Nonuniform Distribution of Tundra Snow Cover in Eastern Siberia

Transcription

1 VOLUME 5 JOURNAL OF HYDROMETEOROLOGY JUNE 2004 Nonuniform Distribution of Tundra Snow Cover in Eastern Siberia HIROYUKI HIRASHIMA,* TETSUO OHATA,* YUJI KODAMA,* HIRONORI YABUKI, NORIFUMI SATO,* AND ALEXANDER GEORGIADI # *Institute of Low Temperature Science, Hokkaido University, Sapporo, Japan Frontier Observational Research System for Global Change, Yokohama, Japan # Institute of Geography, Russian Academy of Sciences, Moscow, Russia (Manuscript received 25 April 2003, in final form 20 November 2003) ABSTRACT Characteristics of snow cover in a small watershed in Arctic tundra near Tiksi, eastern Siberia, were studied by observation and model simulation. First, helicopter observation, snow survey on three traverse lines, and trigonometric survey of snowdrift were carried out at the end of the winters of 1999 and 2000 to estimate the amount and distribution of snow. The observed locations of snowdrifts in the two years were mostly the same. The area of the snowdrift, including shallow snowdrifts, was larger in the year of high winter precipitation than in the year of low winter precipitation. The snowdrifts were formed on the riverbed and lee sides of cols rather than on steep leeward-facing slopes. Second, snow distribution was simulated using a snow distribution model. The results of simulated snow distribution at the end of winter agreed well with the results of the observations. The results of the simulations showed that approximately 40% of winter precipitation was sublimated. The simulations also indicated that snowdrifts were formed in limited areas such as the valley bottom and riverbed in the first half of winter because of strong wind conditions. In the latter half of winter, when strong wind was rare, shallow snowdrifts were formed in various areas. 1. Introduction Strong global warming is predicted in the continental polar region (Watson et al. 2001). Early melt of snow cover and melting of permafrost have also been predicted. Therefore, prediction of the effect of global warming on the hydrologic cycle in the continental polar region is important because changes in the hydrologic cycle affect evaporation, soil moisture, and river runoff, which in turn affects the feedback to atmospheric conditions, surface conditions, and water resources. A snowpack has significant effects on the energy cycle because of its high albedo and low thermal conductivity, and it also affects the hydrological cycle because of its supply of meltwater. In tundra regions, winter is very long and snowfall occurs from the middle of September till the end of May. Snowpack on the flatlands and mountains is eroded by strong wind, and snow accumulates in depressions, resulting in a nonuniform distribution of snow. Here, considerations of the nonuniformity of snow distribution is important in studies on hydrological and energy cycles. These considerations are less important in southern forest regions where the distribution of snow is relatively uniform. Corresponding author address: Dr. Hiroyuki Hirashima, Institute of Low Temperature Science, Hokkaido University, Sapporo , Japan. hirashi@hucc.hokudai.ac.jp The distribution of snow can be determined by observation. However, such observation is laborious, and determination of seasonal variation in snow distribution based on observations is difficult. Seasonally continuous snow distribution can be estimated by using a snow distribution model with meteorological and topographical input data. Various snow distribution models have been developed in recent years. Uematsu et al. (1991) developed a numerical simulation model of snowdrift for a snow fence. Liston et al. (1993) improved this model by incorporating a k- turbulence model. Naaim et al. (1998) made further improvements by incorporating snow erosion and snow deposition models based on results of field measurement by Takeuchi (1980) and wind tunnel experiments, and they reproduced snow accumulation windward and leeward of a solid fence with a bottom gap by using their model. Yamashita and Kawamura (2000) simulated snow accumulation around snow fences of various shapes by using a generalized coordinate system. Snowdrift modeling for a complex terrain has been carried out more recently. Pomeroy et al. (1997) developed a distributed blowing snow model (DBSM) and applied to Trail Valley Creek (TVC), Canada. This model divided the domain into source and sink regions for blowing and drifting snow based on topography and vegetation characteristics. This model was validated by comparing with mean snow water equivalent (SWE) for 2004 American Meteorological Society 373

2 374 JOURNAL OF HYDROMETEOROLOGY VOLUME 5 FIG. 1. (a) Location of Tiksi, (b) location of the station and simulated area in Tiksi, and (c) simulated area. The broken line is the boundary of the watershed. The three long lines in the watershed are the lines used for estimation of snow water equivalent by the traverse method. Lines A G are the lines used for determination of a snowdrift by the trigonometric method. each vegetation type at the end of winter. Essery et al. (1999) also simulated the snow distribution in TVC using a terrain wind-flow model (MS3DJH/3R) (Walmsley et al. 1982, 1986; Taylor et al. 1983) and DBSM and reproduced several quantitative features of redistributed snow covers found from snow surveys. Liston and Sturm (1998) developed a snow distribution model named SnowTran3D for an Alaskan tundra watershed and succeeded in reproducing snow distribution at Imnavait Creek in Alaska. Hasholt et al. (2003) applied SnowTran3D for Mittivakkat Glacier, Ammasalik region, southeast Greenland. Simulated mean SWE in the

