Distributed simulation of snowcover mass- and energy-balance in the boreal forest

Transcription

1 HYDROLOGICAL PROCESSES Hydrol. Process. 13, 2439±2452 (1999) Distributed simulation of snowcover mass- and energy-balance in the boreal forest Timothy Link 1 and Danny Marks 2 * 1 Oregon State University, Environmental Sciences Graduate Program, at: USEPA-NHEERL, 200 SW 35th Street, Corvallis, OR USDA Agricultural Research Service, NW Watershed Research Center, 800 Park Blvd., Suite 105, Boise, ID Abstract: The accurate distributed simulation of snowpack deposition and ablation beneath forest canopies is complicated by the fact that vegetation canopies strongly a ect the snow surface energy balance. The canopy alters the radiation balance of the snowcover and reduces the wind speed at the snow surface. Simple canopy adjustment algorithms for solar and thermal radiation and wind speed are used in conjunction with commonly available land cover classi cations to spatially distribute sub-canopy solar and thermal radiation, air and soil temperature, humidity, wind speed, and precipitation. The distributed climate surfaces are used to drive a twolayer coupled energy- and mass-balance snowmelt model over two areas within the BOREAS study region for the 1994±1995 snow season. Model results are validated using both automatic and manually collected snow depth data. The simulated timing and rate of snowpack development and ablation at both study areas are well represented beneath the canopy types where validation data are present. Rigorous evaluation of model performance beneath the full range of canopy types requires information regarding the spatial distribution of snow covered area during the ablation period. This study demonstrates that given basic landcover parameters, relatively simple canopy adjustments coupled with an energy balance model can be used to estimate climate conditions and snowcover processes across a range of boreal forest covers. Copyright # 1999 John Wiley & Sons, Ltd. KEY WORDS snowmelt; forest canopy e ects; boreal forest INTRODUCTION Spatially distributed snowcover simulations can contribute to hydrologic investigations by providing estimates of the timing and rate of snowmelt delivery over heterogeneous regions. Spatial variation in the transfer of energy to the snowcover principally results from topographic and vegetation canopy e ects which alter the radiation climatology and rates of turbulent energy transfer to the snow surface. Accurate simulation of regional snowcover dynamics therefore requires the preparation of climate surfaces which are corrected for both topographic and vegetation e ects. Topography principally in uences the intensity of incoming solar radiation (0.3±2.8 mm) due to simple geometric e ects. The presence of a forest canopy overlying a seasonal snowcover alters the processes of mass and energy transfer and snowpack properties relative to open sites. Canopies strongly in uence the radiation balance of a snowcover by absorbing and re ecting incoming solar radiation, and by altering the emission of thermal radiation (2.8±100 mm) (Male and Granger, 1981; La eur and Adams, 1986). A forest canopy also * Correspondence to: Dr D. Marks, USDA Agricultural Research Service, NW Watershed Research Center, 800 Park Blvd, Suite 105, Boise, ID 83712, USA. danny@quercus.ars.pn.usbr.gov Contract grant sponsor: NASA. Contract grant number: NAG5-2301; ref BOREAS-U107. CCC 0885±6087/99/142439±14$1750 Received 30 April 1998 Copyright # 1999 John Wiley & Sons, Ltd. Revised 20 January 1999 Accepted 18 March 1999

