Reduce Exposure to Reduce Risk

CLIMATE CHANGE AFFECTED TO LAND SUITABILITY: CASE STUDY “EUCALYPTUS SUITABILITY IN NAKHONRATCHASIMA PROVINCE, THAILAND” Rujee Rodcha1 , Chudech Losiri2, and Asamaporn Sitthi3 1School of Engineering and Technology, Asian Institute of Technology, Pathum Thani, Thailand. 2Department of Geography, Srinakharinwirot University, Bangkok, Thailand. 3Department of Geography, Mahasarakham University, Mahasarakham, Thailand. E-mail: [email protected] ABSTRACT: During past 20 years, climate was changed fluctuate especially, rainfall, it has been caused to crop yield of land suitability. Thailand is an agricultural country which crop yield depends on the natural water resources and the major natural water resource is rainfall. Each crop has a vary requirement of water level even the small amount of water level changes, it can be harmful to those crops. Eucalyptus is a crash crop and it is being expanded rapidly during past recently year due to need of substitution of energy plant. Generally, land requirement for eucalyptus plantation is not fixed much for criteria to simulate the suitable area. Eucalyptus suitable area is able to calculate accurately until the period of climate change, eucalyptus suitable area becomes to be more fluctuated. This study emphasizes how the change of amount of rainfall has an effect to eucalyptus suitable area. The set of data of amount of rainfall from 1991 to 2009 are as one parameter as input to the model of eucalyptus suitability by using Analytic Hierarchical Process (AHP) and techniques of Geographic Information Systems (GIS) and Remote Sensing as tools. The outputs from the eucalyptus suitability model are compared and consider how much eucalyptus suitable area is changed. KEY WORDS: Eucalyptus, rainfall, land suitability, GIS, Remote Sensing 1. INTRODUCTION This study emphasizes how the change of amount of rainfall has an effect to eucalyptus suitable area. The set Nowadays, climate change has effects to many things on of data of amount of rainfall from 1991 to 2009 are as the earth especially the cycle of crop. Land suitability is one parameter as input to the model of eucalyptus an efficient model to evaluate the crop suitability. The suitability by using Analytic Hierarchical Process (AHP) output of the model of land suitability follows by many and techniques of Geographic Information Systems (GIS) input factors. Rainfall is an important input to feed into and Remote Sensing as tools. The outputs from the the model. During past 20 years, the amount of rainfall eucalyptus suitability model are compared and consider had been fluctuated due to climate change. It has been how much eucalyptus suitable area is changed. caused to crop yield of land suitability. Thailand is an agricultural country which crop yield depends on the 2. MATERIAL AND METHODOLOY natural water resources and the major natural water resource is rainfall. Each crop has a vary requirement of 2.1 The study area water level even the small amount of water level changes, it can be harmful to those crops. Eucalyptus is a crash The study area is situated at Nakhon Ratchasima crop and it is being expanded rapidly during past recently province in the North eastern of Thailand. The study area year due to need of substitution of energy plant. covers approximately 2,049,400 hectares mainly engaged Generally, land requirement for eucalyptus plantation is in agricultural activities, growing such diverse crops as not fixed much for criteria to simulate the suitable area. rice, sugarcane, cassava, corn and fruits etc., as shown in Eucalyptus suitable area is able to calculate accurately figure 1. until the period of climate change, eucalyptus suitable area becomes to be more fluctuated. Reduce Exposure to Reduce Risk 40

Maha Sarakham formation or the Khok Kruat formation. The related major soil series, in this case, are Korat and Warin. Soil texture of the soil series varies from loamy sand (as in Nam Phong series) to Clayey (as in Phimai series). Korat series has sandy loam (SL). Similarly, saturation percentage of the major soil series ranges from 34 to 50.5 % (Yingjajaval and Sangkhasial, 1990). The Mun river and its tributaries are the main source of drainage in this area. These tributaries are distributed in such as way that upper and lower portion are drained separately. Besides these, numerous shallow lakes are also dominant to store some of rain water. This area has Lam Takong irrigation project covers 20.4% of the whole study area. Such area is mainly concentrated in flood pain and basin that has worse ground water quality. Although the climate of whole northeast region belongs to Tropical Savannah type (“Aw” I Koppen’s climatic classification system), rainfall is low but erratic one (Miura et al., 1990). The mean annual rainfall of this locality for the past 34 years (1961-1994) is 1,060 mm. which is even less (774 mm) for 1994. Out of this, nearly 85% occurs within six months (May to October). Figure 1: The study area Nakhon Ratchasima province is located at 140 58’ 28’’ N and Longitudes 1020 5’ 53” E. The topography is a high/middle terrace, low terrace, peneplain, flood plain, and basin. Among them, major area is under flood plain and high/middle terrace that cover 37.95 and 37.03% of the whole study area, respectively. Flood plain and basin have flat topography while terraces and peneplain have gently undulating. Besides these, some portion is occupied by alluvial complex. Altitude of the study area ranges from 170 to 210 m. above mean sea level in figure 2. Although the earlier consideration was that soil parent Figure 2: The topography of the study area materials in Nakhon Ratchasima province are mainly alluviocolluvium (Michael, 1981), it was later concluded The average annual evaporation for 1994 and 34 years as the weathered mantle of bedrock and finer matrix of (1961-1994) is 1,755 and 1,873 mm. respectively. This is gravel bed. These materials are transported for a much higher as compared to the annual rainfall which relatively shorter distance by wash, creep, wind action etc. clearly indicates the intensity of dryness in the locality. (Miura et al., 1990). The study area consists of 14 Furthermore, this condition prevails in the most of identified and one unidentified soil units with associated months. soil series, phases and variants. The two major soil series are Phimai and Korat covering 25.6 and 17.0% of the whole study area. On the other hand, Lopburi low phase with 0.1% has the lowest coverage. Along the Mun and its tributaries, the soil series such as Phimai and Udon are present. These have fertile soils such as Tropaquepts (Hydromorphic Alluvial soils). On the other hand, undulating terraces have the soil series that have been directly or indirectly derived from the Reduce Exposure to Reduce Risk 41