3 JUNE 2004 HIRASHIMA ET AL. 375 Mittivakkat Glacier agreed well with observed mean SWE. Purves et al. (1998) also developed a snow distribution model in which it was assumed that snow transport rate is proportional to the cube of the difference between wind speed and threshold wind speed, and this model was applied to Aonach Mor in the Western Highlands of Scotland, though the results of the simulation were not verified by the results of observations. Gauer (2001) developed a complex numerical model of drifting snow for an alpine region, and snow redistribution during a snowfall events around Gaudergrat ridge, Switzerland, was reproduced using this model. Winstral et al. (2002) developed a spatial snow model that uses an Sx parameter, which quantifies the degree of shelter/ exposure provided by upwind terrain and a regression tree model of snow distribution. It was applied to Green Lakes Valley, Colorado. This model coincided with the general field observation that snow primarily accumulates in areas sheltered from the strong winds. Winstral and Marks (2002) utilized the Sx parameter to calculate the wind field and snow accumulation factor to simulate snow distribution in Reynolds Mountain East study area, Idaho, where simulated snow distributions were correlated with aerial photographs. The snow distribution model developed by Liston and Sturm (1998) was most suitable for our study in terms of reproducibility and spatial and temporal scales. However, snow distribution was not reproduced accurately when the same parameters as those used by Liston and Sturm (1998) were used since the characteristics of snow distribution in our watershed were different from those at Imnavait Creek. The difference in snow distributions is discussed in section 6. The purposes of this study were to observe the actual conditions of nonuniform snow distribution, to develop a model for reproducing this snow distribution, and to estimate seasonal variation in snow distribution using this model. The snow distribution model presented herein is part of a land surface model that combines a snow and subsurface multilayer submodel, spatially distributed hydrological submodel, and snow distribution submodel and can reproduce a land surface condition for the whole year (Hirashima and Ohata 2001a). The development of the land surface model was carried out as a part of the project of the Global Energy and Water Cycle Experiment (GEWEX) Asian Monsoon Experiment (GAME) in Siberia, whose main purpose it is to study water and energy cycles in the Lena River basin. 2. Study region and climate The study site is a watershed, 5.5 km 2 in area, near Tiksi, eastern Siberia (71 40 N, E). It is located near the mouth of the Lena River and 5 km west of the Laptev Sea coast. Figure 1 shows the location of the study site and a map of the watershed. The altitude of the watershed ranges from 40 to 300 m. Permafrost completely underlies this region. The ground surface of the wet land is covered by various types of vegetation such as moss and sedge. They are distributed on flat plains and the lower parts of relatively gentle slopes. The top and steep-sloping part of the mountain is covered by gravel with lichen. The simulation of snow distribution (described in section 5) was carried out in a wider area than the area of the watershed in order to take into account snow blowing into and out of the watershed. Figures 1b and 1c show the area, including the watershed for which the simulation was carried out (15 km 2 ). Meteorological data were obtained at the Automatic Climate Observation System (ACOS) station established at an altitude of 40 m at a flat site (ACOS point in Fig. 1c), and all input data used in the simulations were obtained from this site, except for data on precipitation. The data on precipitation were obtained at the Russian Weather Station at Polyarka (Fig. 1b), located about 5 km east of the ACOS station, since data on winter precipitation at the ACOS station were not available. Figure 2 shows meteorological elements during the cold period (from 21 September to 31 May) in 1998/ 99 and in 1999/2000. Air temperature remained below 0 C from the middle of September to the end of May, and daily air temperature in the coldest period, from December to March, was sometimes below 40 C. Relative humidity ranged from 60% to 100%. Daily mean wind speed reached 20 m s 1 on windy days, and mean wind speed over a period of 30 min sometimes exceeded 30 m s 1. Because of such conditions, blowing and drifting snow were frequently observed. A southwest wind was dominant during the cold period because of stable Siberian anticyclones. When a cyclone approached, air temperature rose to 20 to 30 C, wind became strong accompanied by precipitation, and the amount of drifting snow increased. Figure 3 shows the monthly air temperature, precipitation, and seasonal variation in 10-days-mean wind speed in Tiksi. Mean annual air temperature is 13.5 C and annual precipitation is 331 mm. The average precipitation during a 20-yr period is 173 mm (Hirashima and Ohata 2001b). During the cold period in Tiksi, the ground is exposed to strong winds, as are western Siberia and Alaska tundra regions, unlike the eastern part of the east Siberia tundra region, in which the wind is gentle with small precipitation (Hirashima and Ohata 2001b). 3. Observation of snow cover a. Determination of spatial distribution by helicopter and satellite observations 1) METHOD AND DATA ANALYSIS Observations from a helicopter were carried out in the snowmelt seasons in 1999 and 2000 in order to determine the spatial distribution of snow cover (Ohata

