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Environmental Statistics and Decision Support Systems for Disaster Risk Management - Technical Session 1


Themes and Objectives of the Technical Session 1

Environmental Statistics for Disaster Risk Management

Environmental statistics is an established system in India and Social Statistics Division of MoSP annually publishes a compendium of environmental statistics. The compendium of environmental statistics at National/State and District level can be put into use for assessments and planning pertaining to DRM:
Objectives: Inform about available data and sources that are of relevance for DSS/DRM, their structure and quality (with examples) and the use of statistics as a tool

Disaster Databases and Applications in Disaster Management (DM)

Disaster Databases concerning disaster events and their impacts: date/time, location/extent, damages/losses, relief, institutions involved, recovery and costs, etc. for developing disaster reports and to help in HRVC analysis and formulating Disaster Management Plans at District & State levels and departmental plans.
Objectives: Discuss about the available data, data needs, data gaps, issues related to quality, availability and scales; cooperation between different departments at different levels (district/State/national/regional)

Applications of Geoinformatics for Developing a Decision Support System (DSS)

Role and potential of space technology in Disaster Risk Reduction for hazard monitoring, mapping, risk assessment and impact assessment
Geoinformatics tools and methodologies for developing DSS on environment and Disaster Management including integration of spatial non spatial data, mapping and interpretation allowing overlay, scaling/integration, catastrophic risk models and DSS for floods, drought, cyclones etc.
Objectives: Discuss with examples on the potential of space technology and geoinformatics tools and technologies, data availability, issues and challenges and way forward to optimal use of geoinformatics for Disaster Risk Reduction.

The importance of Agriculture Information System for Disaster Risk Reduction (DRR)

Natural resources system and in particular agriculture as primary concern in environment disasters management, and complex set of information needed viz. on soil, species/cropping system, water-use/availability, pests, fertilizers, diseases, fire, agro-wastes, livestock, fodder, fuels, reservoirs/water bodies, climate, etc.
Objectives: Discuss the data available in agricultural information systems that are of relevance for DRR/DRM; use for predictions of production losses and long term food supply planning – sources of data, availability, quality


Accepted Contributions - Abstracts


Environmental Knowledge for Disaster Risk Management, Challenges in Integrating Geospatial Technologies
Indian Institute of Remote Sensing ISRO, Dept. of Space, Govt. of India, Dehradun

FLOOD WARNING IN BAVARIA, GERMANY
Alfons Vogelbacher (on behalf of GIZ)
Flood Information Centre, Bavarian Environment Agency, Munich, Germany


Timely warning of flood hazard is an essential part of precautious flood protection. But effective flood mitigation also requires the preparedness of the recipients. The flood warning service in Bavaria has performed valuable work for successful mitigation of flood damage for more than 100 years. It covers the river basins in Bavaria with response times ranging from 6 to 12 hours through to the larger basin of the Danube with response times ranging from 1 to 3 days. In the last century it used to be rather an information service, the forecasting of water levels was limited to only a few gauging stations. Since the flood catastrophe in May 1999 in Bavaria, the action program 2020 for a sustainable flood protection in Bavaria integrates structural and nonstructural means to minimize flood damage. The improvement of the flood warning service was part of this program. Five flood forecast centres corresponding to the main river basins (Main, Danube, Inn) and tributary basins where large reservoirs have to be operated (Iller-Lech, Isar) are responsible for operational flood forecast. They closely co-operate with the flood information centre and the 17 state offices for water management. Transboundary water courses like the Danube and Inn river in Bavaria requires a close cooperation with Austria in terms of flood forecast and flood warning and has lead to a unique forecast system in these basins.

As a prerequisite for flood forecast a meteorological and hydrological information system and database with a fully automated data communication system has been created in the last ten years. Following the lessons learned during the floods of May 1999 and August 2002, the focus was on the reliability and availability of the main system-parts. Realtime data collection, online data base, generation of automated products and the dissemination of the data and products are based on redundant systems at several locations.

Hydrological forecasts have become an important part of the flood warning scheme since they are calculated for all river basins in the Bavarian Danube Catchment. For large parts of Bavaria, flood forecast models of modular structure have been developed, verified and adopted. The hydrodynamic models WAVOS (Danube and Main) and FLORIS 2000 (Lech, Inn and Danube) are in operation. For the tributaries, rainfall–runoff models based on the program LARSIM are implemented. They are grid or sub-basin oriented. Numerical weather forecast of the German Weather Service are mainly used as input to the hydrological forecast models. The latest product is the short-term precipitation forecast using the results of online-adjusted radar. Development of snow cover and the total water release from snowmelt and rainfall is pre-processed by the results of the SNOW3-model.

