Measurements and Modeling of Snow Energy Balance and Sublimation from Snow

Snow melt runoff is an important factor in runoff generation for most Utah rivers and a large contributer to Utah\u27s water supply and periodically flooding. The melting of snow is driven by fluxes of energy into the snow during warm periods. These consist of radiant energy from the sun and atmosphere, sensible and latent heat transfers due to turbulent energy exchanges at the snow surface and a relatively small ground flux from below. The turbulent energy exchanges are also responsible for sublimation from the snow surface, particularly in arid environments, and result in a loss of snow water equivalent available for melt. The cooling of the snowpack resulting for sublimation also delays the formation of melt runoff. This paper describes measurements and mathematical modeling done to quantify the sublimation from snow. Measurements were made at the Utah State University drainage and evapotranspiration research farm. I attempted to measure sublimation directly using weighing lysimeters. Energy balance components were measured, by measuring incoming and reflected radiation, wind, temperature and humidity gradients. An energy balance snowmelt model was tested against these measurements. The model uses a lumped representation of the snowpack with two state variables, namely , water equivalent and energy content relative to a reference state of water in the solid phase at 0 degrees Celcius. This energy content is used to determine snowpack average temperature or liquid fraction. The model is driven by inputs of air temperature, precipitation, wind speed, humidity and solar radiation. The model uses physically based calculations of radiative, sensible, latent and advective heat exchanges. An equilibrium parameterization of snow surface temperature accounts for differences between snow surface temperature and average snowpack temperature without having to introduce additional state variables. This is achieved by incorporating the snow surface thermal conductance, which with respect to heat flux is equivalent to stomatal and aerodynamic conductances used to calculate evapotranspiration from vegetation. Melt outflow is a function of the liquid fraction, using Darcy\u27s law. This allows the model to account for continued melt outflow even when the energy balance is negative. The purpose of the measurements presented here was to test the sublimation and turbulent exchange parameterizations in the model. However the weighing lysimeters used to measure sublimation suffered from temperature sensitive oscillations that mask short term sublimation measurements. I have therefore used the measured data to test the models capability to represent the overall seasonal accumulation and ablation of snow

The Source Hydrology of Severe Sustained Drought in th Southwestern U.S.

This paper considers the risk of drought and develops drough scenarios for use in the study of severe sustained drought in the Southwestern United States. The focus is on the Colorado River gbasin and regions to which Colorado River water is exported, especially southern California, which depends on water from the Colorado River as well as the four major rivers in northern California. Drought scenarios are developed using estimates of unimpaired historic streamflow as well as reconstructions of streamflow based on tree ring widths. Drought scenarios in the Colorado River are defined on the basis of annual flow at Lees Ferry. Possible spatial manifestations of the Colorado River drough scenarios for input into a Colorado River system simualation model are developed by disaggregating the Lees Ferry flow to monthly flows at twenty nine source locations required by the model. The risk, in terms of retun period, of the drough scenarios developed, is assessed using stochastic models applied to both the Colorado River basin and the comvined flow in four major California rivers. The risks of severe sustained drought occurring concurrently in the Colorado River basin and California is also assessed

A New Method for the Determination of Flow Directions and Contributing Areas in Grid Digital Elevation Models

A new procedure for the representation of flow directions and calculation of upslope areas using rectangular grid digital elevation models is presented. The procedure is based on representing flow direction as a single angle taken as the steepest downward slope on the eight triangular facets centered at each grid point. Upslope area is then calculated by proportioning flow between two downslope pixels according to how close this flow direction is to the direct angle to the downslope pixel. This procedure offers improvements over prior procedures that have restricted flow to eight possible directions (introducing grid bia) or proportioned flow according to slope (introducing unrealistic dispersion). The new procedure is more robust than prior procedures based on fitting local planes while retaining a simple grid based structure. Detailed algorithms are presented and results are demonstrated through test examples and application to digital elevation data sets

Modeling the effect of vegetation of the accumulation and melting of snow

This work investigates the variability of snow accumulation and differences in the timing of melt and sublimation between open (grass/shrubs) and forest (conifer/deciduous) locations at a mountain study site in the Western US, using a combination of field observations and modeling. Observations include continuous automated climate and snow depth measurements supported by periodic field measurements of snow water equivalent and temperature in four different vegetation classes (grass, shrubs, coniferous forest, deciduous forest) at the TW Daniel Experimental Forest located 30 miles N-E of Logan. The Utah Energy Balance physically based snowmelt model, was enhanced by adding new parameterizations of: i) snow interception and unloading; ii) transmission of radiation through the canopy; and iii) atmospheric transport of heat and water vapor between the snow on the ground, intercepted snow in the canopy and the atmosphere above; to better simulate snow processes in forested areas. The enhanced model was evaluated by comparing model simulations of meteorological conditions (temperature, wind, radiation) and snow properties (water equivalent, depth, temperature) in and beneath the canopy with observations. Observations showed approximately 10 to 20% more snow accumulation in open areas compared to forest areas. Ablation rates were also found to be higher in open areas than in forest areas. In comparison to coniferous forest, deciduous forest had high rates of accumulation and ablation. The model performed well in representing these effects based on inputs such as canopy height, canopy coverage, leaf area index and leaf orientation; thereby improving our ability to simulate and predict snow processes across heterogeneous watersheds. (KEYWORDS: snow accumulation, melt timing, sublimation, interception, snowmelt

