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[1] Digital image profiles of snowpack surfaces were acquired concurrently with 1-cm resolution manual measurements. The manual measurements confirmed that unaltered digital images accurately represented a two-dimensional roughness profile of the snowpack surface. Roughness indices, such as random roughness, that have been used to represent soil surfaces were computed, and their utility for quantifying snowpack surface roughness is illustrated. Variogram analysis was used to determine the fractal dimension and scale break. Surface characteristics were a function of the scale, with a rough snow surface and graupel yielding similar results. A relatively smooth snow surface showed no crystal-scale features and had a fractal dimension approaching that of a random surface.

This paper presents a prototype Carbon Monitoring System (CMS) developed to produce regionally unbiased annual estimates of aboveground biomass (AGB). Our CMS employed a bottom-up, two-step modeling strategy beginning with a spatially and temporally biased sample: project datasets collected and contributed by US Forest Service (USFS) and other forestry stakeholders in 29 different project areas in the northwestern USA. Plot-level AGB estimates collected in the project areas served as the response variable for predicting AGB primarily from lidar metrics of canopy height and density (R2 = 0.8, RMSE = 115 Mg ha−1, Bias = 2 Mg ha−1). This landscape model was used to map AGB estimates at 30 m resolution where lidar data were available. A stratified random sample of AGB pixels from these landscape-level AGB maps then served as training data for predicting AGB regionally from Landsat image time series variables processed through LandTrendr. In addition, climate metrics calculated from downscaled 30 year climate normals were considered as predictors in both models, as were topographic metrics calculated from elevation data; these environmental predictors allowed AGB estimation over the full range of observations with the regional model (R2 = 0.8, RMSE = 152 Mg ha−1, Bias = 9 Mg ha−1), including higher AGB values (>400 Mg ha−1) where spectral predictors alone saturate. For both the landscape and regional models, the machine-learning algorithm Random Forests (RF) was consistently applied to select predictor variables and estimate AGB. We then calibrated the regional AGB maps using field plot data systematically collected without bias by the national Forest Inventory and Analysis (FIA) Program. We found both our project landscape and regional, annual AGB estimates to be unbiased with respect to FIA estimates (Biases of 1% and 0.7%, respectively) and conclude that they are well suited to inform forest management and planning decisions by our contributing stakeholders. Social media abstract Lidar-based biomass estimates can be upscaled with Landsat data to regionally unbiased annual maps.

Microtopographic features known as terracettes are found throughout semiarid rangelands. Their soil properties and hydrologic function, however, are virtually unknown. This research aimed to quantify whether or not terracetted hillslopes retain more soil water. The objectives of this research were to: (i) assess soil moisture measured at two terracetted field sites comparing bench and riser; (ii) identify to what extent microtopography, soil properties, and land use affect soil moisture; and (iii) quantify differences in soil moisture at the field scale between terracetted and control sites (grazed and ungrazed) in eastern Washington. We measured volumetric water content (q v ), bulk density, soil texture, saturated hydraulic conductivity, and organic matter in addition to compaction, vegetative cover, and cattle density. Our results show significant q v differences between terracette benches and risers in the upper 10 cm, with benches exhibiting higher mean q v than risers across all sites during both wet (+6.14%) and dry (+6.63%) seasons. Soil texture and organic matter did not vary between bench and riser features, and microtopography itself was not driving observed soil moisture differences. Soil moisture differences were attributed to land use (i.e., cattle) impact on soil bulk density and vegetative cover. Greater water content on terracette benches is partially attributed to shifts to smaller pore sizes with compaction and a reduction in transpiration resulting from lower vegetative cover due to root impedance. However, this increased water storage is not plant accessible and does not contribute to increased forage production. This work is intended to provide a mechanistic understanding of terracette hydrology for semiarid rangeland management.Abbreviations: EMD, site with east aspect and moderate-to low-density cattle stocking; OC, organic carbon; PSA, particle size analysis; WHD, site with west aspect and highdensity cattle stocking.