Experiences with published forecasts during former flood events have shown the need for communicating the uncertainties associated with these forecasts to the civil protection and the public. Therefore, methods for quantifying and representing these uncertainties have been developed and incorporated in the flood warning routine.

Keywords: flood warning Bavaria, flood forecast, flood information system, rainfall-runoff model, Danube river, uncertainty, ensemble forecast, transboundary forecast system, flood preparedness, flood protection, realtime data collection, online data base.

Dr. Alfons Vogelbacher

Cyclone hazard risk profile of coastal districts of India
M. Mohapatra, India Meteorological Department, Mausam Bhavan, Delhi - mohapatra_imd@yahoo.com

Hazards associated with tropical cyclones are long duration rotatory high velocity winds, very heavy rain and storm tide. India has a coastline of about 7,516 km of which 5,400 km is along the mainland. The entire coast is affected by cyclones with varying frequency and intensity. The India Meteorological Department (IMD) is the nodal government agency that provides weather services related to cyclones in India. However, IMD has not identified cyclone prone districts following any specific definition though the districts for which cyclone warnings are issued have been identified. On the other hand, for the purpose of better cyclone disaster management in the country, it is necessary to define cyclone proneness and identify cyclone prone coastal districts. It is also necessary to decide degree of hazard proneness of a district by considering cyclone parameters so that mitigation measures are prioritized. In this context, an attempt has been made to prepare a list of cyclone hazard prone districts by adopting hazard criteria.

In general, the coastal districts of West Bengal, Orissa, Andhra Pradesh and Tamil Nadu are more prone and are in the high to very high category. The Proneness factor is very high for the districts of Nellore, East Godawari, & Krishna in Andhra Pradesh; Yanam in Puducherry and districts of north coastal Orissa & West Bengal. The results give a realistic picture of degree of cyclone hazard proneness of districts, as they represent the frequency and intensity of land falling cyclones along with all other hazards like rainfall, wind and storm surge. The categorization of districts with degree of proneness also tallies with observed pictures. Therefore, this classification of coastal districts based on hazard may be considered for all the required purposes including coastal zone management and planning. However, this classification is based on only hazard criteria. Vulnerability of the place has not been taken into consideration. Therefore, composite cyclone risk of a district, which is the product of hazard and vulnerability, needs to be assessed separately through detailed study.

M. Mohapatra
India Meteorological Department, Mausam Bhavan, Delhi - mohapatra_imd@yahoo.com↵

Extreme weather events in India- a preliminary analysis on Impact
Ajay Singh, Post doctoral fellow, SJM SOM IIT Bombay-ajay@som.iitb.ac.in
Anand Patwardhan, Professor, SJM SOM IIT Bombay-anand@som.iitb.ac.in
Abhijat Arun Abhyankar, Post doctoral fellow, CSE IIT Bombay-abhijat@iitb.ac.in
Nandlal L. Sarda, Professor, CSE IIT Bombay-nls@cse.iitb.ac.in


Extreme weather events have enormous impacts to human society and environment. India is highly vulnerable to climatic extremes due to high population density, poor infrastructure, low human development index and minimal coping capacity. In this scenario it is important to look at damage caused by climate extremes over India spatially and temporally. Impacts data constitute information about mortality, persons affected, village affected, crops affected and total economic loss. All events combined show significant increasing trend in impact. It was found that significant increasing impacts are observed in case of dust storm, flood, hail storm and lightening. Floods share maximum impacts caused by climate extremes. Total mortality due to the extreme events is maximum in Orissa. It also stands first in normalized mortality. Cold wave has significant increasing trend in impact on Haryana, Rajasthan and West Bengal, whereas significant decreasing trend in Madhya Pradesh. All the states have shown increasing trend in heat wave occurrences. Finally policy implications of impacts of these events and future work have been discussed.


Knowledge and Data Integration for Modelling of Risk for Development of a DSS
J. Durgaprasad Professor, Civil Engg. Department, Gyan Ganga College of Tech., Jabalpur - 482 003, India. E-mail: jdprasad4@gmail.comand
P. Subba Rao Professor and Head, Civil Engg. Department, JNTU College of Engineering, JNTU Campus, Kakinada - 533 003, India.