Canopy Radiation Transmission for an Energy Balance Snowmelt Model

To better estimate the radiation energy within and beneath the forest canopy for energy balance snowmelt models, a two stream radiation transfer model that explicitly accounts for canopy scattering, absorption and reflection was developed. Upward and downward radiation streams represented by two differential equations using a single path assumption were solved analytically to approximate the radiation transmitted through or reflected by the canopy with multiple scattering. This approximation results in an exponential decrease of radiation intensity with canopy depth, similar to Beer\u27s law for a deep canopy. The solution for a finite canopy is obtained by applying recursive superposition of this two stream single path deep canopy solution. This solution enhances capability for modeling energy balance processes of the snowpack in forested environments, which is important when quantifying the sensitivity of hydrologic response to input changes using physically based modeling. The radiation model was included in a distributed energy balance snowmelt model and results compared with observations made in three different vegetation classes (open, coniferous forest, deciduous forest) at a forest study area in the Rocky Mountains in Utah, USA. The model was able to capture the sensitivity of beneath canopy net radiation and snowmelt to vegetation class consistent with observations and achieve satisfactory predictions of snowmelt from forested areas from parsimonious practically available information. The model is simple enough to be applied in a spatially distributed way, but still relatively rigorously and explicitly represent variability in canopy properties in the simulation of snowmelt over a watershed

Ensemble Streamflow Forecasting Using an Energy Balance Snowmelt Model Coupled to a Distributed Hydrologic Model with Assimilation of Snow and Streamflow Observations

In many river basins across the world, snowmelt is an important source of streamflow. However, detailed snowmelt modeling is hampered by limited input data and uncertainty arising from inadequate model structure and parametrization. Data assimilation that updates model states based on observations, reduces uncertainty and improves streamflow forecasts. In this study, we evaluated the Utah Energy Balance (UEB) snowmelt model coupled to the Sacramento Soil Moisture Accounting (SAC‐SMA) and rutpix7 stream routing models, integrated within the Research Distributed Hydrologic Model (RDHM) framework for streamflow forecasting. We implemented an ensemble Kalman filter for assimilation of snow water equivalent (SWE) observations in UEB and a particle filter for assimilation of streamflow to update the SAC‐SMA and rutpix7 states. Using leave one out validation, it was shown that the modeled SWE at a location where observations were excluded from data assimilation was improved through assimilation of data from other stations, suggesting that assimilation of sparse observations of SWE has the potential to improve the distributed modeling of SWE over watershed grid cells. In addition, the spatially distributed snow data assimilation improved streamflow forecasts and the forecast volume error was reduced. On the other hand, the assimilation of streamflow observations did not provide additional forecast improvement over that achieved by the SWE assimilation for seasonal forecast volume likely due to there being little information content in streamflow at the forecast date prior to its rising during the melt period and this application of particle filter being better suited for shorter timescales

A Comparison of National Water Model Retrospective Analysis Snow Outputs at Snow Telemetry Sites Across the Western United States

This study compares the US National Water Model (NWM) reanalysis snow outputs to observed snow water equivalent (SWE) and snow-covered area fraction (SCAF) at snow telemetry (SNOTEL) sites across the Western United States. SWE was obtained from SNOTEL sites, while SCAF was obtained from moderate resolution imaging spectroradiometer (MODIS) observations at a nominal 500 m grid scale. Retrospective NWM results were at a 1000 m grid scale. We compared results for SNOTEL sites to gridded NWM and MODIS outputs for the grid cells encompassing each SNOTEL site. Differences between modelled and observed SWE were attributed to both model errors, as well as errors in inputs, notably precipitation and temperature. The NWM generally under-predicted SWE, partly due to precipitation input differences. There was also a slight general bias for model input temperature to be cooler than observed, counter to the direction expected to lead to under-modelling of SWE. There was also under-modelling of SWE for a subset of sites where precipitation inputs were good. Furthermore, the NWM generally tends to melt snow early. There was considerable variability between modelled and observed SCAF as well as the binary comparison of snow cover presence that hampered useful interpretation of SCAF comparisons. This is in part due to the shortcomings associated with both model SCAF parameterization and MODIS observations, particularly in vegetated regions. However, when SCAF was aggregated across all sites and years, modelled SCAF tended to be more than observed using MODIS. These differences are regional with generally better SWE and SCAF results in the Central Basin and Range and differences tending to become larger the further away regions are from this region. These findings identify areas where predictions from the NWM involving snow may be better or worse, and suggest opportunities for research directed towards model improvements

A Tool for Downscaling Weather Data from Large-grid Reanalysis Products to Finer Spatial Scales for Distributed Hydrological Applications

A downscaling tool was developed to provide sub-daily high spatial resolution surfaces of weather variables for distributed hydrologic modeling from NASA Modern Era Retrospective-Analysis for Research and Applications reanalysis products. The tool uses spatial interpolation and physically based relationships between the weather variables and elevation to provide inputs at the scale of a gridded hydrologic model, typically smaller (∼100 m) than the scale of weather reanalysis data (∼20–200 km). Nash-Sutcliffe efficiency (NSE) measures greater than 0.70 were obtained for direct tests of downscaled daily temperature and monthly precipitation at 173 SNOTEL sites. In an integrated test driving the Utah Energy Balance (UEB) snowmelt model, 80% of these sites gave NSE \u3e 0.6 for snow water equivalent. These findings motivate use of this tool in data sparse regions where ground based observations are not available and downscaled global reanalysis products may be the only option for model inputs