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Abstract
Significant differences in snow deposition, development of the seasonal snow cover, and the timing of melt can occur over small spatial distances because of differences in topographically controlled wind exposure and canopy cover. To capture important intrabasin hydrological processes related to heterogeneous snow cover and energy inputs, models must explicitly account for these differences. The “SNOBAL” point snow cover energy and mass balance model is used to evaluate differences in snow cover energy and mass balance at two sites in a small headwater drainage of the Reynolds Creek Experimental Watershed (RCEW) in the Owyhee Mountains of southwestern Idaho. Though these sites are separated by only 350 m, they are located in distinctly different snow cover regimes. The “ridge” site (elevation 2097 m) is located on a broad shelf on the southern ridge of RCEW, and the “grove” site (elevation 2061 m) is sheltered by topography and forest canopy in a grove of aspen and fir trees just in the lee of the ridge on which the ridge site is located. Precipitation and climate data from these sites were used to drive SNOBAL for three water years: the 1984 water year, the largest discharge year on record; the 1992 water year, the smallest discharge year on record; and the very windy 1999 water year. Simulated energy balance during meltout at the ridge site was dominated by sensible heat flux; at the grove site the primary energy input during meltout was net allwave radiation. Comparison of total annual snowmelt with measured stream discharge from this headwater drainage showed that neither site adequately represented the basin input of water in any of the model years. This analysis details the disparity in energy and mass fluxes during the snow accumulation and ablation cycle between two proximal sites, illustrating the importance of understanding and spatially accounting for variable energy inputs and snow deposition patterns.
Abstract
Significant differences in snow deposition, development of the seasonal snow cover, and the timing of melt can occur over small spatial distances because of differences in topographically controlled wind exposure and canopy cover. To capture important intrabasin hydrological processes related to heterogeneous snow cover and energy inputs, models must explicitly account for these differences. The “SNOBAL” point snow cover energy and mass balance model is used to evaluate differences in snow cover energy and mass balance at two sites in a small headwater drainage of the Reynolds Creek Experimental Watershed (RCEW) in the Owyhee Mountains of southwestern Idaho. Though these sites are separated by only 350 m, they are located in distinctly different snow cover regimes. The “ridge” site (elevation 2097 m) is located on a broad shelf on the southern ridge of RCEW, and the “grove” site (elevation 2061 m) is sheltered by topography and forest canopy in a grove of aspen and fir trees just in the lee of the ridge on which the ridge site is located. Precipitation and climate data from these sites were used to drive SNOBAL for three water years: the 1984 water year, the largest discharge year on record; the 1992 water year, the smallest discharge year on record; and the very windy 1999 water year. Simulated energy balance during meltout at the ridge site was dominated by sensible heat flux; at the grove site the primary energy input during meltout was net allwave radiation. Comparison of total annual snowmelt with measured stream discharge from this headwater drainage showed that neither site adequately represented the basin input of water in any of the model years. This analysis details the disparity in energy and mass fluxes during the snow accumulation and ablation cycle between two proximal sites, illustrating the importance of understanding and spatially accounting for variable energy inputs and snow deposition patterns.
Abstract
Highly heterogeneous mountain snow distributions strongly affect soil moisture patterns; local ecology; and, ultimately, the timing, magnitude, and chemistry of stream runoff. Capturing these vital heterogeneities in a physically based distributed snow model requires appropriately scaled model structures. This work looks at how model scale—particularly the resolutions at which the forcing processes are represented—affects simulated snow distributions and melt. The research area is in the Reynolds Creek Experimental Watershed in southwestern Idaho. In this region, where there is a negative correlation between snow accumulation and melt rates, overall scale degradation pushed simulated melt to earlier in the season. The processes mainly responsible for snow distribution heterogeneity in this region—wind speed, wind-affected snow accumulations, thermal radiation, and solar radiation—were also independently rescaled to test process-specific spatiotemporal sensitivities. It was found that in order to accurately simulate snowmelt in this catchment, the snow cover needed to be resolved to 100 m. Wind and wind-affected precipitation—the primary influence on snow distribution—required similar resolution. Thermal radiation scaled with the vegetation structure (~100 m), while solar radiation was adequately modeled with 100–250-m resolution. Spatiotemporal sensitivities to model scale were found that allowed for further reductions in computational costs through the winter months with limited losses in accuracy. It was also shown that these modeling-based scale breaks could be associated with physiographic and vegetation structures to aid a priori modeling decisions.