East coast of India is frequently battered by intensive tropical cyclones. Due to these natural hazards, damage to infrastructure is predominant including heavy losses of life and property. From post disaster damage surveys, it is noted that many industrial buildings, houses, roads, power and communication lines suffer varying degrees of damage resulting in socio-economic losses. How risky the system is important for projects/problems, which are generally of high cost or unique or strategically important and multidisciplinary in nature. One aspect of effective risk management is accurate risk analysis, which is of vital significance to decision makers. Hence, it is necessary to develop continually efficient methodologies and techniques for moderating risks to be within acceptable limits. A storehouse of knowledge of experts and data are now available in the area of risk analysis, wherein extensive work has been done by professionals and researchers who have also studied natural hazards and strategies for mitigating the damage or loss. Acquired chunks of knowledge from the domain expert’s must be processed to identify the conflicting chunks of knowledge, gaps, and redundancies and improved before using for risk analysis. Further, it is necessary to eliminate conflicts and represent the knowledge in a consistent and complete manner suitable for carrying out risk analysis. The use of a graph theoretic technique is proposed in this paper for processing of knowledge and its use in the creation of a Knowledge Base (KB) for developing Decision-Support System (DSS). To demonstrate the approach, a case study on risk analysis of a roof structure against damage due to cyclonic winds is used.


Applications of Natural Disaster Database in Vulnerability mapping and Disaster risk reduction for the South Andaman Island, India – A GIS Based Probabilistic Risk Assessment study.

Shrikant Maury Department of Coastal Disaster Management, Pondicherry University, Brookshawbad campus, Port Blair, Andaman and Nicobar Islands 744103, India. Email: chrissmariah@yahoo.co.in,
S. Balaji - Department of Coastal Disaster Management, Pondicherry University, Port Blair 744103


The South Andaman Island is one of the most natural disaster prone zone and very frequent to earthquakes which are often most destructive and also inherently poses various vulnerable natural hazards such as catastrophic tsunamis, coastal floods, coastal land subsidence and landslides etc. The South Andaman Island lies in the Bay of Bengal in N-S direction and administratively ambit between 10°00' to 12°12' North latitudes and 92° to 94° East longitude. Major landscape of these islands are divided into low to moderately high and steep hills, intermountain narrow valleys and gradually sloping coastal tracts including swamps. The 26th December 2004 tsunami disaster not only posed geographical and environmental changes but it also infringed the infrastructure of social life of this island. The post math tsunami impacts still triggering imbalance on various consequences and driving major environmental and climatic change on this Island. A GIS based Probabilistic Risk Assessment (PRA) is an ardent need for the assessment of the vulnerability before work on any development policies and implement the development plans. Therefore to evaluate the vulnerability risks, a culmination of available disaster database from the past events in various aspects and contemporary issues related to sustainable community developmental activities and their scenarios on changing environment have been undertaken. This article provides cognitive understanding and integrates the perception on various aspects of GIS based Probabilistic Risk Assessment (PRA) and its importance in disaster management, environmental issues and developmental activities for the South Andaman Island.


Environmental Statistics for Disaster Management – Indian Scenario
Sreeja S Nair & Anil K. Gupta - National Institute of Disaster Management

It has been increasingly recognized that development, disaster risk and Environment are interlinked. However the exact nature is poorly understood due to the non availability of datasets. Disaster management being a multi -disciplinary subject involving complex subjects like Atmosphere, Water, Biodiversity, Land and Soil and Human Settlements and socio economic factors it is difficult to collect, analyze and study relationships among them. It, therefore, became necessary to develop an efficient statistical system on environment including the disasters that could meet the growing demand of various governmental agencies, environmentalists and general public for data on various aspects of environment and disasters.
The Central Statistical Organization has taken various initiatives in close cooperation with various data source line Ministries / Departments/ Organization for the development of environment statistics in the country. CSO created a division of environmental statistics in 1996 and prepared broad framework for capturing statistics. The Compendium of Environment Statistics by CSO has been prepared under the broad Framework for Development of Environment Statistics provided by the United Nations Statistics Division and adopted by the Steering Committee on Environment Statistics set up by CSO during 1996.The five parameters of the framework, namely, biodiversity, atmosphere, land/soil, water, and human settlements have been used in this compendium. There is a dedicated chapter on Land Uses, Agriculture, Mining and Natural disasters included in the compendium. CSO in collaboration with NIDM came up with a framework for developing a national disaster statistical system in India covering hazard related statistics and disaster statistics.
The present paper gives a detail analysis on how the compendium of environmental statistics can be utilized by the disaster managers for the various phases of disaster management cycle. The paper also highlights the limitations of the databases in the perspective of a disaster management professionals and strategies to overcome the challenges.


