Proceedings, International Snow Science Workshop, Breckenridge, Colorado, 2016

Similar documents
Comparison of a snowpack on a slope and flat land by focusing on the effect of water infiltration

An operational supporting tool for assessing wet-snow avalanche danger

Snow Disaster Forecasting System on Roads. Suyoshi, Maeyama, Nagaoka, Japan

Development of Snow Avalanche Forecasting System in Japan

SLOPE SCALE AVALANCHE FORECASTING IN THE ARCTIC (SVALBARD)

Proceedings, International Snow Science Workshop, Banff, 2014

PREDICTING SNOW COVER STABILITY WITH THE SNOW COVER MODEL SNOWPACK

On modelling the formation and survival of surface hoar in complex terrain

Variations of snow cover in the source regions of the Yangtze and Yellow Rivers in China between 1960 and 1999

Depth-hoar crystal growth in the surface layer under high tedl.perature gradient

MONITORING SNOWPACK TEMPERATURE GRADIENT USING AUTOMATIC SNOW DEPTH SENSOR

Estimated seasonal snow cover and snowfall in Japan

- SNOW - DEPOSITION, WIND TRANSPORT, METAMORPHISM

Proceedings, International Snow Science Workshop, Banff, 2014

Proceedings, International Snow Science Workshop, Breckenridge, Colorado, 2016

Multiple Choice Identify the choice that best completes the statement or answers the question.

Forecasting Experiments Using the Regional Meteorological Model and the Numerical Snow Cover Model in the Snow Disaster Forecasting System

Proceedings, International Snow Science Workshop, Breckenridge, Colorado, 2016

Preliminary Runoff Outlook February 2018

Proceedings, International Snow Science Workshop, Innsbruck, Austria, 2018

USING GIS FOR AVALANCHE SUSCEPTIBILITY MAPPING IN RODNEI MOUNTAINS

ROLE OF SYNOPTIC ATMOSPHERIC CONDITIONS IN THE FORMATION AND DISTRIBUTION OF SURFACE HOAR

Regional influence on road slipperiness during winter precipitation events. Marie Eriksson and Sven Lindqvist

Proceedings, 2012 International Snow Science Workshop, Anchorage, Alaska

A STUDY ON CHARACTERISTICS OF GROUND MOTION IN PERMAFROST SITES ALONG THE QINGHAI-TIBET RAILWAY

Extreme Weather and Climate Change: the big picture Alan K. Betts Atmospheric Research Pittsford, VT NESC, Saratoga, NY

2011 National Seasonal Assessment Workshop for the Eastern, Southern, & Southwest Geographic Areas

AVALANCHE WINTER REGIMES A SYSTEM FOR DESCRIBING AVALANCHE ACTIVITY CHARACTERISTICS

Since the winter of , when the studded tire

Flood Risk Assessment

CLIMATE. UNIT TWO March 2019

Pass, San Juan Mountains, Southwest Colorado

P. Marsh and J. Pomeroy National Hydrology Research Institute 11 Innovation Blvd., Saskatoon, Sask. S7N 3H5

World Geography Chapter 3

1.Introduction 2.Relocation Information 3.Tourism 4.Population & Demographics 5.Education 6.Employment & Income 7.City Fees & Taxes 8.

Definitions Weather and Climate Climates of NYS Weather Climate 2012 Characteristics of Climate Regions of NYS NYS s Climates 1.

Use of the models Safran-Crocus-Mepra in operational avalanche forecasting

Weather Report. PCAS Camp 2009/10. By Chris Mckenzie

Seasonal & Diurnal Temp Variations. Earth-Sun Distance. Eccentricity 2/2/2010. ATS351 Lecture 3

POTENTIAL DRY SLAB AVALANCHE TRIGGER ZONES ON WIND-AFFECTED SLOPES

Proceedings, International Snow Science Workshop, Banff, 2014

Monthly Long Range Weather Commentary Issued: APRIL 18, 2017 Steven A. Root, CCM, Chief Analytics Officer, Sr. VP,

Analysis of meteorological measurements made over three rainy seasons in Sinazongwe District, Zambia.