The monthly mean, maximum and minimum Temper- Monthly - 1995, nt temperatures of this locality remain higher during March - Department to May. The average of 34 years shows that monthly ature Tempera 1:50,000 1991 Meteorolog maximum temperature reaches up to 37.0 0C while its - ical lowest range is 29.8 0C in December. This temperature is -ture Department even higher for 1994 which ranges from 33.3 0C to 40 0C. 2009 The monthly mean temperature for both cases, 1994 and Rainfall Monthly 1991 Meteorolog 34 years period (1961 – 1994), does not vary much but Rainfall - ical the monthly minimum temperature for 1994 has reached Department 14.0 0C as compared to 16.5 0C for 34 years period. Slope Topogra- 2009 RTSD phic map 1992 Monthly mean relative humidity over 30 year’ period Table 1: Physical data for the study (1961-1990) clears that it ranges from 62 to 81% with an average of 71.7%. 2.2 Methodology The average amount of rainfall from 1991-2009 has been The methodology of study is mainly aimed to rainfall vary, it was less than 1,000 mm. in some years and quantity affected to eucalyptus suitability. Rainfall data reached to more than 1,000 mm. in many years as shown sets from 1991 to 2009 were interpolated by using the in figure 3. equation of Inverse Distance Weight (IDW). Each interpolated rainfall data set from 1991- 2009 was feed to the model of eucalyptus suitability. (LDD, 2005) This step will be use a multi-criteria factor evaluation based on a series of GIS with Analytical Hierarchy Processing (AHP) and a pair wise comparison method for ranking and weighting of the various factors. A linear combination of each experiment will be used to identify eucalyptus suitability. The Flowchart of methodology can be described as chart below: Figure 3: Graph shows the annual rainfall from 1991- 2009 From the sceinario of the average amount of rainfall, it was very flacutuated. It is not easy to expect the amount of rainfall for each year. It caused to predict unaccurated crop suitability. On the other hand, temperature is rarely changed. Data type The following data used in this study are presented in table 1. Data Base Scale Year Publisher Figure 3: Research methodology flowchart type Source 1:100,000 Soil Soil map 1982 Land 1:50,000 - Developme Land Land use nt use map 1991 Department Land 1982, Developme 1993, Reduce Exposure to Reduce Risk 42

3. RESULTS 2009 The results of interpolation the amount of rainfall by Figure 4: The percentage of the amount of rainfall using data set from 1991-2009 take into the model of from 1991-2009 eucalyptus suitability by using techniques of AHP and GIS. These outputs can be explained in two categories. The amount of rainfall is less than 1000 mm. per year covered the area in the less percentage. It varies between 3.1 The output of interpolation of the amount of 0.02 – 37.67 percentage. The fewest one occurred in rainfall. 2008 and the greatest one occurred in 1997. Remarkably, the amount of rainfall in 1997 is fewest one among them. The results of the interpolation of the amount of rainfall The amount of rainfall in range of 1000-1200 mm. covers by using data set from 1991–2009 have fluctuated. The the area intensively included the amount of rainfall in average of the amount of rainfall in this area is in the range of 1200-1400 mm. The maximum amount of range of 1000-1600 mm. per year. There were more rainfall occurred in 2008. The percentage of covering abundant in some years and insufficient in some years. area in range of the amount of rainfall more than 1600 mm. is highest one. It shows 36.06% covered the area. 1991 1992 1993 All results of interpolation of the amount of rainfall are shown in Figure 4, Table 2 and Figure 5(graph). 1994 1995 1996 1997 1998 1999 Table 2 : Shows the percentage of the amount of rainfall from 1991-2009 2000 2001 2002 2003 2004 2005 2008 2006 2007 Figure 5: Graph shows the percentage of the amount of rainfall from 1991-2009 3.2 The output of Eucalyptus land suitability The outputs from the interpolation of the amount of rainfall feed into the model of eucalyptus suitability by using technique of GIS. The results of eucalyptus Reduce Exposure to Reduce Risk 43

suitability have four classes such as mostly suitable, 2009 moderately suitable, less suitable and not suitable. The moderate suitability covers the largest area, and the most Figure 6: Eucalyptus suitability area from 1991-2009 suitability is the second one to cover the area. Less The results of eucalyptus suitability from 1991-2009 can suitable area is very rare. Not suitable area is nearly none be shown in the details as Figure 6, tables 3 and Figure 7 or zero percentage. The results imply this area is very (graph). suitable for growing eucalyptus. Although the amount of rainfall is quiet change year by year but it has not effect much to suitability of eucalyptus plantation. The amount of rainfall has effects to eucalyptus suitability only transferring from the moderate suitable area to the most suitable area. This case is just only reduced the difference between the moderate suitable area and the most suitable area. 1991 1992 1993 1994 1995 Table 3: Shows the percentage of Eucalyptus suitability area 1996 1997 1998 1999 Figure 7: The percentage of Eucalyptus suitability 2000 2001 2002 area 2003 2004 2005 2006 2007 2008 4. CONCLUSTION AND RECOMMENDATION Change the amount of rainfall occurred from natural phenomena that it cannot be changed or adjusted. The situation of change of annual rainfall is increased every year and it has affected to the distribution of monthly rainfall, then it can be caused to reduce the growing of the plant. Unlike eucalyptus, the changes of the amount of rainfall are not caused to decrease of eucalyptus suitability. 5. ACKNOWLEDGEMENTS First of all, the authors would like to express our gratitude to Dr.Nitin K.Tripathi, Dr.Rajendra Prasad Shrestha and Dr.Taravudh Tipdecho for invaluable guidance and support in the study. Reduce Exposure to Reduce Risk 44