2 2440 T. LINK AND D. MARKS shelters the snow surface from wind, greatly decreasing the e ciency of turbulent energy transfer (Berris and Harr, 1987; Ohta et al., 1993; Marks et al., 1998). This investigation presents two distributed simulations of the development and ablation of a seasonal snowcover in the boreal forest ecosystem of northern Saskatchewan and Manitoba. In this region, snow surface energy uxes are primarily controlled by canopy e ects, with minor controls exerted by topography. Thus, this area can be used to develop and validate general techniques which can be used to simulate snowpack dynamics beneath vegetation canopies. OBJECTIVES The primary objective of this investigation is to simulate the energy- and mass-balance of the seasonal snowcover over northern and southern regions in the boreal forest. The speci c objectives of this investigation are to: 1. Spatially distribute and adjust climate data for topography and canopy cover over both areas to estimate the climate at the snow surface throughout the course of a winter season. 2. Simulate the development and ablation of the seasonal snowcover at each study site using a distributed two-layer mass- and energy-balance snowmelt model (ISNOBAL) (Marks, Domingo and Frew, 1999; Marks et al., 1999, this issue). 3. Evaluate the use of simple canopy corrections over heterogeneous areas, and identify measures which may be taken to improve distributed results. BACKGROUND Snowcover energy-balance simulations were successfully completed beneath conifer canopies for the 1994 ablation period using physically-based radiation transfer and snowcover models (Hardy et al., 1997; Ni et al., 1997). Sensitivity studies on the e ects of conifer canopy height and density on snowcover ablation in the boreal environment demonstrated the strong control canopies exert on the timing and rates of snowmelt (Davis et al., 1997). Simpli ed algorithms were developed and applied to adjust climate data collected above boreal forest canopies for conditions beneath a range of deciduous, mixed and conifer canopies. The resulting data were used to simulate the development and ablation of the seasonal snowcover at point locations for the 1994±1995 winter season (Link and Marks, in press). The transition from point to distributed simulations requires the development of techniques to distribute climate parameters based on commonly available geographic information, such as elevation, species cover and canopy height. Spatially distributed snowmelt models can be run over a regular grid representing a spatially continuous parameter, typically a digital elevation model (DEM). Grid-based distributed snowmelt modelling using a physically based mass- and energy-balance approach was successfully completed in several mountainous basins, where variations in energy transfer resulted from topographic e ects (Garen and Marks, 1996; Marks et al., 1999, this issue). The simulations presented herein build on radiation studies in mountainous areas (Garen and Marks, 1996; Dubayah, 1994), detailed analyses of canopy climate e ects, and point snowcover simulations (Link, 1998; Link and Marks, in press; Hardy et al., 1997), to produce distributed snowcover simulations accounting for both canopy and topographic structure. APPROACH Data preparation and snowcover simulations were completed within the Image Processing Workbench (IPW) environment (Marks, Domingo and Frew, 1999; Frew, 1990). IPW is a suite of software tools which are designed to process geospatial data and support hydrologic and bio-physical simulations. The snowcover investigations were completed using the following procedure: 1. Reclassify existing landcover data into simple canopy cover and height classes. 2. Adjust and spatially distribute meteorological data collected as part of the BOREAS project (Sellers et al., 1995) based on the distribution of canopy cover types and heights. 3. Simulate the mass- and energy-balance of the seasonal snowcover from 1 October 1994 through complete ablation in the spring of 1995, on a one-hour timestep using ISNOBAL.