Drought hazard and vulnerability analysis for Bundelkhand region using geo-spatial tools
Anjali Singh TERI University, New Delhi 110 070, India
Anil K. Gupta Sreeja S. Nair National Institute of Disaster Management, New Delhi 110 002 India
P.K. Joshi and V.K. Sehgal Indian Agriculture Research Institute, New Delhi 110 012 India


Drought is one of the most complex natural disasters in the world in terms of the number of person directly affected. It is considered to be least understood among all natural disasters due to wider range of environmental inputs and influences over the occurrences, impacts and mitigation strategies. A major challenge for drought research is to develop suitable methods and techniques for forecasting the onset and termination points of a drought. Drought indices plays an important role in drought studies, they assimilate thousands of bits of data of drought variable into a comprehensible big picture. A drought index value is typically a single number, far more useful than raw data for decision making. Since drought studies require huge amount of continuous data and because of its peculiar characteristics there has been a lack of progress in drought management throughout the world in general and in particular in India. Present study has scientifically identified and assessed the hazards and vulnerability on spatial and temporal scales, by using (SPOT Vegetation images) remote sensing and GIS techniques.

The study has developed a new methodology for a comprehensive/ detailed drought analysis covering three major types i.e. meteorological, hydrological and agricultural with limited and freely available data. It was an attempt to fill the knowledge gap by using statistical techniques and Geoinformatics tools and of drought monitoring. Range of indices like Deciles of percentile, SPI, Percent by normal SWLI, NDVI and VCI were used to capture multi-dimension impact of drought. Different software such as ArcGIS 9.1, ENVI 4.4, SPI_SL_6 etc, has been applied to analyse the nature of the droughts and calculate their frequency and intensity. The report ends with innovative presentation of hazard, vulnerability and composite drought risk map of the region to educate the reader and the decision makers about the recurring nature of the drought. It presents an intelligent information base to assist the decision makers and mitigation analysts in taking informed decisions in drought mitigation action. The aim of the study was to develop a drought risk map for the Bundelkhand region which is not available and can serve as a major basis for risk management planning and programmes along effective warning and monitoring framework.


Application of analytic hierarchy process (AHP) to dust storm risk assessment, Khuzestan province southwest of Iran
Fatemeh Matroud -Department of mathematic Islamic Azad University (IAU), Abadan Branch, fati.matroud@gmail.com
Ahad Nazarpour - Department of geology, Islamic Azad University (IAU), North Tehran branch


Dust storm is the synthesize result of the action of atmospheric motion and physical geographic environment. Formation dust storm depends on the interaction between atmosphere and sandiness surface, which are different physics medium in their density. Recently Khuzestan province in southwest of Iran is affected by dust storm phenomena. This feature makes some problem in agriculture, Transportation, Communication and human health side effect in this province. Evaluation, ranking and management strategies of environmental impacts of dust storms are the reasonable ways to achieve sustainable development. These ways can be as a management tools in decision making for program planners. So they can be able to identify solutions and logical and best choice option for reducing potential environmental impacts from implementation of a plan or severity of the actual effects of a natural phenomenon. Analytical Hierarchy Process (AHP) is an approach to decision making that involves structuring multiple choice criteria into a hierarchy, assessing the relative importance of these criteria, comparing alternatives for each criterion, and determining an overall ranking of the alternatives. In this investigation ranking of environmental impacts in physicochemical, biological, social, economic and cultural environmental has been done by using AHP methods. Results of this study showed that dust has the greatest effects on health than education.

Key words: Analytical Hierarchy process (AHP), Ranking, dust storm, Environment


Hot Spot Analysis of Tornado Due to Climate Change for Bangladesh: A GIS and Remote Sensing Approach
Md. Abul Hasem, hasem058@gmail.com
M.Sc; Geography and Environment, University of Dhaka, Bangladesh


Bangladesh ranked the top of among world’s ten deadliest tornados from death toll with more than 16 million people stretching a small proportion of landmass and highly susceptible due to climate change. It has the potential impact as there’s more energy in the atmosphere, more water vapor evaporating and greater likelihood for stronger heating events that lead to stronger thunder storms-super cells and tornado production. Death toll from tornados is the highest due to lack of communication, information, warnings, week housing and appropriate shelters etc. Tornados and Norwester’s is a short lived event and occurring regularly which is creating an adverse situation to the poor people. The risk and vulnerability of tornado hazard is crucial and causing a great proportion of economical, social, administrative and health problems to the affected people. Coping strategy for the development of household employment, water supply and health is very little in relation to damage and injuries. The study aims to develop a tornado Hot Spot zone by calculating Getis-ord Gi* statistics and Spatial Weight Matrix. The Delauny Triangulation conceptualization ensured the relationships of neighborhood features. A ranking matrix and linear interpolation method adopted to find the risk areas throughout the country. Data over 40 years will help to analysis tornado hot spot zone. Capacity building, damage management and development are very important for the spatial allocation of affected people which will help the decision makers to launch multifaceted initiatives and projects aiming at mitigation of risks.
  Key words: Climate change, Hotspot zone, Weight matrix, Shelters, Capacity Building and Development.