4 376 JOURNAL OF HYDROMETEOROLOGY VOLUME 5 FIG. 2. Meteorological data. The wind directions 0, 90, 180, and 270 mean winds blowing from north, east, south, and west directions, respectively. The dominant wind direction in the cold period is southwest (a) in 1998/99 and (b) in 1999/2000. and Georgiadi 2001). These observations were carried out six times (from 28 May to 21 July) in 1999 and four times (from 19 May to 13 June) in The area covered was larger than the area of the watershed. Video images of the ground surface were obtained and analyzed by connecting a digital video deck and personal computer through a video capture board (Sato et al. 2001). Snow distribution was also determined by analysis using satellite data [Satellite Pour l Observation de la Terre (SPOT)]. FIG. 3. Climate conditions in Tiksi: (a) monthly mean air temperature and precipitation, and (b) 10-day-mean wind speed. 2) RESULTS Figure 4 shows the snow distributions determined by helicopter observation in (a) 1999 and (b) 2000 and (c) by analysis of satellite data in White shows snow cover, black shows snow free, and gray shows datamissing area (i.e., we could not determine whether it was covered with snow or not). According to data obtained at Polyarka, precipitation in the cold period in 1998/99 was only 92.3 mm but was mm in 1999/ 2000 and mm in 2000/01. In all of these years, snowdrift was formed on the riverbed and several places on the slope. The snowdrift was formed at almost the same place in these 3 yr. This characteristic corresponded to the results of measurements in Kuparuk Basin, Alaska, which had shown that the snow depth distribution pattern is the same year after year (König and Sturm 1998). When the helicopter observation was carried out, snowmelt had already started. Therefore, these figures are melt-period snow vegetation patterns. The

5 JUNE 2004 HIRASHIMA ET AL. 377 FIG. 4. Snow distributions obtained by helicopter observation. White shows snow cover, black shows snow free, and gray shows data-missing area on (a) 4 Jun 1999 and (b) 26 May 2000, and (c) satellite data on 5 Jun 2001 (Copyright 2001 by CNES/SPOT IMAGE Distribution) and (d) 5 Jun 2000.

6 378 JOURNAL OF HYDROMETEOROLOGY VOLUME 5 FIG. 5. Distribution of SWE (mm) along the traverse line. (a) (d) Observation results, and (e) (h) simulation results. Panels (a), (b), (c), and (d) are on the SP line in 1999, SP line in 2000, RR line in 2000, and PV line in 2000, respectively. Snow surveys were not carried out on the RR and PV lines in 1999 because of lack of snow. Panels (e), (f), (g), and (h) show the results of the simulation at the same place and in the same year as those for which results are shown in (a), (b), (c), and (d), respectively. amount of snowmelt before the helicopter observation was estimated by the degree-day method, which is described in section 3d. b. Estimation of snow water equivalent by a traverse method 1) MEASUREMENT METHOD At the end of the cold period, snow depth and snow density were measured on three traverse lines [station peak (SP), ridge ridge (RR), and peak valley (PV) lines] to determine the distribution of SWE in the watershed (see Fig. 1c). Snow depth was measured at 25- m intervals along these lines, and snow density was measured at several points along the lines by the use of a snow sampler. The SWE at each point was calculated by multiplying snow depth and snow density measured at the nearest point. These measurements were carried out on 5 June 1999 (after helicopter observation) and on 29 and 30 May 2000 (after helicopter observation). 2) RESULTS The SWEs along the three traverse lines are shown in Fig. 5. On the SP line, snowdrift had formed near the points of intersection with the PR and PV lines, and on the RR and PV lines, snowdrift had formed near the points of intersection with the SP line and T river. Since there was much winter precipitation in 2000, the SWE was larger than that in 1999, but the observed locations of snowdrift in 1999 and 2000 were the same. In 1999 (a year with little precipitation), a large amount of snow accumulated in a small area and formed deep snowdrifts. In 2000 (a year with much precipitation), the depth of deep snowdrift was almost the same as that in 1999, but the area in which snow accumulated was larger. Based on these results, the mean SWE of the whole watershed excluding the snowdrift on the riverbed was estimated. Since snow depth data on the RR and PV lines in 1999 were not available, the SWEs of points on the SP line were averaged and used to calculate mean SWE in the watershed. The mean SWEs in 1999 and

7 JUNE 2004 HIRASHIMA ET AL. 379 FIG. 6. Cross sections of snow surface and ground surface at lines D and F on 5 Jun 1999 and 29 May (a) Results of observation at line D in 1999, (b) results of observation at line D in 2000, (c) results of observation at line F in 1999, and (d) results of observation at line F in (h) The (e), (f), (g) results of the simulation at the same line and in the same year as those for which results are shown in (a), (b), (c), and (d), respectively. The x axis and y axis are horizontal and vertical distances from the datum point were 62.1 and mm, respectively, and were regarded as the mean SWE of the watershed excluding the snowdrift on the riverbed. The SWE of the snowdrift on the riverbed is discussed in the next section. These data are used for validation of the results of the simulated snow distributions in section 5. c. Determination of a snowdrift by a trigonometric method 1) MEASUREMENT METHOD Accumulation of snow exceeding 10 m in depth exists in the upstream area of T river in the watershed. This snowdrift plays an important role in supplying meltwater to the river in summer. Measurements of snowdrift surface level before snowmelt and bare ground surface level after snowmelt were carried out by trigonometric surveys at several survey lines along the river shown in Fig. 1c (lines A to G). The results of these two surveys were compared to determine snow depth. 2) RESULTS Figure 6 shows cross sections at surveyed points D and F. The amount of snowdrift that had formed on the riverbed was estimated using these data. Estimation of snow density is necessary to obtain snow mass from snow volume. According to previous studies, the snow density of a perennial snow patch ranges from 600 to 800 kg m 3 (Martinelli 1959; Higuchi et al. 1971; Tsuchiya 1984). The dry snow density in a snow patch for first-year snow ranges from 500 to 550 kg m 3 (Wak-