3 SNOW HYDROLOGY 49: SIMULATION OF SNOWCOVER MASS- AND ENERGY-BALANCE 2441 Site descriptions The BOREAS study region covers most of Saskatchewan and Manitoba, and contains southern and northern study areas (SSA and NSA), which are 130 km 90 km, and 100 km 80 km, respectively (Figure 1). A snowcover modelling sub-area was selected within each study area to encompass experimental watersheds and automated meteorological stations (AMSs), and to contain a complete range of vegetation species and heights. The snowcover modelling sub-areas are 15 km 18.5 km in the SSA, and 18 km 12 km in the NSA, and are indicated within the SSA and NSA maps on Figure 1. Each model grid cell represents a 100 m 100 m area. Topographic relief is subtle at both locations, with elevation ranges under 100 m for both snowcover modelling sub-areas. The landcover at both study areas is composed of a patchwork of lakes, open areas (fen and muskeg), and forests of predominantly aspen (Populus tremuloides), jack pine (Pinus banksiana) and black spruce (Picea mariana) with other minor species. Canopy heights tend to be higher in the SSA, with the maximum about 22 m, whereas the maximum canopy height is estimated to be 15 m in the NSA. Description of data products Two AMSs operated by the Saskatchewan Research Council are located within both the SSA and NSA (Shewchuk, 1997). In the SSA, AMSs are located within mature jack pine (OJP) and aspen (OA) stands. In the NSA, AMSs are located within mature jack pine (OJP), and mixed spruce/poplar (YTH) stands. The AMSs provide 15-minute average values for incoming global and di use solar radiation, thermal radiation, air temperature, relative humidity, and windspeed measured from 3 to 6 m above the top of the canopy. Below-canopy air (2 m), soil ( 0.5 m), and canopy temperatures, precipitation and snow depth are also recorded by the AMSs. In addition, manual snow depth measurements were taken on a bi-weekly basis beneath a range of canopy types. Spatial data products for the study areas consist of digital elevation models, land cover and canopy height images. Landcover and height class distributions in the SSA were derived by the Saskatchewan Environment and Resource Management Forestry Branch from infrared aerial photography. In the NSA, landcover and cutting class distributions were prepared by Manitoba Natural Resources. Landcover distributions were reclassi ed into ve major cover classes: open, deciduous (e.g. aspen), mixed conifer/deciduous, medium conifer (e.g. jack pine) and dense conifer (e.g. black spruce). The SSA height class image contained ve height classes: open, 2.5±7.5 m, 7.5±12.5 m, 12.5±17.5 m, and 17.5±22.5 m. In the NSA, the cutting class image and class descriptions were used to derive an image composed of six height classes: open, 0±2 m, 2±3 m, 3±5 m, 5±10 m, 10±13 m, 13±15 m. Data preparation Previous studies noted that net snowcover radiation is the dominant component of the snowcover energy balance in the boreal environment (Link and Marks, in press; Davis et al., 1997; Price and Petzold, 1984). Distribution and canopy correction techniques used in these investigations are most accurately speci ed for net solar radiation which is adjusted for both topographic and canopy e ects. Distribution methods for thermal radiation and parameters a ecting sensible, latent, and soil heat uxes are based on canopy class. Solar radiation is therefore treated as a spatially continuous variable, whereas other meteorological parameters are treated as categorical variables. Input climate surfaces for ISNOBAL were prepared by distributing meteorological data recorded at the AMSs in each of the study areas, and adjusting the values for canopy cover using simple algorithms developed and validated at point locations (Link and Marks, in press). The snowcover modelling sub-areas are relatively small (5300 km 2 ), therefore it is assumed that the above-canopy meteorological parameters recorded at the AMSs (SSA-OJP and NSA-OJP) are representative of conditions across the entire sub-area. In addition, we assume that climate conditions measured below-canopy at the SSA-OJP,

4 2442 T. LINK AND D. MARKS Figure 1. Locations of BOREAS study sites, snowcover modelling sub-areas, and ux tower sites

5 SNOW HYDROLOGY 49: SIMULATION OF SNOWCOVER MASS- AND ENERGY-BALANCE 2443 Figure 2. Schematic diagram describing the technique used to distribute net snowcover radiation over the modelling sub-areas SSA-OA, NSA-OJP, and NSA-YTH sites are representative of conditions beneath all grid cells of the corresponding canopy class. Solar radiation. The distribution of solar radiation over the snowcover modelling sub-areas is simulated according to the ow diagram depicted in Figure 2. The snowcover albedo for the visible (0.28±0.7 mm) and near infrared (0.7±2.8 mm) band varies as a function of sun angle, therefore the distribution of each radiation band is calculated separately and recombined to determine the net solar radiation. The theoretical clear sky topographically-corrected solar radiation is calculated using the model STOPORAD which uses exoatmospheric solar radiation, a two-stream atmospheric radiative transfer model, and DEM as described by Dozier

