A New Approach to Avalanche Forecasting and Disaster Management for Western Himalaya

Transcription

1 A New Approach to Avalanche Forecasting and Disaster Management for Western Himalaya Ashwagosha Ganju and Amreek Singh Synopsis Every winter snow avalanches claim a number of precious lives of civil and military personnel as well as damage property worth millions in high altitude, snow bound regions of Western Himalaya. Many active and passive methods have been developed to mitigate the danger posed by the avalanches. The preferred methods for the mitigation of the hazard remain to be structural control and artificial release of avalanches (active methods). Avalanche forecasting (passive method) is implemented where the active methods cannot be applied, and aims at avoiding the avalanches by forecasting their occurrence accurately. However, the shift to active control methods may assume priority with the increasing need for absolute safe travel and stay in snow bound mountainous regions. Conventionally, avalanche forecasting is attempted by inferring current snowpack stability and assessing its behaviour subject to projected weather conditions. This approach is subjective in nature and relies on other important factors e.g. future weather, physical and mechanical state of snow cover, and past avalanche occurrence, which by itself are subjective and unavailable to the forecaster in time. Site specific avalanche forecast is difficult and generally it refers to average slope stability condition over a region. Any improvement in the above mentioned approach is bound to result in improved prediction of avalanches. Research in snow and mountain meteorological science may take some time to bridge the above-mentioned gap but men and material need to be protected from the menace of avalanches. Besides the above, avalanche disaster management has always been a problem area so far as Indian Himalaya is concerned. Remoteness of area, inhospitable terrain, extremely cold climate and bad weather conditions are some of the impediments that pose serious problems in avalanche disaster management. The paper brings out a new approach to prediction of avalanches and management of avalanche disasters by integrating modern communication and information technology with conventional techniques. The communication of field data through remote sensing satellites and modern telemetry system has the potential to overcome the limitations of non-availability of timely data on snow stability and magnitude of disaster. Similarly digital maps, GIS and GPS are going to help in assessing quickly the gravity of situation and in better perspective. This will help in mobilizing resources in shortest possible time to evade major disaster. INTRODUCTION Due to strategic as well as commercial reasons, human venturing in snow bound regions of western Himalaya has multiplied manifolds in recent times. Many new routes have been opened and habitation areas have spread out deep into mountains. With this exodus has also increased the vulnerability of mankind to avalanche threat. This is an issue of deep concern and demands immediate attention. Ultimate control of avalanches is possible only through active methods, which include installation of structures in the three zones of avalanches, afforestation and artificial release of avalanches under controlled conditions. However, these methods are often either economically unviable or! Snow & Avalanche Study Establishment, HIM PARISAR, Sec - 37A, Chandigarh

2 difficult to execute. The best option thus zeros down to passive methods; that is prediction of avalanches and generating awareness among backcountry travellers. The avalanche forecasting involves assessment of current stability of the snow pack lying on the mountain slope and subjecting it to future weather conditions. In operational mode, the task involves collection of snow pack and meteorological data at various locations in the region (preferably in avalanche formation zones), transmission of the data to avalanche forecasting centres for detailed analysis, inferring the present and future snow pack stability and dissemination of the warning to end-users. This is achieved by following an integrated approach (Ganju, 1997) for the prediction of avalanches. The key components of avalanche forecasting thus are communication, knowledge about physical and mechanical properties of snow, snow pack variability across mountain slopes, terrain information, weather forecast and useful inferences from past data. Improvement in any of these components will have significant implications on the accuracy of avalanche forecast. Conventionally, an avalanche forecaster mentally assimilates current snow and meteorological information with respect to snow stability in the backdrop of his knowledge and experience about local terrain to arrive at the final decision. This approach is subjective in nature and the decisions may vary from forecaster to forecaster. The inherent variability in the properties of the snow pack across the slope is also poorly represented in the conventional techniques followed by the avalanche forecasters. The strategy used for data assimilation is difficult to explain. The final forecast remains region based, far from the desired requirement of area and period based. In nutshell this technique is learned through long experience and exposure in the field with very little to gain. With the increasing demand to cover larger area, the requirement of more experienced avalanche forecasters, data observatories, and disaster management infrastructure is also to be thought of. In this scenario, the technological developments in the fields of communication, logical computation, database management and Geographical Information System (GIS) are proving very helpful in optimizing the available resources (Ganju, 2000). Automatic Weather Stations (AWS) are proving a suitable alternate to manned observatories. Data transmission through satellite-based networks is more robust and error-free. The experience and knowledge of forecasters is translated in the development of expert systems. The terrain information is now available on Digital Elevation Maps (DEM) and layers of information can be made available through powerful Geographical Information System (GIS) packages. The state-of-the-art communication tools and Global Positioning System (GPS) are proving helpful in mobilizing the resources to the affected areas in any eventuality. With all these advancements, the site and time specific dream forecast may be still far away, yet apparently above the horizon. This paper presents an approach to avalanche forecasting where the emphasis is on integrating state-of-the-art technology with conventional tools to meet the general desire of area and period specific forecast. A disaster management plan has also been discussed emphasising on reduction of time elapsed in mobilising rescue operations. GENERAL CLIMATOLOGY OF WESTERN HIMALAYA The western Himalayan region comprises diverse snow climatic zones. Sharma and Ganju (2000) broadly classified the Western Himalaya into three zones namely Lower Himalayan zone, Middle Himalayan zone and the Upper Himalayan zone (Figure 1). A brief description of the important terrain and meteorological factors of these three main zones of Western Himalaya is presented in Table 1. There are sub-zones within these zones, which are characterized by similar

