Kelly Elder

A Meteorological Distribution System for High-Resolution Terrestrial Modeling (MicroMet)

An intermediate-complexity, quasi–physically based, meteorological model (MicroMet) has been developed to produce high-resolution (e.g., 30-m to 1-km horizontal grid increment) atmospheric forcings required to run spatially distributed terrestrial models over a wide variety of landscapes. The following eight variables, required to run most terrestrial models, are distributed: air temperature, relative humidity, wind speed, wind direction, incoming solar radiation, incoming longwave radiation, surface pressure, and precipitation. To produce these distributions, MicroMet assumes that at least one value of each of the following meteorological variables are available for each time step, somewhere within, or near, the simulation domain: air temperature, relative humidity, wind speed, wind direction, and precipitation. These variables are collected at most meteorological stations. For the incoming solar and longwave radiation, and surface pressure, either MicroMet can use its submodels to generate these fields, or it can create the distributions from observations as part of a data assimilation procedure. MicroMet includes a preprocessor component that analyzes meteorological data, then identifies and corrects potential deficiencies. Since providing temporally and spatially continuous atmospheric forcing data for terrestrial models is a core objective of MicroMet, the preprocessor also fills in any missing data segments with realistic values. Data filling is achieved by employing a variety of procedures, including an autoregressive integrated moving average calculation for diurnally varying variables (e.g., air temperature). To create the distributed atmospheric fields, spatial interpolations are performed using the Barnes objective analysis scheme, and subsequent corrections are made to the interpolated fields using known temperature–elevation, wind–topography, humidity–cloudiness, and radiation–cloud–topography relationships.

A Distributed Snow-Evolution Modeling System (SnowModel)

Abstract SnowModel is a spatially distributed snow-evolution modeling system designed for application in landscapes, climates, and conditions where snow occurs. It is an aggregation of four submodels: MicroMet defines meteorological forcing conditions, EnBal calculates surface energy exchanges, SnowPack simulates snow depth and water-equivalent evolution, and SnowTran-3D accounts for snow redistribution by wind. Since each of these submodels was originally developed and tested for nonforested conditions, details describing modifications made to the submodels for forested areas are provided. SnowModel was created to run on grid increments of 1 to 200 m and temporal increments of 10 min to 1 day. It can also be applied using much larger grid increments, if the inherent loss in high-resolution (subgrid) information is acceptable. Simulated processes include snow accumulation; blowing-snow redistribution and sublimation; forest canopy interception, unloading, and sublimation; snow-density evolution; and snowp...

Evaluation of forest snow processes models (SnowMIP2)

Thirty-three snowpack models of varying complexity and purpose were evaluated across a wide range of hydrometeorological and forest canopy conditions at five Northern Hemisphere locations, for up t ...

Spatial Snow Modeling of Wind-Redistributed Snow Using Terrain-Based Parameters

Abstract Wind is widely recognized as one of the dominant controls of snow accumulation and distribution in exposed alpine regions. Complex and highly variable wind fields in rugged terrain lead to similarly complex snow distribution fields with areas of no snow adjacent to areas of deep accumulation. Unfortunately, these complexities have limited inclusion of wind redistribution effects in spatial snow distribution models. In this study the difficulties associated with physically exhaustive wind field modeling are avoided and terrain-based parameters are developed to characterize wind effects. One parameter, , was based on maximum upwind slopes relative to seasonally averaged winds to characterize the wind scalar at each pixel location in an alpine basin. A second parameter, , measured upwind breaks in slope from a given location and was combined with an upwind application of to create a drift delineator parameter, D0, which was used to delineate sites of intense redeposition on lee slopes. Based on 504 ...

Estimating the spatial distribution of snow water equivalence in a montane watershed

An approach to model distributed snow water equivalence (SWE) that merges field measurements of depth and density with remotely sensed snow-covered area (SCA) is described. In 1993, two teams conducted an intensive snow survey in the 92. 8k m 2 Blackcap Basin of the Kings River. Snow depth was measured at 709 points and density in five snow pits and along five transects using a Federal Sampler. Sample locations were chosen to be representative of the range of elevation, slope and aspect of the basin. Regression tree models showed that net radiation, elevation and slope angle account for 60‐70% of the variance in the depth measurements. Density was distributed over the basin on a 30 m grid with a multiple linear regression model that explained 70% of the observed variance as a function of the same three variables. The gridded depth estimates, combined with modelled density, produced spatially distributed estimates of SWE. An unsupervised spectral unmixing algorithm estimated snow cover fractions from Landsat-5 Thematic Mapper data acquired at the time as the snow survey. This method provides a snow cover fraction estimate for every pixel. This subpixel map was used as the best estimate for SCA and, combining it with the SWE map, allowed computation of the SWE volume. The estimated volume using the subpixel SCA map was compared with several SCA maps produced with simulations of binary SCA mapping techniques. Thresholds of 40, 50 and 60% fractional cover were used to map binary cases of full snow cover or no snow cover. The diAerence in basin SWE volume was up to 13% depending on the threshold used to classify snow-covered versus snow-free areas. The percentage diAerences in volumes show a significant correlation to the percentage diAerences in SCA between the methods. #1998 John Wiley & Sons, Ltd.