6 2444 T. LINK AND D. MARKS (1980), Dozier and Frew (1981), and Dubayah (1994). The theoretical radiation at each point in the gridded area is scaled by a cloud cover factor de ned as the ratio of measured to the theoretical global radiation. The radiation is split based on the measured proportions of direct and di use radiation to yield above-canopy values for the direct and di use components. The transfer of di use radiation through the forest canopy is calculated: S d;f ˆ t d S d;o 1 where: S d,f is the di use radiation at the snow surface (W m 2 ), S d,o is the di use radiation at the top of the canopy (W m 2 ), and t d is the optical transmissivity for di use radiation (dimensionless). The transfer of direct irradiance is speci ed using a formulation of the Beers-Bouger-Lambert Law: S b;f ˆ S b;o e mhsec y 2 where: S b,f is the beam irradiance at the snow surface (W m 2 ), S b,o is the beam irradiance at the top of the canopy (W m 2 ), m is the extinction coe cient (m 1 ), which is inversely proportional to the optical transmissivity of the canopy to beam irradiance, h is the canopy height (m), and y is the solar zenith angle (degrees). The values for m and t d for each canopy type were derived from concurrent measurements of above- and below-canopy global solar radiation. Individual radiometers within a 10-element sub-canopy radiometer array described by Hardy et al. (1997) were occasionally shaded by canopy elements over the course of a day, yielding sporadic measurements S d,f. When all radiation data from the sub-canopy array were plotted synchronously, an approximate di use radiation curve was produced by the inverted peaks on the individual curves. t d was determined graphically, by iteratively multiplying S d,o by estimated values for t d, until the resulting curve matched the S d,f curve to within 5%. S b,f for each time interval was computed by subtracting S d,f, calculated using equation (1), from the mean S g,f measured by the sub-canopy radiometer array. The total direct sub-canopy solar radiation at the snow surface during the sub-canopy measurement period was calculated by numerically integrating the S b,f over the measurement period. An iterative solution for m was obtained for each canopy type by estimating values for m and numerically integrating the calculated S b,f using equation (2), until the resulting net incident energy converged to within 1% of the measured value. The values applied for each canopy type are given in Table I. Clean snowcover surface albedoes were computed as a function of solar zenith angle and grain size by STOPORAD, which uses empirical relationships described by Warren and Wiscombe (1980), Wiscombe and Warren (1980), and Marshall and Warren (1987), re ned for general application by Marks (1988) and Marks and Dozier (1992). Additional surface albedo reductions were applied to account for the deposition of organic debris on the snow surface beneath forest canopies. The function linearly decreased the snowcover albedo from the point of maximum SWE through complete ablation. The function was de ned so that the snowcover Table I. Canopy transmissivity parameters Canopy Class t d, t L m Open 1 0 Deciduous Mixed conifer/deciduous Medium conifer Dense conifer All parameters were derived from concurrent above- and belowcanopy radiation measurements, except for the mixed conifer/ deciduous parameters which were estimated.

7 SNOW HYDROLOGY 49: SIMULATION OF SNOWCOVER MASS- AND ENERGY-BALANCE 2445 albedo reached 0.55 just prior to complete ablation, as observed in the boreal forest (Pomeroy and Dion, 1996). Adjusted albedo values were applied to the global sub-canopy radiation to yield net solar radiation. Thermal radiation. Assuming negligible thermal re ection from the canopy, thermal radiation at the snow surface (L f ) was estimated as: L f ˆ t L L o 1 t L e c st 4 c 3 where:t L is the transmissivity of the canopy to thermal radiation (dimensionless), L o is the thermal radiation measured above the forest canopy (W m 2 ), e c is the emissivity of the canopy (0.96 dimensionless), s is the Stefan-Boltzman constant ( Wm 2 K 1 ), and T c is the mean surface temperature of the canopy (K). L f radiation surfaces were created by applying the t L from Table I. In the SSA, T c measurements from the OJP site were applied to the mixed and conifer classes, and measurements from the OA site were applied to the deciduous classes. In the NSA, T c measurements from the OJP site were applied to the conifer classes, and measurements from the YTH site were applied to the mixed and deciduous classes. Other forcing data. Point snowcover investigations found that turbulent and soil energy uxes comprise a minor portion of the snowcover energy balance (Link and Marks, in press), therefore simple distribution and adjustment techniques were used. Air temperatures (T a ) measured within the forest canopy were spatially distributed as categorical variables, as described for the T c values in the L f computations. In addition, T a values measured above the forest canopy were applied to all open locations. Relative humidity values measured above-canopy were assumed to be homogeneous both throughout the canopy, and over the DEM grid. Vapor pressures (e a ) at the snow surface were calculated using the distributed T a surfaces in conjunction with the spatially constant humidity values. Wind speeds were adjusted only for the presence of a canopy. In open areas, above canopy windspeeds were applied, and in forested areas, above canopy values were decreased by a factor of ve to account for sheltering of the snow surface. The wind speed reduction in forested areas was based on above- and below-canopy measurements at the SSA-OJP site (Hardy et al., 1997). Soil temperature (T g ) measurements were assumed to be spatially constant over the study areas. Precipitation patterns may vary over the study regions due to localized phenomena or redistribution processes. Precipitation measurements were only available at the one AMS in each snowcover simulation area, therefore it was assumed that precipitation did not vary over the DEM grid. Although snowfall interception can be signi cant in the boreal environment (Pomeroy and Schmidt, 1993), interception losses were assumed to be negligible. This was because detailed interception data and process models for this environment were not available when this investigation was undertaken. Mass- and energy-balance snowmelt model A detailed discussion of mass and energy transfer over a snow surface was presented by Marks (1988), Marks and Dozier (1992), and Marks et al. (1992). Development of the two-layer mass and energy transfer snowmelt model ISNOBAL was discussed by Marks et al. (1998), and Marks et al., 1999 (this issue). A complete description of the model, its input requirements, and output parameters is provided in Marks, Domingo and Frew, A brief overview of the model structure is presented to provide a basic description of the snowcover simulation approach. Seasonal snowcover dynamics are controlled by temperature and vapor gradients within the snowcover, which are caused by energy exchanges at the snow surface and at the snow-soil interface (Colbeck et al., 1979; Male and Granger, 1981). ISNOBAL is driven by net snowcover solar radiation, incoming thermal radiation, air temperature, vapor pressure, wind speed, soil temperature, and precipitation mass, temperature, density, and state (solid/liquid) fraction. The model determines the snowcover depth, density and thermal properties from the meteorological conditions at each time step, throughout the duration of the simulation.