3 Table 1. Terrain & meteorological factors of Western Himalaya (Sharma & Ganju, 2000) Factors Terrain factors Altitude (m) Slope Ground Lower Himalayan Zone (76%) (64%) Tall grassy cover Meteorological factors Snowfall in a storm (cm) (56%) Average total yearly snowfall 1800 (cm) Temperatures ( o C) Highest max 20.2 Mean max 6.8 Mean min -1.6 Lowest min Middle Himalayan Zone (100%) (75%) Scree and boulders (81%) Upper Himalayan Zone (100%) (67%) Rocky, scree and glacial (51%) snow-meteorological conditions. The conventional forecasting scheme generates forecast for each of these sub-zones, which could be hundreds of kilometers apart. The task ahead is to convert this regional forecast into local forecast as per the user s requirement. STATE-OF-THE-ART Snow and Avalanche Study Establishment (SASE) is the only institute in south Asia working in the field of snow and avalanche research. The main operational area of SASE is Indian western Himalaya including the snow bound regions of Jammu & Kashmir, Himachal Pradesh and Uttaranchal. Since its inception in 1969, SASE has taken many giant leaps for improving the state of avalanche forecasting in western Himalaya. Data Observation and analysis SASE has a network of 46 manned observatories spread over the various parts of Indian Western Himalaya, wherefrom snow-meteorological data is regularly passed to its Research & Development Centre (RDC) at Chandigarh through radio/satellite communication. These observatories are located between 1500m to 5400m above mean sea level. The locations of some of the observatories are shown in Figure 1. Snowmeteorological parameters being observed here include air temperature, snow-surface temperature, wind speed and direction, relative humidity, standing snow, fresh snowfall amount, fresh snow density, incident and reflected short-wave solar radiation, atmospheric pressure, cloud amount & type and sunshine duration. These observatories also conduct snowpack stability tests and pit observations. Avalanche occurrences taking place in the representative areas of the observatories are also recorded by the observers. In addition to above, SASE has installed 16 Automatic Weather Stations (AWS) also in different parts of Western Himalaya. These AWS are equipped with sensors to measure air temperature, incident and reflected shortwave radiation, wind speed and direction, sunshine duration, relative humidity, water equivalent of precipitation, snow depth and atmospheric pressure. These stations operate on battery powered by solar panels. A station can be configured to measure different parameters on given frequency and store the representative value over the preset time interval in the data-logger. Presently the stations store representative value of each parameter over a time period of one hour. The stored data is then transmitted to RDC at regular intervals through satellite transponder. The data received at RDC both from manned observatories as well as from AWS is compiled and archived in a central database. The data thus collected from different observatories and automatic

