Snow measurement techniques for land-surface-atmosphere exchange studies in boreal landscapes

Theor. Appl. Climatol. 70, 215±230 (2001) 1 Division of Water Resources Research, LuleaÊ University of Technology, LuleaÊ, Sweden 2 Institute of Earth Sciences/Hydrology, Uppsala University, Uppsala, Sweden Snow measurement techniques for land-surface-atmosphere exchange studies in boreal landscapes A. Lundberg 1 and S. Halldin 2 Received August 27, 1999 Summary Snow has been studied widely in hydrology for many decades whereas recent meteorological interest in snow is caused by increased emphasis on high latitudes and wintertime in climate-change research as well as by the need to improve weather-forecast models during these conditions. Ground-based measurements of snow properties are needed both to improve understanding of surface-atmosphere exchange processes and to provide ground truth to new remotesensing algorithms. This justi es a review of techniques to measure snow in combination with establishment of criteria for the suitability of the methods for process studies. This review assesses the state-of-art in ground-based snowmeasurement techniques in the end of the 1990s in view of their accuracy, time resolution, possibility to automate, practicality and suitability in different terrain. Methods for snow-pack water equivalent, depth, density, growth, quality, liquid-water content and water leaving the snow pack are reviewed. Synoptic snow measurements in Fennoscandian countries are widely varying and there is no single standard on which process-related studies can build. A long-term, continuous monitoring of mass and energy properties of a snow cover requires a combination of point-measurement techniques. Areally representative values of snow properties can be achieved through a combination of automatically collected point data with repeated manual, areally covering measurements, remote-sensing data and digital elevation models, preferably in a GIS framework. 1. Introduction Snow hydrology has been an established research area for many decades. Snow acts as a seasonal storage of surface water and thereby in uences the timing and size of the spring ood, which is of importance for many parts of the society, not the least the waterpower industry. Snow-related studies have not been prominent in meteorological research until recently. Possible increased global warming is primarily expected at high latitudes and during winter. Models used for simulation of present-day climate and weather-prediction models are still treating both the permanent and the seasonally varying snow cover in a poor way (e.g., Bonan et al., 1992; Thomas and Rowntree, 1992; Foster et al., 1996; Yang et al., 1998). Characterisation of the snow cover has, thus, become an interest for many scienti c elds outside the traditional sphere of snow hydrologists. In order to limit the uncertainty of present predictions of global climate change, international organisations like the International Geosphere- Biosphere Programme (IGBP) and the World Climate Research Programme (WCRP) are promoting regional- and mesoscale experiments in the boreal zone and the tundra. Such experiments must be carried out during dif cult weather conditions and often with poor logistics. It is thus important to prepare such experiments carefully. One key question is whether present snow-measurement methods are appropriate and suf cient for the new experiments and applications. NOPEX (a NOrthern hemisphere climate Processes landsurface EXperiment, as one of the IGBP-promoted mesoscale land-surface experiments, has the objective to study exchange processes during the whole annual cycle, including the winter (Halldin

216 A. Lundberg and S. Halldin et al., 1999). One preparatory study for a coming NOPEX wintertime experiment has been an investigation of existing methods for measuring snow-pack properties, both in permanently snowcovered situations and in situations where snow melt and accumulation occur haphazardly during the dark half of the year. Previous land-surface exchange-process studies have been concentrated to the light and warm part of the year. The prospect of wintertime experiments has forced the development of methods for long-term, continuous measurements of uxes and states of water and energy at the land-surfaceatmosphere interface. The focus on long-term data collection is motivated, e.g., by the need to cover seldom-occurring phenomena, which may have a large importance for both water and energy balances. Only techniques, which require minor maintenance and which can be automated, are likely to produce reliable data during winter. Remote-sensing techniques are among the most advocated to get surface information at high latitudes and during winter. In order to be trusted they must be correlated with reliable ground-truth data. The aim of this study was to review existing ground-based methods to measure snow-cover properties and to evaluate the usefulness of these methods for land-vegetation-atmosphere exchangeprocess studies in the boreal landscape. Remotesensing methods are discussed in this review only in relation to ground-truth measurements. The reviewed methods have been combined with suitability criteria in order to screen the best available methods and to suggest further development of these to meet the new demands. The treatise on ``Snow Hydrology'', made by U.S. Army Corps of Engineers (1956) was long a major reference for snow-related studies, both with regard to measurements and analysis methods. The ``Handbook of snow'', edited by Gray and Male (1981), later served the same purpose. The operational monitoring of snow by various national services have been inspired by the continuously updated WMO ``Guide to hydrological practices'' (WMO, 1994). Thorough reviews of methods to measure snowpack properties (both ground-based and remote) are presented by Goodison et al. (1981), Trabant and Clagett (1990), Martinec and Sevruk (1992), Pomeroy and Gray (1995) and Killingtveit and Saelthun (1995). The snow-cover extent, depth and snow-water equivalent (SWE) are the main properties of the snow cover but other characteristics such as free liquid-water content, radiation re ectance, radiation transmissivity, temperature, thermal properties, impurities, chemistry and snow accumulation are also important. Snow impurities and chemistry are summarised by Viklander (1994) and Bales and Harrington (1995) and are not dealt with in this study. Thermal, mechanical, frictional and other physical snow properties, less central for the study of exchange processes in the soil-snowvegetation-atmosphere system, are treated by, e.g., Male (1981), Langham (1981), Mellor (1975; 1977), and Sturm et al. (1997). Snow stored in forest canopies also in uences exchange processes but is not considered here. Lundberg (1993) and Lundberg and Halldin (2001) review such measurement techniques. Radiation conditions in forests during winter is treated by, e.g., Pomeroy and Dion (1996), Price and Petzold (1984), Betts and Ball (1997), Davis et al. (1997), Loechel et al. (1997), and Ni et al. (1997) 2. Point- and line-measurement methods 2.1 Snow depth Snow depth is important for heat exchange between ground and snow because of the high thermal insulation capacity of the snowpack. Manual determination of snow depths are made either by inserting a probe or by observing snow height on xed markers. Care should be taken when using xed markers to avoid errors because of local melt or snow drift around the marker. Sighting along the snow surface a few metres from the marker can make this error small. If snow depth is measured at xed points, care should be taken not to disturb the snow close to the markers. Automatic recording of snow depth can be made with ultrasonic techniques. Several snowdepth sensors with low power consumption and low maintenance requirements are now marketed. The distance from a sensor to the snow surface is determined from the time required by an emitted signal to be re ected and received by the sensor. Complementary air-temperature measurements are required to compensate for temperature-dependent variations in the speed of sound in air. Anomalous

