What drives basin scale spatial variability of snowpack properties in northern Colorado?

This study uses a combination of field measurements and Natural Resource Conservation Service (NRCS) operational snow data to understand the drivers of snow density and snow water equivalent (SWE) variability at the basin scale (100s to 1000s km²). Historic snow course snowpack density observations were analyzed within a multiple linear regression snow density model to estimate SWE directly from snow depth measurements. Snow surveys were completed on or about 1 April 2011 and 2012 and combined with NRCS operational measurements to investigate the spatial variability of SWE near peak snow accumulation. Bivariate relations and multiple linear regression models were developed to understand the relation of snow density and SWE with terrain variables (derived using a geographic information system (GIS)). Snow density variability was best explained by day of year, snow depth, UTM Easting, and elevation. Calculation of SWE directly from snow depth measurement using the snow density model has strong statistical performance, and model validation suggests the model is transferable to independent data within the bounds of the original data set. This pathway of estimating SWE directly from snow depth measurement is useful when evaluating snowpack properties at the basin scale, where many time-consuming measurements of SWE are often not feasible. A comparison with a previously developed snow density model shows that calibrating a snow density model to a specific basin can provide improvement of SWE estimation at this scale, and should be considered for future basin scale analyses. During both water year (WY) 2011 and 2012, elevation and location (UTM Easting and/or UTM Northing) were the most important SWE model variables, suggesting that orographic precipitation and storm track patterns are likely driving basin scale SWE variability. Terrain curvature was also shown to be an important variable, but to a lesser extent at the scale of interest

Soil biochemical properties in brown and gray mine soils with and without hydroseeding

Surface coal mining in the eastern USA disturbs hundreds of hectares of land every year and removes valuable and ecologically diverse eastern deciduous forests. Reclamation involves restoring the landscape to approximate original contour, replacing the topsoil, and revegetating the site with trees and herbaceous species to a designated post-mining land use. Re-establishing an ecosystem of ecological and economic value as well as restoring soil quality on disturbed sites are the goals of land reclamation, and microbial properties of mine soils can be indicators of restoration success. Reforestation plots were constructed in 2007 using weathered brown sandstone or unweathered gray sandstone as topsoil substitutes to evaluate tree growth and soil properties at Arch Coal\u27s Birch River mine in West Virginia, USA. All plots were planted with 12 hardwood tree species and subplots were hydroseeded with a herbaceous seed mix and fertilizer. After 6 years, the average tree volume index was nearly 10 times greater for trees grown in brown (3853 cm3) compared to gray mine soils (407 cm3). Average pH of brown mine soils increased from 4.7 to 5.0, while gray mine soils declined from 7.9 to 7.0. Hydroseeding doubled tree volume index and ground cover on both mine soils. Hydroseeding doubled microbial biomass carbon (MBC) on brown mine soils (8.7 vs. 17.5 mg kg−1), but showed no effect on gray mine soils (13.3 vs. 12.8 mg kg−1). Hydroseeding also increased the ratio of MBC to soil organic C in both soils and more than tripled the ratio for potentially mineralizable nitrogen (PMN) to total N. Brown mine soils were a better growth medium than gray mine soils and hydroseeding was an important component of reclamation due to improved biochemical properties and microbial activity in mine soils

A New Sampler for the Collection and Retrieval of Dry Dust Deposition

Atmospheric dust can influence biogeochemical cycles, accelerate snowmelt, and affect air, water quality, and human health. Yet, the bulk of atmospherically transported material remains poorly quantified in terms of total mass fluxes and composition. This lack of information stems in part from the challenges associated with measuring dust deposition. Here we report on the design and efficacy of a new dry deposition sampler (Dry Deposition Sampling Unit (DSU)) and method that quantifies the gravitational flux of dust particles. The sampler can be used alone or within existing networks such as those employed by the National Atmospheric Deposition Program (NADP). Because the samplers are deployed sterile and the use of water to remove trapped dust is not required, this method allows for the recovery of unaltered dry material suitable for subsequent chemical and microbiological analyses. The samplers were tested in the laboratory and at 15 field sites in the western United States. With respect to material retention, sampler performance far exceeded commonly used methods. Retrieval efficiency was \u3e97% in all trials and the sampler effectively preserved grain size distributions during wind exposure experiments. Field tests indicated favorable comparisons to dust-on-snow measurement across sites (r2 0.70, p \u3c 0.05) and within sites to co-located aerosol data (r2 0.57–0.99, p \u3c 0.05). The inclusion of dust deposition and composition monitoring into existing networks increases spatial and temporal understanding of the atmospheric transport on materials and substantively furthers knowledge of the effects of dust on terrestrial ecosystems and human exposure to dust and associated deleterious compounds

Subgrid snow depth coefficient of variation spanning alpine to sub-alpine mountainous terrain