STUDY ON SNOW TYPE QUANTIFICATION BY USING SPECIFIC SURFACE AREA AND INTRINSIC PERMEABILITY

Monthly Long Range Weather Commentary Issued: APRIL 1, 2015 Steven A. Root, CCM, President/CEO

Near-surface Faceted Crystal Formation and Snow Stability in a High-latitude Maritime Snow Climate, Juneau, Alaska

Modeling variation of surface hoar and radiation recrystallization across a slope

1. GLACIER METEOROLOGY - ENERGY BALANCE

Plan for operational nowcasting system implementation in Pulkovo airport (St. Petersburg, Russia)

THE INVESTIGATION OF SNOWMELT PATTERNS IN AN ARCTIC UPLAND USING SAR IMAGERY

SNOW CREEP MOVEMENT IN THE SAN JUAN MOUNTAIN SNOWPACK RED MOUNTAIN PASS

3) What is the difference between latitude and longitude and what is their affect on local and world weather and climate?

Test Study on Strength and Permeability Properties of Lime-Fly Ash Loess under Freeze-Thaw Cycles

Minnesota s Climatic Conditions, Outlook, and Impacts on Agriculture. Today. 1. The weather and climate of 2017 to date

FACETED SNOW AND DEEP SLAB INSTABILITIES IN THE MARITIME CLIMATE OF THE CASCADES. Jon Andrews* Stevens Pass Ski Area

OBSERVATION AND MODELING OF A BURIED MELT-FREEZE CRUST

Rooster Comb Ridge Cornice Incident

Science 1206 Chapter 1 - Inquiring about Weather

Regional stability evaluation with modelled snow cover data

WHAT CAN MAPS TELL US ABOUT THE GEOGRAPHY OF ANCIENT GREECE? MAP TYPE 1: CLIMATE MAPS

Mass Wasting: The Work of Gravity

ESTIMATION OF NEW SNOW DENSITY USING 42 SEASONS OF METEOROLOGICAL DATA FROM JACKSON HOLE MOUNTAIN RESORT, WYOMING. Inversion Labs, Wilson, WY, USA 2

Climate.tgt, Version: 1 1

Proceedings, International Snow Science Workshop, Banff, 2014

Ch. 3: Weather Patterns

Climates of NYS. Definitions. Climate Regions of NYS. Storm Tracks. Climate Controls 10/13/2011. Characteristics of NYS s Climates

The Northern Hemisphere Sea ice Trends: Regional Features and the Late 1990s Change. Renguang Wu

The Extremely Low Temperature in Hokkaido, Japan during Winter and its Numerical Simulation. By Chikara Nakamura* and Choji Magono**

3. The map below shows an eastern portion of North America. Points A and B represent locations on the eastern shoreline.

Novel Snotel Data Uses: Detecting Change in Snowpack Development Controls, and Remote Basin Snow Depth Modeling

Dust Storm: An Extreme Climate Event in China

SENSITIVITY ANALYSIS OF THE RAMMS AVALANCHE DYNAMICS MODEL IN A CANADIAN TRANSITIONAL SNOW CLIMATE

ATMOSPHERIC CIRCULATION AND WIND

1' U. S. Forest Products Laboratory. Weathering and decay. U.S. Forest Serv. Forest Prod. Lab. Tech. Note 221 (rev,), 2 pp. 1956, (Processed.

Assimilation of satellite derived soil moisture for weather forecasting

AIR MASSES. Large bodies of air. SOURCE REGIONS areas where air masses originate

Local Ctimatotogical Data Summary White Hall, Illinois

1 Earth s Oceans. TAKE A LOOK 2. Identify What are the five main oceans?

Lesson 2C - Weather. Lesson Objectives. Fire Weather

Recent fluctuations of meteorological and snow conditions in Japanese mountains

M.Sc. in Meteorology. Physical Meteorology Prof Peter Lynch. Mathematical Computation Laboratory Dept. of Maths. Physics, UCD, Belfield.

