Modeling Postfire Effects on Snow Albedo and Forest Recovery Over a Chronosequence of Burned Forests in the Triple Divide Region of the Rocky Mountains

Wildfires impact snow albedo, forest cover, and forest structure and thus snow melt rate and snowpack supply for as long as 15 years following burn. These effects have not been quantified at fine spatial resolutions and long time periods at a watershed scale. I modeled the effects of postfire effects on snow albedo, snow-mass energy balance, and resulting snow-water equivalent (SWE) depth over a long-time scale and at a fine spatial resolution. Using a spatially and temporally distributed snow evolution model called SnowModel, I modeled postfire effects on snow albedo and forest structure over postfire recovery within 8 forest fires between 2000 and 2020 in a region in Northwestern Wyoming. SnowModel does not currently incorporate the effects of postfire effects on snow albedo, forest structure nor the recovery of the postfire effects, so I developed and incorporated postfire snow albedo decay functions from Gleason and Nolin (2016) into SnowModel and developed a 15-year postfire recovery of postfire effects on snow albedo and forest structure parameterization informed by remotely-sensed measurements of surface snow albedo from the MODIS-MOD10A1 dataset. I then compared the parameterized model (postfire albedo) with a base model to quantify changes in peak SWE, snow volume, and snow disappearance date (SDD) due to postfire effects on snow and recovery within the burn regions and at the watershed scale for up to 20 years following fire. To partition the postfire impacts on snow due to forest structure from the albedo impacts, I also parameterized a third model with only forest structure impacts (postfire forest) and compared the results with the postfire albedo model and the base model. My hypothesis was that modeled results would show significant and lasting alterations in peak SWE, total snow volume, and SDD for up to 15 years following fire. Postfire parameterizations caused peak SWE losses of between 2.81% and 31.91% (474K m3 to 12.7M m3) and an average 9.93 to 87.97% reduction in ablation season SWE in the year immediately following fire. Immediately following fire, snow disappearance occurred 33 (SD: 3 days) to 58 days (SD: 9 days) earlier than in the base model. Over recovery, losses in total SWE and peak SWE, and shifts in disappearance date tended to shrink relative to the losses observed immediately following fire, but remained negative throughout. In two fires modeled for the entire 15 year postfire recovery period, the greatest losses in peak SWE did not occur immediately following fire, but instead 4-9 years following fire. Postfire effects on snow summed over the entire 15-year recovery period caused total reductions in peak SWE of between 0.76% and 12.45% (5.5M m3 and -20.5M m3) over 1 to 15 years following fire - losses between 2 and 18 times greater than the losses incurred in the first year immediately following ignition. Postfire impacts were most severe in burns occurring at lower elevation. Beyond 15 years following fire, postfire effects on snow persisted due to the shift from forest to open meadow over the course of the 15-year recovery period. The Boulder fire (ignition date:2000) showed significant increases in snow volume (+2.32%; +196K m3) 16 years following fire while the Green Knoll fire (ignition date: 2001) showed peak SWE losses (-2.20%; -241K m3) 16 years following fire. Postfire impacts in the Lower Granite Creek subbasin, a heavily burned watershed (Ryan et al., 2011) within the study region (43% burned over 20 years), caused average annual reductions in ablation season (May 1st) SWE of -6.30 ± 6.95% (5.9M m3 ± 6.5M m3). Postfire effects in the Lower Granite Creek subbasin caused earlier melting of 5.85% of snowpack over 20 years in total, an amount equal to 94M m3 of additional runoff added to the watershed over 20 years. Overall, parameterizations of postfire impacts and recovery showed significant changes in snow volume and spring snowmelt following fire that lasted 16+ years beyond the initial ignition date. Quantifying the changes in snow accumulation and snowmelt due to severe wildfire using postfire recovery parameterizations will provide critical understanding needed for anticipating wildfire effects on water supply under a changing climate and increasingly severe fire regime