6. REFERENCES Shrestha, R.P. (1999). Developing Sustainable Land Use Systems through Soil and Water Conservation in Beek, K.J. (1978). Land evaluation for agricultural the Sakae Krang Watershed, Central Thailand. development. Wageningen, Netherlands: (Doctoral dissertation No. AC-99-1, Asian University of Agriculture. Institute of Technology, 1999). Bangkok: Asian Institute of Technology. Brinkman, R. and Smyth, A.J.(1973). Land evaluation for rural purposes. Wageningen, Netherlands: Son, N.T. (2005).GIS Aided Land Evaluation for International institute for land reclamation and Sustainable Agricultural Development in Tri Ton, improvement. An Giang, Vietnam. (Master Thesis No.NR-05-06, Asian Institute of Technology, 2005). Bangkok: FAO (1976). A framework for land evaluation. FAO Asian Institute of Technology. Soils Bulletin No.32, Rome. Ugsang, D. M. (1995). Land evaluation approach using FAO (1983). Guideline: Land evaluation for rainfed GIS in modeling farming systems for sustainable agriculture. Soil Bulletin No. 52. Rome. development of marginal agricultural lands: A case study of Abo, Cebu Province, Philipines. AIT Food and Agriculture Organization of the United Nations Thesis, Thailand: Asian Institute of Technology. [FAO]. (1976). A Framwork for Land Evaluation. Soil Resources Development and Conservation Service, Land and Water Development Division, Netherlands: Food and Agriculture Organization of the United Nations. http://www.sciencedirect.com/science?_ob=Articl eURL&_udi=B6V4-91CGN-013 1&_coverDate=2%03F2%28F2005&_alid= 399836510&_rdoc=1&_fmt=&_orig=search&_qd =1&_cdi=5885&_sort=d&view=c&_acct=C 000052592&_version=1&_urlVersion=0&_userid =1402360&md=5ff4a48a80f6754f039842007946a 68a4 Ojeda-Trejo, E. (1997). Land Evaluation and Geographical Information Systems for Land Use Planning: A Case Study of the Municipality of Texcoco, Mexico. Retrieved March 1, 2005, from http://www.efita.net/apps/accesbase/bindocload.as p?d=5507&t=0&identobj=XNqynevy&uid=57305 290&sid=57305290&idk=1 Phua, M.H., Minowa, M. (2005). A GIS-based multi criteria decision making approach to forest conservation planning at a landscape scale: a case study in the Kinabalu Area, Sabah, Malaysia. Landscape and Urban Planning, 71 (2005) 207- 222. Retrieved March 13, 2006, from Shivakoti, G.P. (2005). Land Expropriation Policy Suitable for Timor-Leste. USA: USAID Shivakoti, G.P. (2005). Land Valuation and Taxation Policy for Timor-Leste. USA: USAID. Shrestha, R. P. and Eiumnoh, A. (2000). GIS and Multicriteria Evaluation Techniques for Land-Use Allocation: The Case of Sakae Krang Watershed Thailand. Asian-Pacific Remote Sensing and GIS Journal, Volume 13, December 2000. Reduce Exposure to Reduce Risk 45

GIS-BASED SITE LOCATION PROCEDUREFOR SOLID WASTE MANAGEMENT IN SRILANKA:A CASE STUDY OF MORATUWA URBAN COUNCIL Suresh Pakshaweera and NandanaPathirage [email protected], [email protected] ABSTRACT: Selecting suitable sites for waste disposal is one of the challenges of modern metropolises in the 21st century. With rapid urbanization consuming most of the potential disposal sites and unrelenting thrust of hyper consumerism amassing greater quantities of waste materials, authorities all around the world are struggling to find alternative ways to process waste other than using them as a landfill material which, despite all efforts, remains to be the most commonly use of waste materials. Given their predominance as the most widely used use of waste material, the inappropriate selection of disposal sites lead to economic losses of chaotic proportions in terms of wasteful expenditure incurred in maintaining and managing substandard sites and their environmental, health, and litigation costs. Hence, selection of sites demands sensitivity towards sustainability and suitability so that strategic use of all resources involved within the entire process can be guaranteed. As they are responsible for the governing of one of key metropolises in Sri Lanka, Moratuwa Urban Council is too plagued with similar concerns. In this research, the feasibility of using Geographical Information System (GIS) enabled site selection procedure for Moratuwa Urban Council was investigated so that the economic losses of selecting unsustainable and unsuitable sites can be prevented. In order to assess the degree of feasibility; the research analyzed the potential solution from technological, economical, operational, socio- cultural & political perspectives. The use of GIS is expected to provide an alternative approach which facilitates quick and easy remodeling for slight changes in citing criteria and produces results such as maps eminently suitable for analysis and presentation. The maps are then embedded to generate a master map which identifies potential sites for waste disposal. Finally the identified sites are expected to be ranked according to their suitability for waste disposal. The research revealed that introduction of GIS enabled site selection procedure for solid waste management in urban councils is feasible considering its long term benefits. KEY WORDS: GIS, Site Selection, Waste Management, Waste Disposal, Moratuwa Urban Council 1. INTRODUCTION taken into consideration in identifying potential 1.1 Background disposal sites. Developed countries make use of One of the main challenges faced by modern various tools which integrate al concerning criteria to metropolises is to seek sustainable waste identify such sites. management mechanisms. This task is all the more difficult for developing countries such as Sri Lanka Some of these tools make use of Geographical where they have to find solutions to amid shortages Information System (GIS) to integrate physical and of resources and political motivation. If these demographic criteria in graphical forms to identify challenges are not met with adequate solutions sustainable and suitable disposal sites and expedite consequences will be severe. Improper solid waste the entire process. Therefore, this report investigates management cause atmospheric and water pollution, the feasibility of using GIS technology in site aesthetic nuisance, odor nuisance, spreading of location procedure for solid waste management so diseases, and fire hazards. All these consequences that the Moratuwa Urban Council can avoid financial results in grave economic losses. Daily production of losses caused by the extant procedure. waste per capita is in the range of 0.4kg-0.85kg in 1.2 Presenting Problem and its Origin major cities in Sri Lanka and 0.2kg of that daily It is the usual practice of the individuals, institutions waste reach final disposal points. The waste disposal and organizations to undertake the task of searching mechanism of Moratuwa Urban Council (MUC) for the optimal site for their intended project such as handles only a 1/3rd of the daily production which is a new school, new bus terminus, or a new airport and at about 350 ton/day. most of these tasks involve going through a large number of maps and huge volumes of documents The location of disposal sites of Moratuwa Urban which is a demanding task. Council displays the unconsciousness about the economic losses incurred by the consequences of With the population explosion, proliferation of small improper solid waste management, mainly the and large multistoried residential and commercial selection of unsustainable and unsuitable disposal complexes and the burgeoning suburbs in recent sites. In the waste management mechanisms criteria years, Moratuwa Urban Council has been grappling such as natural landscape, ecology, and land use are Reduce Exposure to Reduce Risk 46