8 2446 T. LINK AND D. MARKS The model approximates the snowcover as being composed of two layers, a basal layer, and a xed-thickness surface layer. At each time-step, the model computes the energy balance of each layer, and adjusts the thickness, thermal properties, and measurement heights of the forcing data accordingly. The model calculates the energy balance of a snowcover as: DQ ˆ R n H L v E G M 4 where: DQ is the change in snowcover energy (W m 2 ), R n is the net radiative energy ux (W m 2 ), H is the sensible energy ux (W m 2 ), L v E is the latent energy ux (W m 2 ), G is the soil energy ux, and (W m 2 ), and M is the advected energy ux (W m 2 ). When the snowcover is in thermal equilibrium, there is no change in the net snowcover energy (i.e. DQ ˆ 0). A positive change in snowcover energy will warm the snowpack, whereas a negative change in energy will cool the snowcover. Signi cant amounts of melt cannot occur until the entire snowcover reaches 0 8C. When the entire snowpack is isothermal at 0 8C, a positive energy balance will result in melt, and a negative energy balance will result in refreezing of any water contained within the snowpack. The model simulates each component of the energy balance, calculates the addition or depletion of mass by deposition, melt, evaporation or runo, and adjusts snowcover mass and thermal conditions at each time-step. RESULTS Comparison with 30-year records indicate that 1994 and 1995 were warmer and dryer than the long-term means (Shewchuk, 1997). The seasonal snowcover in both the SSA and NSA began to develop in early November 1994, and lasted until late April to mid-may, 1995, depending on site cover and local topography. The snowcover at both sites is characterized by low densities and shallow depths reaching approximately 50 cm in the SSA and 60 cm in the NSA. Peak snow water equivalent was approximately 13 cm in the SSA and 12 cm in the NSA. Extensive measurements of snow properties were not available when this investigation was initiated, therefore within-stand spatial variation of snow properties were not considered. Recent investigations completed in the SSA indicate that snow depth variations in forested environments are related to the proximity to tree stems (Davis et al., 1998). Little variation was noted in the deciduous stands, except for late in the season. SWE under conifer crowns was found to be less than SWE measured along the snow courses, with pronounced tree wells early and late in the season. Density was found to be spatially conservative, therefore SWE variations arose mainly from variations in snow depth. Measured and modelled depths are therefore most likely to be overestimated within the conifer canopies. Model validation Measured and modelled snowcover depths for several landcover classes within the SSA and NSA are presented in Figures 3 and 4. Modeled depths represent the mean of all grid cells in the cover type and height class corresponding to the locations where veri cation depth measurements were recorded. As noted above, open site conditions were simulated using meterological data collected above the OJP canopy. Manual measurements are represented by points on the Figures, and automatic depth measurements are represented by the continuous line plots. Model e ciencies were found to range from 0.82 to 0.94, with root mean square di erences ranging from 3.5 to6.0 cm of snow depth, at point locations. A detailed discussion of model tting statistics and model sensitivity is provided in Link and Marks (in press). Southern study area. In the SSA, continuous depth measurements were made only at the OJP site, within the snowcover modelling sub-area. Manual depth measurements in open, medium conifer, and dense conifer covers were made at locations approximately 15 km south of the modelling area. Manual depth measurements for the deciduous cover type were made approximately 80 km to the west of the modelling area, and