The avalanche climate of Glacier National Park, B.C., Canada during

Monthly Long Range Weather Commentary Issued: February 15, 2015 Steven A. Root, CCM, President/CEO

Chapter 2: Physical Geography

ATMOSPHERIC ENERGY and GLOBAL TEMPERATURES. Physical Geography (Geog. 300) Prof. Hugh Howard American River College

Basic Hydrologic Science Course Understanding the Hydrologic Cycle Section Six: Snowpack and Snowmelt Produced by The COMET Program

The weather in Iceland 2012

DOWNLOAD PDF SCENERY OF SWITZERLAND, AND THE CAUSES TO WHICH IT IS DUE.

Lecture 07 February 10, 2010 Water in the Atmosphere: Part 1

Planed treatment recommendations

ESCI 344 Tropical Meteorology Lesson 7 Temperature, Clouds, and Rain

CHARACTERISTICS OF TRAFFIC ACCIDENTS IN COLD, SNOWY HOKKAIDO, JAPAN

Weather and Climate of the Rogue Valley By Gregory V. Jones, Ph.D., Southern Oregon University

Objective 3: Earth and Space Systems

1 A 3 C 2 B 4 D. 5. During which month does the minimum duration of insolation occur in New York State? 1 February 3 September 2 July 4 December

Proceedings, 2012 International Snow Science Workshop, Anchorage, Alaska

Chapter 15: Weather and Climate

SNOW CLIMATOLOGY OF THE EASTERN SIERRA NEVADA. Susan Burak, graduate student Hydrologic Sciences University of Nevada, Reno

HyMet Company. Streamflow and Energy Generation Forecasting Model Columbia River Basin

Great Lakes Update. Volume 199: 2017 Annual Summary. Background

Transcription:

CHARACTERISTICS OF AVALANCHE RELEASE AND AN APPROACH OF AVALANCHE FORECAST- ING SYSTEM USING SNOWPACK MODEL IN THE TIANSHAN MOUNTAINS, CHINA Osamu ABE 1*, Lanhai LI 2, Lei BAI 2, Jiansheng HAO 2, Hiroyuki HIRASHIMA 3, and Junrong XU 2 1 Snow and Ice Research Center, National Research Institute for Earth Science and Disaster Resilience, Shinjo, Japan 2 Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, China 3 Snow and Ice Research Center, National Research Institute for Earth Science and Disaster Resilience, Nagaoka, Japan ABSTRACT: We summarized the characteristics of avalanche releases in the Tianshan Mountains of western China based on observations of snow cover and avalanche occurrences. Grain types and stability indexes simulated by SNOWPACK models were then compared with actual observation results. Finally, the potential of using SNOWPACK modeling for predicting surface dry and full-depth avalanches was discussed. KEYWORDS: avalanche release, forecasting system, SNOWPACK model, Tianshan Mountains 1. INTRODUCTION The Tianshan Mountains, which are located in western China, make up an avalanche-prone area in the world. Recently, as economic activities in China have increased, transportation and communication networks have been improved, even in mountainous areas. According to the previous studies, avalanches in the Tianshan Mountains are characterized by frequent occurrences resulting from limited snow depths, thick depth hoar layers, snowy weather, and rapid increases in air temperature (Ma and Hu, 1990). In their studies, Hu et al. (1992) and Wei (1992) stated that small loose avalanches, normally released during or shortly after heavy snowfalls, were the most common avalanches occurring in the Tinashan Mountains. However, there have been few detailed investigations into avalanche release mechanisms (Wang, 1988) and no attempt has been made to apply an avalanche forecasting system. This is significant as traffic safety is becoming increasingly important, even in mountainous areas, and the need for an avalanche forecasting system is becoming increasingly self-evident. In this paper, we will begin by simulating snow by grain type and create a stability index (SI) for a typical slope using SNOWPACK model that is based on meteorological data obtained from an automatic weather station. We will then compare the results of our simulation with actual observed results. Finally, we will discuss the possibility of an avalanche forecasting system in the Tianshan Mountains based on the use of the SNOWPACK model. 2. STUDY AREA 2.1 Location For our study area (Fig. 1), we selected the central portion of the Tianshan Mountains where Route * Corresponding author address: Osamu ABE, Snow and Ice Research Center, National Research Institute for Earth Science and Disaster Resilience, 1400 Tokamachi, Shinjo 996-0091, Japan; tel: +81-233-23-8006; fax: +81-233-23-3353; email: oabe@bosai.go.jp Fig. 1 Location of the Tianshan Station for Snowcover and Avalanche Research and the surrounding study area. 1250