with the formidable problem of locating suitable and management system would maximize the harmful sustainable sites to dispose huge mass of solid waste impact of waste on the natural environment and collected from the City of Moratuwa. This has endanger the very existence of human and non caused their main problem which is the unnecessary human inhabitants of that environment resulting in expenditure incurred due to selecting unsustainable financial setbacks. and unsuitable sites for solid waste management. In the consumer economy where main driver of The current solid waste management procedure market is mass consumption, the gross production of consists of manually locating suitable sites for human waste has reached mammoth proportions and dumping, recycling and incinerating solid waste. related phenomena such as industrialization, Conventional method of site identification greatly urbanization and uncontrolled urban sprawl have lacks comprehensiveness and objectiveness. They are made effective solid waste management, one of the more subjective in nature and content. Intensive prominent problems of the world (Figure 1). study of a large number of maps, containing data about land use, geology, geomorphology, slope, soil Figure 1: Projection of Demand and Supply type, land ownership and other relevant factors which Relationships Land for Landfill Purposes is imperative to find and optimum solution to locate Source: Leaoet al., (2004) suitable and sustainable dumping sites, is not feasible Accordingly, the researcher understands the in a conventional approach which, therefore, results importance of integrating mechanisms of solid waste in inappropriate conclusions. management in order to ensure sustainable development. Following a similar thread of argument Further, hazardous garbage dumping permitted by the Leaoet al., (2004) identifies that in a integrated waste manual and irregular nature of the extant procedure management system focus should be on minimizing made Urban Council to go through costly public the quantity of materials identified as wastes; reuse, litigation process. Such litigation processes resulted recycle and disposing solid waste in sustainable in judiciary issuing mandamus orders prohibiting the landfill sites. Similarly, the importance of identifying use of concerning land for waste disposal purposes such sustainable landfill sites is stressed by despite the exorbitant leases paid by the Urban Tchobanoglouset al., (2007), as it facilitates the Council for the use of these lands. As a result of this control of environmental contamination, ecological Moratuwa Urban Council is forced to change sites damage, and socioeconomic costs. Furthermore, it is regularly and find temporary disposal sites incurring researcher’s belief that landfill sites if improperly further financial losses. Moreover, the extant site selected, can contribute to severe financial loses and location procedure is time consuming, unsuitable and public health and safety hazards. inadequate in analyzing a large number of criteria. Consequently, the best sites are often missed. 2.2 Use of GIS to Select Landfill Sites According to Burrough and McDonnel (1998), a GIS 1.3 Research Question is defined as a powerful set of tools for collecting, The research question around which this research is storing, retrieving, at will, displaying, and based is presented below: transforming spatial data. The ability of GIS to incorporate data of large geo spatial entities to How can the unnecessary expenditures of selecting multilayered models has endeared it to many subject unsuitable and unsustainable sites for solid waste areas including geology and seismology. Likewise, management be avoided by introducing the GIS site selection procedures in solid waste management technology into site location procedure? have also used GIS technology. 2. REVIEW OF LITERATURE Usually in such instances initially a number of spatial 2.1Introduction to the Solid Waste Management data sets each siting criterion such as topography, As indicated by Robinson et al., (2005), solid waste settlements, roads, railways, airport, wetlands, management is a multilayered procedure which includes steps such as collection, transportation, and disposal of waste. It also includes processing and treatment of the solid waste before disposing. According to McDougall et al., (2006), a proper solid waste management procedure intends to create an uncontaminated environment in which waste generated as a results of human activities does not harm the well being of natural resources. Therefore, in our opinion, a proper solid waste management procedure facilitates safe disposal, recycling and re- use of human generated waste whereas a faulty waste Reduce Exposure to Reduce Risk 47

infrastructures, slope, geology, land use, floodplains, within an overall inductive research paradigm. The aquifers and surface water are generated from characteristics of the quantitative and qualitative conventional and remote sensing sources. methodologies and the variables of the research necessitate a coherent synthesis between these two After acquiring the necessary data, they are methodologies. distributed into layers according to geo spatial characteristics they share with the aid of digitizing The main stimuli behind using such a combination is techniques like scanning and geo coding. Then, that the research demands the predictive buffer zones for each layer of data are identified generalization of the feasibility of a GIS enabled site using the limitations identified as inherent of the selection procedure and develop an understanding of concerning geo spatial characteristics. For example, socio-economic dimensions of solid waste a buffer zone in the slope layer map generated from a management by studying the problem environment contour map or digital elevation model indicate the first hand and interprets related phenomena in terms areas that have steep slopes which should be avoided of meanings employees bring to them. Therefore, this in selecting a landfill sites. Individual raster maps are combination made the research more in-depth and created for each layer after signifying a class per each productive. Within the larger frame work of selected layer using buffer zones. methodology, several research techniques were employed to gather the necessary data. Researcher identified this as a potent way of Finally, to theorize and reach conclusions, an identifying the capabilities of each criterion as inductive approach which uses data to formulate reflected in raster maps. Further, it enables the real theories was applied to the qualitative data and a time feeding of updated geo spatial data and make, deductive approach which applies a tentative according to researcher perspective, the analysis hypothesis to the concerned data to ascertain its much easier and more accurate. validity was applied to the quantitative data. After the forming individual raster maps, GIS 3.1 Selection of the Samples software is then used to combine these maps together The selection of a sample for the research was done creating a master map showing the locations of based on the non-probability sampling technique. number of sites suitable for solid waste disposal The reason for selecting non-probability sampling which satisfied all the specified criteria. Methods technique for determining the sample population is such as ‘‘Simple Additive Weighting method’’ and that the entire research population consists of a ‘‘Analytical Hierarchy Method’’ are usually used for limited number of individuals. Therefore, members ranking selected sites. of the sample could be selected based on subjective judgment of the researcher. This ensured the After studying the pros and cons of two multi accuracy and applicability of the data gathered from attribute decision technique, the researcher felt the sample. Simple Additive Weighting method is the most appropriate method in terms of simplicity and Following this line of reasoning, the authorities of the recognition. Moratuwa Urban Council who are responsible for the solid waste management process were selected as During the data presentation stage 0 value is given to members of the sample population. In selecting areas restricted by physical constraints and rules and members for the sample population from the they are excluded from further consideration. A mask authorities, their characteristics and attributes such which is prepared by multiplying all layers with as, experience and the knowledge in handling this individual layer values so that layer values of 0 yield process, the position in the commanding chain, a result of 0 is used to exclude unsuitable areas for amount of hands on experience and the willingness to land filing from the final analysis. participate in the research were considered. In looking at the process it is clear to the researcher Similarly, the selection of members of the sample that this technique strains the end user to a minimum from the residents of the area and other interested extent since feeding of updated geo spatial data is the parties non-probability sampling techniques was only demand made from them. Further, the multiple used. In order to select members from these groups, layer technique enables manipulation of criteria at the proximity between their places of residence and will so that the end users can easily customized the disposal sites, the severity of the consequences they selection. were facing, the number of complaints lodged by them and their willingness to participate in the 3. RESEARCH METHODOLOGY research were considered. Considering the nature of the research, as the chosen research methodology for the research, a mix of quantitative and qualitative methodologies was used Reduce Exposure to Reduce Risk 48

