TOPOFIRE: A Topographically Resolved Wildfire Danger and Drought Monitoring System for the Conterminous United States

Abstract

Patterns of energy and available moisture can vary over small (<1 km) distances in mountainous terrain. Information on fuel and soil moisture conditions that resolves this variation could help to inform fire and drought management decisions. Here, we describe the development of TOPOFIRE, a web-based mapping system designed to provide finely resolved information on soil water balance, drought, and wildfire danger information for the contiguous United States. We developed 8-arc-second-resolution (~250 meter) daily historical, near real-time, and 4-day forecast radiation, temperature, humidity, and snow water equivalent data and used these grids to calculate a suite of drought and wildfire danger indices. Large differences in shortwave radiation and surface air temperature with aspect contribute to greater snow accumulation and delays in melt timing on north-facing slopes, delaying fuel conditioning on shaded slopes. These datasets will help advance our understanding of the role of topography in wildland fire spread and ecological effects. Integration with national programs like the Wildland Fire Assessment System, the Wildland Fire Decision Support System, and drought early warning systems could support more proactive management of wildland fires and refine the characterization of drought in mountainous regions of the United States.

A changing climate is snuffing out post‐fire recovery in montane forests

“…To address this limitation of data resolution, we developed annual gridded climate datasets for the recent (1981–2015) and future (2021–2099) periods at 250‐m resolution using spatial gradient and inverse distance squared interpolation (GIDS; Flint & Flint, 2012) of data from the Gridded Surface Meteorological (Abatzoglou, 2013) and Multivariate Adaptive Constructed Analogs datasets (Abatzoglou & Brown, 2012). We used 30‐m digital elevation models (DEMs; United States Geological Survey, 1999) and 250‐m water balance metrics of Holden et al (2019) as ancillary data in downscaling (Supporting Information Appendix S2). Gridded climate data are inherently uncertain, but GIDS‐interpolated outputs have been shown to closely match local climatic conditions in complex terrain (McCullough et al, 2016).…”

The Topographic Signature of Ecosystem Climate Sensitivity in the Western United States

“…The climatic water balance imparts a strong control over the spatial distribution of plant functional types and is an important driver of ecosystem productivity. We used daily estimates of PET and AET output from a 8 arc‐second (~250 m) gridded soil water balance model, evaluated from 1986 to 2015 (Holden et al, ), to calculate the climatic water deficit (deficit = PET − AET). We then calculated the total annual deficit for each year, which represents the unmet atmospheric demand for moisture.…”

High‐Resolution Soil Moisture Data Reveal Complex Multi‐Scale Spatial Variability Across the United States

“…SM spatial variability also impacts on wildfires by controlling the spatial distribution of vegetation fuel load and flammability through vegetation water content (O et al., 2020; Taufik et al., 2017). As such, the inability to represent wet and dry SM hotspots at sub‐kilometer scales results in underestimated risks of propagating wildfires (Holden et al., 2019). Similarly, local wet hotspots lead to conditions that trigger landslides and local flash floods.…”

“…SM spatial variability leads to changes in surface temperature and evapotranspiration (Rouholahnejad Freund et al., 2020), altering drought impacts (Vergopolan et al., 2021) as well as the formation of clouds and convective storms (Simon et al., 2021; Zheng et al., 2021). SM hotspots can alter runoff generation, resulting in faster and peakier flood events (Zhu et al., 2018), and trigger wildfires (Holden et al., 2019; Taufik et al., 2017) and landslides (Brocca et al., 2016; Wang et al., 2020). SM spatial variability influences the distribution of soil fauna and flora, by controlling its habitats, food sources, and dynamics (He et al., 2015; Mathys et al., 2014; Sylvain et al., 2014; Youngquist & Boone, 2014).…”