Handling of Uncertainty for Modelling of Risk for Development of a DSS
J. Durgaprasad - Professor, Civil Engg. Department, Gyan Ganga College of Tech., Jabalpur- 482003, India. E-mail: jdprasad2@yahoo.com
P. Subba Rao - Professor and Head, Civil Engg. Department, JNTU College of Engineering, JNTU Campus, Kakinada - 533 003, India.


One aspect of effective risk management is accurate risk analysis. Decision Support Systems (DSS) are being widely used for risk analysis. Risk arises from uncertainty about the future behaviour of a system. In the absence of uncertainty there would be no risk. In spite of the great significance and practical success of probabilistic information theory, it has increasingly been recognized that probability theory captures only one type of uncertainty, i.e., aleatory uncertainty. The greater challenge is to treat epistemic (due to vagueness) uncertainties that come from incomplete (or lack of) knowledge about fundamental phenomena. And, the problem of accounting for epistemic uncertainty in risk management decisions is conceptually straightforward, but is riddled with practical difficulties. In addition, complexity is an important source of uncertainty and hence risk. Complexity makes it difficult to understand or predict a system's behavior, thus giving rise to epistemic uncertainty. The more complex a system is, the more possibility for components to interact in unforeseen and possibly undesirable ways. One goal of risk management and development of DSS and operation in general, is to reduce uncertainty as much as possible, and to make sure that the remaining uncertainty is identified and understood. These issues are addressed, in this paper, by making use of Bayesian networks. And expert opinions are aggregated into full (composite) probability distributions that can be combined through Bayesian computations with the other variables of the model. To demonstrate the approach, a case study on risk analysis of a roof structure against damage due to cyclonic winds is used.


Landslide Hazard Zonation Mapping Using Remote Sensing and GIS In Kodaikanal Taluk, Dindigul District

Dr. N. Prabhakaran, K.S. Preethi Magdalene: Department of Civil Engineering, PSNA College of Engineering and Technology, Dindigul

India is vulnerable to different natural hazards of which 15% of total area of the country is susceptible to landslides exceeding 0.49million km2. Landslide is defined as “the movement of mass of rock, debris or earth down a slope”. Landslides are caused in hilly terrains due to factors like gravity, weathering, deforestation, earthquake, heavy precipitation etc; Landslide may be major or minor disaster which results in loss to property and life. Kodaikanal is a Taluk division of Dindigul district in the state of Tamil Nadu, India. It lies between 10 0 6’38”N to 10 0 26’57”N Latitudes and 77 0 16’00”E to 77 0 44’56”E longitudes, covering an area of about 1081.33 sq.km. The methodology involves generation of drainage pattern map, geology, structural map, land use/land cover map, slope angle, slope aspect, DEM, lineament map, landuse/landcover, rainfall distribution and water level map. DEM of study area has been generated from SRTM image and topographic map at a scale of 1:25000. Landuse/landcover map has been largely interpreted from Landsat TM and Cartosat image. To identify the vulnerable areas, the above-mentioned parameters were analyzed in a GIS by assigning appropriate ranks and weights. The result is a landslide hazard zonation map showing regions with varying degrees of vulnerability to landslides. It is opined that such a map (which is derived from the analysis of the causative factors) will enable to propose and implement suitable mitigating measures like blasting thus preventing loss of life and property in the Kodaikanal hills.


Participatory GIS Approach for Flood Vulnerability Assessment
Arnab Kundu Research Scholar, Centre for the Study of Regional Development, School of Social Sciences, Jawaharlal Nehru University, New Delhi-110067, India, arnknd@live.in

Flood is a most frequent and risky hazard as well as a disaster in the world. In the present study, Participatory GIS (PGIS) approach has been used to assess flood vulnerability and risk. The application of Participatory GIS is common and effective where local people have the ability to accumulate knowledge and experiences. The PGIS integrates qualitative information (mental maps, public opinions) with quantitative data. The main objective of this study is to design a method of the vulnerability assessment and also for further risk analysis and management. In this study, building materials, land use map, flood depth map, population (day and night), building types of study area have used to clarify social vulnerability. For the community based study, sites or houses of the area have been selected by Random sampling method. A questionnaire has been prepared for the collection of qualitative as well as quantitative data from the people. The vulnerability maps and curves have been created using Spatial Multi Criteria Analysis (SMCA) technique. For this, some of the invented data has used together with the existing data. The PGIS survey of the area help in obtaining many information which are needed for the assessment of vulnerability of the study area.
Keywords: Flood, Vulnerability, Participatory GIS (PGIS), Social Vulnerability, Random Sampling, Questionnaire, SMCA, Risk Analysis and Management.