4. DISCUSSION OF DATA Hence, it can be concluded that an automation of 4.1 Analysis of the Data: Extant Site Selection tasks that brings efficiency and accuracy is needed if Procedure the extant procedure to reach its maximum In the questionnaire, the authorities’ adherence to the utility.Another important aspect of site selection protocol when selecting sites for waste disposal was procedure its financial feasibility and questions were questioned. In analyzing these data, it is apparent that designs to determine its financial feasibility. although there is a protocol in place for selecting disposal sites in majority of time it was not followed. This leads the conclusion that this might be due to several reasons. The issue of waste disposal is one of the burning issues that demands immediate solution but existing procedure because of its heavily time consuming nature resulting from an overt emphasis on manual processes seems to have become redundant. Figure 2: Adherence to a Protocol on Selecting Sites Figure 4: Financial Feasibility of Site Selection Procedure Figure 3: Manual Nature of the Site Selection Process After a critical analysis of the data, it was clear that a In addition, it seems that there is a complexity or relative negligence of financial viability in two fold weaknesses within the extant protocols that compels manners was apparent. First, in a direct sense, the the authorities to bypass the demands of the protocol. financial cost of procuring and maintaining sites were In searching for reasons that might have rendered the neglected when confronted with immediate demand protocols redundant, it was probed whether the for disposal sites. Secondly, in a more indirect sense, manual nature of site selection process has any hand ecological, health and social cost of acquiring and in this matter. maintaining sites was also neglected at times. It was the opinion of near majority that the maximum use of When this statistical data is viewed in conjunction selected sites was not taken and this might be a result with the current state of extant procedure, majority of of changing dumping site regularly due to litigation respondents stated that extant site selection procedure or other social pressures. Obviously, this leads to the did not automate any of its functions and its conclusion that extant procedure incurs unnecessary individual tasks were not handled systematically. As expenses. a result, the extant procedure entailed a significant amount of unnecessary repetition of tasks. Therefore, the need of a wider sophistication in Furthermore, the extant procedure did not give selection procedure to facilitate selection of adequate priority to socio-economic and legal factors sustainable disposal sites is evident. With such a but they are of significant financial strategic value financial condition, it was important to gauge the that is not apparent on the surface and the indirect authorities’ perception of the extant procedure. costs incurred by negligence of socio-economic and legal factors are grave. Figure 5: Authorities Perception of the Extant Procedure Furthermore, in accordance with above findings, it was reiterated in the analysis of statistical data presented in the figure 5 that the extant procedure Reduce Exposure to Reduce Risk 49

failed to yield any substantial strategic, financial and social gains. Hence, the authorities seems to entertain largely negative attitude towards the extant site selection procedure for which the inability of the procedure to execute and correlate its individual tasks smoothly and efficiently was indicated as one of the reasons and it is potent to surmise under such circumstances, the authorities will favor a positive change in the extant procedure. 4.2 Analysis of the Data: Knowledge Pertaining to Figure 7: Awareness of GIS in Site Selection GIS Technology Procedure In the questionnaire, the authorities’ awareness of GIS and related technologies was questioned. As the initial step, it was probed whether the authorities were familiar with GIS technology and its applications. The overwhelming popularity of mobile technologies in Sri Lanka and the use of GIS based applications in different industries with in Sri Lankan context may have had an influence in creating such literacy. All these determinants indicate that introducing a GIS enabled site location procedure would not pose severe obstacles in terms of technical literacy of stake holders. Figure 8: Support towards Introduction of GIS enabled Site Selection Procedure In summary, the majority of respondents believed that the GIS enabled site selection solution can remedy the loopholes in the extant procedure and they also believed that it would enhance the entire process and would bring substantial economic gains. Hence, it is apparent that authorities had some understanding of the strategic value of such incorporation. Figure 6: Awareness of GIS and Related 5. CONCLUSIONS AND GENERALIZATIONS Technologies The GIS enabled site location procedure is to bring Furthermore, this assumption is further supported by accuracy and efficiency to the site selection respondents as shown in above figure. The existence procedure of solid waste management. The solution of such a level of knowledge will be an encouraging is strategically aligned to enhance the existing site factor when introducing a GIS enabled solution since selection mechanism by making use of the GIS these informed respondents can influence rest to react technology and its peripheral elements. Hence, the in a positive way. solution would process the geo-cultural data With such a knowledge based it was important to pertaining to the scope of the municipal area using examine extent to which the respondents were aware multilayered mapping system the results of which is of the uses of GIS in site selection. The importance further analyzed through a filtering method to of this level of awareness as shown in the figure 6 produced a list of alternative plots of lands which are that it may be an indicator that the respondents know rated and ranked depending on their suitability. themselves that there working process can be enhanced by incorporating such a technology in the As demonstrated in the research, such automation is process and this knowledge would function as a destined to be brought by introducing process strong motive in adopting GIS in to site selection. integration, reliability, accuracy, ability to Therefore, this data reiterates the ability of the users incorporate updated information and reducing to successfully cope with GIS enabled solution in site selection procedure. Reduce Exposure to Reduce Risk 50

process time of the extant procedure. This Burrough P.A, McDonnell R.A, (1998). Principals of introduction would enhance the entire solid waste Geographical Information Systems: Oxford management process by bringing operational value. University Press. This solution with all its benefits can be applied in numerous other problem contexts. By changing the Chau, K., (2006). An Expert System on Site selection criteria and geo-spatial data sets to suit the Selection of Sanitary Landfill [Electronic Version], demands and the requirements of a particular International Journal of Environment and Pollution, problem context, this solution can be easily utilized. 2006 (28) (3) 402-411 In the same manner this solution can be applied to Effat, H., Hegazy, M.N., and Kader, O.A., (2010). other areas of solid waste management such as Solid Waste Landfill Site Selection Using R S and locating suitable sites for waste recycling and GIS: A Case Study in South Sinai Governorate, incinerating. Egypt [Electronic Version], 3rd International Conference on Cartography and GIS 15-20 June, Moreover, the applicability of this solution 2010, Nessebar, Bulgaria. transcends the barriers of field. Hence, this solution can be adapted for purposes that share features with Jarrah, O.A., and Qdais, H.A., (2006). Municipal the concerned problem domain. For example in real- solid waste landfill siting using intelligent estate industry different real-estates agencies can use [Electronic Version] system, Journal of Waste similar solutions to keep track of their land Management, (2006) (26) 299–306 entitlements, similarly, in order to find tenants for vacated households and apartments a database of Javaheri, H., Nasrabadi, T., Jafarian, M.H., Rowshan, vacated and available homesteads can be maintained G.R., and Khoshnam, H (2006). Site Selection of with the aid of similar system, in large scale Municipal Solid Waste Landfills Using Analytical developments schemes to produce environmental Hierarchy Process Method in Geographical reports that prevents the intrusion into Information Technology Environment in Giroft environmentally sensitive areas and zones, the aid of [Electronic Version], Iran. J. Environ. Health. Sci. such a system can be adapted. Eng., 2006 (3) (3) 177-184 In addition, the business establishments with Heywood, I., Cornelins, S., Carver, S., &Raju, S. numerous amounts of outlets can strategically plan (2007).An introduction to geographical information where to locate their branches so that they can make systems (2nded.). India: Dorling Kindersley. the best use of them. Finally, in whichever context a similar solution is used, the importance rests in incorporating the accurate selection criteria to the selection mechanism. REFERENCES Akbari, V., Rajabi, M.A., Chavoshi, S.H., & Shams, R. (2008). Landfill Site Selection by Combining GIS and Fuzzy Multi Criteria Decision Analysis, Case Study: Bandar Abbas, Iran [Electronic version]. World Applied Sciences Journal 3 (1), 39-47. Apaydin, O., &Gonullu, M.T. (2007). Route optimization for solid waste collection: Trabzon (Turkey) case study [Electronic version]. Global NEST Journal, 9 (1), 6-11. Babu, B.V., & Ramakrishna, V. (2003).Extended studies on mathematical modeling of site sensitivity indices in the site selection criteria for hazardous waste treatment, storage and disposal facility [Electronic version]. Journal of the Institution of Public Health Engineers India, 2003, 11-17. Bagchi, A., (2003). Design, Construction and Monitoring of Landfills.2nd ed., John Wiley & Sons. Inc., New York Reduce Exposure to Reduce Risk 51