8 380 JOURNAL OF HYDROMETEOROLOGY VOLUME 5 FIG. 7. Method of analysis of snowdrift: (a) determination of h and S, (b) image of inverse triangle, (c) image of reduction of snow depth, and (d) estimation of snow volume V from each S. ahama et al. 1968). The snowdrift on the riverbed was a first-year snow patch and had a large load for densification since mean snow depth was about 2 m. The snow in this watershed had already undergone several cycles of snowmelt and refreezing before this snow survey. In this study, snow density was estimated as follows. First, the cross-sectional area S (meters squared) and maximum snow depth h (meters) on each surveyed line were estimated (Fig. 7a), and the cross section of the snowdrift was treated as an inverse triangle whose height was h and width was 2Sh 1 (meters) (Fig. 7b). The horizontal and vertical ratio of this triangle, Ar, was determined to be equal to 2Sh 2 and was assumed to be temporally constant. Then the amount of snowmelt was calculated by the energy balance method (Zhang et al. 2000) and divided by presumed snow density to obtain reduction of snow depth, h (meters per day) (Fig. 7c). The seasonal variation of h was calculated by subtracting h every day, and the seasonal variation of S was determined to be equal to Arh 2 (meters squared). The actual seasonal variations of h and S were also obtained by a trigonometric survey carried out five times in 1999 (from 6 June to 2 August). When the snow density was presumed to be 520 kg m 3, the calculated seasonal variations of h and S agreed with the observation results. This value of snow density was obtained by trial and error and is similar to the value obtained in a previous study (Wakahama et al. 1968). The total snow volume in the snowdrift on the riverbed V (meters cubed) was estimated by integrating the snowdrift volumes at all survey points. The volume between two survey points was calculated by multiplying the distance and the mean cross-sectional area between two survey points. Estimated snow volume was multiplied by snow density, 520 kg m 3, to obtain total snow mass. The snow masses of the snowdrift on the riverbed were calculated to be kg in 1999 and kg in This snow mass was divided by the area of the snowdrift to obtain the mean SWE in this snowdrift. The area of the snowdrift on the riverbed was calculated to be km 2 by multiplying the mean width of the snowdrift at each surveyed point and the distance from point A to point G along the river. Thus, the mean SWEs on this snowdrift in 1999 and 2000 were 675 and 1320 mm, respectively. Using these values and the mean SWE of the whole watershed excluding for the snowdrift on the riverbed described in the previous subsection, the mean SWE of the whole watershed was estimated by the following expression: AndSwnd AdSwd Sww, (1) A A where Sw w is the mean SWE of the whole watershed, Sw nd and A nd are the mean SWE and area of the whole watershed except for the snowdrift on the riverbed, respectively, and Sw d and A d are the mean SWE and area of the snowdrift on the riverbed, respectively. The mean SWEs in the watershed in 1999 and 2000 were 74.8 and mm, respectively. d. Estimation of snowmelt When the observations (helicopter observation and snow survey) were carried out, snowmelt had already begun. The amount of snowmelt before observations must therefore be taken into account when the results obtained by using the snow distribution model discussed section 5 are compared with the results of the observations. The amount of snowmelt before the start of nd d

9 JUNE 2004 HIRASHIMA ET AL. 381 FIG. 8. Outline of the snow distribution model. observations was estimated by the degree-day method, that is, M C max(t T, 0), (2) 0 T T0 0 where C is the degree-day factor (mm day 1 C 1 ), T is daily mean air temperature, and T 0 is threshold air temperature for snowmelt and is assumed to be 0 C. The degree-day factor, which was calculated by dividing cumulative amount of snowmelt determined by observations by cumulative temperature, was 5.3 mm day 1 C 1. The estimated amount of snowmelt before helicopter observations carried out on 4 June 1999 and 26 May 2000 were 32 and 107 mm, respectively, and the estimated amount of snowmelt before snow surveys carried out on 5 June 1999 and 29 May 2000 were 83 and 121 mm, respectively. 4. Model description The snow distribution model developed by Liston and Sturm (1998) was adopted to reproduce snow distribution. Figure 8 shows the structure of the snow distribution model. The schemes in the model are 1) input of snowfall (it is assumed that accumulated snow from snowfall is distributed uniformly); 2) calculation of wind field; 3) calculation of wind shear velocity; 4) calculation of the transport of snow by saltation; 5) calculation of the transport of snow by suspension; 6) calculation of the sublimation of saltating and suspended snow; and 7) calculation of the accumulation and erosion of snow at the snow surface and redistribution of the snow water equivalent for each grid. Liston and Sturm (1998) generated a daily precipitation dataset from relative humidity and air temperature. The total amount of winter precipitation was adjusted until the results of the simulation of end-of-winter SWE were the same as the observed SWE. However, with this technique, the model can only be used in years when the snow survey was carried out. In the present study, the amount of new snowfall was determined from meteorological data obtained at the Russian weather station at Polyarka. These data were used without any correction as precipitation input. The meteorological data obtained at ACOS station were used for the other required input data to run the model. To estimate the snow transport, the wind field affected by the topography of the watershed must be reproduced. In this study, a simplified method (Liston and Sturm 1998) was used. The wind speed for each grid was determined as the product of wind speed measured at ACOS and the linear function of slope and curvature of the grid: W 1.0 s s c c and (3) ugrid W u sta, (4) where W is weighting factor for wind speed, s and c are topographic slope and curvature, respectively, s and c are parameters on modifying wind speed, u grid is wind speed at the grid, and u sta is wind speed at the ACOS station. Change in wind direction caused by topography was not considered in this study. At first, the parameters used by Liston and Sturm were used [ s 11 and c 360 in Eq. (3)]. These parameters were used successfully for estimation of the snow distribution at Imnavait Creek. Simulation carried out in the present study using the same parameterization showed that most