Abstract
Highly heterogeneous mountain snow distributions strongly affect soil moisture patterns; local ecology; and, ultimately, the timing, magnitude, and chemistry of stream runoff. Capturing these vital heterogeneities in a physically based distributed snow model requires appropriately scaled model structures. This work looks at how model scale—particularly the resolutions at which the forcing processes are represented—affects simulated snow distributions and melt. The research area is in the Reynolds Creek Experimental Watershed in southwestern Idaho. In this region, where there is a negative correlation between snow accumulation and melt rates, overall scale degradation pushed simulated melt to earlier in the season. The processes mainly responsible for snow distribution heterogeneity in this region—wind speed, wind-affected snow accumulations, thermal radiation, and solar radiation—were also independently rescaled to test process-specific spatiotemporal sensitivities. It was found that in order to accurately simulate snowmelt in this catchment, the snow cover needed to be resolved to 100 m. Wind and wind-affected precipitation—the primary influence on snow distribution—required similar resolution. Thermal radiation scaled with the vegetation structure (~100 m), while solar radiation was adequately modeled with 100–250-m resolution. Spatiotemporal sensitivities to model scale were found that allowed for further reductions in computational costs through the winter months with limited losses in accuracy. It was also shown that these modeling-based scale breaks could be associated with physiographic and vegetation structures to aid a priori modeling decisions.
Abstract
Rangelands are often characterized by a patchy mosaic of vegetation types, making measurement and modeling of surface energy fluxes particularly challenging. The purpose of this study was to evaluate surface energy fluxes measured using three eddy covariance systems above and within two rangeland vegetation sites and use the data to improve simulations of turbulent energy fluxes in a multilayer plant canopy model: the Simultaneous Heat and Water (SHAW) model. Model modifications included adjustment of the wind profile roughness parameters for sparse canopies, extending the currently used K-theory approach to include influence of the roughness sublayer and stability functions within the canopy, and in a separate version of the model, introducing Lagrangian far-field turbulent transfer equations (L theory) in lieu of the K-theory approach. There was relatively little difference in simulated energy fluxes for the aspen canopy using L-theory versus K-theory turbulent transfer equations, but L theory tracked canopy air temperature profiles better during the growing season. Upward sensible heat flux was observed above aspen trees, within the aspen understory, and above sagebrush throughout the active snowmelt season. Model simulations confirmed the observed upward sensible flux during snowmelt was due to solar heating of the aspen limbs and sagebrush. Thus, the eddy covariance (EC) systems were unable to properly quantify fluxes at the snow surface when vegetation was present. Good agreement between measured and modeled energy fluxes suggest that they can be measured and simulated reliably in these complex environments, but care must be used in the interpretation of the results.
Abstract
Rangelands are often characterized by a patchy mosaic of vegetation types, making measurement and modeling of surface energy fluxes particularly challenging. The purpose of this study was to evaluate surface energy fluxes measured using three eddy covariance systems above and within two rangeland vegetation sites and use the data to improve simulations of turbulent energy fluxes in a multilayer plant canopy model: the Simultaneous Heat and Water (SHAW) model. Model modifications included adjustment of the wind profile roughness parameters for sparse canopies, extending the currently used K-theory approach to include influence of the roughness sublayer and stability functions within the canopy, and in a separate version of the model, introducing Lagrangian far-field turbulent transfer equations (L theory) in lieu of the K-theory approach. There was relatively little difference in simulated energy fluxes for the aspen canopy using L-theory versus K-theory turbulent transfer equations, but L theory tracked canopy air temperature profiles better during the growing season. Upward sensible heat flux was observed above aspen trees, within the aspen understory, and above sagebrush throughout the active snowmelt season. Model simulations confirmed the observed upward sensible flux during snowmelt was due to solar heating of the aspen limbs and sagebrush. Thus, the eddy covariance (EC) systems were unable to properly quantify fluxes at the snow surface when vegetation was present. Good agreement between measured and modeled energy fluxes suggest that they can be measured and simulated reliably in these complex environments, but care must be used in the interpretation of the results.