FEASIBILITY OF USING ENERGY COST BASED GEO- INFORMATICS MODELS FOR DECISION SUPPORTING IN INFRASTRUCTURE DEVELOPMENT AND MANAGEMENT PROJECTS IN ELEPHANT RANGING AREAS M.S.L.R.P. Marasinghe1, Ranjith Premalal De Silva2, N.D.K. Dayawansa2 1Department of Wildlife Conservation, 811/A, Jayanthipura Road, Battarmulla, Sri Lanka, E-mail [email protected] 2Department of Agricultural Engineering, Faculty of Agriculture University of Peradeniya, Sri Lanka, Email [email protected], [email protected]. ABSTRACT: High rate of human population growth in Asia has caused increasing conversion of natural habitats for Infrastructure Development Projects, bringing elephants and humans into greater contact. Both governmental and non-government agencies commonly initiate such projects based on immediate socio-economic and political needs, without consideration of ecological and conservation consequences in the absence of an accepted National Land Use Zoning. In introduction of mega level Infrastructure projects replacing natural habitats, there should be a concern on land use zones and the ecological issues. However, in practice, those conversions are occurred with little foresight or consideration of such issues. In Sri Lanka, it was observed that the Human Elephant Conflict, which was escalated due to the impact of Infrastructure Development projects, caused unexpected levels of economic losses in the long run. Implementation and management of Infrastructure projects, in areas where elephants are habitually present is a greater concern in today’s context as development and elephant conservation has given the same level of importance. The importance placed on elephants considering the cultural, ecological economical and esthetic grounds on one hand, and the grater damages it can cause on human lives and infrastructure on the other hand, made it a unique challenge with no solution for the decision makers. When the ecological and behavioral factors of elephant movement is observed, it can be seen that the Elephants attempts to minimize the energy usage while maximize the energy intake. It is also noted that the Elephant movements are affected by the attributes such as vegetation types and water sources, etc. Seasonal variables such as water availability, time of the day have an impact on factors effecting Elephant movements. Male and Female elephants respond differently for all those variables. However those factors which are spatial in nature and affecting elephant’s energy budget could be modeled and evaluated using Geo-Informatics. It can be emphasized that the energy minimizing behavior of elephant, modeled with Geo-Informatics could be effectively used in modeling preferred elephant movement paths and in predicting new areas to which elephants might move in case if the most preferred path is blocked. Geo-Informatics based energy cost models enable evaluation of different scenarios in infrastructure development project planning, and designing protection infrastructure such as electric fences for the protection of such infrastructure and rationalize those decisions scientifically. This is a vital requirement in mega infrastructure development and management projects in areas where elephant habitats are present. KEY WORDS: Geo-informatics, Elephant corridors, Multi criteria cost surface, Remote Sensing, Infrastructure project planning 1. INTRODUCTION than 5,879 elephants exist in Sri Lanka. With almost 20 High rate of human population growth in Asia has caused million people (Department of Censes Statistics, 2009), increasing conversion of natural habitats (Sukumar, Sri Lanka is one of the most densely populated countries 1989) for Infrastructure Development Projects, bringing in the world (United Nations, 2008). The growing human elephants and humans into greater contact. In Sri Lanka, needs along with their complexity and voracious strive elephant has been closely associated with its history, towards never ending development targets has created a culture, religion (Jayewardene, 1994), mythology and greater demand on accelerated, multipurpose mega even politics. At the turn of the 18th century, an estimated development programs. 12,000 (McKay, 1973) to 19,500 Asian elephants (Jayewardene, 1994) lived in the jungles of Sri Lanka. In this context both governmental and non-government However their numbers have fallen so drastically and agencies commonly initiate infrastructure development during the early nineties only 2,000 elephants appear to projects based on immediate socio-economic and have survived in Sri Lanka (Clarke 1901). According to political needs, with little consideration on ecological and the finding of the recent elephant survey conducted by conservation consequences in the absence of an accepted the Department of Wildlife Conservation (2012) more National Land Use Zoning. In introduction of mega level Reduce Exposure to Reduce Risk 52