10 382 JOURNAL OF HYDROMETEOROLOGY VOLUME 5 TABLE 1. Meteorological conditions and averaged snow water equivalent for simulated year, and comparison of averaged SWE obtained from results of simulation and observation. Observed result Air temperature ( C) Relative humidity (%) Wind speed (m s 1 ) Wind direction ( ) Ratio of wind speed 5ms 1 (%) Ratio of wind speed 10 m s 1 (%) Ratio of wind speed 20 m s 1 (%) Precipitation (mm) Averaged SWE in the watershed (mm) Simulated result Sublimation loss (mm) Averaged SWE in all simulated area of simulated (mm) Averaged SWE in the watershed before snowmelt (mm) Averaged SWE in the watershed on the day of snow survey (mm) 1998/ / (SW) (SW) of the snow had accumulated on the leeward slope and little had accumulated on the valley bottom and riverbed. This disagreement is thought to have arisen for the following two reasons: 1) the watershed is surrounded by some small hills with steep slopes, whereas the topography at Imnavait is characterized by gently rolling ridges and valleys, and 2) the meteorological condition is different from Imnavait Creek. Because of the stronger wind in Tiksi, snow did not accumulate on the slopes behind the ridge but on the valley bottom and riverbed. Thus, the parameter for the slope was decreased and the parameter for the curvature was increased [ s 2 and c 720 in Eq. (3)]. These parameters were obtained by trial and error. The snow distribution was then reproduced more accurately. Equations used for processes from schemes 3 7 listed above were quoted from Liston and Sturm (1998). Formation of a snowdrift results in a change in the topography, which, in turn, affects the formation of the snowdrift. Therefore, new elevation is calculated in each grid at each time step after a snowdrift event. 5. Model simulation Snow distribution in a 15-km 2 area in the period from 20 September to 31 May in 1998/99 and 1999/2000 was simulated using this model (Figs. 1b,c). In this model, the time step was set to 30 min and the grid size was 50 m. Table 1 shows the meteorological conditions and mean SWEs in all simulated areas and in the watershed for each year. The results indicate that 40% of precipitation was sublimated. Pomeroy et al. (1997) and Liston and Sturm (1998) suggested that sublimation loss was about 10% 30% of total precipitation in the cold period. Sublimation amount in our results was larger than that in the results of Pomeroy et al. (1997) and Liston and Sturm (1998). On the other hand, Essery et al. (1999) suggested that 47% of snowfall amount was sublimated in open tundra, and most of this watershed in Tiksi consists of open tundra. One of the reasons for the large degree of sublimation in our study was that much snow was driven by very strong winds of more than 30 m s 1, and the mass concentration of snow particles became large. Thus, a lot of snow was sublimated. As shown in Table 1, the mean SWE in the watershed was about 30% larger than that in the entire area used for the simulation (15 km 2 ), indicating that this watershed works as a sink for blowing and drifting snow. This might be due to the topography of the watershed, such as the steep slopes and the riverbed. Figure 9 shows the results of simulation of snow distribution. The results show that deep snowdrift formed over a large area of the riverbed. For validation of the snow distribution model, the results of the simulation were compared with the results of the observations at the end of the cold periods in 1999 and The mean SWEs, spatial distributions of snow, SWEs at each point on the three traverse lines, and cross sections of snowdrift were compared. a. Validation of mean SWE in the watershed Table 2 shows the mean SWEs calculated from results of observation and simulation. The mean SWE calculated from results of simulation was about 20% smaller than that determined from observations. This disagreement was thought to be due to the precipitation input for the simulation. Yang and Ohata (2001) pointed out that the catch efficiency of a precipitation gauge in polar regions is small. They suggested using precipitation corrected by catch efficiency for analysis. However, when the corrected values were used for the input of the simulation, the mean SWE calculated from the results of the simulation was about 50% larger than that determined from the observations. In order to find the precipitation correction factor for increment, C i, simulations were carried out using the following equation and

