Utah Energy Balance Model Research Articles

Evaluation of Temperature-Index and Energy-Balance Snow Models for Hydrological Applications in Operational Water Supply Forecasts

In the western United States, snow accumulation, storage, and ablation affect seasonal runoff. Thus, the prediction of snowmelt is essential to improve the reliability of water supply forecasts to guide water allocation and operational decisions. The current method used at the Colorado Basin River Forecast Center (CBRFC) couples the SNOW-17 temperature-index snow model and the Sacramento Soil Moisture Accounting (SAC-SMA) runoff model in a lumped approach. Limitations in parameter transferability and calibration requirements for changing conditions with the temperature-index model motivated this research, in which new avenues were investigated to assess and prototype the application of an energy-balance snow model in a distributed modeling approach. The Utah Energy Balance (UEB) model was chosen to compare with the SNOW-17 model because it is simple and parsimonious, making it suitable for distributed application with the potential to improve water supply forecasts. Each model was coupled with the SAC-SMA model and the Rutpix7 routing model to simulate basin snowmelt and discharge. All the models were applied on grids over watersheds using the Research Distributed Hydrologic Model (RDHM) framework. Case studies were implemented for two study sites in the Colorado River basin over a period of two decades. The model performance was evaluated by comparing the model output with observed daily discharge and snow-covered area data obtained from remote sensing sources. Simulated evaporative components of sublimation and evapotranspiration were also evaluated. The results showed that the UEB model, requiring calibration of only a snow drift factor, achieves a comparable performance to the calibrated SNOW-17 model, and both provided reasonable basin snow and discharge simulations in the two study sites. The UEB model had the additional advantage of being able to explicitly simulate sublimation for different land types and thus better quantify evaporative water balance components and their sensitivity to land cover change. UEB also has a better transferability potential because it requires calibration of fewer parameters than SNOW-17. The majority of the parameters for UEB are physically based and regarded as constants characterizing spatially invariant properties of snow processes. Thus, the model remains valid for different climate and terrain conditions for multiple watersheds.

Exploration of the Snow Ablation Process in the Semiarid Region in China by Combining Site-Based Measurements and the Utah Energy Balance Model—A Case Study of the Manas River Basin

Understanding the snow accumulation and melting process is of great significance for the assessment and regulation of water resources and the prevention of meltwater flooding, especially for the semiarid region in the Manas River Basin. However, the lack of long snow measurement time series in this semiarid region prevents a full understanding of the detailed local-scale snow ablation process. Additionally, the modeling of snow accumulation and melting is challenging due to parameter uncertainty. In this study, the snow ablation process in the Manas River Basin was quantitatively explored with long time-series of 3-h measurements of snow depth, snow density and snow water equivalent (SWE) at the Wulanwusu (WLWS), Hanqiazi (HQZ), and Baiyanggou (BYG) sites. This study explored the ability of the Utah energy balance (UEB) snow accumulation and melt model to simulate SWE, energy flux and water loss in the study area. Furthermore, the uncertainty in the ground surface aerodynamic roughness index zos in the UEB model was also analyzed. The results showed that: (1) noticeable variations in snow depth, SWE and snow density occurred on seasonal and interannual time scales, and variations in melting time and melting ratios occurred on short time scales; (2) a rapid decrease in snow depth did not influence the variations in SWE, and snow melting occurred during all time periods, even winter, which is a typical characteristic of snow accumulation in arid environments; (3) the UEB model accurately simulated the snow ablation processes, including SWE, snow surface temperature, and energy flux, at WLWS, HQZ, and BYG sites; (4) the lowest contribution of net radiation to melting occurred in the piedmont clinoplain, followed by the mountain desert grassland belt and mountain forest belt, whereas the contributions of net turbulence exhibited the opposite pattern; (5) the optimal zos in the UEB model was experimentally determined to be 0.01 m, and the UEB model-simulated SWE based on this value was the most consistent with the measured SWE; and (6) the results may provide theoretical and data foundations for research on the snow accumulation process at the watershed scale.