Abstract
Previous studies have shown that gauge-observed daily streamflow peak times (DPTs) during spring snowmelt can exhibit distinct temporal shifts through the season. These shifts have been attributed to three processes: 1) melt flux translation through the snowpack or percolation, 2) surface and subsurface flow of melt from the base of snowpacks to streams, and 3) translation of water flux in the streams to stream gauging stations. The goal of this study is to evaluate and quantify how these processes affect observed DPTs variations at the Reynolds Mountain East (RME) research catchment in southwest Idaho, United States. To accomplish this goal, DPTs were simulated for the RME catchment over a period of 25 water years using a modified snowmelt model, iSnobal, and a hydrology model, the Penn State Integrated Hydrologic Model (PIHM). The influence of each controlling process was then evaluated by simulating the DPT with and without the process under consideration. Both intra- and interseasonal variability in DPTs were evaluated. Results indicate that the magnitude of DPTs is dominantly influenced by subsurface flow, whereas the temporal shifts within a season are primarily controlled by percolation through snow. In addition to the three processes previously identified in the literature, processes governing the snowpack ripening time are identified as additionally influencing DPT variability. Results also indicate that the relative dominance of each control varies through the melt season and between wet and dry years. The results could be used for supporting DPTs prediction efforts and for prioritization of observables for DPT determination.
Abstract
Previous studies have shown that gauge-observed daily streamflow peak times (DPTs) during spring snowmelt can exhibit distinct temporal shifts through the season. These shifts have been attributed to three processes: 1) melt flux translation through the snowpack or percolation, 2) surface and subsurface flow of melt from the base of snowpacks to streams, and 3) translation of water flux in the streams to stream gauging stations. The goal of this study is to evaluate and quantify how these processes affect observed DPTs variations at the Reynolds Mountain East (RME) research catchment in southwest Idaho, United States. To accomplish this goal, DPTs were simulated for the RME catchment over a period of 25 water years using a modified snowmelt model, iSnobal, and a hydrology model, the Penn State Integrated Hydrologic Model (PIHM). The influence of each controlling process was then evaluated by simulating the DPT with and without the process under consideration. Both intra- and interseasonal variability in DPTs were evaluated. Results indicate that the magnitude of DPTs is dominantly influenced by subsurface flow, whereas the temporal shifts within a season are primarily controlled by percolation through snow. In addition to the three processes previously identified in the literature, processes governing the snowpack ripening time are identified as additionally influencing DPT variability. Results also indicate that the relative dominance of each control varies through the melt season and between wet and dry years. The results could be used for supporting DPTs prediction efforts and for prioritization of observables for DPT determination.
Abstract
In the Pacific Northwest (PNW), concern about the impacts of climate and land cover change on water resources and flood-generating processes emphasizes the need for a mechanistic understanding of the interactions between forest canopies and hydrologic processes. Detailed measurements during the 1999 and 2000 hydrologic years were used to modify the Simultaneous Heat and Water (SHAW) model for application in forested systems. Major changes to the model include improved representation of rainfall interception and stomatal conductance dynamics. The model was developed for the 1999 hydrologic year and tested for the 2000 hydrologic year without modification of the site parameters. The model effectively simulated throughfall, soil water content profiles, and shallow soil temperatures for both years. The largest discrepancies between soil moisture and temperature were observed during periods of discontinuous snow cover due to spatial variability that was not explicitly simulated by the model. Soil warming at bare locations was delayed until most of the snow cover ablated because of the large heat sink associated with the residual snow patches. During the summer, simulated transpiration decreased from a maximum monthly mean of 2.2 mm day−1 in July to 1.3 mm day−1 in September as a result of decreasing soil moisture and declining net radiation. The results indicate that a relatively simple representation of the vegetation canopy can accurately simulate seasonal hydrologic fluxes in this environment, except during periods of discontinuous snow cover.
