Rae A. Melloh

Winter methane dynamics in a temperate peatland

Methane (CH4) dynamics in pore water, snow pore air, and surface emissions were investigated in a temperate poor fen in New Hampshire over several winters. Total snowfall and average air temperatures during winter months (defined as December, January, and February) were climatologic indicators of significant flux rates from this midlatitude poor fen. Average winter emissions, for the five winters ending in 1994–1995, were 20, 39, 53, 56, and 26 mg m−2 d−1, amounting to 2.0, 5.2, 6.6, 9.2, and 2.0% of the total annual fluxes, respectively. Totaling emissions over 5 years that represent low to average snowfall, winter accounted for 4.3% of emissions to the atmosphere. Winter flux rates were near 55 mg m−2 d−1 for years with average snowfall, and 25 mg m−2 d−1 for years with low snowfall. Concentrations of CH4 sampled in pore water immediately beneath the ice were highly variable (0 to 1.1 mM). The concentration magnitude and standard deviation increased toward the fen center and correlated with spatial variation in hydrology, peat texture, and peat depth. CH4 stores increased in the near-surface pore water as the ice cover formed. Seasonal CH4 buildup in deeper peat began near the end of the growing season, probably due to changing transport mechanisms and temperature effects on solubility. Stored CH4 in the 25- to 75-cm peat layer decreased by 2.7 g m−2 between January and June 1995.

Radiative Transfer Modeling of a Coniferous Canopy Characterized by Airborne Remote Sensing

Abstract Solar radiation beneath a forest canopy can have large spatial variations, but this is frequently neglected in radiative transfer models for large-scale applications. To explicitly model spatial variations in subcanopy radiation, maps of canopy structure are required. Aerial photography and airborne laser scanning are used to map tree locations, heights, and crown diameters for a lodgepole pine forest in Colorado as inputs to a spatially explicit radiative transfer model. Statistics of subcanopy radiation simulated by the model are compared with measurements from radiometer arrays, and scaling of spatial statistics with temporal averaging and array size is discussed. Efficient parameterizations for spatial averages and standard deviations of subcanopy radiation are developed using parameters that can be obtained from the model or hemispherical photography.

Incorporating effects of forest litter in a snow process model

Net solar radiation often dominates the snow surface energy exchange during ablation in many conifer forests. Reflection of solar radiation from the snow surface depends not only on snow properties, but also on forest litter lying on and within the snowpack. We know of no validated model reported in the literature that accounts for the influence of forest litter on snow surface energy exchanges. The purpose of this work is to test an existing algorithms ability to accumulate forest litter in snow layers and to predict the subsequent effect of litter on the snow surface albedo. Field studies in a conifer stand of red spruce-balsam fir in northern Vermont, USA, provided key data for validation, including subcanopy radiation, meteorology, snow depth, and images of litter accumulation. We ran the litter algorithm coupled with the snow model SNTHERM for the ablation season, and predictions compared well with measurements of snow depth, snow surface litter coverage, and snow surface albedo beneath the conifer canopy. Model results suggest that for this forest and ablation season, the current litter algorithm realistically distributes litter in the snowpack through time with validated effects on snow surface litter concentration and albedo, The poor relationship between mean wind speed and change in litter coverage on the snow surface suggest that, for this forest and ablation season, incorporating wind events into the algorithm will not improve the results.

Modeling the View Angle Dependence of Gap Fractions in Forest Canopies: Implications for Mapping Fractional Snow Cover Using Optical Remote Sensing

Abstract Forest canopies influence the proportion of the land surface that is visible from above, or the viewable gap fraction (VGF). The VGF limits the amount of information available in satellite data about the land surface, such as snow cover in forests. Efforts to recover fractional snow cover from the spectral mixture analysis model Moderate Resolution Imaging Spectroradiometer (MODIS) snow-covered area and grain size (MODSCAG) indicate the importance of view angle effects in forested landscapes. The VGF can be estimated using both hemispherical photos and forest canopy models. For a set of stands in the Cold Land Field Processes Experiment (CLPX) sites in the Fraser Experimental Forest in Colorado, the convergence of both measurements and models of the VGF as a function of view angle supports the idea that VGF can be characterized as a function of forest properties. A simple geometric optical (GO) model that includes only between-crown gaps can capture the basic shape of the VGF as a function of vie...