Given the substantial variability of snow in complex mountainous terrain, a considerable challenge of coarse scale modeling applications is accurately representing the subgrid variability of snowpack properties. The snow depth coefficient of variation (CVds) is a useful metric for characterizing subgrid snow distributions but has not been well defined by a parameterization for mountainous environments. This study utilizes lidar-derived snow depth datasets spanning alpine to sub-alpine mountainous terrain in Colorado, USA to evaluate the variability of subgrid snow distributions within a grid size comparable to a 1000 m resolution common for hydrologic and land surface models. The subgrid CVds exhibited a wide range of variability across the 321 km2 study area (0.15 to 2.74) and was significantly greater in alpine areas compared to subalpine areas. Mean snow depth was the dominant driver of CVds variability in both alpine and subalpine areas, as CVds decreased nonlinearly with increasing snow depths. This negative correlation is attributed to the static size of roughness elements (topography and canopy) that strongly influence seasonal snow variability. Subgrid CVds was also strongly related to topography and forest variables; important drivers of CVds included the subgrid variability of terrain exposure to wind in alpine areas and the mean and variability of forest metrics in subalpine areas. Two statistical models were developed (alpine and subalpine) for predicting subgrid CVds that show reasonable performance statistics. The methodology presented here can be used for characterizing the variability of CVds in snow-dominated mountainous regions, and highlights the utility of using lidar-derived snow datasets for improving model representations of snow processes

Soil aggregates as massively concurrent evolutionary incubators

Soil aggregation, a key component of soil structure, has mostly been examined from the perspective of soil management and the mediation of ecosystem processes such as soil carbon storage. However, soil aggregation is also a major factor to consider in terms of the fine-scale organization of the soil microbiome. For example, the physico-chemical conditions inside of aggregates usually differ from the conditions prevalent in the bulk soil and aggregates therefore increase the spatial heterogeneity of the soil. In addition, aggregates can provide a refuge for microbes against predation since their interior is not accessible to many predators. Soil aggregates are thus clearly important for microbial community ecology in soils (for example, Vos et al., 2013; Rillig et al., 2016) and for microbially driven biogeochemistry, and soil microbial ecologists are increasingly appreciating these aspects of soil aggregation. Soil aggregates have, however, so far been neglected when it comes to evolutionary considerations (Crawford et al., 2005) and we here propose that the process of soil aggregation should be considered as an important driver of evolution in the soil microbial community

Snow sublimation and seasonal snowpack variability

2016 Fall.Includes bibliographical references.In the western United States, seasonal melt from snow in mountainous regions serves as an essential water resource for ecological and anthropological needs, and improving our abilities to quantify the amount of water stored in the seasonal snowpack and provide short-term forecasts of snowmelt inputs into river systems is a critical science endeavor. Two important uncertainties in characterizing the seasonal evolution of snow in mountainous environments are related to the inherent spatial variability of snow in complex terrain and the magnitude and variability of snow sublimation fluxes between snow and the atmosphere; these uncertainties motivate this collection of research which includes three studies conducted in the north-central Colorado Rocky Mountains. The first study uses fine resolution airborne lidar snow depth datasets to evaluate the spatial variability of snow within areas comparable to coarse scale model grids (i.e. subgrid variability at 500 m resolution). Snow depth coefficient of variation, which was used as a metric for evaluating subgrid snow variability, exhibited substantial variability in mountainous terrain and was well correlated with mean snow depth, land cover type, as well as canopy and topography characteristics. Results highlight that simple statistical models for predicting subgrid snow depth coefficient of variation in alpine and subalpine areas can provide useful parameterizations of subgrid snow distributions. Given that snow sublimation fluxes are expected to exert important influences on snow distributions, the second and third studies focus on measuring and modeling the variability and importance of snow sublimation. To evaluate the relative merits and measurement uncertainty of methods for quantifying snow sublimation in mountainous environments, a comparison was made between the eddy covariance, Bowen ratio-energy balance, bulk aerodynamic flux, and aerodynamic profile methods within two forested openings. Biases between methods are evaluated over a range of environmental conditions, which highlight limitations and uncertainties of each method as well as the challenges related to measuring surface sublimation in snow-covered regions. Results provide guidance for future investigations seeking to quantify snow sublimation through station measurements and suggest that the eddy covariance and/or bulk aerodynamic flux methods are superior for estimating surface sublimation in snow-covered forested openings. To evaluate the spatial variability and importance of snow sublimation, a process-based snow model is applied across a 3600 km2 domain over five water years. In-situ eddy covariance observations of snow sublimation compare well with modeled snow sublimation at sites dominated by surface and canopy sublimation, but highlight challenges with model evaluation at sites where blowing sublimation is prominent. Modeled snow sublimation shows considerable spatial variability at the hillslope scale that is evident across elevation gradients and between land cover types. Snow sublimation from forested areas (canopy plus surface sublimation) accounted for the majority of modeled sublimation losses across the study domain and highlights the importance of sublimation from snow stored in the forest canopy in this region. Model simulations suggest that snow sublimation is a significant component of the winter water balance, accounting for losses equivalent to 43 percent of total snowfall, and strongly influences snow distributions in this region. Results from this study have important implications for future water management and decision making