Abstract
In the Pacific Northwest (PNW), concern about the impacts of climate and land cover change on water resources and flood-generating processes emphasizes the need for a mechanistic understanding of the interactions between forest canopies and hydrologic processes. Detailed measurements during the 1999 and 2000 hydrologic years were used to modify the Simultaneous Heat and Water (SHAW) model for application in forested systems. Major changes to the model include improved representation of rainfall interception and stomatal conductance dynamics. The model was developed for the 1999 hydrologic year and tested for the 2000 hydrologic year without modification of the site parameters. The model effectively simulated throughfall, soil water content profiles, and shallow soil temperatures for both years. The largest discrepancies between soil moisture and temperature were observed during periods of discontinuous snow cover due to spatial variability that was not explicitly simulated by the model. Soil warming at bare locations was delayed until most of the snow cover ablated because of the large heat sink associated with the residual snow patches. During the summer, simulated transpiration decreased from a maximum monthly mean of 2.2 mm day−1 in July to 1.3 mm day−1 in September as a result of decreasing soil moisture and declining net radiation. The results indicate that a relatively simple representation of the vegetation canopy can accurately simulate seasonal hydrologic fluxes in this environment, except during periods of discontinuous snow cover.
Abstract
This paper develops a multivariate mosaic subgrid approach to represent subgrid variability in land surface models (LSMs). The k-means clustering is used to take an arbitrary number of input descriptors and objectively determine areas of similarity within a catchment or mesoscale model grid box. Two different classifications of hydrologic similarity are compared: an a priori classification, where clusters are based solely on known physiographic information, and an a posteriori classification, where clusters are defined based on high-resolution LSM simulations. Simulations from these clustering approaches are compared to high-resolution gridded simulations, as well as to three common mosaic approaches used in LSMs: the “lumped” approach (no subgrid variability), disaggregation by elevation bands, and disaggregation by vegetation types in two subcatchments. All watershed disaggregation methods are incorporated in the Noah Multi-Physics (Noah-MP) LSM and applied to snowmelt-dominated subcatchments within the Reynolds Creek watershed in Idaho. Results demonstrate that the a priori clustering method is able to capture the aggregate impact of finescale spatial variability with O(10) simulation points, which is practical for implementation into an LSM scheme for coupled predictions on continental–global scales. The multivariate a priori approach better represents snow cover and depth variability than the univariate mosaic approaches, critical in snowmelt-dominated areas. Catchment-averaged energy fluxes are generally within 10%–15% for the high-resolution and a priori simulations, while displaying more subgrid variability than the univariate mosaic methods. Examination of observed and simulated streamflow time series shows that the a priori method generally reproduces hydrograph characteristics better than the simple disaggregation approaches.
Abstract
This paper develops a multivariate mosaic subgrid approach to represent subgrid variability in land surface models (LSMs). The k-means clustering is used to take an arbitrary number of input descriptors and objectively determine areas of similarity within a catchment or mesoscale model grid box. Two different classifications of hydrologic similarity are compared: an a priori classification, where clusters are based solely on known physiographic information, and an a posteriori classification, where clusters are defined based on high-resolution LSM simulations. Simulations from these clustering approaches are compared to high-resolution gridded simulations, as well as to three common mosaic approaches used in LSMs: the “lumped” approach (no subgrid variability), disaggregation by elevation bands, and disaggregation by vegetation types in two subcatchments. All watershed disaggregation methods are incorporated in the Noah Multi-Physics (Noah-MP) LSM and applied to snowmelt-dominated subcatchments within the Reynolds Creek watershed in Idaho. Results demonstrate that the a priori clustering method is able to capture the aggregate impact of finescale spatial variability with O(10) simulation points, which is practical for implementation into an LSM scheme for coupled predictions on continental–global scales. The multivariate a priori approach better represents snow cover and depth variability than the univariate mosaic approaches, critical in snowmelt-dominated areas. Catchment-averaged energy fluxes are generally within 10%–15% for the high-resolution and a priori simulations, while displaying more subgrid variability than the univariate mosaic methods. Examination of observed and simulated streamflow time series shows that the a priori method generally reproduces hydrograph characteristics better than the simple disaggregation approaches.