4 Figure 1: Snow-avalanche climatic zones and the network of SASE observatories in Western Himalaya. weather stations is analyzed and used for generating forecast from different avalanche forecast models developed at SASE. Use of predicted weather The forecast weather is an important ingredient in the prediction of avalanches. This information till recently was available only on regional scale and was of qualitative character. However, recent advances at SASE have seen generation of meso-scale forecast for the mountainous region. Generation of forecast The avalanche forecast at SASE is generated with the help of a few statistical models developed over a period of time. However, these models are used as guides only. The actual forecast is drawn by experienced forecasters using a combination of schemes that includes building of snow cover at various stations and using contributory factors approach (Agrawal & Ganju, 1994). The final forecast issued to various users is regional in nature. The forecast is disseminated to various users directly or through SASE observatories using different modes of communication. Recently an attempt has been made to generate visual forecasts for easy comprehension, which would take some time to stabilize. Disaster management The disaster management is entirely done by either state governments or by army units who have drawn their own contingency plans for meeting such eventualities. The plans, though elaborate are not based on speed. Also operating procedures being followed are needed to be redrawn. Limitations/bottlenecks The entire avalanche forecasting and disaster management schemes need to be given a fresh thought to help people to carry out effective operations in mountains. The forecasting scheme at SASE has to be made user oriented and not left to them for guessing situations in their respective areas.

5 This is possible through user s participation only. Similarly the contingency plans for meeting any avalanche eventuality need to be beefed up keeping rapidity the main criterion in mind. APPLICATIONS OF MODERN TECHNOLOGY Besides improvements in current forecasting scheme, the expansion of avalanche forecasting and disaster mitigation services to newly adopted areas is the need of the hour. All the key components of avalanche forecasting are required to be spruced up to address the changing environment. The avalanche disaster management plan should be reliable enough in responding to the never before demands. With limited resources at our disposal, the potential of modern technological developments has been explored in this direction. Following is an account of some of the modern technology developments being put to use in the field of avalanche forecasting in western Himalaya Automatic Weather Stations Automatic Weather Stations (AWS) are the future of data observation. Working in an autonomous mode on solar panel and battery, these are capable of observing snow-meteorological data in most harsh condition at any desired interval. The data is directly received at Earth Receiving Station at SASE through satellite transponder. The advantage with the AWS is that, they can be installed even in the formation zones of an avalanche sites before the onset of winter. Since the data observation is completely software guided, it is free from any human error. Knowledge about Physical and Mechanical Properties of Snow (Snow Cover Model) Snowpack information is vital to stability assessment. At present, this is observed manually by digging a pit in the snowpack. Snow Cover Model (SCM) is an attempt to Figure 2: Temperature profile simulated by Snow Cover Model and comparison with two actual observations for station Dhundi (HP) during winter. bypass the need of digging a pit for snowpack observation. SCM is a computer aided numerical method to simulate structural characteristics of snowpack as a function of meteorological data observed above the snowpack surface (Brun et al, 1989; Ganju et al, 1994; Durand et al 1999). The numerical analogues of physical and mechanical properties of snow are used to simulate density profile, temperature profile, grain size, shape parameters - dendricity & sphericity (Brun et al, 1992) and water content of snowpack. The recent version of SCM developed at SASE, after incorporating the latest snow physics and mechanics research outputs (Satyawali, 2000; Mahajan, 1993), is capable of simulating the snowpack at any location, subject to the availability of above-surface meteorological data of the location. Temperature profile as simulated by SCM for one of the SASE research station named Dhundi (Altitude 3050m) in Pir Panjal range of Himachal Pradesh for winter is presented in Figure 2. The model may be integrated with AWS to simulate the snowpack structure. Such an arrangement can prove an alternative to manual observatory as far as snowpack and meteorological data observation is concerned. However, the avalanche

6 occurrence observation in the area is not possible through this arrangement. Statistical Models With the development of high-end computational and data storage facilities, it has been possible to develop complex numerical/statistical tools for avalanche forecasting, using past snow-meteorological and avalanche occurrence data. In this direction, Nearest Neighbour Model (Buser, 1983), a statistical tool to forecast avalanche occurrence probability in a pre-defined area, has been very successful in western Himalayan region. It compares the present snow-meteorology condition with the past ones and analyses the avalanche activity of similar days in the past to arrive at a conclusion. The results of avalanche prediction by Nearest Neighbour Model during winter in Chowkibal- Tangdhar axis in Jammu & Kashmir region are presented in Figure 3. Knowledge Based Expert System The number of experienced forecasters is limited. It takes years of continuous observations and experience for a layman to master the technique of avalanche forecasting. Still because of high level of subjectivity involved, the decisions vary from forecaster to forecaster. The attempts are on to make the computers learn the knowledge and experience gathered by avalanche forecasters using advanced artificial intelligence and fuzzy logic based tools such as expert system (Schweizer, 1996), which are capable of handling symbolic and logical analysis. Our initial experiments with these tools for avalanche forecasting in western Himalayan regions have been very encouraging. Figure 4 presents the results of avalanche forecasting using Fuzzy expert system for Chowkibal- Tangdhar axis in Jammu & Kashmir in western Himalaya. Satellite Imageries Real-time weather pictures received through HRPT (High resolution Picture Transmission) systems in various Visible, Near Infrared and thermal bands through NOAA, Feng-Yun and METEOSAT series satellites can provide the forecaster with a sound idea of possible precipitation pattern. The µm band sensor onboard Figure 3: Results of avalanche prediction by Nearest Neighbour model during winter for Chowkibal-Tangdhar axis in Jammu & Kashmir (India).