Snow measurements techniques for land-surface-atmosphere exchange studies in boreal landscapes 217 results may occur during falling or blowing snow conditions. The sensors can not distinguish lowdensity new-fallen snow from air. Sauter and McDonnell (1994) report inaccurate snow-depth measurements during 16% of their measurement time. A resolution of 1 mm can otherwise be achieved and the sensor gives a spatial average with a radius of 0.2±2 m depending on the mounting height of the sensor (Goodison et al., 1988). Thermistors and light-diodes placed on vertical rods have been tested in Norway (Andersen et al., 1982) to measure snow depth but results are not yet published. Andersen (1995) suggests that pro- les of temperature, measured in small increments, can be used to determine snow depth by analysing the daily temperature amplitude (small within snowpack, large above it). A method to measure snow depth along xed pro les was presented by Kennett et al. (1996). The method is using a high precision barometer attached to a PC to nd the elevation along the pro les by measuring changes in barometric pressure. Pro les are de ned by xed stakes. The elevation along pro les and distance between stakes are surveyed when the ground is snow-free. The barometric pressure is measured on the snowpack along the line. The barometer is located on a sledge equipped with an odometer and pulled by a snowmobile at a constant speed of 10±20 km/h, using a trigger to mark the stakes in the data le. The snow depth at stakes and distance between stakes are used to calibrate the barometer and odometer readings. The snow depth along the pro le is calculated as the difference between the surface (from the barometer) and the bare ground (surveyed). The method has been tested both on snow-covered pro les and on bare ground. The nal testing was performed on bare ground along a roughly 1 km long pro le. Tests were performed during both calm and windy conditions. The method worked well during calm conditions when the average depth value was reported to 04cm with a random square error (RSE) of 11 cm. The method did not perform so well during strong wind since a test during such conditions (on bare ground) gave an average snow depth of 3023 cm with RSE ˆ 48 cm. Compaction of the snow pack from the weight of the snowmobile and the sledge may be a problem when applied on low-density snow. 2.2 Snow accumulation Snow accumulation can be measured with precipitation gauges and/or snow trays. The WMO Guide to hydrological practices (WMO, 1994) recommends criteria for an ideal location: a) Sites having protection from strong wind movement. b) In forested areas, at sites located in open spaces suf ciently large so that snow can fall to the ground without being intercepted by the trees. 2.2.1 Precipitation gauges Precipitation gauges can theoretically be used to record snow accumulation (water equivalent) and snowfall depth but a variety of problems make their use highly unreliable. The great under-catch at high wind speeds because of wind- eld distortion around the precipitation gauge (e.g., Sevruk et al., 1989) is a major problem with solid precipitation for most types of precipitation gauges. Different kinds of shields have been suggested (e.g., Tabler et al., 1990; Huovila et al., 1988) to minimise this error. Empirical functions relating the true catch to the gauge catch and the wind speed are used (e.g., Tabler et al., 1990; Fùrland and Aune, 1985). Martinec and Sevruk (1992) summarise several snow-precipitation-correction methods for some shielded gauges and one unshielded gauge. Allerup et al. (1997) and Vejen (1996) present combinations of wind-speed and air-temperature corrections. Fùrland (1996) presents a manual for operational correction of precipitation data. Drifting snow may cause large errors. Such errors can be reduced by mounting the gauges three to six metres above the surface (WMO, 1994). Snowfall depth can be measured in a xed container of uniform cross section after the snow has been levelled without compressing (WMO, 1994). The receiver should be at least 20 cm in diameter and should either be suf ciently deep to protect the catch from being blown out or else be tted with a snow cross (i.e., two vertical partitions at right angles, subdividing it into quadrants). The automatic recording of output from precipitation gauges adds special problems during wintertime conditions. There are essentially four

218 A. Lundberg and S. Halldin types of sensors for measuring solid precipitation at the ground: heated tipping bucket, heated siphon, weighing, and optical ones. Heated tipping-bucket (Hansson et al., 1983) and heated siphon gauges (Sevruk, 1983) record too little solid precipitation. Weighing gauges give roughly the same monthly totals as manual gauges but the daily totals may vary considerably when the precipitation is sticky (Goodison and Metcalfe, 1988; Bakkehùi et al., 1985). Optical gauges are claimed to have a capacity to distinguish both precipitation type and intensity. Van der Meulen (1992) and Gaumet and Salomon (1992) tested the ability of optical gauges to distinguish type of precipitation, but the capacity to measure intensity is not well documented. A test by Stepek et al. (1992) of an optical gauge with snow precipitation gave inconsistent results. Lundberg and Johansson (1994) tested an optical prototype gauge and showed that it was dif cult to discriminate between rain and snow meteors at high wind speeds and that intensity could not be measured accurately. A prototype gauge (now marketed), based on aerodynamic principles to minimise wind losses, gave good agreement with a reference gauge (Wiesinger et al., 1993). 2.2.2 Snow trays The depth of fresh snow can be measured with a graduated ruler or scale. A mean of several vertical measurements should be made in places where drifting snow is no problem (WMO, 1994). Precautions should be taken so old snow is not measured. This can be made by sweeping a suitable patch clear beforehand or by covering the top of the snow surface with a piece of suitable material ± such as wood, with a slightly rough surface, painted white (this tray is also called board and sampler). The trays are cleared either at regular intervals or after each snowfall. The SWE of fresh snow can be determined by collecting the snow from a tray and melting or weighing it. 2.3 Snow wetness 2.3.1 Melt water leaving the snow pack The amount of melt water leaving the snowpack is a key variable in a variety of water- and energybalance models. Snow-melt lysimeters (e.g., Martinec, 1989) record liquid-water out ow from a snow pack. A snow-melt lysimeter consists of a tray where the melt water is collected and the rate of the runoff is monitored (e.g., with tipping buckets). Like snow pillows, lysimeters give point values and block heat and mass exchange between snow pack and ground. If applied to shallow snow packs there is risk for runoff delay caused by ice clogging the outlet after nocturnal freezing. 