Evaluating the spatial variability of snowpack properties across a northern Colorado basin

2012 Fall.Includes bibliographical references.Knowledge of seasonal mountain snowpack distribution and estimates of its snow water equivalent (SWE) can provide insight for water resources forecasting and earth system process understanding, thus, it is important to improve our ability to describe the spatial variability of SWE at the basin scale. The objectives of this thesis are to: (1) develop a reliable method of estimating SWE from snow depth for the Cache la Poudre basin, and (2) characterize the spatial variability of SWE at the basin scale within the Cache la Poudre basin. A combination of field and Natural Resource Conservation Service (NRCS) operational-based snow measurements were used in this study. Historic (1936 - 2010) snow course data were obtained for the study area to evaluate snow density. A multiple linear regression model (based on the historical snow course data) for estimating snow density across the study area was developed to estimate SWE directly from snow depth measurements. To investigate the spatial variability and observable patterns of SWE at the basin scale, snow surveys were completed on or about April 1, 2011 and 2012 and combined with NRCS operational measurements. Bivariate relations and multiple linear regression models were developed to understand the relation of SWE with physiographic variables derived using a geographic information system (GIS). SWE was interpolated across the Cache la Poudre basin on a pixel by pixel basis using the model equations and masked to observe SCA (from an 8-day MODIS product). The independent variables of snow depth, day of year, elevation, and UTM Easting were used in the model to estimate snow density. Calculation of SWE directly from snow depth measurement using the snow density model has strong statistical performance and model verification suggests the model is transferable to independent data within the bounds of the original dataset. 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. Bivariate relations of SWE and snow depth measurements (from WY 2011 and WY 2012) with physiographic variables show that elevation and location (UTM Easting and UTM Northing) are most strongly correlated with SWE and snow depth. Multiple linear regression models developed for WY 2011 and WY 2012 include elevation and location as independent variables and also include others (e.g., eastness, slope, solar radiation, curvature, canopy density) depending on the model dataset. The final interpolated SWE surfaces, masked to observed SCA, generally show similar patterns across space despite differences in the 2011 and 2012 snow years and differing estimation of SWE magnitude between the combined dataset of field-based and operational-based measurements (modelO+F) and the dataset of operational-based measurements only (modelO). Within each of the model surfaces, interpolated volume of SWE was greatest within Elevation Zone 5 (3,043 - 3,405 m). The percentage of the total interpolated SWE volume for each model was distributed similarly among elevation zones

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

Long Term Interactions of Microorganisms and Prudhoe Bay Crude Oil in Tundra Soils at Barrow, Alaska

Oil was recovered from tundra soils two and seven years after spillage. Oil persisted in the upper soil layer. The depth of penetration appears to depend on soil moisture and drainage characteristics. Maximal penetration seems to occur within one year of spillage. Biodegradation of the oil was indicated by changes in the ratio of gas chromatographically resolved to unresolved components. Individual components appear to be preferentially degraded, but no evidence was found for significant preferential degradation of structural classes of hydrocarbons. Numbers of microorganisms were different in oil contaminated and reference soils generally showing continued enrichment, but in some soils showing inhibition of microbial populations

Hydrocarbons and Microbial Activities in Sediment of an Arctic Lake One Year After Contamination with Leaded Gasoline

Hydrocarbons were found to persist in the sediment of an Arctic lake one year after the lake was accidentally contaminated with leaded gasoline. The contaminating gasoline was continuing to spread from the original site of contamination. High numbers of hydrocarbon utilizing microorganisms were found in the contaminated sediment. Rates of nitrogen fixation did not appear to be affected by hydrocarbon contamination, but potential denitrification activities appeared to be altered by the gasoline. Fertilizer application resulted in a moderate decrease of hydrocarbon concentrations in the sediment