Application of “High Tech Agriculture” to overcome Disasters in Agriculture Sector
Prof. Dhiren Vandra & Asha Tank, 35,Jaydeepnagar, Jail Road, At Mangrol Dist Junagadh (GUJARAT), E-mail : dhirenvandra@yahoo.com

Disasters are the events of environmental extremes which are inevitable entities of this living world. The impact and frequency of the disasters is augmenting. During last decade, there has been greater focus on the interface between environment, livelihoods and disasters with environment and Disaster Risk Reduction for agriculture especially. Because Indian agriculture is a field which is fully depends on rainfall, temperature fluctuation, climate and other natural factors. The irrigation water is limiting factor of agriculture production. In natural disaster Drought is most serious disaster. 43% of total area and 60% of Agricultural area are under Drought forever. 27% of total population of Gujarat state facing Drought Problems permanently. During the 1960 to 1990 (30 yrs) were 18 droughts in Gujarat. This ignored aspect need scientific study. With the help of various references this study was carried out with objectives to know proportion of farmers who adopts High Tech Agriculture systems like Micro Irrigation, Green / Glass Houses, Mulching etc in five Talukas of Junagadh District of Gujarat State. The short questioner giving farmers and data collected was analyzed by simple percentage/ proportion method. The results shows that 50 to 60 percent farmers are adopting mulching to maintain soil temperature and soil moisture in horticultural crops. It reduces 25 to 35 percent water requirement of crops during drought condition; 30 to 35 percent farmers are adopting low cost Green houses which reduce 20% of water requirement in controlled condition and increase 18 - 24 % agricultural production; 70 to 80 percent farmers are adopting Drip Irrigation systems and 25 to 40 percent farmers adopting sprinkle irrigation which saves 35 to 70 percent and 25 to 35 percent of water respectively. So the suggestions are, every farmer should adopt mulching practices to maintain soil moisture and soil temperature in water deficiency, Green House may be used as and when require to create controlled atmospheric condition to sustain crop yield in any disaster. Every farmer of arid region should adopt any Micro irrigation system to minimize water requirement of crops during drought condition. This way with the help of various High Tech Agriculture systems, we can reduce effects of any drought on agricultural productivity.


Air Pollution Episode Prediction Using Extreme Value Theory
Pragati Sharma, Assistant Professor, Department of Applied Sciences, Bharati Vidyapeeth’s College of Engineering, Paschim Vihar, New Delhi-110063, Email: pragatimails@gmail.com
Prateek Sharma, Associate Professor, Department of Natural Resources, TERI University, Delhi, 10, Institutional Area, Vasant Kunj, New Delhi – 110070, Email: prateeks@teri.res.in
Suresh Jain, Associate Professor, Department of Natural Resources, TERI University, Delhi, 10, Institutional Area, Vasant Kunj, New Delhi – 110070, Email: sureshj@teri.res.in


The extreme air pollution event, i.e. the maximum Air Pollution Concentration (APC) is governed by many complex and interrelated factors. In the first place, the causative source emissions and secondly, the cumulative effect of typically complex climatological conditions such as low surface wind speed, temperature inversion, anticyclonic conditions, mixing height, atmospheric stability etc. exert a large influence on the APC. As a result of this and the inherent uncertainty associated with turbulent flow, the deterministic models generally fail to predict extreme event adequately. Thus, on account of these complexities, the statistical methods offer an alternative and pragmatic approach to analyze the extreme air pollution phenomenon.
The Extreme Value Theory (EVT) has mostly been applied in hydrology for the statistical treatment of floods and draughts. In air pollution literature many studies applying Statistical Distribution Models (SDMs) to air quality data, have been undertaken. However, the EVT has not been applied in air pollution area that much frequently, perhaps because of non-availability of adequate data set necessary for its application. In the present study application EVT has been described to through illustrative example for making predictions of the expected number of violations of the National Ambient Air Quality Standards (NAAQS), for two primary pollutants attributed to vehicular sources – carbon monoxide (CO), and nitrogen dioxide (NO2) monitored at a receptor location near a busy urban road intersection in Delhi. This has been done fitting Type I asymptotic distribution of extreme values, more popularly known as the Gumbel distribution. The parameters of the distribution have been estimated using the Gumbel’s method. A comparison of the predicted violations of the National Ambient Air Quality Standards (NAAQS) and the exceedence of the maximum pollution concentration with that of the observed data indicates that Type I asymptotic distribution adequately fits to the observed extreme value data. Finally applicability of EVT for local air quality management has been suggested.