218 crosses the mountains from east to west, because it is a location where numerous avalanches are observed every winter. This location (43.26 N, 84.40 E, 1776 m above sea level (ASL)) is also home to the Tianshan Station for Snowcover and Avalanche Research operated by the Xinjiang Institute of Ecology and Geography of the Chinese Academy of Sciences. 2.2 Topographical conditions The upstream portion of the Yili River flows from east to west in a deep valley along Route 218. The mountain peaks along the river are higher than 4000 m. No remarkable forest growth is present on the south-facing slopes, but dense forests can be seen on the north-facing slopes. Consequently, there are numerous avalanche paths on the southfacing slopes, especially during clear weather conditions when the snow cover is exposed by strong solar radiation. Since most of the slope angles are steeper than 40 (Wei, 1992), we restricted our investigations to avalanches that occurred on the south-facing slopes. 2.3 Climate conditions The climate of the study area is classified as continental, which means dry and cold in winter. Warm and wet air masses driven by southwesterly winds sometime cross over the Tianshan Mountains in winter, bringing significant amounts of snowfall to this area (Hu, 2004). The average air temperature measured at Tianshan Station for the winter half of the year (October to March) is -3.7 C, and the average maximum snow depth is 80 cm (Guo et al., 2012). As is well known from previous studies (Ma and Hu, 1990; Hu et al., 1992; Wei, 1992), the most predominant snow type is depth hoar. 3. OBSERVATIONS 3.1 Meteorological observation At Tianshan Station, manual observations are conducted daily. Fig. 2 shows the maximum and minimum air temperatures for observations taken at 02:00, 08:00, 14:00, and 20:00 (Beijing time), along with the snow depth at 08:00 in the winter of 2015/16. The minimum temperature was -29.2 C on Jan. 22, 2016, and the maximum snow depth was 108 cm on Mar. 3, 2016. Additional meteorological data were obtained from an automatic weather station located at the foot of the mountains and adjacent to the snowpit field (Fig. 3), which is located at an elevation of 1790 m ASL. While the station records numerous elements every 30 minutes, only air temperature, relative humidity, wind speed, downward and upward solar radiation amounts, long-wave radiation, and snow depth were used in this study. Fig. 2 Maximum and minimum air temperatures and snow depth observed at Tianshan Station in the winter of 2015/16. Dates on which avalanche occurrences were observed and snowpit observations were carried out are also shown. 1251

wet avalanche, respectively. For the dry avalanche, snowpit observations were carried out at release zone slope on Jan. 23 (Fig. 3), at which time the slip surface (S) was observed just below the interface (B) between old and new layers. Fig. 3 Automatic weather station. However, it should be noted that during times of strong snowfall, downward solar radiation R d measurements were sometimes incorrect because accreted snow covered the pyrheliometer. In such cases, we reproduced R d by dividing the upward solar radiation R u value by the albedo. The selected albedo value was a measurement taken at 15:00 on a date just after the observed no-snow accretion. At this location, maximum solar radiation usually appears at 15:00 Beijing time (12:00 local solar time). Additionally, since snow depth records include some noise, especially during heavy snowfalls, resulting data losses were supplemented from correct data recorded before and after the lost data, thereby resulting in a complete revised data set. Fig. 4 Surface dry avalanche (a) observed on Jan. 23, 2016. B and S in (b) are the boundary between old and new snow layers, and the slip surface of the avalanche at P in (a). 3.2 Snowpit observation Snowpit observations were carried out in the field next to the automatic weather station approximately every three days from Jan. 24 to Mar. 12, 2016 (see bottom of Fig. 2). Measured parameters were grain type and diameter, and snow temperature and density. These results will be compared with our simulated results later. 3.3 Avalanche occurrences Avalanche occurrences were observed on slopes around Tianshan Station almost every day, and the dates when avalanches occurred after the middle of January 2016 were recorded (see symbol (*) in Fig. 2). Figs. 4 and 5 show typical examples of a surface dry avalanche and a full-depth Fig. 5 Full-depth wet avalanche observed on Feb. 27, 2016. 1252