Exploring the impact of forcing error characteristics on physically based snow simulations within a global sensitivity analysis framework

Abstract. Physically based models provide insights into key hydrologic processes but are associated with uncertainties due to deficiencies in forcing data, model parameters, and model structure. Forcing uncertainty is enhanced in snow-affected catchments, where weather stations are scarce and prone to measurement errors, and meteorological variables exhibit high variability. Hence, there is limited understanding of how forcing error characteristics affect simulations of cold region hydrology and which error characteristics are most important. Here we employ global sensitivity analysis to explore how (1) different error types (i.e., bias, random errors), (2) different error probability distributions, and (3) different error magnitudes influence physically based simulations of four snow variables (snow water equivalent, ablation rates, snow disappearance, and sublimation). We use the Sobol' global sensitivity analysis, which is typically used for model parameters but adapted here for testing model sensitivity to coexisting errors in all forcings. We quantify the Utah Energy Balance model's sensitivity to forcing errors with 1 840 000 Monte Carlo simulations across four sites and five different scenarios. Model outputs were (1) consistently more sensitive to forcing biases than random errors, (2) generally less sensitive to forcing error distributions, and (3) critically sensitive to different forcings depending on the relative magnitude of errors. For typical error magnitudes found in areas with drifting snow, precipitation bias was the most important factor for snow water equivalent, ablation rates, and snow disappearance timing, but other forcings had a more dominant impact when precipitation uncertainty was due solely to gauge undercatch. Additionally, the relative importance of forcing errors depended on the model output of interest. Sensitivity analysis can reveal which forcing error characteristics matter most for hydrologic modeling.

Integration of an energy balance snowmelt model into an open source modeling framework

This paper presents a data model for organizing the inputs and outputs of an energy balance snowmelt model (the Utah Energy Balance Model, UEB) that provides a foundation for its integration into the EPA BASINS modeling framework and enables its coupling with other hydrologic models in this system. Having UEB as a BASINS component has facilitated its coupling with the Geospatial Streamflow Forecast Model (GeoSFM) to compute the melting of glaciers and subsequent streamflow in the Himalayas. The data model uses a combination of structured text and network Common Data Form (netCDF) files to represent parameters, geographical, time series, and gridded space-time data. We describe the design and structure of this data model, integration methodology of UEB and GeoSFM and illustrate the effectiveness of the resulting coupled models for the computation of surface water input and streamflow for a glaciated watershed in Nepal Himalayas.

Impact of errors in the downwelling irradiances on simulations of snow water equivalent, snow surface temperature, and the snow energy balance

AbstractThe forcing irradiances (downwelling shortwave and longwave irradiances) are the primary drivers of snowmelt; however, in complex terrain, few observations, the use of estimated irradiances, and the influence of topography and elevation all lead to uncertainties in these radiative fluxes. The impact of uncertainties in the forcing irradiances on simulations of snow is evaluated in idealized modeling experiments. Two snow models of contrasting complexity, the Utah Energy Balance Model (UEB) and the Snow Thermal Model (SNTHERM), are forced with irradiances with prescribed errors of the structure and magnitude representative of those found in methods for estimating the downwelling irradiances. Relatively modest biases have substantial impacts on simulated snow water equivalent (SWE) and surface temperature (Ts) across a range of climates, whereas random noise at the daily scale has a negligible effect on modeled SWE and Ts. Shortwave biases have a smaller SWE impact, due to the influence of albedo, and Ts impact, due to their diurnal cycle, compared to equivalent longwave biases. Warmer sites exhibit greater sensitivity to errors when evaluated using SWE, while colder sites exhibit more sensitivity as evaluated using Ts. The two models displayed different sensitivity and responses to biases. The stability feedback in the turbulent fluxes explains differences in Ts between models in the negative longwave bias scenarios. When the models diverge during melt events, differences in the turbulent fluxes and internal energy change of the snow are found to be responsible. From this analysis, we suggest model evaluations use Ts in addition to SWE.

In-situobservations and modeling of spring snowmelt processes in an Altay Mountains river basin

Snowmelt is a principal source for ground-water recharge and stream flows in moun- tainous regions of northwestern China. Knowledge of the timing, magnitude, and processes of snowmelt under changing climate conditions is required for appropriate water resource manage- ment. The Utah energy balance (UEB) model was used to simulate the development and melting of spring (March 2012) snow cover at an observation site in the Kayiertesi River Basin in the Altay Mountains in Xinjiang. The modeled results were validated by field measurements and remotely sensed data. Results show that the simulation of the snowmelt process lasted for 24 days and the modeled snow water equivalent (SWE) closely matched the observed SWE, with a mean relative error of 7.2%. During the snowmelt process, net radiation was the major energy source of the snow layer. The variation of the snowmelt outflow was closely related to the snowmelt amounts and air temperature. The initial results of this modeling process show that our calibrated parameters were reasonable and the UEB model can be used for simulating and forecasting peak snowmelt outflows in this region. © 2014 Society of Photo-Optical Instrumentation Engineers (SPIE) (DOI: 10.1117/1.JRS.8.084697)