Forest Fire Effects on Landscape Snow Albedo Recovery and Decay

Surface snow albedo (SSA) darkens immediately following a forest fire, while landscape snow albedo (LSA) brightens as more of the snow-covered surface becomes visible under the charred canopy. The duration and variability of the post-fire snow albedo recovery process remain unknown beyond a few years following the fire. We evaluated the temporal variability of post-fire snow albedo recovery relative to burn severity across a chronosequence of eight burned forests burned from 2000 to 2019, using pre- and post-fire daily, seasonal, and annual landscape snow albedo data derived from the Moderate Resolution Imaging Spectroradiometer (MOD10A1). Post-fire annual LSA increased by 21% the first year following the fire and increased continually by 33% on average across all eight forest fires and burn severity classifications over the period of record (18 years following a fire). Post-fire LSA measurements increased by 63% and 53% in high and moderate burn severity areas over ten years following fire. While minimum and maximum snow albedo values increased relative to annual post-fire LSA recovery, daily snow albedo decay following fresh snowfall accelerated following forest fire during the snowmelt period. Snow albedo recovery over 10 years following fire did not resemble the antecedent pre-fire unburned forest but more resembled open meadows. The degradation of forest canopy structure is the key driver underlying the paradox of the post-fire snow albedo change (SSA vs. LSA)

Monitoring Drought Conditions in the Navajo Nation Using NASA Earth Observations

The Navajo Nation, a 65,700 sq km Native American territory located in the southwestern United States, has been increasingly impacted by severe drought events and changes in climate. These events are coupled with a lack of domestic water infrastructure and economic resources, leaving approximately one-third of the population without access to potable water in their homes. Current methods of monitoring drought are dependent on state-based monthly Standardized Precipitation Index value maps calculated by the Western Regional Climate Center. However, these maps do not provide the spatial resolution needed to illustrate differences in drought severity across the vast Nation. To better understand and monitor drought events and drought regime changes in the Navajo Nation, this project created a geodatabase of historical climate information specific to the area, and a decision support tool to calculate average Standardized Precipitation Index values for user-specified areas. The tool and geodatabase use Tropical Rainfall Monitoring Mission (TRMM) and Global Precipitation Monitor (GPM) observed precipitation data and Parameter-elevation Relationships on Independent Slopes Model modeled historical precipitation data, as well as NASA's modeled Land Data Assimilation Systems deep soil moisture, evaporation, and transpiration data products. The geodatabase and decision support tool will allow resource managers in the Navajo Nation to utilize current and future NASA Earth observation data for increased decision-making capacity regarding future climate change impact on water resources

Forest Fire Effects on Landscape Snow Albedo Recovery and Decay

Surface snow albedo (SSA) darkens immediately following a forest fire, while landscape snow albedo (LSA) brightens as more of the snow-covered surface becomes visible under the charred canopy. The duration and variability of the post-fire snow albedo recovery process remain unknown beyond a few years following the fire. We evaluated the temporal variability of post-fire snow albedo recovery relative to burn severity across a chronosequence of eight burned forests burned from 2000 to 2019, using pre- and post-fire daily, seasonal, and annual landscape snow albedo data derived from the Moderate Resolution Imaging Spectroradiometer (MOD10A1). Post-fire annual LSA increased by 21% the first year following the fire and increased continually by 33% on average across all eight forest fires and burn severity classifications over the period of record (18 years following a fire). Post-fire LSA measurements increased by 63% and 53% in high and moderate burn severity areas over ten years following fire. While minimum and maximum snow albedo values increased relative to annual post-fire LSA recovery, daily snow albedo decay following fresh snowfall accelerated following forest fire during the snowmelt period. Snow albedo recovery over 10 years following fire did not resemble the antecedent pre-fire unburned forest but more resembled open meadows. The degradation of forest canopy structure is the key driver underlying the paradox of the post-fire snow albedo change (SSA vs. LSA)