Key words: extreme value theory, Gumbel distribution, time series, statistical modelling, air quality standards.


Air Quality Alerts Using Univariate Linear Stochastic Model
Pragati Sharma, Assistant Professor, Department of Applied Sciences, Bharati Vidyapeeth’s College of Engineering, Paschim Vihar, New Delhi-110063, pragatimails@gmail.com
Prateek Sharma, Associate Professor, Department of Natural Resources, TERI University, Delhi, 10, Institutional Area, Vasant Kunj, New Delhi – 110070, prateeks@teri.res.in
Suresh Jain, Suresh Jain, Associate Professor, Department of Natural Resources, TERI University, Delhi, 10, Institutional Area, Vasant Kunj, New Delhi – 110070, sureshj@teri.res.in


Air pollution is relevant in urban regions in view of the large number of people suffering from its effects on health. Moreover, in many urban areas, pollutant concentrations become really critical in the presence of particularly unfavourable meteorological conditions. Such conditions lead to the formation of an urban “heat island”, where prolonged pollutant accumulation takes place. In polluted areas there is, thus, a need for issuing warnings to the general public so that sensitive individuals can take necessary precautions. For an adequate health warning system and for management of control and public warning strategies for pollutant levels at densely populated areas, reasonably accurate forecasts of pollutant concentrations as a function of time and location are necessary so that those persons with pollutant affected health problems can plan their activities in advance. There is also the possibility that foreknowledge of high pollution potential could be used to reduce future atmospheric pollutant concentrations through timely reduction of emissions by traffic control or industrial shut-down. The air quality “predictor” for pollutants can be developed either by analytical or by statistical means. Analytical models are, in general, more suitable for making long-term forecasts/planning decisions. For air pollution “episodes”' characterised typically by fast dynamics, these models do not give satisfactory results. Moreover, in the absence of additional parameters required as input, such as, wind vector, temperature, traffic characteristics (for emission factor computations), the analytical models fail to provide quantitative description of the atmospheric pollution. Stochastic modelling of the pollution time-series provides an alternative approach. In the present study, an attempt has been made to determine the degree of prediction possible using only a limited data set, restricted only to the past record of CO and NO2 time series. For this purpose, the theoretical details of the univariate linear stochastic model based on the Box-Jenkins modelling techniques has been first described. The methodology has then been described through an illustrative example in which models for CO and NO2 for a major traffic intersection in Delhi City (ITO crossing) have been developed. The models can be utilised for supplying real-time forecasts of extreme CO and NO2 concentrations, predicting future concentration levels on the basis of data recorded in previous periods.
Key words: Air quality alert, time-series analysis, real-time prediction, linear stochastic models, early warning system


Macro-analysis of occurrence of climate extremes in India
Ajay Singh, Post doctoral fellow, SJM SOM IIT Bombay-ajay@som.iitb.ac.in
Anand Patwardhan, Professor, SJM SOM IIT Bombay-anand@som.iitb.ac.in
Abhijat Arun Abhyankar, Post doctoral fellow, CSE IIT Bombay-abhijat@iitb.ac.in
Nandlal L. Sarda, Professor, CSE IIT Bombay-nls@cse.iitb.ac.in


Climate change studies suggest shift in extreme weather events. The changes in patterns in extreme weather events would lead to issues related to energy, water and food security including national security. In the present study we have selected ten key climate extreme events namely, flood, tropical cyclone, heat wave, cold wave also gale, squall, lightning, dust-storm, hailstorm and thunderstorm to study spatio-temporal pattern over India. Data on the occurrence of extreme climate events have been acquired from India Meteorological Department and other relevant government agencies. Flood constitutes major share of the events. Cyclonic events which constitute only 1% of the events have sizeable impact on socio-economic system of the coastal region. Regression analysis on the total number of occurrences of these events reveals a significant increasing trend. Among several events showing significant increasing trend are flood, heat wave and lightning. Only cyclone among the all reported events has insignificant decreasing trend. Leading states by event category has also been computed and found that few states are relatively more prone to the repeated occurrence of particular events. Finally, we have concluded with the suggestions for the improvement in data collection and key recommendations for further study.