11 JUNE 2004 HIRASHIMA ET AL. 383 FIG. 9. Simulated SWE distribution at the end of winter (a) in 1998/99 and (b) in 1999/2000. making the simulated mean SWE to agree with the observed value by trial and error: P P0 (Pc P 0) C i, (5) where P 0 is the noncorrected precipitation, and P c is the corrected precipitation proposed by Yang and Ohata (2001). When C i was 0.3, calculated mean SWE from TABLE 2. Mean snow water equivalents obtained from results of the observations and simulations. Before melt indicates mean SWE in the watershed before snowmelt (mm), and snow survey indicates mean SWE in the watershed on the day of snow survey (mm). 1998/ /2000 Observation Mean SWE in the watershed (mm) Simulation case 1 a Before melt Snow survey Simulation case 2 b Before melt Snow survey Simulaton case 3 c Before melt Snow survey a Simulation without any precipitation correction. b Simulation with precipitation correction suggested by Yang and Ohata (2001). c Simulation with precipitation correction using the precipitation correction factor C i 0.3. the results of the simulation agreed with that calculated from the results of the observations with an increase in precipitation of about 25%. However, the value for C i ( 0.3) was obtained as a tuning parameter, and it is not a precipitation correction factor physically related with terms like those done by Yang and Ohata (2001). Hereafter, uncorrected values of input precipitation were used. b. Comparison with spatial distribution data Since data obtained by helicopter observation are only data on surface conditions (snowcovered or snow free), SWE can not be estimated from these data. However, these data can be used for validation of the snow distribution model. Figure 10 shows results of the simulation of surface conditions for the time when the helicopter observation was carried out. Since the snowpack had already begun to melt when the helicopter observation was carried out, the amount of snowmelt calculated by the degree-day method before the helicopter observation (described in section 3d) was subtracted from SWE estimated by the simulation for each grid. The white zone means that the grid was estimated to be covered by remaining snow. A comparison with the results obtained by the helicopter observation (Fig. 4) shows that the simulation accurately reproduced the snowdrift formed on the riverbed. However, there was disagreement between the results in some places. Thus,

12 384 JOURNAL OF HYDROMETEOROLOGY VOLUME 5 FIG. 10. Simulated surface condition on the day when helicopter observation was carried out. A white zone means that the grid was estimated to be covered by remaining snow: (a) 4 Jun 1999, (b) 26 May 2000, and (c) 5 Jun the pattern of surface conditions obtained by simulation did not agree well with that obtained from observations. One example of disagreement is discussed in the next section, and the reason for this disagreement is discussed in section 6. As snowmelt advances, the area covered by snow becomes smaller, and only deep snowdrift remains. Data obtained by helicopter observation on 5 June 2000 (Fig. 4d) were compared with data for surface conditions on the same day obtained by simulation (Fig. 10c) to estimate the reproducibility of an area of deep snowdrift. The amount of snowmelt up to this day was 206 mm [about 2 times greater than that before 26 May 2000 (107 mm; Fig. 4b)]. The simulated snow-covered area agreed well with the observed one, suggesting this model can reasonably reproduce areas of deep snowdrift.

13 JUNE 2004 HIRASHIMA ET AL. 385 c. Comparison with snow depth data SWEs calculated from the results of the observation and SWEs calculated from the results of simulations are shown in Fig. 5. On the SP line, a snowdrift existed near the point of intersection with the RR line and PV line, and on the RR and PV lines, a snowdrift existed near the point of intersection with the SP line and the river. These results of the simulation agree with the observed data. However, at the bending point of the SP line (BP in Fig. 5), the results of the simulation showed that a snowdrift had formed, but there was in fact no snow at this point. The reason for this disagreement in results is discussed in section 6. d. Comparison with snowdrift data Figure 6 also shows a cross section of the topography expressed by digital elevation model (DEM) data and simulated snowdrift. The topography based on DEM data was smoother than the real topography, and the DEM data were not sufficient to resolve the microtopography as it was determined by a trigonometric survey. Therefore, the form of snowdrifts cannot be reproduced accurately. 6. Discussion Snow distribution was simulated using the parameterization of the SnowTran3D model. As stated in previous sections, the results of simulations of the snow distribution at the end of winter agreed well with the results of observations. In the following section, we will discuss characteristics of tundra snow distribution, influence of grid scale, seasonal variation of snow distribution, and the versatility and shortcomings of the model. a. Characteristics of tundra snow distribution Observations and simulations in the Arctic tundra region have been carried out in various areas. Pomeroy et al. (1997) and Essery et al. (1999) simulated a snow distribution and verified their results with a snow survey in Trail Valley Creek (68 43 N, W). According to Pomeroy et al. (1997) and Essery et al. (1999), vegetation has a strong control on snow accumulation, and taller vegetation such as shrub of sparse forest traps more wind-blown snow. Averaged snow mass in shrub tundra and sparse forest is times as that in open tundra. The vegetation in TVC is forest tundra transition. On the other hand, in colder tundra regions that have no tree and tall vegetation, snow distribution is determined mainly by topographic effect. Bruland et al. (2001) carried out a snow survey in 100 m 100 m grid scale for a 3-km 2 site in Svalbard, Norway (78 55 N, E), where there is no tall vegetation. In their results, large snowdrifts were found in the bottom of long west-facing slopes (leeward slopes). Woo (1998) mentioned that drifting effect produces cornices and deep accumulations on slope concavities, sometimes accompanied by snow-free patches on slope crests and slope concavities. König and Sturm (1998) provided the methods of snow distribution of mapping in the southern part of the Kuparuk Basin (69 N, 149 W) using aerial photograph and topographic data. Snow patterns were classified to some patterns (creek, river, lake, patchy, flat, slope, and bare). Liston and Sturm (1998) developed the SnowTran3D model based on the fact that lee and concave slopes produce reduced wind speeds and snowdrifts were formed there. These studies suggested the snowdrifts are formed on the following places: 1) tall vegetation such as shrub and tree, 2) concave slopes and riverbed, and 3) leeward slopes. In Tiksi, since there are no tall trees, the difference in roughness lengths caused by vegetation type is small, and snowdrift formation is mainly affected by topography. The helicopter observation and satellite data showed the existence of snowdrifts on the riverbed. These snowdrifts correspond to creek pattern and river pattern in the classification of König and Sturm (1998). Although snowdrifts usually form on leeward slopes (Liston and Sturm 1998; Bruland et al. 2001), snowfree areas were also shown, even on the steep leeward slope in Tiksi. Prevailing wind blows from the southwest, but most of the snowdrifts formed on the east- or southeast-facing slopes rather than on northeast-facing slopes (Fig. 4c) except on the riverbed. We can recognize these snowdrifts located on lee sides of notches or cols (gaps in a ridge), and this is one of the causes for disagreement of snow distributions between the simulation and the observation. This characteristic has never been discussed at other tundra regions. It will be discussed more in section 6e. b. Influence of grid scale In this study, the model grid scale was set to 50 m, which is known to affect the results of simulation. Liston and Sturm (1998) suggested that if the resolution exceeds the general scale of the feature, it will be lost by the simulation entirely, or at least smoothed to the extent. In this case, the shape of snowdrift on the riverbed was smoothed (see Fig. 6). When the simulation with high resolution (20 m) was carried out, snow distribution was obtained more in detail, but discrepancies discussed in section 5 still existed. In fact, the simulation with small grid scale needs much computational expense. In particular, when this model is incorporated into a land surface model, snow and soil temperature is also calculated and needs more computational expense. Therefore, a larger grid scale is desirable as far as the snow distribution is reproduced reasonably. Liston and Sturm (1998) suggested that a computational grid of 100 m would be quite appropriate. We tried the simulation with the grid scales 100, 200, and 300 m as well. Although in the cases of 100 and 200 m, the snowdrifts on the