2.3.2 Snow quality Present-day remote-sensing methods are capable to determine the quality of a snowpack and research is carried out to also determine the free liquid-water content. The thermal quality of snow is de ned as the quotient of the heat required to produce a given volume of water from snow to the heat needed to melt the same volume of water from pure ice at 0 C. The term is used here to differentiate between wet and dry snow. Accurate measurement of snow temperature (radiation shielded) can be used as ground truth of snow quality since snow temperatures below zero are associated with dry snow. 2.3.3 Free liquid-water content The free liquid-water content is also called unfrozen water content and snow wetness. Ground truth for liquid-water content can be achieved from manual calorimetric methods (freezing or melting) where the phase-change energy is used as a measure of the unfrozen water content (e.g., Radok et al., 1961). Davis et al. (1985) describe a more accurate and less time-consuming method where the dilution of a solution, when mixed with a known mass of wet snow, is taken as a measure of the liquid-water content. The free liquid-water content is dif cult to record automatically and is nowhere measured operationally. Most recording methods are based on measurements of the dielectric constant (permittivity) and may be used to determine both density and liquid-water content. A real part and an imaginary part make up the dielectric constant. The real part is 3 foriceand88 for liquid water for the frequencies used. Sihvola and Tiuri (1986), and Kendra et al. (1994) describe manual electromagnetic sensors based on measurement of both the real and the imaginary parts. Denoth

Snow measurements techniques for land-surface-atmosphere exchange studies in boreal landscapes 219 (1994) presents a at capacitive device working with radio-frequencies (20 MHz) which can record snow density and liquid-water content at four different levels. Denoth (1997) tests a monopole antenna to record snow and soil wetness. The large sizes (19.5 cm 12.5 cm 1.7 cm for the capacitive device and an aluminium plate with radius 12 cm for the monopole antenna) are disadvantages with Denoth's (1994, 1997) devices. The devices will absorb solar radiation and are only suited for short-term measurements since they are sensitive to air-gap formation around the probes. The time-domain-re ectometry (TDR) method, which has gained widespread use for soilmoisture measurements, has also been used for snow wetness and density determinations (e.g., Kane and Stein, 1983; Schneebeli and Davis, 1992; Lundberg, 1996; Schneebeli et al., 1997). The probe designed by Schneebeli et al. (1997) has the advantage of a low mass compared to the devices used by Denoth (1994, 1997). A disadvantage with the TDR method is the complicated signal requiring interpretation before the dielectric constant can be determined. The free liquid-water contents estimated with the TDR probe are reasonable but additional measurements are required to evaluate the performance of the probe during continuous measurements (Schneebeli et al., 1997). 2.4 Snow density The insulation capacity of snow depends on the density. Most density measurements are manual. Volume and weight of a snow sample are measured with different types of snow tubes (for vertical density averages). Care should be taken to use tubes with the cutting end sharpened (to minimise over- or undersampling) and treated with wax, silicon or Te on (to avoid sticking of snow in the sampler). The errors associated with snow tubes are discussed by Martinec and Sevruk (1992). Vertical resolution of density variations requires digging of snow pits and samples extracted at different depths in the snowpack. Automatic recording of snow density has been made with an automatic pro ling snow gauge, which gives non-destructive snow depth-density pro les according to Smith et al. (1970). The gauge records gamma-ray transmission between two xed tubes through which the transmitter and the detector move in parallel. Some such gauges have been operated remotely (e.g., Wheeler and Huffman, 1984). The units are costly and the handling of a radioactive source is a disadvantage. Schneebeli et al. (1997) showed that their lightweight TDR probes in combination with a multiplexer could be used to monitor dry snow density at several locations simultaneously with high accuracy (5 kg/m 3 ) over an extended period. 2.5 Snow water equivalent 2.5.1 Snow courses Hydropower companies who need to know the basin-maximum SWE traditionally use the snowcourse technique with manual measurement of depth and density. Snow courses (or surveys) are discussed by, e.g., Andersen et al. (1982), Kuusisto (1984), WMO (1992, 1994), and Killingtveit and Saelthun (1995). Snow courses are considered to give the most precise information about SWE for a whole catchment, but the high costs and the dif culties with access because of bad weather can lead to loss of data (Killingtveit and Saelthun, 1995). The WMO guide to hydrological practices (WMO, 1994) gives criteria for ideal locations of snow-course measurements in mountainous areas. In addition to the requirements already given for precipitation gauges, snow courses should be undertaken at sites suf ciently accessible to ensure continuity of surveys and, if total seasonal accumulation should be measured, at elevations and exposures where there is little or no melting prior to the peak accumulation. The ground surface should be cleared of rocks, stumps and bushes for two metres in all directions from each sampling point. In order to avoid any systematic error because of snow drift it may be necessary to perform an extensive survey, with long traverses and a large number of sampling points, prior to nally determining location, length and sampling distance for a snow course. Once the prevailing length and direction of the snowdrifts have been ascertained it should be possible to reduce the number of sampling points (WMO, 1994). Gottschalk and Jutman (1979) gives snow survey recommendations and error estimates for Swedish conditions. They suggest that the area should be divided into homogenous subareas

220 A. Lundberg and S. Halldin considering differences in physiographic factors. Snow survey should be performed in each subarea and a practical number of observations is 20. The distance between each sampling point is estimated to 50±100 m and density should be measured approximately every 100 m. Analysis of variances is then applied on the survey data to decide the most ef cient subdivision of the total area. 2.5.2 Ground-based radar Radar waves can penetrate soil, snow and ice and are re ected at interfaces between different materials. Ground-based radar systems have been used for measurements of ice thickness on rivers and lakes, glacier thickness and strati cation, depth to groundwater level, permafrost thickness. Radar measurements of seasonal snow-cover SWE are presented by Ulriksen (1982, 1985), Killingtveit and Sand (1988), Bruland and Sand (1996), Sand and Bruland (1999), and, of Antarctic snow cover, by Richardson et al. (1997). SWE (from snow courses) is determined empirically with linear regression from the two-way travel time of a radarwave propagation. Early studies (e.g., Ulriksen, 1982) neglected the in uence of snow density on radar-derived SWE. Bruland and Sand (1996) show that slope of the regression varies as a function of density. For density values in the range 250±500 kg m 3 (at the end of the accumulation period) this relationship should be accounted for. This was con rmed by Lundberg et al. (1999) who also pointed out the importance of restricting measurements to dry snow. If applied to wet snow, the regression slope should be adjusted for the free liquid-water content. 