Contribution of Geographic Information Science (GIS) to Emergency Preparedness and Response

Alok Singh, Assistant Professor, Accurate Institute of Advanced Management, Greater Noida, aloksinghiiit@gmail.com
Sunil Kumar Yadav, Assistant Professor, Greater Noida Institute of Technology, Greater Noida, yadavsoft@gmail.com


The emergency preparedness and response application challenge is mainly concerned with the interaction between humans and their environment under conditions thought to be hazardous either to habitat or life. This application challenge is not only multifaceted as its title implies but also covers a wide range of disasters, many with fundamentally different underlying processes (such as earthquakes, hurricanes, and wildfires). Even though the processes that generate the disaster might be fundamentally different, techniques to assess risk, evaluate preparedness, and assist response appear to have much in common and can share and benefit from advances in geographic information science (such as data acquisition and integration; data ownership, access, and liability issues; and interoperability).
Understanding geographic information is critical if we are to build and maintain liveable communities. Since computing has become almost ubiquitous in planning and managing our communities, it is probable that advances in geographic information science will play a founding role in smarter decision making. This paper examines the challenges that occur between humans and their environment under conditions thought to be hazardous to life and habitat. Emergency preparedness and response are reviewed, and recommended priorities for research, educational, and policy contributions to emergency preparedness and response are documented.
Keywords: Geographic Information Science (GIS), Emergency Preparedness, Emergency Response, Risk assessment, Emergency Planning.


Estimating the Hazard from Landslides Using Historical Data and GIS Spatial Model

Janak Bahadur CHAND, Yasuhiro MITANI, Ibrahim DJAMALUDDIN
Hiro IKEMI, PhD Scholar, Department of Civil and Structural Engineering, Kyushu University, Motooka 744, Nishi-Ku, Fukuoka 819-0395 JAPAN, chand_jb@yahoo.com


Landslides are important natural hazards that often result in significant damage to society every year in Japan. The nature of damage that can be caused by landslides is complex and diffuse because of the many interacting factors that are involved, and it may involve loss of life and injury or economic loss. For instance, in Itoshima area, many landslides occur around residence buildings and some of them have caused damage to the environment. A Japanese practical method for measuring slope hazardous area in the vicinity of buildings has been established for Itoshima area. A rational assessment of a slope hazard, including the consideration of potential travel distance of debris and spatial distribution of the vulnerable building population is rarely carried out, and landslide consequences are commonly gauged only the basis of engineering judgment. Traditionally, the main emphasis of this method has often been placed on the evaluation of the likelihood of slope failure based on the slope angle. However, in comparison with fairly advanced geographic information system (GIS) technology that has been developed and applied in the landslide hazard assessment, there seem to be a lack of a systematic and comprehensive framework for rigorous assessing the likelihood of slope hazard on widely area. In addition, in developing risk management methods, it is important to keep in mind the wide range of landslide and slope instability problems, which need to be considered. This paper provides an overview of the factors that need to be considered in landslide hazard and risk assessment in Itoshima area, and the approaches that could be adopted in quantifying whole area of the likelihood of slope hazard around residence buildings. An integrated methodology has been developed to generalize a landslide hazard assessment map based on historical data and spatial criteria, and examples are given to verify the slope hazard assessment by three-dimensional (3D) instability analysis by GIS-based integrated tool.


VULNERABLE COMMUNITIES AND DISASTER MANAGEMENT -A GIS based Decision Support Systems for slums and low income housing of Ahmedabad, India

Rutool Sharma & Somesh Sharma, Faculty of Planning and Public Policy, CEPT University, Kasturbhai Lalbhai Campus ,University Road Navrangpura, Ahmedabad 380 009

It is a known fact that the vulnerable communities like people living in slums, low income groups, migrant colonies and EWS housing societies are much more susceptible to disasters than other communities living in the city. Also it has been witnessed that the impact of any disaster is also much higher on this group of people as they are at a higher risk of exposure. These communities may be directly affected during the disaster as they are generally not prepared and excessively affected. In event of a disaster, they don’t have enough knowledge and resources to respond to these disasters. These people may take several months or years to rehabilitate completely, as they may lose multiple things like livelihood means, house, other assets, important documents or family member during the disaster. The paper emphasis the need for developing a comprehensive GIS based Decision Support System (DSS) focusing specifically on vulnerable communities living in urban areas. The developed DSS will enable urban local bodies to identify vulnerable communities based on nature of disaster, plan mitigative measures, which will ultimately help to respond quickly to emergency situations. The DSS can also be used to assist the urban local bodies for post disaster planning like estimate losses, design R&R packages and distribute resources like construction of houses, social infrastructure etc. The GIS based DSS can also be used for recording disaster related data and also facilitate easy upgradation of records. The information generation from DSS can be a base for preparing environment action plans and environment management plans of the city. The paper presents the need and framework for preparing a Decision Support System taking Ahmedabad (India) as case study.
Keywords: Disaster, GIS, Decision Support System, Vulnerable communities, Slums, environment


 

 
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