Observations show that most avalanches occur either after a rapid increase in snow depth or when the maximum air temperature exceeds 0 C continuously for several days. It should also be noted that a strong earthquake occurred at 21:10 Feb. 11 Beijing time, and numerous avalanches were observed in the vicinity of Tianshan Station the next day. For example, avalanche debris was surveyed along Route 218 near the Tianshan Station on Jan. 25, Feb. 6, and Feb. 9. Ultimately, 22 debris locations were found along a 5 km-long stretch of the highway. All avalanches occurred on south-facing slopes, and two avalanches were confirmed to have crossed over the highway itself. 4. COMPARISON OF SIMULATED RESULTS WITH ACTUAL OBSERVATIONS 4.1 SNOWPACK model The SNOWPACK model is one of the most accurate types for simulating snow layer structures and producing slope SIs (Bartelt and Lehning, 2002; Lehning et al., 2002a, b). In this study, two ver- Fig. 6 Comparison between observational and simulated grain type results. Fig. 7 Simulated results produced by the revised version of the SNOWPACK model. 1253

sions of the SNOWPACK model were used: the original version and a revised version that was created by implementing a dry snow metamorphism (DSM) factor to enhance dry snow avalanche predictions (Hirashima et al., 2009; Hirashima et al., 2011). 4.2 Grain type Fig. 6 shows a snow grain type comparison between the simulated and observed results. In the observed results, more than half of the depth hoar layer appeared near the bottom until the end of February 2016, and the simulated results produced by the original SNOWPACK model were found to correspond closely with the observed results. However, the simulated results produced by the revised version did not reproduce the thick depth hoar layers, primarily because it was modified based on grain type descriptions for snowy regions of Japan (Hirashima et al., 2009). 4.3 Stability index To calculate the SI, we assumed that the slope angle and aspect were 40 and 180 (south), respectively, thereby matching the conditions in Section 2.2. Fig. 7 shows the SI simulated by the revised version. As can be seen in this figure, unstable periods are sometimes shown just after strong snowfalls (cases A to D). Note that the longest unstable period appeared during case A, but unfortunately no record on avalanche occurrences during that period. In case B, a weak layer appeared just below the boundary between the old and new snow layers. In cases C and D, as case B, a weak layer appeared just below the boundary, and full-depth wet avalanches occurred during these periods. In the same periods, however, no unstable layers were reproduced at the bottom layers, and a minimum value of SI = 4.8 was calculated on Feb. 24. 5. POSSIBILITY TO PREDICT AVALANCHE DANGER USING SNOWPACK MODEL 5.1 Surface dry avalanche In case B (see Section 4.3), a weak layer is simulated just below the old snow layer. This weak layer was assumed to consist of faceted crystals. Fig. 8 shows two types of time variations for the minimum SI values simulated by SNOWPACK models both with and without the DSM factor observed during the period from Jan. 17 to 24 (see Fig. 6). The lowest value, SI min, along with a number of avalanches was observed on Jan. 19. However, the SI min with the DMS factor is lower than that without the DMS factor. Fig. 9-a shows a time series of SI profiles from Jan. 18 to 22 at 12:00 local solar time. As shown, the minimum value occurred at a height of 57 cm on Jan. 19, and a slip surface was observed on a slope at almost the same height, as shown in Fig. 4. The same weak layer was observed at just below the snowpit field boundary on Feb. 24 (Fig. 9-b). After conducting a compression test, we confirmed that this layer was the weakest in the snow cover. As shown in Fig. 2, weather conditions tended to clear immediately after snowstorms. However, Fig. 8 Changes to the minimum SI value, SI min, from Jan. 17 to Jan. 24, 2016. *D indicates the period shown in Fig. 9. (a) (b) Fig. 9 SI profiles simulated from Jan. 18 to 22 (a: left), and a weak layer (*) observed in the snowpit field (b: right). * 1254