2.5.3 Heated plastic-sheet gauges Calder (1990) and Lundberg et al. (1998) used heated plastic-sheet throughfall gauges to measure snow throughfall and drip in a forest. The plastic sheets covered areas of 40 m 2 and were heated in order to melt the throughfall and record it as runoff. The heating requirement may be large and Lundberg et al. (1998) report that insuf cient heating to instantaneously melt the snow caused a delay in the recorded throughfall. 2.5.4 Snow pillows A snow pillow can continuously monitor the SWE ofasnowpack.thesnowpillowisapressure sensor consisting of a circular pillow (usually 2± 4 m wide) made by reinforced rubber or stainless steel and lled with an antifreeze liquid. Trabant and Clagett (1990) state that snow pillows are not suited for use in snow packs which bridge over the pillow, i.e., which contain ice lenses (commonly found in coastal mountain ranges) or where bridging from wind effects occur. The pressure change can be sensed either by recording the uid level in a standpipe or by a pressure transducer. If a standpipe is used and the antifreeze liquid has a density differing from 1000 kg m 3 this must be corrected for. The pressure can be remotely recorded when pressure transducers are used. Another, less important, disadvantage is that the pillow modi es the temperature gradient of the snowpack by blocking the temperature-driven vapour ux at the interface between soil and snow (Trabant and Clagett, 1990). Large mammals may disturb or destroy snow pillows. 2.5.5 Weighing lysimeters A large weighing lysimeter (25 m 2 ) is used by Storck and Lettenmaier (1999) to compare ground snow-pack accumulation in a forest with accumulation in a shelterwood. The large surface area of the beneath-canopy lysimeters, installed around a r tree, is said to remove the variability of canopy throughfall. 2.5.6 Other techniques Changes of snow pack SWE has been monitored with a neutron probe by Harding (1986), who observed increased melt around the access tubes for the probe. Isotopic snow gauges have been tested in France, Japan, Russia and USA (Martinec and Sevruk, 1992). Attenuation of the emission of a radioactive source is related to the SWE of a snowpack in these gauges. The units are costly and the handling of a radioactive source is a disadvantage. Determination of SWE from the attenuation in a snow pack of cosmic-ray-produced neutrons have been performed by Kodama and Nakai (1979), who discuss uncertainties caused by, e.g., soil-moisture content. 3. Area averages The majority of hydrological and meteorological models and applications need area-average values

Snow measurements techniques for land-surface-atmosphere exchange studies in boreal landscapes 221 of snow properties, not point or line values. A key problem in snow research is, thus, to relate point and line measurements to representative area averages. The methods to do this varies with surface type ± forests, agricultural land, and mountains. Differences in snow deposition and redistribution because of wind are main problems for mountains and open elds. Deposition differences in mountains are caused by orography (distance to sea, height above sea level, inclination and prevailing wind direction), by aerodynamics (lee-/wind-ward side, surface type [ eld/forest], concavity/convexity). The snow cover is also in uenced by radiation exposure (slope, aspect, eld/forest) at the end of the winter. The most important factors affecting snow accumulation are a) altitude, b) type of vegetation (forest/ eld), and c) slope (steepness and aspect) according to Martinec and Sevruk (1992). Martinec and Sevruk (1992) describe six different methods used in practice to estimate areal SWE from point measurements. The more advanced methods include modelling of snowmelt by a distributed model and inclusion of betweenyear uctuations of snow cover. Tveit (1980) developed a complex model to simulate areal SWE from snow-course measurements in Norwegian fell regions. An abbreviated description of this model can be found in Andersen et al. (1982). The model includes factors such as location in eastwest and north-south directions, slope, concavity/ convexity (on local and regional scales) and surface type (forest/ eld). A similar method, called strati edsampling(unitisedsampling),isbased on the assumption that the snow-deposition pattern is closely related to landscape features. A watershed is divided into different classes based on land use, vegetation characteristics and terrain. SWE for each class is determined by depth and density samples taken from a few units of that class. The weighed average watershed SWE is determined by weighing SWE in the different classes according to their relative area (McKay, 1970). Some recent investigations dealing with spatial estimates and variability of areal depth and SWE are presented by, e.g., Ling et al. (1995), Carroll (1996), Lapen and Martz (1996), and Shook and Gray (1996). Many researchers have combined, in second half of the 1990s, geographical information systems (GIS) with digital terrain models (DEM), and in some cases also with remote-sensing (RS) techniques, in order to improve the areal description of the snow pack. The studied areas are usually subdivided into different landscape types based on parameters such as elevation, type of vegetation, slope, aspect, wind exposition and land-surface curvature. Atkinson and Kelly (1997) propose a way to scale up point measurement of snow depth for comparison with remotely-sensed data. Many studies have been made in connection to hydropower applications (e.g., Faanes and Kolberg, 1996; Kristensen, 1996; Nilsson, 1995; Lundquist 1998) and runoff forecasting (e.g., VehvilaÈinen and Lohvansuu, 1996). Faanes and Kolberg (1996) suggest an optimised snow-survey design for hydropower use in Norway through the use of GIS and DEM. They propose a division into ``homogenous areas'' based on wind exposition. Nilsson (1995) applies helicopter impulse-radar measurements combined with GIS as a tool to estimate snowmelt volume for a reservoir. Lundquist (1998) describes a decision-support system designed for planning of hydropower production based on remotely-sensed snow cover in a GIS framework. VehvilaÈinen and Lohvansuu (1996) present a GIS interface for a watershed simulation and forecasting system. Lapen and Martz (1996) examine the relation between snow-cover depth (SCD), topography and land use on the Canadian prairie with the help of DEM. Their analysis involve a subdivision of an agricultural study area into six terrain units de ned by relative topographic position and land-use variables. Their terrain classi cation delineated the major patterns of observed SCD and illustrated the spatial relationships between SCD and landscape attributes but did not substantially reduce the SCD variance. Ehrler et al. (1997) incorporate snow-cover units (SCU) into a GIS framework to differentiate snow-cover depletion in different elevation zones. The SCUs are de ned as assemblies of patches with similar snow cover. They are obtained by superimposing biophysical features (ground properties), topographic properties (elevation, aspect, slope) and af liation to climatic regions (regions of snow accumulation). The procedure enabled extrapolation of the areal snow cover into remotesensing scenes partially obscured by clouds. Baumgartner and Ap (1997) suggest that hydrological models should be combined with not only RS and GIS but also with a database management