9 SNOW HYDROLOGY 49: SIMULATION OF SNOWCOVER MASS- AND ENERGY-BALANCE 2447 Figure 3. Measured and simulated snow depths at the SSA modelling sub-area are included to validate the general depth trends. The fact that many of the validation measurements were made outside of the study area serves to validate the performance of the data preprocessing procedures as a general tool for simulating snowcover processes beneath large forested areas. Mean simulated snow depths accurately reproduced measured depths within most land cover classes. The simulated meltout date for the medium conifer class was delayed by approximately ve days relative to the automatic measurement; however, the meltout date appears to match closely the manually measured location. The measured depths for the deciduous cover are similar to the modelled depths; however, the measured depths are consistently higher. This result appears to be due to a larger quantity of snow water equivalent deposited within this canopy cover. Northern study area. In the NSA, continuous depth measurements were available only at the OJP site. Manual depth measurements within open, mixed and dense conifer covers were all made within the snowcover modelling sub-area. Modelled snowcover depths were similar to measured depths within the modelling area. Depth measurements are consistently higher than simulated values during portions of the season, particularly in the

10 2448 T. LINK AND D. MARKS Figure 4. Measured and simulated snow depths at the NSA modelling sub-area open and dense conifer covers. These di erences may be due to variations in precipitation deposition and redistribution patterns, or undercatch by the precipitation gauge at the OJP site. The simulated meltout date at the medium conifer site closely matches the date observed with the automated depth sensor. Distributed simulations Results from distributed simulations are presented in Plate 1 and 2. The images cover the active ablation period from 21 April to 7 May at the SSA and from 21 April to 13 May at the NSA, to illustrate the relative timing of snowcover depletion both between study areas, and between land cover classes. Simulated snow depths are depicted by varying shades of gray, with darker values indicating deeper snow. When the snow is completely ablated from an individual pixel, the color corresponding to the land cover class is displayed. Southern study area. The meltout date for the di erent cover classes in the SSA varies by approximately two weeks, as shown on Plate 1. Most of the open areas exhibit complete ablation by 21 April, followed by the mixed and deciduous covers roughly two days later. Some areas of medium conifer also exhibit complete ablation at this time. Areas of medium conifer cover which ablate at this early date correspond to relatively

11 Copyright # 1999 John Wiley & Sons, Ltd. HYDROLOGICAL PROCESSES, VOL. 13, (1999) Plate 1. Distributed snowcover simulation results at the SSA modelling sub-area, Snow depths are indicated by the grayscale. When no snow is present, the canopy species class is indicated by the colour scale

12 Copyright # 1999 John Wiley & Sons, Ltd. HYDROLOGICAL PROCESSES, VOL. 13, (1999) Plate 2. Distributed snowcover simulation results at the NSA modelling sub-area, Snow depths are indicated by the grayscale. When no snow is present, the canopy species class is indicated by the colour scale

13 Plate 3. Mean snowcover energy balance for all grid cells in the given canopy classes (12.5 to17.5 m height class) at the SSA. The individual energy balance components are indicated by the coloured bars and the net snowcover energy balance is shown by the crosshatched bars Copyright # 1999 John Wiley & Sons, Ltd. HYDROLOGICAL PROCESSES, VOL. 13, (1999)