Abstract
Forecasting the timing and magnitude of snowmelt and runoff is critical to managing mountain water resources. Warming temperatures are increasing the rain–snow transition elevation and are limiting the forecasting skill of statistical models relating historical snow water equivalent to streamflow. While physically based methods are available, they require accurate estimations of the spatial and temporal distribution of meteorological variables in complex terrain. Across many mountainous areas, measurements of precipitation and other meteorological variables are limited to a few reference stations and are not adequate to resolve the complex interactions between topography and atmospheric flow. In this paper, we evaluate the ability of the Weather Research and Forecasting (WRF) Model to approximate the inputs required for a physics-based snow model, iSnobal, instead of using meteorological measurements, for the Boise River Basin (BRB) in Idaho, United States. An iSnobal simulation using station data from 40 locations in and around the BRB resulted in an average root-mean-square error (RMSE) of 4.5 mm compared with 12 SNOTEL measurements. Applying WRF forcings alone was associated with an RMSE of 10.5 mm, while including a simple bias correction to the WRF outputs of temperature and precipitation reduced the RMSE to 6.5 mm. The results highlight the utility of using WRF outputs as input to snowmelt models, as all required input variables are spatiotemporally complete. This will have important benefits in areas with sparse measurement networks and will aid snowmelt and runoff forecasting in mountainous basins.
Abstract
Forecasting the timing and magnitude of snowmelt and runoff is critical to managing mountain water resources. Warming temperatures are increasing the rain–snow transition elevation and are limiting the forecasting skill of statistical models relating historical snow water equivalent to streamflow. While physically based methods are available, they require accurate estimations of the spatial and temporal distribution of meteorological variables in complex terrain. Across many mountainous areas, measurements of precipitation and other meteorological variables are limited to a few reference stations and are not adequate to resolve the complex interactions between topography and atmospheric flow. In this paper, we evaluate the ability of the Weather Research and Forecasting (WRF) Model to approximate the inputs required for a physics-based snow model, iSnobal, instead of using meteorological measurements, for the Boise River Basin (BRB) in Idaho, United States. An iSnobal simulation using station data from 40 locations in and around the BRB resulted in an average root-mean-square error (RMSE) of 4.5 mm compared with 12 SNOTEL measurements. Applying WRF forcings alone was associated with an RMSE of 10.5 mm, while including a simple bias correction to the WRF outputs of temperature and precipitation reduced the RMSE to 6.5 mm. The results highlight the utility of using WRF outputs as input to snowmelt models, as all required input variables are spatiotemporally complete. This will have important benefits in areas with sparse measurement networks and will aid snowmelt and runoff forecasting in mountainous basins.
Abstract
The spatial variation of melt energy can influence snow cover depletion rates and in turn be influenced by the spatial variability of shortwave irradiance to snow. The spatial variability of shortwave irradiance during melt under uniform and discontinuous evergreen canopies at a U.S. Rocky Mountains site was measured, analyzed, and then compared to observations from mountain and boreal forests in Canada. All observations used arrays of pyranometers randomly spaced under evergreen canopies of varying structure and latitude. The spatial variability of irradiance for both overcast and clear conditions declined dramatically, as the sample averaging interval increased from minutes to 1 day. At daily averaging intervals, there was little influence of cloudiness on the variability of subcanopy irradiance; instead, it was dominated by stand structure. The spatial variability of irradiance on daily intervals was higher for the discontinuous canopies, but it did not scale reliably with canopy sky view. The spatial variation in irradiance resulted in a coefficient of variation of melt energy of 0.23 for the set of U.S. and Canadian stands. This variability in melt energy smoothed the snow-covered area depletion curve in a distributed melt simulation, thereby lengthening the duration of melt by 20%. This is consistent with observed natural snow cover depletion curves and shows that variations in melt energy and snow accumulation can influence snow-covered area depletion under forest canopies.