Infrastructure projects replacing natural habitats, there With this background, the study was carried out in order should be a concern on land use zones and the ecological to examine how the elephants decide on the best path for issues. In practice, those conversions are occurred with movement, and determination of alternative elephant little foresight or consideration of such issues. In this movement paths if an existing corridor is blocked by an context Santiapillai and Jackson (1990) argues that, there infrastructure development project. The specific is a necessity to give a highest priority to conserve the objectives of the study were to identify the factors elephant while satisfying the human interests. affecting elephant’s preference on to a given place, their relative contribution and developing a spatial model in In Sri Lanka, it was observed that the Human Elephant predicting elephant ranging areas. Conflict, which was escalated due to the impact of Infrastructure Development projects, including several 2. METHODOLOGY large river diversions and irrigation schemes designed to The study was carried out in Mahaweli Wildlife Region develop commercially viable agricultural practices of the Department of wildlife Conservation. The selected caused unexpected levels of economic losses in the long area is falling in side of the “Hurulu” Forest Reserve run (Jayewardene, 1998). Jayewardene (1998) estimates managed by the Forest Department. Factors that led to the annual losses incurred by farmers in System G of the the selection of this region for the study include the high Accelerated Mahaweli Development Program ranged number of elephants present, the severity of the Human from Rupees 10, 000 ($106.40) to Rupees 30,000 Elephant Conflict, the availability of agricultural ($319.10) per farmer per annum. Fernando (1993) argues settlements and infrastructure, and the availability of that most of these development schemes did not pay large irrigations systems and mega developments adequate attention to the habitat requirements of the projects, which provide all possible factors influencing elephant in the adjacent nature reserves and that this the behavior of elephants. oversight may have increased the severity of economic damage by elephants. In this study, ranging data from an elephant herd Implementation and management of Infrastructure monitored through the satellite telemetry by the projects, in areas where elephants are habitually present is a greater concern in today’s context where Department of Wildlife Conservation was used. The development and elephant conservation has given the same level of importance. The major difficulty faced by maps prepared with the location details of the herd the decision makers is rational location of the infrastructure project with minimal disturbance to the recorded in 8 hour intervals were digitized and a layer of existing elephant ranging areas and forecast new ranging area when such projects are unavoidable and act as a elephant heard locations was prepared. The heard was barrier within an existing ranging area. monitored from 11th May 2009 and it was noted that the heard suddenly shifted to a new location and settled in It is a fact that any living being absorbs energy from the new location from 23rd May 2010 onward. An acute outside world and uses that energy in existence and disturbance was suspected as the cause of this maintenance of the progeny. In the process of using the energy for the functions of life, living beings convert displacement. Even though finding the cause was beyond energy from one form to another causing energy losses as such energy conversions are not 100% efficient. The one, the scope of the study, the same phenomenon inspired who can improve the efficiency of energy use, can survive on less energy compared to the ones who are this study to be conducted. inefficient in energy conversion. Hence any living being including elephants tries to optimize the energy budget in The elephant herd location layer was used to extract the every aspect of their lives and in modeling the behavior two home ranges of the elephant herd and spatial loyalty this basic concept could be used. This fact is explained raster was generated by calculating the kernel density of by the past findings. It was established that the the same dataset. Many other raster layers were created movement of elephants is linked with landscape, in order to represent the factors influencing the elephant topography such as avoidance of hills (Wall, et.al., 2006) behavior as shown in Table 1. etc.. Harris, et.al. (2008), Smith, et.al.,(2007) and Cushman et.al., (2010), found that the surface water Table 1: Raster Data used in the study availability is the best predictor of seasonal range use by elephants. Smith, et al., (2007) and Hoare (1999) noted Raster Layer Source Representing that the movement of elephants may be dictated by water availability. Dimer (2003) argues that the land cover and Name Factor the slope were the variables that best represent the elephant distribution. According to Clark et.al. (1993), Spatial loyalty Digitized Spatial loyalty GIS based habitat models are more effective for evaluating species with generalized environmental Telemetry requirements and Diemer, (2003) has obtained better results in estimating the correlation of environmental Data variables with elephant distribution, using only the Digital Elevation Model (DEM) and a Satellite image. Percentage Slop 1:50000 land Terrain (energy Reduce Exposure to Reduce Risk Raster use maps usage) NDVI Landsat TM Forest Type (food/ energy intake) Brightness Landsat TM Thin grass lands (Tasseled Cap) (food/ energy Intake) Greenness Landsat TM Forest Type (food/ (Tasseled Cap) energy intake) Wetness Landsat TM Water (energy (Tasseled Cap) usage) The correlation coefficients were calculated using the Band Collection Statistics Tool of ArcMap software for 53

both the home ranges. The raster layers were again requirement. However the wetness raster was not reclassified on a scale of 1 to 5 and a multi criteria cost included in developing multi criteria cost raster as there surface was generated for the entire study area using the was no considerable contribution. It is also noted that the relative weight determined by the outcome of the contribution from the slope is also low. The area is a flat correlation statistics. The generated multi criteria cost terrain. Although it is classified in to five classes for the surface was used in determining the least cost corridor analysis, the variability is low. However the slope layer between two home ranges. The intermediate stopover was included in the model despite the low correlation location recorded during the movement from first home with the spatial loyalty of elephants. range to second home range was compared with the derived movement path in order to validate the results. In order to generate the multi criteria cost raster, the The methodology adopted in the study is outlined in Figure 01. layers were reclassified giving the value five to the most resistant classes and value one for the least resistant class. The relative importance of different layers was 3. RESULTS AND DISCUSSION incorporated in to the model by multiplying each raster The two home ranges of the herd within the study area are shown in the Figure 2. In calculating the correlation by a weight which was determined by the shown level of between spatial loyalty with forest types, slope, open areas and thin grasslands, water availability; derived correlation. The summation of the weights was one and raster of NDVI, slope, brightness (band 01 of tasseled cap), greenness (band 02 of tasseled cap) and wetness the individual weights were calculated using the (band 03 of tasseled cap) was used. The used layers are shown in Figure 3. The estimated correlation coefficients following equation where Wn is the weight assigned to (Table 2) show lower correlation between the wetness the nth resistance layer, LCn is the Correlation coefficient layer and the spatial loyalty. This cannot be considered as a contradiction with the past research findings as the of the layer n and was the summation of all the wetness raster of the study area shows lesser variability and the effect of the water variation is homogeneous correlation coefficients. within the study area. Even though elephants locate their home ranges close to a water source, existence of a water The calculated weight values for each layer are shown in source within a home range is not a mandatory Table 3. Digitized Elevation Multi Spectral Telemetry Data Details Satellite Image Kernel TIN Tasseled Cap Density Transformation Function Slope Raster Home Range Spatial NDVI Brightness Greenness Wetness Polygon loyalty Multivariate Analysis Re Classification Layer Correlation Coefficient Relative Determination of Cost Raster Weights Relative Weight among Layers Multi Criteria Cost Surface Least cost corridor Figure 1: Methodology adopted in determination of the corridor Reduce Exposure to Reduce Risk 54