14 SNOW HYDROLOGY 49: SIMULATION OF SNOWCOVER MASS- AND ENERGY-BALANCE 2449 short (2.5±7.5 m) canopies, which permit greater transmittance of direct radiation, as indicated by equation (2). A small depositional event occurs between 25 April and 27 April, but melts within 24 hours. The majority of the grid cells in the medium conifer class melt out by 5 May, followed by cells in the dense conifer class on 7 May. Some small snow covered areas are still evident on 7 May, corresponding to dense conifer stands in the tallest (17.5±22.5 m) height class. Northern study area. A smaller proportion of the snowcover in the northern modelling sub-area is completely ablated by 21 April, as a result of cooler temperatures and less radiation loading relative to the southern area. The rst canopy classes to exhibit complete meltout in this area are associated with the relatively short height classes (2±4 m), occurring on south facing slopes. These classes melt out before the open areas, due to relatively low degrees of shading from the short canopies coupled with enhanced thermal emission from the overlying canopy, and slightly higher incident solar radiation resulting from topographic e ects. Depletion of snow covered area proceeds at a slower rate relative to the SSA, with the majority of the open areas ablating by 23 April, followed by the remainder of the deciduous and mixed classes by 3 May, medium conifer by 5 May, and dense conifer from 7 May to 13 May. The snow covered portions of the 13 May image correspond to dense conifer classes located on north-facing slopes. These results demonstrate that the canopy exerts the primary control on the snow surface energy balance in this region with minor e ects resulting from subtle topographic variations. Energy balance. Plate 3 shows the mean daily snowcover energy balance components for all SSA grid cells within each landcover class for three days during the active ablation period to illustrate the contributions of the various components during snowmelt. For the forested classes, means for grid cells in the 12.5±17.5 m height class are shown in order to eliminate energy transfer variations arising from di erences in canopy height. The net snowcover energy uxes are also shown to illustrate di erences between the canopy classes. Variable e ects of forest canopies on the net radiation and turbulent heat uxes are clearly indicated by this gure. 14 April is a relatively clear day where the dominant component of the snowcover energy balance is net radiation. The open areas exhibit higher net turbulent (sensible latent) heat uxes, resulting from warmer air temperatures and higher wind speeds in the open areas. 16 April is a cloudy day with some precipitation. The snowcover exhibits much smaller net energy uxes associated with reduced solar radiation. Although net radiation dominates the energy balance within some cover classes, turbulent uxes comprise a greater proportion of the energy balance with a minor contribution from the advected heat ux associated with rainfall on the snowpack. The net energy ux in the open is much larger than the forested locations due to higher wind speeds and net radiation. 21 April represents a shift back to warm, sunny conditions, where the energy balance is again dominated by radiative uxes. Net turbulent uxes are close to zero, due to negative latent uxes which tend to balance positive sensible heat uxes. On the days shown in Plate 3, the snowcover in open areas is cooler than the snowcover in the forested areas due to a larger degree of radiative cooling. This phenomenon causes larger soil heat uxes in open areas, even though soil temperatures are assumed to be constant over the simulation domain. On clear days, the net energy uxes can be seen to decrease with increasing canopy optical density as a result of variable canopy shading. On cloudy days, net ux di erences are greatest between forested and open areas due to reduced windspeeds beneath forest canopies, which greatly decrease the amount of turbulent transfer relative to open areas. DISCUSSION Results of measured and simulated snow depths indicate that the canopy adjustments and snowmelt model accurately describe the development and ablation of the seasonal snowcover beneath a range of forest canopies. The distributed climate data adjustments explicitly account for spatial variations in canopy structure and subtle topographic variations a ecting snowcover dynamics in the modelling sub-areas.