Abstract
The spatial variation of melt energy can influence snow cover depletion rates and in turn be influenced by the spatial variability of shortwave irradiance to snow. The spatial variability of shortwave irradiance during melt under uniform and discontinuous evergreen canopies at a U.S. Rocky Mountains site was measured, analyzed, and then compared to observations from mountain and boreal forests in Canada. All observations used arrays of pyranometers randomly spaced under evergreen canopies of varying structure and latitude. The spatial variability of irradiance for both overcast and clear conditions declined dramatically, as the sample averaging interval increased from minutes to 1 day. At daily averaging intervals, there was little influence of cloudiness on the variability of subcanopy irradiance; instead, it was dominated by stand structure. The spatial variability of irradiance on daily intervals was higher for the discontinuous canopies, but it did not scale reliably with canopy sky view. The spatial variation in irradiance resulted in a coefficient of variation of melt energy of 0.23 for the set of U.S. and Canadian stands. This variability in melt energy smoothed the snow-covered area depletion curve in a distributed melt simulation, thereby lengthening the duration of melt by 20%. This is consistent with observed natural snow cover depletion curves and shows that variations in melt energy and snow accumulation can influence snow-covered area depletion under forest canopies.
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.
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.
Abstract
The utility of a snow–vegetation energy balance model for estimating surface energy fluxes is evaluated with field measurements at two sites in a rangeland ecosystem in southwestern Idaho during the winter of 2007: one site dominated by aspen vegetation and the other by sagebrush. Model parameterizations are adopted from the two-source energy balance (TSEB) modeling scheme, which estimates fluxes from the vegetation and surface substrate separately using remotely sensed measurements of land surface temperature. Modifications include development of routines to account for surface snowmelt energy flux and snow masking of vegetation. Comparisons between modeled and measured surface energy fluxes of net radiation and turbulent heat showed reasonable agreement when considering measurement uncertainties in snow environments and the simplified algorithm used for the snow surface heat flux, particularly on a daily basis. There was generally better performance over the aspen field site, likely due to more reliable input data of snow depth/snow cover. The model was robust in capturing the evolution of surface energy fluxes during melt periods. The model behavior was also consistent with previous studies that indicate the occurrence of upward sensible heat fluxes during daytime owing to solar heating of vegetation limbs and branches, which often exceeds the downward sensible heat flux driving the snowmelt. However, model simulations over aspen trees showed that the upward sensible heat flux could be reversed for a lower canopy fraction owing to the dominance of downward sensible heat flux over snow. This indicates that reliable vegetation or snow cover fraction inputs to the model are needed for estimating fluxes over snow-covered landscapes.
Abstract
The utility of a snow–vegetation energy balance model for estimating surface energy fluxes is evaluated with field measurements at two sites in a rangeland ecosystem in southwestern Idaho during the winter of 2007: one site dominated by aspen vegetation and the other by sagebrush. Model parameterizations are adopted from the two-source energy balance (TSEB) modeling scheme, which estimates fluxes from the vegetation and surface substrate separately using remotely sensed measurements of land surface temperature. Modifications include development of routines to account for surface snowmelt energy flux and snow masking of vegetation. Comparisons between modeled and measured surface energy fluxes of net radiation and turbulent heat showed reasonable agreement when considering measurement uncertainties in snow environments and the simplified algorithm used for the snow surface heat flux, particularly on a daily basis. There was generally better performance over the aspen field site, likely due to more reliable input data of snow depth/snow cover. The model was robust in capturing the evolution of surface energy fluxes during melt periods. The model behavior was also consistent with previous studies that indicate the occurrence of upward sensible heat fluxes during daytime owing to solar heating of vegetation limbs and branches, which often exceeds the downward sensible heat flux driving the snowmelt. However, model simulations over aspen trees showed that the upward sensible heat flux could be reversed for a lower canopy fraction owing to the dominance of downward sensible heat flux over snow. This indicates that reliable vegetation or snow cover fraction inputs to the model are needed for estimating fluxes over snow-covered landscapes.