7 Figure 4: comparison of actual and predicted avalanche activity using expert system in Chowkibal-Tangdhar axis in Jammu & Kashmir (India). NOAA-15 is good for snowcover-clouds discrimination. The images received in this spectral range can be used to assess the change in spatial extent of snowcover. Thermal band images can provide variation in snowpack surface temperature. Avalanche occurrence feedback is important for the fine-tuning of statistical models and expert system. This feedback can also be useful in subsequent identification of the areas where avalanche risk has reduced among a high-risk area. The avalanche occurrence observation is beyond the present capability of AWS and human observers have their own limitations. A potential application of 250m resolution MODIS (Moderate Resolution Imaging Spectroradiometer) imageries is the observation of avalanche occurrences through change detection methods. With this methodology in practice, the requirement of manual observatories can be completely done away with, as the necessary snowmeteorological information can be received through SCM-AWS team. High resolution IRS and PAN images may be used to generate Digital Elevation Maps (DEM). A DEM may be subjected to analysis with respect to terrain parameters to generate digital avalanche atlases for reference by the users. Using a combination of imageries and data of optical (IRS, LANDSAT, SPOT) and microwave sensors (RADARSAT, ERS), it is possible to delineate information related to snow pack characteristics. Using SSM/I data snow thickness, scattering index, snow water equivalent, etc. can be known by carrying out the analysis of brightness temperature data (Dash et al, 1999). The spatial and temporal knowledge of snowcover characteristics can help in identifying avalanche prone areas. Although the techniques would take some time to stabilize, the information level obtained through satellite would be of immense help to avalanche forecaster Geographical Information System Avalanche formation is a localised phenomenon and highly depends on the terrain parameters (altitude, slope & aspect of the formation zone) besides the local weather pattern around it. While terrain factor is somewhat fixed, the weather needs to be continuously monitored on real time basis for avalanche risk assessment. Having a Digital Elevation Map (DEM) of entire Himalaya at its background, a

8 Figure 5: A conceptual example of varying avalanche danger levels over a snowcovered area as depicted by GIS. customized Geographical Information System (GIS) can provide an easy and comprehensive access of the current weather and snowpack status. The various stability levels deduced through SCM can be projected on DEM and zones with varying degree of avalanche danger can be delineated. A conceptual example of representation of zones of various degrees of avalanche danger over a snow-covered area as depicted by GIS is presented in Figure 5. The identification of safer and dangerous routes at a particular time would thus be easy to make this way. Weather Forecast Knowledge of approaching weather is vital to avalanche forecasting. It is further better, if weather forecast is available at higher spatial and temporal resolution. Under the project PARWAT (A joint project between SASE, Indian Meteorological Department and Indian Army), weather forecast is now available at a spatial resolution of 30 km grid for up to day-4 (Figure 6). The 5 th generation model - MM5 (Das, 2000), to simulate meso-scale or regional scale atmospheric circulation, has been customised for this purpose. Further attempts are on to draw forecast at a spatial resolution of 10 kms grid. Figure 6: A sample of meso-scale (30km grid) precipitation forecast as generated under project PARWAT for western Himalaya Operational Avalanche Forecasting With the incorporation of above-mentioned applications of modern technology, we envisage a comprehensive scheme of operational avalanche forecasting for western Himalaya. A schematic for the same is presented in Figure 7. The following explains in detail the overall implementation of the scheme The knowledge of variability in snowcover properties across an area is achieved through SCM using meteorological data provided by a dense network largely of AWS. SCM is connected to the central database server at SASE. All observatories and AWS have a direct communication with central database through Earth Receiving Station (ERS) at SASE. Every hour, as the new snowmeteorological observations reach SASE and database is updated, SCM simulates the latest snowpack status for the corresponding location and writes the results back into the database. Further, the output of knowledgebased expert system and statistical Nearest Neighbour model is considered for corroboration of the decisions about the current stability conditions on the slopes. In the next step, projected weather provided under the project PARWAT is considered to assess the implication of future weather on the snowpack stability across the area.