Fate of Crude and Refined Oils in North Slope Soils

Prudhoe Bay crude oil and refined diesel fuel were applied to five topographically distinct tundra soils at Prudhoe Bay, Alaska. The penetration of hydrocarbons into the soil column depended on soil moisture and drainage characteristics. Biodegradation, shown by changes in the pristane to heptadecane and resolvable to total gas chromatographic area ratios, appeared to be greatly restricted in drier tundra soils during one year exposure. Some light hydrocarbons, C9-C10, were recovered from soils one year after spillages. Hydrocarbons were still present in soils at Fish Creek, Alaska, contaminated by refined oil spillages 28 years earlier, attesting to the persistence of hydrocarbons in North Slope 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

Evidence for Acquisition in Nature of a Chromosomal 2,4-Dichlorophenoxyacetic Acid/(alpha)-Ketoglutarate Dioxygenase Gene by Different \u3ci\u3eBurkholderia\u3c/i\u3e spp.

We characterized the gene required to initiate the degradation of 2,4-dichlorophenoxyacetic acid (2,4-D) by the soil bacterium Burkholderia sp. strain TFD6, which hybridized to the tfdA gene of the canonical 2,4-D catabolic plasmid pJP4 under low-stringency conditions. Cleavage of the ether bond of 2,4-D by cell extracts of TFD6 proceeded by an (alpha)-ketoglutarate-dependent reaction,characteristic of TfdA (F. Fukumori and R. P. Hausinger, J. Bacteriol. 175:2083-2086, 1993). The TFD6 tfdA gene was identified in a recombinant plasmid which complemented a tfdA transposon mutant of TFD6 created by chromosomal insertion of Tn5. The plasmid also expressed TfdA activity in Escherichia coli DH5(alpha), as evidenced by enzyme assays with cell extracts. Sequence analysis of the tfdA gene and flanking regions from strain TFD6 showed 99.5% similarity to a tfdA gene cloned from the chromosome of a different Burkholderia species (strain RASC) isolated from a widely separated geographical area. This chromosomal gene has 77.2% sequence identity to tfdA from plasmid pJP4 (Y. Suwa, W. E. Holben, and L. J. Forney, abstr. Q-403, in Abstracts of the 94th General Meeting of the American Society for Microbiology 1994.). The tfdA homologs cloned from strains TFD6 and RASC are the first chromosomally encoded 2,4-D catabolic genes to be reported. The occurrence of highly similar tfdA genes in different bacterial species suggests that this chromosomal gene can be horizontally transferred

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

Snowpack Relative Permittivity and Density Derived from Near-Coincident Lidar and Ground-Penetrating Radar

Depth-based and radar-based remote sensing methods (e.g., lidar, synthetic aperture radar) are promising approaches for remotely measuring snow water equivalent (SWE) at high spatial resolution. These approaches require snow density estimates, obtained from in-situ measurements or density models, to calculate SWE. However, in-situ measurements are operationally limited, and few density models have seen extensive evaluation. Here, we combine near-coincident, lidar-measured snow depths with ground-penetrating radar (GPR) two-way travel times (twt) of snowpack thickness to derive \u3e20 km of relative permittivity estimates from nine dry and two wet snow surveys at Grand Mesa, Cameron Pass, and Ranch Creek, Colorado. We tested three equations for converting dry snow relative permittivity to snow density and found the Kovacs et al. (1995) equation to yield the best comparison with in-situ measurements (RMSE = 54 kg m−3). Variogram analyses revealed a 19 m median correlation length for relative permittivity and snow density in dry snow, which increased to \u3e 30 m in wet conditions. We compared derived densities with estimated densities from several empirical models, the Snow Data Assimilation System (SNODAS), and the physically based iSnobal model. Estimated and derived densities were combined with snow depths and twt to evaluate density model performance within SWE remote sensing methods. The Jonas et al. (2009) empirical model yielded the most accurate SWE from lidar snow depths (RMSE = 51 mm), whereas SNODAS yielded the most accurate SWE from GPR twt (RMSE = 41 mm). Densities from both models generated SWE estimates within ±10% of derived SWE when SWE averaged \u3e 400 mm, however, model uncertainty increased to \u3e 20% when SWE averaged \u3c 300 mm. The development and refinement of density models, particularly in lower SWE conditions, is a high priority to fully realize the potential of SWE remote sensing methods