Fractal Distribution of Snow Depth from Lidar Data

Abstract Snowpack properties vary dramatically over a wide range of spatial scales, from crystal microstructure to regional snow climates. The driving forces of wind, energy balance, and precipitation interact with topography and vegetation to dominate snow depth variability at horizontal scales from 1 to 1000 m. This study uses land surface elevation, vegetation surface elevation, and snow depth data measured using airborne lidar at three sites in north-central Colorado. Fractal dimensions are estimated from the slope of a log-transformed variogram and demonstrate scale-invariant, fractal behavior in the elevation, vegetation, and snow depth datasets. Snow depth and vegetation topography each show two distinct fractal distributions over different scale ranges (multifractal behavior), with short-range fractal dimensions near 2.5 and long-range fractal dimensions around 2.9 at all locations. These fractal ranges are separated by a scale break at 15–40 m, depending on the site, which indicates a process cha...

An Improved Snow Scheme for the ECMWF Land Surface Model: Description and Offline Validation

A new snow scheme for the European Centre for Medium-Range Weather Forecasts (ECMWF) land surface model has been tested and validated. The scheme includes a new parameterization of snow density, incorporating a liquid water reservoir, and revised formulations for the subgrid snow cover fraction and snow albedo. Offline validation (covering a wide range of spatial and temporal scales) includes simulations for several observation sites from the Snow Models Intercomparison Project-2 (SnowMIP2) and global simulations driven by the meteorological forcing from the Global Soil Wetness Project-2 (GSWP2) and by ECMWF Re-Analysis ERA-Interim. The new scheme reduces the end of season ablation biases from 10 to 2 days in open areas and from 21 to 13 days in forest areas. Global GSWP2 results are compared against basinscale runoff and terrestrial water storage. The new snow density parameterization increases the snow thermal insulation, reducing soil freezing and leading to an improved hydrological cycle. Simulated snow cover fraction is compared against NOAA/National Environmental Satellite, Data, and Information Service (NESDIS) with a reduction of the negative bias of snow-covered area of the original snow scheme. The original snow scheme had a systematic negative bias in surface albedo when compared against Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data. The new scheme reduces the albedo bias, consequently reducing the spatial- and time-averaged surface net shortwave radiation bias by 5.2 W m 22 in 14% of the Northern Hemisphere land. The new snow scheme described in this paper was introduced in the ECMWF operational forecast system in September 2009 (cycle 35R3).

Combining binary decision tree and geostatistical methods to estimate snow distribution in a mountain watershed

We model the spatial distribution of snow across a mountain basin using an approach that combines binary decision tree and geostatistical techniques. In April 1997 and 1998, intensive snow surveys were conducted in the 6.9-km2 Loch Vale watershed (LVWS), Rocky Mountain National Park, Colorado. Binary decision trees were used to model the large-scale variations in snow depth, while the small-scale variations were modeled through kriging interpolation methods. Binary decision trees related depth to the physically based independent variables of net solar radiation, elevation, slope, and vegetation cover type. These decision tree models explained 54–65% of the observed variance in the depth measurements. The tree-based modeled depths were then subtracted from the measured depths, and the resulting residuals were spatially distributed across LVWS through kriging techniques. The kriged estimates of the residuals were added to the tree-based modeled depths to produce a combined depth model. The combined depth estimates explained 60–85% of the variance in the measured depths. Snow densities were mapped across LVWS using regression analysis. Snow-covered area was determined from high-resolution aerial photographs. Combining the modeled depths and densities with a snow cover map produced estimates of the spatial distribution of snow water equivalence (SWE). This modeling approach offers improvement over previous methods of estimating SWE distribution in mountain basins.

Interannual Consistency in Fractal Snow Depth Patterns at Two Colorado Mountain Sites

Fractal dimensions derived from log–log variograms are useful for characterizing spatial structure and scaling behavior in snow depth distributions. This study examines the temporal consistency of snow depth scaling features at two sites using snow depth distributions derived from lidar datasets collected in 2003 and 2005. The temporal snow accumulation patterns in these two years were substantially different, but both years represent nearly average 1 April accumulation depths for these sites, with consistent statistical distributions. Two distinct fractal regions are observed in each log–log variogram, separated by a scale break, which indicates a length scale at which a substantial change in the driving processes exists. The lag distance of the scale break is 15 m at the Walton Creek site and 40 m at the Alpine site. The datasets show consistent fractal dimensions and scale break distances between the two years, suggesting that the scaling features observed in spatial snow depth distributions are largely determined by physiography and vegetation characteristics and are relatively insensitive to annual variations in snowfall. Directional variograms also show consistent patterns between years, with smaller fractal dimensions aligned with the dominant wind direction at each site.

Persistent reduced ecosystem respiration after insect disturbance in high elevation forests

Amid a worldwide increase in tree mortality, mountain pine beetles (Dendroctonus ponderosae Hopkins) have led to the death of billions of trees from Mexico to Alaska since 2000. This is predicted to have important carbon, water and energy balance feedbacks on the Earth system. Counter to current projections, we show that on a decadal scale, tree mortality causes no increase in ecosystem respiration from scales of several square metres up to an 84 km2 valley. Rather, we found comparable declines in both gross primary productivity and respiration suggesting little change in net flux, with a transitory recovery of respiration 6–7 years after mortality associated with increased incorporation of leaf litter C into soil organic matter, followed by further decline in years 8–10. The mechanism of the impact of tree mortality caused by these biotic disturbances is consistent with reduced input rather than increased output of carbon.