High-resolution surface soil moisture variability at a midwest site

Soil moisture is highly variable in space and time and affects the performance of electromagnetic sensors through its effects on thermal and dielectric properties. This research focused on characterizing soil moisture variability at spatial scales relevant to the sensing of small targets. Surface moistures of the top 6 cm of soil were collected on regular grids with an impedance probe. Measurements were made at 0.1-m resolution over 3- × 4-m and 3- × 5-m grids at a short grass site on silt loam. Tall grass and bare soil sites on gravelly silt loam were sampled at 1.0-m resolution over 20- × 30-m and 10- × 30-m grids. Exponential models fit to sample variograms of the 0.1-m resolution data show that soil moistures were spatially dependent over a distance of 0.5 m. Maximum variances (variogram sill), for data collected over a four-day span following a rainfall event, increased linearly with decreased mean moisture level as the soil dried. The revealed structures can be exploited to simulate soil moisture variation temporally and spatially. The impedance probe’s ability to reproduce variation in volumetric water content observed with conventional oven drying methods was demonstrated prior to the field experiment. Separate tests demonstrated that the probes can be used interchangeably. The impact of sparse surface grass on the moisture variation measured with the probe was also demonstrated to be small under the conditions tested.

Spatial and temporal variation of 10-cm background soil moisture

Soil moisture affects soil thermal and dielectric properties and may cause false alarms in detecting manmade objects when dielectric or thermal discontinuities exist in the soil. The spatial variability of soil moisture changes with time and it is important to understand this behavior because it is relevant for detection of small targets, and for modeling background moisture and temperature. Surface moisture of the top 6 cm of soil was sampled on regular grids with an impedance probe at a 0.1-m interval during wetting and drying events, both four days in duration. Maximum variances for data collected in August 2004 increased with decreasing mean moisture, as soil dried following a soaking rainfall. Maximum variances in June 2005 decreased over several days of intermittent rain as the soil rewetted following a prolonged drought. Spatially dependent ranges of approximately 0.5-m lag distance and exponential model fits were consistent among all the data sets, despite changes in moisture, moisture trend, and sample variance. The procession of spatial variation is described by variograms that transition from high to low maximum variances (sills) for wetting events, and from low to high maximum variances for drying events. A linear relationship between the maximum variance and mean of square root of ε was consistent for both years, except when the soil was incompletely wetted after a drought. The highest spatial variance in moisture that produced the most variable background for small target detection occurred as a consequence of the incomplete or uneven wetting following a drought.

METHODS OF CHARACTERIZING CLOUD DROP SPECTRA SPATIAL VARIATION

Cloud liquid water content (LWC), particle concentration, median volume droplet diameter (MVD), mixed-phase conditions, and spectra of drop and particle sizes vary in space and time. Aircraft traversing clouds accumulate ice as a function of the spatially changing microphysical conditions within the clouds. Remote sensing system performance also varies as cloud microphysical conditions change, thus potentially affecting information provided to pilots. Since remote sensing devices operating in millimeter wave (MMW) frequencies have some ability to operate within or through clouds, it is essential that a better understanding be developed of the fluctuation of cloud microphysical properties for modeling and simulation. This report investigates and evaluates three methods of quantifying the spatial variation of cloud drop spectra: 1) Drop Size Correlation, 2) Constrained Clustering, and 3) Spectral Shape Analysis. The three methodologies were evaluated against timeseries of natural cloud measurements. The three techniques show promise as methods of quantitatively evaluating spatial fluctuation of drop size spectra. However, they vary considerably in ease of use and interpretation, and in the type and quality of information provided. Theory and examples of each are demonstrated and evaluations of the potential of each are presented.