while clear weather days would then continue until the next snowfall, the maximum air temperatures did not normally exceed 0 C. During the daytime of this period, sunlight penetrates the snow surface and increases the temperature just below the surface (Yoshida, 1960; Colbeck, 1989; Ma et al., 1992), especially on the south-facing slope. In contrast, the snow layer near the surface is exposed to a steep heat loss gradient at night due to the absence of warming solar radiation, which causes a weak layer consisting of faceted crystals to form quickly (Akitaya and Shimizu, 1987). Therefore, a surface dry snow avalanche prediction can be made easily for case B by detecting a low SI. However, in cases C and D, after the snow melt season begins, it is necessary to consider the potential for full depth wet avalanches in addition to surface dry snow avalanches. 5.2 Full depth wet avalanche As mentioned in Section 4.3, even though fulldepth avalanche occurrences were observed during the period of Feb. 25 to 27, 2016, low SIs that indicate avalanche occurrences were not simulated at the snow cover bottom. However, based on the snowpit observations, temperatures near the bottom of the snow cover were observed at the melting point of snow (0 C) between Feb. 23 and 27 (Fig. 10). Fig. 11 shows the simulated snow temperatures produced by the revised SNOWPACK model from Feb. 23 to 27 at 12:00 local solar time. As shown, snow temperatures near the surface vary widely due to the presence or absence of warming solar radiation, while remaining nearly constant at a height of 50 cm because of the existence of a layer saturated with water. However, snow temperatures between 0 and 50 cm in height were found to gradually approach 0 C, and the simulation period when temperatures near the bottom of the snow cover reached 0 C agreed well with actual observations. Therefore, for full-depth wet avalanches, temperatures at the snow cover bottom provide a good indicator of the avalanche danger level because the strength of the wet snow layer declines rapidly (McClung and Schaerer, 2006). Recently, a water transport model with constant diameter particles for each layer of layered show was incorporated into the SNOWPACK model (Hirashima et al., 2010). For the near future, however, in order to accurately simulate weak layers consisting of depth hoar crystals and liquid water, Fig. 10 Snow temperature profiles observed from Feb. 20 to Mar. 4, 2016. Fig. 11 Snow temperature profiles simulated from Feb. 23 to Feb. 27, 2016. it will be necessary to conduct further investigations on liquid water movement through depth hoar layers, and to more fully determine the effect of water content on shear strength. 1255