222 A. Lundberg and S. Halldin systems (DBMS). Such a combination allows a variety of hydrological information to be derived, e.g., snow-cover accumulation and ablation, variation of the snow-line elevation, elevationand aspect-dependent snow coverage, snow-cover depletion curves (elevation- and aspect-dependent) and snow-melt runoff. Chang and Zhaoxing (1997) combine DEM data with data from 194 snow courses with 30-years continuous records into a GIS framework to model SWE variations in Idaho watersheds. They use the following topographic variables: elevation, slope and aspect, landsurface curvature, and the combined effect of slope and aspects. They conclude that GIS is well suited for multivariate regression modelling because of its capabilities to integrate and analyse diverse spatial datasets. Guneriussen et al. (1998), Solberg et al. (1998), and Standley et al. (1998) present different aspects of new remote-sensing methods for applications in snow hydrology, carried out in the SNOWTOOLS project. Pertziger (1998) uses relief, orientation and surface curvature in a GIS framework to identify areas with increased risk for avalanches. Differences in snow deposition and redistribution because of orographic effects and wind redistribution are small for at and homogenous forested areas even if local aerodynamical effects have been observed in clearings and at the border between eld and forest (e.g., Gary, 1974). The small-scale variation of snow properties within a few metres or less is a big problem in area averaging for forested areas. When rain or snow, intercepted by a forest canopy, leaves branches as lumps or drops, it causes a small-scale redistribution of the original precipitation eld. Throughfall generally increases from the tree trunk towards the spacing between trees (Johnson, 1990). Crockford and Richardsson (1990) compared troughs and funnels for throughfall measurements of rain. They showed that the number of gauges required to achieve an area average at a certain con dence level could be reduced to approximately one fth by using troughs instead of funnels. Lundberg et al. (1997) obtained similar results with a GIS technique on the basis of published throughfall data. Woo and Steer (1986) developed a Monte- Carlo snow-depth simulation model for a at, single-species forest. A heated plastic-sheet gauge gives a small-scale area average of throughfall but disturbs the natural snow pack and blocks in ltration of water into the soil. The snowinterception evaporation may be in uenced by a heating large enough to produce direct throughfall runoff at air temperatures well below zero. Spatial variations in SWE may also be caused by differences in radiation absorption at the end of the winter. The albedo () of snow-covered forests is much lower than the albedo of a snow pack. Harding and Pomeroy (1996) measured ˆ 12± 14% above a snow-covered, slightly open jack pine canopy in Canada. Nakai (1996) reports -values from 16% and up in a Todo- r canopy in Japan. The differences in SWE at the end of the winter between south- and north-facing slopes are larger in forested areas than in elds because of the larger radiation absorption. 4. Operational snow monitoring in the Nordic countries Ground-based measurements of snow-cover extent and SWE are not performed regularly in most countries. Operational snow monitoring in Fennoscandia has largely been governed by the need of the hydropower industry. Snow-course measurements are done operationally in Finland and Norway (Kuittinen, 1996), whereas synoptic weather stations in Sweden only note local snow depth (Table 1). The most extensive snow-course measurements are found in Finland with its relatively at topography. Data are regularly (every 10 days) reported from 160 snow courses of 2±4 km length with 150±180 depth and 8±10 density measurements (PeraÈlaÈ, 1995). Neither snow quality nor SWE are measured operationally in Sweden. The most complete snow data in Fennoscandia is found in Finland and Kuittinen (1989) presents a combination of satellite images, gamma-ray spectrometry, snow-course measurements and air-temperature measurements to calculate daily melt (Table 1). The use of satellite remote sensing is not so advanced in the Nordic countries as in north America where it is now operational (Rango, 1997) for both snow areal extent (using visible and near infrared sensors) and SWE (using passive microwave techniques). NOOA/AVHRR satellites are used on operational basis in Finland and Norway to determine snowcover extent (Table 1). The airborne gamma-ray technique is operational in Norway and Finland

Snow measurements techniques for land-surface-atmosphere exchange studies in boreal landscapes 223 Table 1. Snow-cover measurement in Finland (F), Norway (N) and Sweden (S) separated into operational and research methods. The capability of methods to determine depth d, SWE and snow quality (wet/dry) is indicated Method Extent d SWE Quality Operational Research References Ground-based methods Point snow depth S, N, F Snow course N, F S Andersen et al., 1982 Impulse radar N S, F Bruland and Sand, 1996 Satellites 1 NOAA/AVHRR N, F S HaÈggstroÈm, 1994 SPOT (S) Moberg and Brandt, 1988 2 Landsat S Moberg and Brandt 1988 Radarsat N Guneriussen, pers comm., 1997 ERS-1 N Guneriussen, et al. 1996, Koskinen and Hallikainen,1997 Airborne methods Gamma ray N, F S BergstroÈm and Brandt, 1985 Impulse radar S N, F Ulriksen, 1985; Lundberg et al., 2000 Combined methods N, F Kuittinen, 1989 1 Satellites do not directly give SWE but can be used to estimate it by merging remotely-sensed data, snow surveys and a hydrological model (e.g., Fortin and Bernier, 1995). 2 No useful images were identi ed for the observation area because of clouds during the two snowmelt seasons studied. and airborne radar is used to some extent in Sweden (Table 1). 5. Techniques suited for process studies The snow-measurement methods reviewed above cover a majority of existing ground-based techniques. They have been developed for different purposes and not all of them are suited for studies of exchange processes. Such studies are expensive in terms of equipment and manpower, which motivates a suitability assessment of techniques for process studies. 5.1 Requirements on ideal techniques Models of soil-vegetation-atmosphere transfer of heat, water and carbon require an understanding of the physics of frost formation and snow dynamics. Research on remote-sensing algorithms of interest for process studies require information on snow-cover extent, quality and SWE. Measurements must have a time resolution of the same order as the time constant for the processes, i.e., typically an hour or less. The measurement must resolve the dynamics of state variables, which means that water/ice/snow quantities should be recorded with an accuracy of at least 1 mm. Yearto-year variations in snow dynamics are very high in the southern part of the boreal zone. The average snowpack in, e.g., Uppsala, Sweden is shallow (17 cm in February according to Taesler, 1972) but winter temperatures range from 20 C to 10 C and sometimes change abruptly (> 20 C in a day). Snow depth, thus, can easily vary from zero to more than a metre. It is still possible to study processes governing such unpredictable dynamics and extreme situations with the help of high-resolution, continuous, and multiannual measurements? An ideal technique for studies of snow characteristics should: 1 cause negligible disturbance on the natural snow pack. 2 measure SWE with hourly or better time resolution and with an accuracy of 1 mm. 3 measure accumulation, depth, SWE, and quality.

