Friday of his dissertation “Effects of post-land abandonment management strategies on water resources, vegetation dynamics, and soil properties and redistribution in the central Spanish Pyrenees”. He used the RHESSys model to simulate the different vegetation and climate scenarios explored in his research.
TagueTeamLab members/friends presenting at AGU:
Chris Heckman, Naomi Tague – How a priori forest adaptations affect drought resilience to the 2012-2015 California drought. Poster B15E-1475, Monday Dec. 13, 2021, 2:00-4:00
Kazi Tamaddun, Louis Graup, Anne Lightbody – Modelling Watershed Sensitivity to Drought: Application of Authentic Online Learning on the HydroLearn Platform, Presentation ED12A-05, Monday Dec. 13, 2021, Room 298-299, 8:06-8:11
Louis Graup, Christina (Naomi) Tague, Adrian Adam Harpold, Sebastian A. Krogh – Will Riparian Refugia be Destabilized by Snow Drought? Poster H25C-1077, Tuesday Dec. 14, 2021, 2:00-4:00
David L Miller, Erin B Wetherley, Dar A Roberts, Christina (Naomi) Tague, Joseph P McFadden – Effects of a multi-year drought in Los Angeles on urban tree, turfgrass, and senesced vegetation cover, Presentation GC33C-02, Room 203-205, Wednesday Dec. 15, 2021, 10:55-11:00
Rachel Torres Christina (Naomi) Tague, David Miller, Michael Alonzo, Joseph P McFadden, Samantha Stevenson – Evaluating urban tree resilience to drought and extreme heat in Santa Barbara, CA, Poster H35X-1299, Wednesday Dec. 15, 2021, 2:00-4:00
Kyotaek Hwang, Holly R Barnard, Adrian Adam Harpold, Christina (Naomi) Tague, Pamela L Sullivan, Katherine B Lininger – Opportunities and challenges in remote sensing-based critical zone ecohydrology, Poster H45F-1239, Thursday Dec. 16th, 2021, 2:00-4:00
Christopher Heckman, Naomi Tague – When and Where Does Storage Matter for Vegetation? Untangling the Non-linear Relationship Between Climate, Storage, and Actual Evapotranspiration, Presentation H51E-06, Friday Dec. 17, 2021, Room 260-262, 6:25-6:30
Erica R Siirila-Woodburn, Alan Rhoades, Benjamin Hatchett, Laurie Huning, Julia Szinai, Christina (Naomi) Tague, Peter S Nico, Daniel Feldman, Andrew D Jones, William Drew Collins, Laurna Kaatz – Evidence of a low-to-no snow future and its impacts on water resources in the western United States, Presentation H52A-04, Friday Dec. 17th, 2021, Room 243-245, 8:00-8:05
Lawrence E Band. Clare Stephens, Lucy Amanda Marshall, Fiona Johnson, Hoori Ajami, Laurence Lin – Investigating spatial patterns and variability in catchment response to climate change using a virtual experiment approach, Presentation H51B-08, Friday Dec. 17th, 2021, New Orleans Theater B, 6:35-6:40
Jianning Ren, Erin J Hanan, Peter M Homyak – How does the mean and variance of rainfall patterns influence nitrogen saturation and export in dryland watersheds? B55O-03, Online 2:00-3:15
Ashley Cale, Erin J Hanan, Jianning Ren, Benjamin W Sullivan – How Effective Will Fuel Treatments Be for Managing Fire Hazard Under a Warming, Drying Climate? Poster B25M-1648, Tuesday Dec. 14, 2021, 2:00-4:00
Jonathan Gendron, Jennifer C Adam, Jianning Ren, Erin J Hanan, Mingliang Liu, Rebecca Gustine, John Abatzoglou, Liane Davis – Changes in Future Wildfire Frequency and Size in the Bull Run Watershed in Response to Different Climate Storylines, Poster GC25H-0733, Tuesday Dec. 14, 2021, 2:00-4:00
In this new publication, functionally and seasonally distinctive remote sensing variables were used to quantify changes in urban vegetation canopy conditions during droughts.
Miller, D.L., Alonzo, M., Meerdink, S.K., Allen, M.A., Tague, C.L., Roberts, D.A., McFadden, J.P. (2021) Seasonal and interannual drought responses of vegetation in a California urbanized area measured using complementary remote sensing indices, ISPRS Journal of Photogrammetry and Remote Sensing 183: 178-195. doi.org/10.1016/j.isprsjprs.2021.11.002
Naomi Tague was recently interviewed by Gilad Barash on his ‘Who’s your Data?‘ podcast about her research predicting and forecasting forest fire frequency and severity, data used in models, machine learning, and her work in developing ways to visualize the results to help officials and the public understand the processes and impacts of fire on our landscapes.
Last week Naomi Tague presented “How Big Data and Machine Learning Can Complement Process-based Ecohydrology Models” at the Artificial Intelligence for Earth System Predictability (AI4ESP) workshop.
The AI4ESP initiative is a collaboration between DOE management and laboratories to understand the paradigm shift required to enable AI across the MODEX enterprise, in part by determining the most impactful applications along the observation-modeling continuum.
156 White Papers were solicited for the development and application of AI methods in areas relevant to EESSD research, with an emphasis on quantifying and improving Earth system predictability, particularly related to the integrative water cycle and extreme events. Submitted white papers were used to inform the design of three sequential workshops (conducted in 2021-2022) focused on answering the question:
How can DOE directly leverage artificial intelligence (AI) to engineer a substantial (paradigm-changing) improvement in Earth System Predictability?