Representation of canopy snow interception, unloading and melt in a parsimonious snowmelt model

Recent improvements in the Utah Energy Balance (UEB) snowmelt model are focused on snow–vegetation–atmosphere interactions to understand how different types of vegetation affect snow processes in the mountains of Western USA. This work presents field work carried out in the Rocky Mountains of Northern Utah to evaluate new UEB model algorithms that represent the processes of canopy snow interception, sublimation, mass unloading and melt. Four years' continuous field observations showed generally smaller accumulations of snow beneath the forest canopies in comparison with open (sage and grass) areas, a difference that is attributed to interception and subsequent sublimation and redistribution of intercepted snow by wind, much of it into surrounding open areas. Accumulations beneath the denser forest (conifer) canopies were found to be less than the accumulation beneath the less dense forest (deciduous) canopies. The model was able to represent the accumulation of snow water equivalent in the open and beneath the deciduous forest quite well but without accounting for redistribution tended to overestimate the snow water equivalent beneath the conifer forest. Evidence of redistribution of the intercepted snow from the dense forest (i.e. conifer forest) to the adjacent area was inferred from observations. Including a simple representation of redistribution in the model gave satisfactory prediction of snow water equivalent beneath the coniferous forest. The simulated values of interception, sublimation and unloading were also compared with previous studies and found in agreement. Copyright © 2013 John Wiley & Sons, Ltd.

Snowmelt and sublimation: field experiments and modelling in the High Atlas Mountains of Morocco

Abstract. Snow in the High Atlas Mountains is a major source for freshwater renewal and for water availability in the semi-arid lowlands of south-eastern Morocco. Snowfall- and snow-ablation monitoring and modelling is important for estimating potential water delivery from the mountain water towers to the forelands. This study is part of GLOWA-IMPETUS, an integrated management project dealing with scarce water resources in West Africa. The Ameskar study area is located to the south of the High Atlas Mountains, in their rain shadow. As a part of the M’Goun river basin within the upper Drâa valley, the study area is characterised by high radiation inputs, low atmospheric humidity and long periods with sub-zero temperatures. Its altitude ranges between 2000 m and 4000 m, with dominant north- and south-facing slopes. Snowfall occurs mainly from November to April but even summit regions can become repeatedly devoid of snow cover. Snow cover maps for the M’Goun basin (1240 km2) are derived from calculations of NDSI (Normalized Difference Snow Index) from MODIS satellite images and snow depth is monitored at four automatic weather stations between 2000–4000 m. Snowfall events are infrequent at lower altitudes. The presence of snow penitentes at altitudes above 3000 m indicates that snow sublimation is an important component of snow ablation. Snow ablation was modelled with the UEB Utah Energy Balance Model (Tarboton and Luce, 1996). This single layer, physically-based, point energy and mass balance model is driven by meteorological variables recorded at the automatic weather stations at Tounza (2960 m) and Tichki (3260 m). Data from snow pillows at Tounza and Tichki are used to validate the model’s physical performance in terms of energy and water balances for a sequence of two snowfall events in the winter of 2003/4. First UEB modelling results show good overall performance and timing of snowmelt and sublimation compared to field investigations. Up to 44% of snow ablation is attributed to snow sublimation in typical winters with subzero temperatures and low atmospheric humidity at an altitude of 3000 m. At altitudes below 3000 m snowmelt generally dominates over sublimation. Unfortunately, the highest altitude zones suffer long periods with direct water loss into the atmosphere by sublimation in the course of which they cannot contribute to direct runoff or groundwater formation in the southern High Atlas Mountains. Keywords: sublimation, snow ablation modelling, energy balance model, High Atlas Mountains

Forest cover algorithms for estimating meteorological forcing in a numerical snow model

The architectural properties of a forest are known to significantly modify meteorological forcing of snowcover. This project develops four numerical modules to simulate canopy processes including attenuation of solar radiation and wind speed, the mixed sky and canopy components of longwave irradiance, and precipitation interception by canopy elements. The four modules and a more realistic atmospheric stability algorithm were included in the Utah Energy Balance (UEB) snow model to estimate water equivalence beneath coniferous and defoliated deciduous forests in northern Michigan. Systematic underestimation of early season snow depth was attributed to the assumption of constant, seasonal average, snow density in the model's lumped treatment of the snowpack processes. The modified UEB model (UEBMOD) improved estimation of snow depth in a clearing and beneath the coniferous site, whereas UEB with original forest parameterizations performed best for the deciduous site.