6. CONCLUSIONS In this paper, we summarized the characteristics of avalanche release based on observations of snow cover and avalanche occurrences in the Tianshan Mountains of western China. In our investigation, grain types and SIs simulated by SNOWPACK models were compared with actual observation results. We then discussed the possibility of avalanche predictions for surface dry and full-depth wet avalanches using the SNOWPACK model. The following conclusions were obtained: Most avalanches occur on south-facing slopes with no remarkable forest cover and where the snow cover is exposed to strong sunlight. Surface dry avalanches occur at the slippery surface of a depth hoar layer just below the boundary between old and new snow layers during strong midwinter snowfalls. Full-depth wet avalanches occur as snow temperatures near the bottom approach 0ºC in spring. When examining grain type, the original SNOWPACK model reproduces the depth hoar layers at the snow cover bottom more accurately than the revised model. Consequently, while a low SI level can provide direct indications of avalanche danger for surface dry avalanches, they provide no direct indicator for full-depth wet avalanches. For those events, snow temperatures at the bottom should be used instead of an SI. Further investigations into liquid water movements through depth hoar layers and determinations on how significantly the water content affects shear strength will be needed. ACKNOWLEDGEMENTS We would like to acknowledge Wang Haicun for his manual observations at Tianshan Station. This study was supported by Chinese Academy of Sciences International Fellowship for Senior International Scientists, Grant No. 2016VEA024. REFERENCES Akitaya, E. and H. Shimizu, 1987: Observations of weal layers in a snow cover, Low Temperature Science, Ser.A, 46, 67-75. (in Japanese with English abstract) Bartelt, P. and M. Lehning, 2002: A physical SNOWPACK model for the Swiss avalanche warning Part I: numerical model. Cold Regions Science and Technology, 35, 123-145. Colbeck, S.C., 1989: Snow-crystal growth with varying surface temperatures and radiation penetration. Journal of Glaciology, 35, 23-29. Guo, L., L. Li, J. Xu, L. Bai, and X. Li, 2012: Experimental study on simultaneous observation of snowmelt and soil moisture content under air temperature increase. Arid Zone Research, 29, 890-897. (In Chinese with English abstract) Hirashima, H., O. Abe, A. Sato and M. Lehning, 2009: An adjustment for kinetic growth metamorphism to improve shear strength parameterization in the SNOWPACK model. Cold Regions Science and Technology, 59, 169-177. Hirashima, H., S. Yamaguchi, A. Sato and M. Lehning, 2010: Numerical modelling of liquid water movement through layered snow based on new measurements of the water retention curve. Cold Regions Science and Technology, 64, 94-103. Hirashima, H., O. Abe and A. Sato, 2011: Parameterization of the shear strength of faceted crystals during equitemperature metamorphism. Annals of Glaciology, 52, 111 118. Hu, R., 2004: Physical geography of the Tainshan Mountains in China. Beijing, China Environment Science Publishing, 443pp. (in Chinese) Hu, R., H. Ma and G. Wang, 1992: An outline of avalanches in the Tien Shan mountains. Annals of Glaciology, 16, 7-10. Lehning, M., P. Bartelt, B. Brown, C. Fierz and P. Satyawali, 2002a: A physical SNOWPACK model for the Swiss avalanche warning Part II: Snow microstructure. Cold Regions Science and Technology, 35, 147-167. Lehning, M., P. Bartelt, B. Brown and C. Fierz, 2002b: A physical SNOWPACK model for the Swiss avalanche warning Part III: meteorological forcing, thin layer formation and evaluation. Cold Regions Science and Technology, 35, 169-184. Liu, Z., L. Sun and C. Cai, 1992: Snowmelt among the western Tianshan Mountains in China. Cold Regions Science and Technology, 20, 313-314. Ma, W. and R. Hu, 1990: Relationship between the development of depth hoar and avalanche release in the Tian Shan mountains, China. Journal of Glaciology, 36, 37-40. Ma, H., Z. Liu and Z. Yang, 1992: Temperature regime studies and mathematical calculations for dry snow covers in the western Tien Shan mountains, China. Annals of Glaciology, 16, 185-189. Ma, H., Z. Liu and Y. Liu, 1992: Energy balance of a snow cover and simulation of snowmelt in the western Tien Shan mountians, China. Annals of Glaciology, 16, 73-78. McClung, D. M. and P. A. Schaerer, 2006: The Avalanche Handbook. 3rd ed The Mountaineers, 347 pp. Wang, Y., 1988: The relation between the growth of seasonal depth hoar and the avalanches in China. Journal of Glaciology and Geocryology, 10, 173-180. (in Chinese with English abstract) Wei, W., 1992:Avalanches in the Tien Shan mountains, China. Annals of Glaciology, 16, 140. Yoshida, J., 1960: Internal melting of snow due to the penetrating sunlight. Low Temperature Science, Ser. A, 19, 97-110. (in Japanese with English abstract) 1256