14 386 JOURNAL OF HYDROMETEOROLOGY VOLUME 5 riverbed were reproduced, in the case of 300 m they were not. Therefore, we can conclude the scale must not be larger than 200 m in this study area. c. Seasonal variation of snow distribution The seasonal variation of the snow distribution can be estimated using this model. Figure 11 shows simulated snow distribution and a histogram of snow depth for each grid at the end of October, January, and April in 1999/2000. In the period from October to January, most of the snow was deposited at the bottom of the valley and riverbed because of strong wind conditions (see Fig. 2). The storm period continued from 22 January to 7 February. In this period, most of the snow was accumulated in the depression, such as on the riverbed. After the storm period, strong wind rarely blew, and shallow snowdrifts were formed in various areas. Thus, the area with snow deeper than 0.2 m increased after 7 February (12%, 16%, and 31% at the end of October, January, and April, respectively). These results could not be validated because there were no data on snow distribution during the cold period. However, these results suggest that the seasonal variation of snow distribution in the cold period is strongly affected by the characteristics of wind. d. Versatility of the snow distribution model The versatility of the snow distribution model is discussed. In general, snowdrifts form in places with a divergent wind field, such as a leeward slope near the ridge (snow cornice) and valley bottom. In the case study at Imnavait Creek by Liston and Sturm (1998), the topography was characterized by gently rolling ridges and valleys, and snowdrift was successfully simulated on the leeward slope near the ridge and valley bottom by their original parameters [ c 360 and s 11 in Eq. (3)]. The topographic characteristics of the watershed in the present study are different from those at Imnavait Creek; the watershed is surrounded by hills with steep slopes. No snow cornices were observed near the tops of the hills, and snowdrifts were mainly seen on the valley bottom. This means that the curvature is more important than the slope for snowdrift formation in this watershed. Thus, the snow distribution was reasonably reproduced by increasing the parameter of curvature, c, and reducing the parameter of the slope, s, in Eq. (3) in this simulation. These results lead to the conclusion that this model can be used for simulation of snow distribution at various sites by adjusting values of parameters based on topographic conditions. e. Shortcomings of the snow distribution model Although the results of the simulations of snow distribution agreed well with the results of observations, improvement in the accuracy of simulation is needed. For example, as pointed out in section 5c, a snowdrift was formed at the bending point of the SP line (Fig. 5) in the simulation, but there was in fact no snowdrift at this point. The results of the simulations showed the formation of a snowdrift on the leeward steep slope of the prevailing wind direction (e.g., the bending point of SP line). However, as mentioned in section 6a, snowdrifts were not always formed on leeward slopes but on the lee side of the prevailing wind direction in notches or cols of hills. The bending point of the SP line is not located on the lee side of notches or cols. The mechanism of snowdrift formation must be parameterized to reproduce the snow distribution accurately. According to Daffern (1988) and McClung and Schaerer (2002), notches or cols in mountain ridges are natural avenues for the wind. Thus, it is assumed that following occurs: The quantity of blowing snow at notches or cols is maximized with maximum wind speed. After passing these points, wind speed decreases because of the wider space. As the wind speed decreases, the amount of blowing snow is reduced, resulting in the accumulation of snow. Therefore, snowdrift is formed on the lee-wind side of the notches or cols. These processes are not considered in this model. If the wind field is calculated using Navier Stokes equations such as the one utilized by Gauer (2001), it may be possible to reproduce this mechanism. However, the time step must be very short to reproduce the fluid mechanism for wind, and the calculation time would be long. For example, the time step used by Gauer (2001) was 0.25 s, and drift simulation was carried out for only 2 days. A fluid equation is therefore not suitable for a simulating a land surface condition over one season or a whole year. Instead of this method, the mass-consistent model developed by Sherman (1978) was tested for wind field calculation. This model enables calculation of a wind field satisfying the equation of continuity and does not require a long computational time. However, in this case, snow was eroded even on the leeward slope and valley bottom by strong wind, and the simulated snow distribution did not agree with the results of the observations. Another problem is the error in the input precipitation data. It is difficult to measure solid precipitation accurately in a cold period because of the small catch efficiency of a precipitation gauge for snow and catchment of drifting snow by the gauge while wind blows. Mean SWE was considerably overestimated in the simulation in which catch efficiency of a precipitation gauge suggested by Yang and Ohata (2001) was taken into account. It is important that the snow distribution model presented herein be capable of accurately simulating spatial snow distributions so that when it is merged with our land surface hydrology model the coupled system will be able to realistically simulate the snow distribution s influence on soil moisture, surface fluxes, and other climate- and hydrology-significant features of the Arctic landscape.