224 A. Lundberg and S. Halldin 4 give area estimates of the snow-cover depth and water equivalent. 5 work in all terrain types. 6 give information about timing and amounts of water leaving the snow pack. 7 distinguish between wet and dry snow and give the liquid-water content. 8 work continuously and have acceptable costs for installation and maintenance. Some type of automated quality control of the measured data is required in addition to these characteristics. A procedure similar to the one developed by Schmidlin et al. (1995) could be used. Their procedure evaluates daily SWE values for common data entry errors, values beyond reasonable limits, and consistency with daily precipitation and estimated melt. Potential effects of drifting in high winds and micro-scale variability of SWE are also considered. 5.2 Evaluation of techniques No single method ful ls all criteria and a combination of several techniques must be used to simultaneously record the snow-pack mass properties (depth, density, SWE), its wetness properties (wet/dry, liquid water content, water leaving the snowpack) and its growth (Table 2). Radioactive methods (neutron sond and gamma rays) are omitted from this evaluation because of their severe safety requirements. The accuracy of the methods is seldom well documented. The accuracy characterisation in Table 2 is, therefore, only qualitative. 5.2.1 Depth The two methods primarily designed for depthmeasurement can both be used for unattended use while the SWE methods giving depth values are manual (Table 2). The ultrasonic snow-depth technique seems well suited for unattended, continuous and long-term measurements. The barometric method seems to be a well suited method for measuring snow depths along a line. 5.2.2 Accumulation Snow trays and precipitation gauges can be used to measure accumulation. The trays give both depth and SWE. The automated, aerodynamically designed precipitation gauge by Wiesinger et al. (1993) can be combined with occasional snowtray measurements. If the air temperature oscillates around 0 C either air temperature measurements (e.g., Rohrer, 1989) or an optical gauge can be used to separate solid from liquid precipitation. 5.2.3 SWE None of the point-measurement techniques in Table 2 give values that represent more than a few square metres. The methods representing the largest area ± the heated plastic-sheet method ± cannot be applied if the interaction with the soil is to be studied. Heating of the plastic sheet may also in uence exchange processes if the heating is large enough to melt the snow at air-temperatures well below zero. If the heating is not large enough the runoff will be delayed. Weighing lysimeters that cover large enough areas may be of interest for land-surface-atmosphere exchange studies in boreal landscapes. Methods giving line averages cannot be automated. Manual methods for determination of snow depth, density and water equivalent (snow courses and radar measurements) are spatially exible whereas automated techniques are bound to speci c locations. The manual methods may be used as a complement to stationary, automated equipment by assessing their representativity. The ground-based snowradar technique, which is sensitive to snow wetness, is not suitable when temperatures frequently exceed zero and the snow becomes wet. Its use can be questioned in forested terrain (where density is highly variable) because of its sensitivity to snow-density variations. 5.2.4 Wetness The calorimetric and dilution methods cannot be automated whereas some of the dielectric methods can: the capacitive sensors, the monopole antenna, and the TDR-methods. The capacitive sensors and the monopole antenna are not well suited for long-term use. The TDR method is not yet fully developed for wetness determinations. Temperature measurements can be used to derive the snow quality but give no information on the liquidwater content. The snowmelt lysimeter is the only method measuring the amount of liquid water leaving the snow pack.

