Sara A. Goeking

This chapter explores how forest disturbance impacts the water cycle. We present an introduction to the scope of forest disturbances in the past, present, and future; a primer on how forest disturbances affect hydrology; approaches for obtaining values of hydrologic variables; tools for ecohydrologic assessment including experiments, statistical or process modeling, satellite remote sensing, and recommendations for forest disturbance assessment and planning. This chapter focuses on forest disturbance (i.e. reductions) and does not consider afforestation.

Seasonal snowmelt from forested mountain catchments is the primary water source for both people and ecosystems in the western United States. As changes in climate and vegetation alter relationships between snow accumulation and water availability, quantifying the influence of ecosystem structure on snow water resources is a critical challenge. To address this knowledge gap, we performed 2 years of distributed snow surveys to quantify how vegetation structure, topography and winter conditions interact to control net snow accumulation. The ratio of net snow water equivalence (SWE) at peak accumulation to winter snowfall (SWE:P) was 0.85 (range 0.67–1.03). This variability in SWE:P was associated with seven distinct vegetation environments that differentially influenced snow accumulation across all sites and years. Greatest snow accumulation was found in open areas immediately north of forests (SWE:P = 1.03 ± 0.05), followed by small gaps in conifer forests (SWE:P = 0.95 ± 0.07), small gaps in aspen forests (SWE:P = 0.88 ± 0.08), nonshaded open areas (SWE:P = 0.88 ± 0.05), under aspen canopies (SWE:P = 0.81 ± 0.04), under coniferous canopies (SWE:P = 0.76 ± 0.04) and lowest in open areas in south‐facing forest edges (SWE:P = 0.67 ± 0.07). These spatial patterns in net SWE input were more pronounced in nontopographically shaded environments, suggesting that the influence of vegetation on local hydroclimate and net snow accumulation was greater in areas with higher solar radiation input. The consistency of these patterns across high and low elevations and during low‐ and high‐snow years suggests that forest structure can be used to distribute spatial patterns in snow water input in both undisturbed forests and in response to forest management.

Whitebark pine (WBP; Pinus albicaulis) is a five-needle conifer tree species native to the western United States (US) and southwestern Canada. In the US, this ecologically important species is listed as ‘threatened’ under the Endangered Species Act because of population declines from compound stressors, including climate change, introduced white pine blister rust (WPBR), widespread outbreaks of mountain pine beetle, and altered fire regimes. Species recovery depends primarily on planting WBP seedlings that are resistant to WPBR, but suitable planting locations are likely to shift under climate warming. We modeled and mapped WBP climatic suitability in the US using forest inventory data and fine-scale (∼220 m) climate datasets under reference period (1961–1990) and mid-21st century climatic conditions. We projected an 80% reduction in the area climatically suitable for WBP by the mid-21st century. Moreover, 75% of the climatically suitable area for WBP under mid-21st century climate is located in designated wilderness areas and national parks. This could challenge WBP recovery efforts, as these protected areas strive to reduce human manipulation of ecological systems. WBP climate suitability maps resulting from our models can be used to identify priority or ‘target areas’ where planting will have the highest likelihood of success and to identify areas within the WPB’s current range that are most vulnerable to rapid change should high-severity wildfires or mountain pine beetle outbreaks occur. Mapping WBP reference period and mid-21st century climate suitability is a fundamental step to efficiently prioritize restoration areas and develop a successful recovery program.

Accurate identification of whitebark and limber pine has become increasingly important following the 2022 listing of whitebark pine as a threatened species under the Endangered Species Act. However, morphological similarities make identification of the two species difficult where ranges overlap. Using a genetic test that differentiates whitebark and limber pine, we compared field identification by Forest Inventory and Analysis field crews with genetic identification for needle samples from 371 trees. Field identifications were 100% correct for the 76 samples collected from outside regions of species’ range overlap. A total of 83% of the field identifications were correct in regions of range overlap (89% for large trees, 88% for saplings, and 78% for seedlings). Field-identified samples were correct 60% of the time for limber pine and >99% for whitebark pine. Random forests analysis revealed that identification accuracy is influenced by crew experience, large (≥ 12.7cm diameter) limber or whitebark pines recorded by field crews on the plot, elevation, Julian day of sample collection, and habitat type. We found that whitebark pine has likely been underestimated, and limber pine overestimated, within their overlapping ranges. We provide insights on improving accuracy of future monitoring where these species overlap. Study Implications: Accurate identification of whitebark pine is critical for monitoring this threatened species, yet distinguishing whitebark from limber pine can be difficult. Genetic analysis determined accuracy of field identification by Forest Inventory and Analysis (FIA) crews was 83% where the species’ ranges overlap. Virtually all individuals identified as whitebark pine were genetically confirmed to be whitebark pine, although nearly 40% of individuals identified as limber pine were actually whitebark pine. Thus, previous data underestimated whitebark and overestimated limber pine abundance in the species’ range overlap. These results quantify reliability of FIA data for whitebark pine assessments and identify areas for improvement.

Search this collection of freely available full text articles on the science behind sustainable management of forests and rangelands, restoration of native plants, wildlife, fish and ecosystems, protecting against wildfires and invasive species, and improving the resilience of our lands in the face of climate change.

Forest change affects the relative magnitudes of hydrologic fluxes such as evapotranspiration (ET) and streamflow. However, much is unknown about the sensitivity of streamflow response to forest disturbance and recovery. Several physically based models recognize the different influences that overstory versus understory canopies exert on hydrologic processes, yet most input datasets consist of total leaf area index (LAI) rather than individual canopy strata. Here, we developed stratum-specific LAI datasets with the intent of improving the representation of vegetation for ecohydrologic modeling. We applied three pre-existing methods for estimating overstory LAI, and one new method for estimating both overstory and understory LAI, to measurements collected from a probability-based plot network established by the US Forest Service’s Forest Inventory and Analysis (FIA) program, for a modeling domain in Montana, MT, USA. We then combined plot-level LAI estimates with spatial datasets (i.e., biophysical and remote sensing predictors) in a machine learning algorithm (random forests) to produce annual gridded LAI datasets. Methods that estimate only overstory LAI tended to underestimate LAI relative to Landsat-based LAI (mean bias error ≥ 0.83), while the method that estimated both overstory and understory layers was most strongly correlated with Landsat-based LAI (r² = 0.80 for total LAI, with mean bias error of -0.99). During 1984-2019, interannual variability of understory LAI exceeded that for overstory LAI; this variability may affect partitioning of precipitation to ET vs. runoff at annual timescales. We anticipate that distinguishing overstory and understory components of LAI will improve the ability of LAI-based models to simulate how forest change influences hydrologic processes.

Data for Spatially distributed overstory and understory leaf area index estimated from forest inventory data