15 JUNE 2004 HIRASHIMA ET AL. 387 FIG. 11. Seasonal variation of snow distribution in the 1999/2000 cold period: (a) 31 Oct 1999, (b) 31 Jan 2000, (c) 30 Apr 2000, (d) 31 Oct 1999, (e) 31 Jan 2000, and (f) 30 Apr Panels (a), (b), and (c) show the snow distributions, and (d), (e), and (f) are histograms of snow depth.

16 388 JOURNAL OF HYDROMETEOROLOGY VOLUME 5 7. Conclusions In order to clarify the spatial and temporal variations of the snow cover near Tiksi, eastern Siberia, a snow survey was carried out to estimate the amount and distribution of snow, and snow distribution was simulated using the parameterization of the SnowTran3D model. This parameterization was applied to a land surface model that takes the effect of nonuniform distribution of snow in a tundra region into account. The main results of this study were as follows: 1) Measurements of snow distribution by observation from a helicopter, a snow survey on three traverse lines, and a trignometric survey of snowdrift were carried out at the end of the winter at 1999 and 2000 to estimate the amount and distribution of snow in the watershed. The observed locations of snowdrifts in the 2 yr were mostly the same. In 2000, when there was large winter precipitation, the area of snowdrift, including shallow drifts, was larger than that in The mean SWEs in the watershed in 1999 and 2000 were estimated to be 74.8 and mm, respectively. 2) The results obtained by using the snow distribution model were compared to the results of observations in order to determine the applicability of the model and determine the seasonal variation of snow distribution. The results of the simulation of the snow distribution agreed well with the results of observations at the end of winter, although the parameter used in previous studies (Liston and Sturm 1998) for calculation of the wind field was unsuitable for this region. The mean SWE in the watershed calculated from the results of the simulation was about 20% smaller than that estimated from the results of the observations. One of the reasons for this underestimation might be an error in the input precipitation data. Snowdrifts were formed in limited areas such as the valley bottom and the riverbed in the first half of winter because of strong wind condition, whereas shallow snowdrifts were formed in various areas in the latter half of winter when strong wind was rare. 3) The results of the simulations indicate that approximately 40% of winter precipitation was sublimated during the cold period. The SWE in the watershed was 30% larger than that in the whole calculated area. This means that the watershed plays a role as a sink for blowing and drifting snow in this area. 4) Snowdrift formation is mainly affected by topography in a tundra region. In previous studies, snowdrift is usually formed on the leeward slope and concave slopes such as riverbeds. However, in the watershed in this study, there were no snow cornices near the tops of the hills, and snowdrifts formed on the valley bottom, mostly because of the topographic characteristics of this watershed (surrounded by hills with steep slopes). The snow distribution was reproduced by adjusting the values of the parameters used for wind field calculation. The results of the observations showed that snowdrifts were located on the lee side of cols rather than the steep leeward slopes. This characteristic was different from other tundra regions previously studied, and these locations of snowdrifts were not accurately reproduced by the simulation. This is one of the shortcomings of this model. The snow distribution model implemented as part of this study has been found to reasonably simulate endof-winter snow distributions in a wind-blown Arctic tundra landscape. As part of the next phase of research effort we will merge this snow distribution submodel with our land surface hydrology model, to work toward our long-term goal of realistically simulating the fullyear-evolution high-latitude snow covers and their influence on surface energy and moisture fluxes, soil and active layer processes, and runoff. Acknowledgments. This work was supported by the GAME-Siberia project funded by the Ministry of Education Science, Sports, and Culture of Japan and the Frontier Research System for Global Change, and we thank the colleagues of this project. We wish to thank Dr. Y. Ishii and Dr. M. Nomura for collecting and supplying data used in this research. We also wish to thank Dr. K. Nishimura for his advice and discussion. REFERENCES Bruland, O., K. Sand, and Å. Killingtveit, 2001: Snow distribution at a high Arctic site at Svalbard. Nord. Hydrol., 32, Daffern, T., 1988: Avalanche Safety for Skiers and Climbers, Seattle. The Mountaineers Books, 172 pp. Essery, R., L. Li, and J. Pomeroy, 1999: A distributed model of blowing snow over complex terrain. Hydrol. Processes, 13, Gauer, P., 2001: Numerical modeling of blowing and drifting snow in Alpine terrain. J. Glaciol., 47, Hasholt, B., G. E. Liston, and N. T. Knudsen, 2003: Snow-distribution modelling in the Ammassalik Region, South East Greenland. Nord. 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