9 Figure 7: A schematic of the operational avalanche forecasting for western Himalaya. Based on the snowpack stability assessment and probabilistic determination of avalanche occurrences, the entire area is delineated in terms of different avalanche danger levels (Low, Medium, High or All round) for short term (24 hours) as well as long term (72-96 hours) periods. Accordingly, Avalanche Warning Bulletins (AWB) for various regions are prepared and are called regional AWB. These AWB can be uploaded on an Info Server. The AWB can be updated after every 3 hours interval, on arrival and analysis of new data. Besides the regular practice of disseminating the warning by Tel, Fax and radio communication, Info Server can be connected to www and users through Internet can thus access the visual warning bulletins. A sample visual forecast bulletin for National Highway 1A in Jammu & Kashmir is presented in Figure 8. On receiving the regional AWB, the local forecasters/users further fine-tunes the forecast according to spot slope stability tests (McClung and Schaerer, 1993) carried out by them to generate local avalanche forecast (limited to a few nearby slopes). Disaster Management The extent to which avalanches can reach the valleys and damage facilities can to a certain extent be calculated. However, same sites behave differently even under similar weather and avalanche situations year after year. Depending on the amount of mass released from the formation zone, avalanche debris fans out in the run-out zone. By knowing exactly the distance to which avalanche debris fans out, the roads through the avalanche terrain can be laid out properly. Similarly, the movement of personnel through the avalanche terrain can be properly marked. GPS can thus be used effectively to pin point the dangerous zones on maps and avoid mishaps. In case of any avalanche eventuality, communication of exact coordinates of the location of the accident through GPS based Avalanche

10 Figure 8: An example of visual avalanche warning bulletin issued for NH-1A road axis in Jammu & Kashmir. Victim detector (AVD), can help rescue attempts in poor visibility conditions. Avalanche forecaster would be able to provide better forecasts, if the exact sites that have triggered earlier are communicated to him with precise dimensions of the avalanche debris. The important aspect of avalanche disaster management is to generate awareness in masses about the snow and avalanches. Regular avalanche awareness cadres are organised by SASE for Indian Army in various parts of western Himalaya. The classroom and field training is imparted to the users on topics such as formation of avalanches, identification of avalanche prone slopes, safe movement in avalanche prone areas, safety & rescue gadgets etc. However, more organised efforts are required in this direction on the part of state governments for the general public. CONCLUSIONS Modern technology tools can be effectively used in the field of avalanche forecasting and disaster management. In general, prediction of any natural phenomenon is as such a difficult task because of many unknowns. Prediction of avalanches has an added disadvantage of non-availability of information in time. Nevertheless, avalanche forecasters have to perform with whatever little information is available to them. With the aim to improve on spatial and temporal resolution of avalanche forecast for Western Himalaya, SASE has initiated integration of Snow Cover Model with remote Automatic Weather Stations and observatories through an automatic receiving and updating system. This task also involves integration of a central relational database management and high-resolution satellite weather monitoring system. This shall provide an objective basis for the assessment of snowcover stability and eventually degree of avalanche danger at regional and local levels. Tools like GIS, GPS and satellite data can be helpful and effective as they give factual information of the ground as well as the changes taking place, periodically. Though at present such tools are in a primitive stage, their potential is very high in near future. ACKNOWLEDGEMENTS Author gratefully acknowledges the constant encouragement and enlightenment extended by Maj. Gen. (Retd.) S.S. Sharma KC, VSM, Director SASE, which paved the way for this paper. REFERENCES Agrawal, K C, A Ganju, 1994: A semiquantitative approach to avalanche forecasting, Proceedings SNOWSYMP- 94. Snow and Avalanche Study Establishment, Manali (HP), INDIA, Brun E, E Martin, V Simon, C Gendre and C Coleou, 1989: An energy and mass model of snowcover suitable for operational avalanche forecasting. Journal of Glaciology, Vol. 35, No 121, Brun E, P David, M Sudul and G Brunot, 1992: A numerical model to simulate snow-cover stratigraphy for operational avalanche forecasting, Journal of Glaciology, Vol. 38, No. 128,

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