Snow measurements techniques for land-surface-atmosphere exchange studies in boreal landscapes 225 Table 2. Ground-based measurement techniques for snow-pack mass (SWE) and growth parameters (Acc ˆ Accumulation), for depth and snow wetness and density parameters (wet/dry, free liquid water content, water leaving the snowpack, and density ) evaluated for their suitability for ground-vegetation-atmospheric exchange-process studies in boreal-forest regions Method Space Time Depth Acc SWE Works in Works with Accuracy Free liquid Wet/dry Water Manual M Referenses resolution resolution Forests wet snow content leaving Automatic A snow Depth methods Ultra-sonic methods Point Continuous Yes No No No Yes Yes Fair 1 No No No A Goodison et al., 1988 Barometric method Line Event Yes No No No Yes Yes Fair 2 No No No M Kennett et al. 1996 Temperature Point Continuous Yes No No No Yes Yes? No Yes No A Andersen, 1995 measurements SWE and accumulation methods Snow courses Line Yes No Yes Yes Yes Yes Good 2 No No No M WMO,1994 Impulse radar Line No No No Yes Yes No Good/Fair 4 No No No M Lundberg et al., 1998 Weighing lysimeter Point Continuous No Yes No Yes Yes Yes Good No No Yes A Storck and Lettenmaier, (25 m 2 ) 1999 Snow trays Point Event-Daily Yes Yes Yes Yes Yes Yes Good No No No M WMO,1994 Snow pillow 5 Point Continuous No No No Yes? Yes Good No No No A Trabant and Clagett, 1990 Precipitation gauges Point Continuous? Yes No Yes (Yes) Yes Poor 6 No No No A Martinec and Sevruk, 1992 Heated-plastic Point Continuous No Yes No Yes Yes Yes Good No No No A Calder, 1990 sheets 7 Wetness and density methods Calorimetric methods Point No No No No Yes Yes Good Yes Yes No M Radok et al., 1961 Dilution methods Point No No No No Yes Yes Good Yes Yes No M Davis et al., 1985 Dielectric methods Snow fork Point No No Yes Yes Yes Yes? 8 Yes Yes No M Sihvola and Tiuri, 1986 Electromagnetic Point No No Yes No Yes Yes? 8 Yes Yes No M Kendra et al., 1994 sensor Capacitive sensor Point Continuous No No Yes No Yes Yes? 8 Yes Yes No A Denoth, 1994 Monopole antenna Point? No No Yes Yes? Yes? 8 Yes Yes No A Denoth, 1997 TDR-methods 9 Point Continuous No No Yes Yes Yes Yes Fair/Good 10 Yes Yes No A Schneebeli and ColeÂou, 1997 Snow-melt lysimeter Point Continuous No No No No Yes Yes Good No No Yes A Martinec, 1989 1 Sauter and McDonnell (1994) report inaccurate snow depth measurements during 16% of their measurement time. 2 Average value 4 cm during low wind conditions, Average value 30 cm 23 cm during strong wind conditions. 3 Gottschalk and Jutman (1979) estimate the error in the areal mean to 3±6% for lowland areas (forest and open elds) and to 10±20% in the mountain region (Swedish conditions). 4 Good accuracy for dry snow, does not work well with wet snow or with snow with very varying density. 5 Problems with bridging. 6 Refers to most precipitations gauges, can be improved with windshields and with empirical correction factors. 7 The SWE-values may be delayed. 8 No detailed comparisions of the accuracy to measure wettnes and density are here made. 9 Some problems at the surface if exposed to sun radiation. 10 The accuracy for density is good, for free liquid content the estimated values are reasonable but additional measurements are required.

226 A. Lundberg and S. Halldin 5.2.5 Density The most promising technique for automated measurements of dry snow density seems to be the low-weight TDR probes by Schneebeli and ColeÂou (1997). The density can also be determined from SWE and depth. 5.2.6 Area averages Dif culties with estimating area averages from point measurements in forests can be minimised by carefully selecting measurement locations. The vicinity of clearings and elds should be avoided to minimise anomalies because of aerodynamical effects. Flat areas should be selected to avoid anomalies because of differences in radiation absorption. Elongated snow pillows could be used to integrate the small-scale variations of a few metres in SWE caused by redistribution when intercepted snow leaves the canopy. Dif culties with wind-formed snow bridging over the pillow should be negligible in forests whereas bridging caused by ice lenses may be a problem. Nakai (1997, personal communication) found shallow layers consisting of needles and ice fractions in a Japanese coniferous forest when studying snowlayer formation. These layers were probably formed from melting snow, which had fallen down from the canopy and refrozen. The installation of snow pillows in a forest will be associated with practical problems. It is dif cult to dig recesses in a forest soil full of roots, stones and boulders. Trustworthy areal averages seem best accomplished with a combination of techniques and data sources. A GIS framework is useful to integrate ground-based point and line data with possible remotely-sensed data. The integration is best achieved when the area is strati ed into suitable classes based on topography, land forms, etc. 6. Conclusions The following techniques may be recommended for ground-based, long-term, unattended point measurements of snow-pack properties: Depth: Ultrasonic snow-depth recorder and the barometric method for line averages. Accumulation: Aerodynamically designed precipitation gauge combined with occasional snow-tray measurements. Air-temperature and/ or optical-gauge measurements are needed to separate solid from liquid precipitation. SWE: Snow pillows or weighing lysimeters located in a way, which accounts for snow redistribution and radiation variations. Elongated snow pillows should be tested for use in forests. Density: The TDR method and/or deductions from SWE and depth Quality: Temperature sensors with small mass and high re ectivity not exposed to direct sunlight. Free liquid-water content: Dielectric-constant method with sensors protected from direct sun light. Water leaving the snow pack: Snow lysimeters. Several point measurements are needed to provide information on areally averaged properties. Automated point measurements for SWE and depth should be complemented at several occasions with manual snow-course, manual barometric depth measurements or ground-radar measurements (the last only when the snow is dry and the density variations are moderate). This will allow an evaluation of their areal representativity and optimise the layout of the measurement network. A combination of remote-sensing and DEM data with GIS techniques seems to have a large potential for estimation of areal averages of SWE and snow coverage. References Allerup P, Madsen H, Vejen F (1997) A comprehensive model for correcting point precipitation. Nordic Hydrology 28: 1±20 Andersen PS (1995) A method for estimating surface temperature, snow surface heat ux and accumulation time series over snow, suitable for high latitude, low power automatic weather stations. Annales Geophysicae (Supplement II) 13: C 545 Andersen T, Gottschalk L, Harestad J, Killingtveit AÊ, Aam S (1982) SnoÈmaÊlinger for kraftverksdrift, rapport til raêdet for den kraftverkshydrologiske tjensten. Projekt nr A-113, VR, Asker, Trondheim, 141 pp Atkinson PM, Kelly REJ (1997) Scaling-up point snow depth data in the UK for comparison with SSM/I imagery. Int J Remote Sensing 18: 437±443 Bakkehùi S, éien K, Fùrland EJ (1985) An automatic precipitation gauge based on vibrating-wire strain gauges. Nordic Hydrology 16: 193±202 Bales RC, Harrington RF (1995) Recent progress in snow hydrology. Reviews of Geophysics 33: 1011±1020