15 2450 T. LINK AND D. MARKS Results from the distributed simulations indicate that canopy structure can delay seasonal snowmelt in densely forested areas for up to three weeks relative to more open areas. The individual energy balance components suggest that net radiation is frequently the dominant source of energy contributing to snowmelt, illustrating the importance of accurate canopy shading adjustments. The snow depth and radiation data used to derive the adjustment algorithms were both collected within closed-canopy mature forests. The canopy adjustments also assumed xed optical transmissivity values for all cover types, regardless of canopy density. We therefore expect the simulated snowcover results to be the most accurate beneath mature, closed canopy forests. In the boreal forest, other researchers found canopy density to be as important as canopy height in controlling transmissions of solar radiation (Davis et al., 1997). However, in the SSA and NSA less than 12% and 2% of the area in the snowcover modelling sub-areas is covered by sparse (i.e. 555% closure) canopies. Errors in the calculated solar radiation beneath short and discontinuous canopies may be partially o set by opposing errors in the thermal radiation, causing the e ect on the net snowcover radiation computation to be reduced in these classes. Snowcover simulations beneath forest canopies may be further improved by additional studies focusing on the distribution of solar and thermal radiation beneath short and discontinuous canopies. In the absence of data beneath a full spectrum of canopy covers, the application of scaleable canopy shading models in which species, height, and tree density are explicitly speci ed (e.g. Ni et al., 1997; Price, 1997) will produce more accurate radiation surfaces for distributed snowcover simulations. Studies have noted that the late winter snowcover beneath coniferous (spruce and pine) canopies are 30±40% less than in deciduous canopies and open areas (Pomeroy and Schmidt, 1993). Simple adjustments for interception losses, based on improved measurement techniques to estimate sub-canopy snowcover deposition (Davis et al., 1998) may improve simulated results. Integration of snow interception models (e.g. Hedstrom and Pomeroy, 1998) with snowcover mass- and energy-balance models can improve distributed snowcover modelling by providing a physically-based representation of depositional processes. Model validation in heterogeneous environments will bene t from the production of spatial data indicating snow covered area over time and measurements of snow depths beneath a wide range cover classes. CONCLUSIONS The results of the spatially distributed energy- and mass-balance snowcover simulations indicate that simple canopy correction algorithms based on commonly available landcover data can be used to simulate subcanopy snowcover dynamics e ectively. The climate data preprocessing accounts for both forest canopy and topographic e ects, indicating that the techniques used in these investigations will also be applicable in mountainous basins. Canopy corrections for radiation at the snow surface can be applied with a good degree of con dence where the optical transmissivity of canopy classes are well characterized. Results of spatially distributed snowmelt simulations can be used to estimate the timing and rate of moisture delivery to the soil during seasonal ablation, to parameterize hydrologic models, and to assess the potential e ects of land cover changes on regional hydrologic systems. ACKNOWLEDGEMENTS Funding for this research was provided by NASA, (grant no. NAG5-2301; reference no BOREAS- U107). O ce and computing facilities were provided by the US Environmental Protection Agency, Western Ecology Division. Meteorological data were provided by the diligent work of many people within the Saskatchewan Research Council. Special thanks go to the observers for their assistance with this project. Sub-canopy radiation, wind, and temperature measurements were collected by Janet P. Hardy and Robert E. Davis, of the Cold Regions Research and Engineering Laboratory (CRREL). The snow course data in the SSA were collected by the study area manager Mary Dalman, assisted by Vivian Heap. The NSA snow

16 SNOW HYDROLOGY 49: SIMULATION OF SNOWCOVER MASS- AND ENERGY-BALANCE 2451 courses were carried out by AES Weather Specialist Bill Palmer, assisted by Martha Evaluardjuk. Site location maps are provided courtesy of the BOREAS Information System ( Mention of trade names or commercial products does not constitute endorsement or recommendation for use. REFERENCES Berris SN, Harr RD Comparative snow accumulation and melt during rainfall in forested and clear-cut plots in the western Cascades of Oregon. Water Resources Research 23: 135±142. Colbeck SC, Anderson EA, Bissel VC, Crook AG, Male DH, Slaughter CW, Wiesnet DR Snow accumulation, distribution, melt, and runo. EOS, Transactions, American Geophysical Union 60: 465±474. Davis RE, Hardy JP, Ni W, Woodcock C, McKenzie JC, Jordan R, Li X Variation of snow cover ablation in the boreal forest: A sensitivity study on the e ects of conifer canopy. 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