Snow measurements techniques for land-surface-atmosphere exchange studies in boreal landscapes 227 Baumgartner MF, Ap GM (1997) Remote sensing, geographic information systems and snowmelt runoff models ± an integrated approach. In: Baumgartner MF, Schults GA, Johnson AI (eds) Remote Sensing and Geographic Information Systems for Design and Operation of Water Resources Systems (Proceedings of Rabat Symposium S3, April 1997). International Association of Hydrological Sciences, IAHS press, Institute of Hydrology, Wallingford, UK, IAHS Publ no 242, pp 73±82 BergstroÈm S, Brandt M (1985) Measurements of areal snow water equivalent of snow by natural gamma radiation ± experiences from northern Sweden. Hydrological Sciences Bulletin 30: 465±477 Betts AK, Ball JH (1997) Albedo over the boreal forest. J Geophys Res 102: 28901±28910 Bonan GB, Pollard D, Thompson SL (1992) Effects of boreal forest vegetation on global climate. Nature 359: 716±718 Bruland O, Sand K (1996) Operational snow surveys by radar. In: Sigur sson O, Einarsson K, Aa lsteinsson (eds) Nordic Hydrological Conference 1996. Akureyri, Iceland 13±15 August 1996. Volume 1. NHP-Report No 40. Icelandic Hydrological Committee, ReykjavõÂk pp 110±119 Calder IR (1990) Evaporation in the uplands. Chichester, England: John Wiley and Sons, 144 pp Carroll SS (1996) Estimating the uncertainty in spatial estimates of areal snow water equivalent. Nordic Hydrology 27: 295±312 Chang K-T, Zhaoxing L (1997) GIS-assisted mapping of snowpack accumulation patterns in Idaho, USA, In: ICC 97, 18th International Cartographic Association ICA/ACI, International Cartographic Conference, Stockholm, 1997, pp 28±34 Crockford RH, Richardson DP (1990) Partitioning of rainfall in an eucalypt forest and pine plantation in South-Eastern Australia. I: Effect of method and species composition. Hydrological Processes 4: 131±144 Davis RE, Dozier J, LaChapelle ER, Perla R (1985) Field and laboratory measurements of snow liquid water by dilution. Water Resources Research 21: 1415±1420 Davis RE, Hardy JP, Ni W, Woodcock C, McKenzie JC, Jordan R, Li X (1997) Variation of snow cover ablation in the boreal forest: A sensitivity study on the effects of conifer canopy. J Geophys Res 102: 29389±29396 Denoth A (1994) An electronic device for long-term snow wetness recording. Annales of Glaciology 19: 104±106 Denoth A (1997) Monopole-antenna: A practical snow and soil wetness sensor. IEEE Transactions on Geoscience and Remote Sensing 35: 1371±1375 Ehrler C, Seidel K, Martinec J (1997) Advanced analysis of snow cover based on satellite remote sensing for the assessment of water resources. In: Baumgartner MF, Schults GA, Johnson AI (eds) Remote Sensing and Geographic Information Systems for Design and Operation of Water Resources Systems (Proceedings of Rabat Symposium S3, April 1997). International Association of Hydrological Sciences, IAHS press, Institute of Hydrology, Wallingford, UK, IAHS Publ no 242, pp 93±101 Faanes T, Kolberg S (1996) Optimal utnyttelse av snùmagasinet. Sintef, Bygg och miljùteknikk, Trondheim, Norway, 47 pp Foster J, Liston G, Koster R, Essery R, Behr H, Dumenil l, Verseghy D, Thomsson S, Pollard D, Cohen J (1996) Snow cover and snow mass intercomparisions of general circulation models and remotely sensed datasets. Journal of Climate 9: 409±426 Fortin J-P, Bernier M (1995) Results from model comparisons with ERS-1 and eld data for snow water equivalent estimation. In: International Geoscience and Remote Sensing Symposium (IGARSS) Proceedings of the 1995 International Geoscience and Remote Sensing Symposium. Part 3 (of 3) Jul 10±14 1995 v 3 Firenze, Italy, Institute of Electrical and Electronics Engineering IEEE 3 IEEE, Piscataway NJ, USA, pp 2176±2178 Fùrland EJ (ed) (1996) Manual for Operational Correction of Nordic Precipitation Data. Nordic Working Group on Precipitation. DNMI Klima Report No 24/96, Oslo, Norway Fùrland EJ, Aune B (1985) Comparison of Nordic methods for point precipitation correction. In: Workshop on the Correction of Precipitation Measurements, Zurich, Switzerland, April 1985, pp 239±244 Gary HL (1974) Snow accumulation and melt as in uenced by a small clearing in lodgepole pine. Water Resources Research 10: 345±353 Gaumet JL, Salomon P (1992) Characterisation des hydromeâteores par meâthode optique (Characterisation of hydrometeors with an optical method, my translation). In: World Meteorological Organisation technical conference on instruments and methods of observation (TECO-92) Vienna, Austria, May 1992. WMO/TD-No 462. pp 310±314 Goodison BE, Metcalfe JR (1988) Canadian participation in the WMO solid precipitation measurement intercomparison. In: Thomsen T, Sùgaard H, Braithwaite R (eds) Applied Hydrology Development of Northern Basins. Proceedings from The Seventh Northern Research Basins Symposium/Work-shop. May 25±June 1, 1988, Ilulissat, Greenland, Danish Society for Arctic Technology, c/o Greenland technical organisation, Copenhagen, Denmark, pp 199±207 Goodison BE, Ferguson HL, McKay GA (1981) Chapter 6. Measurement and data analysis. In: Gray DM, Male DH (eds) Handbook of snow: principles, processes, management and use. Toronto, Oxford, New York, Sydney, Paris, Frankfurt: Pergamon Press, pp 191±265 Gottschalk L, Jutman T (1979) Statistical analysis of snow survey data. Swedish Hydrological and Meteorological Institute, SMHI Report Hydrology and Oceanography, RHO 20, 41 pp Gray DM, Male DH (eds) (1981) Handbook of snow. Principles, processes, management and use. Toronto, Oxford, New York, Sydney, Paris, Frankfurt: Pergamon Press, 776 pp Guneriussen T, Johnsen H, Sand K (1996) DEM corrected ERS-1 SAR data for snow monitoring. Int J Remote Sensing 17: 181±195 Guneriussen T, Solberg R, Kolberg S, Hallikainen M, Koskinen J, Hiltbrunner D, Matzler C, Standley A