Table 2: Correlation Matrix Layer Spatial Slope NDVI Brightness Greenness Wetness Loyalty Spatial Loyalty 0.05270 0.15741 0.32332 0.13896 -0.00067 Slope 1.00000 1.00000 0.00957 0.17584 -0.05221 0.25642 NDVI 0.05270 0.00957 1.00000 0.33025 0.53914 -0.70729 Brightness 0.15741 0.17584 0.33025 1.00000 0.12765 -0.03430 Greenness 0.32332 -0.05221 0.53914 0.12765 1.00000 -0.84125 Wetness 0.13896 -0.70729 -0.70729 -0.03430 -0.84125 1.00000 -0.00067 Table 3: Weights Calculated for Each Layer in In Figure 4 generated movement path of the elephant herd, overlaid on the satellite image. Inside the generated Generating the Multi Criteria Cost Raster. least cost movement path an island of high cost area was noted. Ground verification revealed that this high cost Layer Name Correlation Weight area is a dense forest patch compared to rest of the area. Hence it can be concluded that the elephant preference on coefficient dense forest is low. The selected elephant herd while moving from the old home range to the new home range, Slope 0.05270 0.07837713 rested a while in the middle of the path. According to the telemetry data, they have rested in the place starting from NDVI 0.15741 0.23410521 23rd night till 5.30 p.m. on 24th May 2010. Those points are located inside the determined elephant movement Brightness 0.32332 0.48085189 path and in the edge of the dense forest patch. This situation could be explainable with the natural behavior Greenness 0.13896 0.20666577 of the Elephants. They usually feed on grasslands and rest in the edges of the forest in the nights and the noon. Total 0.67239 1 Falling those points inside the derived movement path is a clear evidence to show that the elephants use the least The multi criteria cost raster and the two home range cost path when moving from place to place. Hence the locations of the selected elephant heard was used in results of the study are valid and could be used in real life determining their movement path form old home range to situations. new home range. The multi criteria cost raster and the generated elephant movement path is shown in the Figure 4. Spatial loyalty Slope NDVI Brightness Greenness Wetness Figure 2: Locations of the Old and New Home Ranges Figure 3: The Raster Layers Used in the Study within the Study Area 55 Reduce Exposure to Reduce Risk

4. CONCLUSIONS where an unavoidable infrastructure projects are required The environment factors affecting the spatial loyalty of to be implemented in an elephant ranging area, disturbed the elephant could be modeled using the geo-informatics elephants’ movement paths could be predicted using the techniques. The factors such as slope, forest types and method adopted in the study. Finally it could also be nature of the habitat have an effect on the elephant’s mentioned that, this procedure could be effectively used spatial loyalty and such factors could be derived using in infrastructure project planning and management in remote sensing techniques. In the study NDVI, slope, elephant ranging area as a decision supporting system. brightness, and greenness derived using band math operations with other geo-informatics techniques were used successfully. The relative weight of contribution by different raster layers could be successfully calculated using the correlation coefficient values estimated in multivariate analysis. According to the results of the study it could be concluded that the geo-informatics models could be effectively used in determining the movement behavior of the elephant. In the situations Less Preferred Old Home Range Moderately Most Prefer Path Preferred Dense Forest Patch Stopover On 24th May 2010 Moderately Preferred from previous night till 5.30 in the evening New Home Range Less Prefered Figure 4: Derived Elephant Movement Paths and the Telemetry Data 56 Reduce Exposure to Reduce Risk

REFERANCES Sukumar, R., 1989, The Asian elephant: ecology and management, Cambridge University Press, Clark, J.D., Dunne. J.E. and Smith, K.G., 1993, A Cambridge. Multivariate Model of female black bear habitat use for a geographic information United Nations, 2008, Statistical Yearbook - Fifty- system. The Journal of Wildlife Manager. second issue, United Nations, New York, 57(3):519-526. USA. Clarke, A., 1901, Sport in the Low country of Wall J, Douglas-Hamilton I, Vollrath F., 2006, Ceylon, Tissara Prakashakayo, Colombo. Elephants avoid costly mountaineering. Curr Biol 16:R527–R529. Cushman, S.A., Chase M.J., and Griffin. C., 2010, Mapping Landscape resistance to identify corridors and barriers for elephant movement in southern Africa. In S.A. Cushman and F. Huettmann (Ed.), Spatial Complexity, Informatics, and Wildlife Conservation, (pp. 349-367). Springer Japan. Department of Census and Statistics, 2009, Statistical Pocket Book-2009, Department of Census and Statistics, Colombo, Sri Lanka. Department of Wildlife Conservation, 2012, First National Survey of Elephants in Sri Lanka, Unpublished Survey Report, Colombo, Sri Lanka. Diemer, N., 2003, Environmental Suitability Analysis for Asian Elephants in Southern India, Unpublished MSc Thesis, International Institute of Geo-information Science and Earth Observation , Enschede, The Netherland. Fernando, A.B., 1993, “Recent Elephant Conservation Efforts in Sri Lanka”, GAJAH,19. Harris, G.M., Russell, G.J., van Aarde, R.I., Pimm, S.L., 2008, Rules of habitat use by elephants Loxodonta africana in southern Africa: insights for regional management. Oryx,42: 66–75. Hoare, R.E., 1999, Determinants of human elephant conflict in a land-use mosaic. Journal of Applied Ecology 36: 689-700. Jayewardene J., 1998, Elephants and Mahaawel; A 15-Year Study. Sri Lanka Nature. p3. Jayewardene, J., 1994, The elephant in Sri Lanka. Wildlife Heritage Trust of Sri Lanka, Colombo. McKay, G.M., 1973, Behaviour and ecology of the Asiatic elephant in southeastern Ceylon. Smithsonian Contrib. Zool. 125 pp.\\ Santiapillai, C. and Jackson, P., 1990, The Asian elephant: an action plan for itsconservation. IUCN/SSC Asian Elephant Specialist Group, IUCN, Gland, Switzerland. Smit, I.P.J., Grant, C.C. & Whyte, I.J., 2007, Landscape-scale segregation in the dry season distribution and resource utilization of elephants in Kruger National Park, South Africa. Diversity and Distributions, 13, 225– 236. Reduce Exposure to Reduce Risk 57

A PLOT BASED LAND CONVERSION MAPPING ON HUMAN BEHAVIOUR PROCESS IN THE CONTEXT OF HOUSEHOLD STABILITY ON LOW-LYING AREAS GPTS Hemakumara* a and Ruslan Rainis b a School of Humanities, Universiti Sains Malaysia, 11800 Minden, Penang, Malaysia Senior Lecturer, Department of Geography, University of Ruhuna, Sri Lanka, E-mail: [email protected] b Geography Section, School of Humanities, Universiti Sains Malaysia, Malaysia 11800 Minden, Penang, Malaysia, E-mail: [email protected] ABSTRACT Living on Low lying areas and emerging of housing on low lying areas is a common issue for developing counties such as Sri Lanka. In this study, it is focus to carry out how individual housing units have been living and behavior in the context of stability on low lying areas in Colombo Metropolitan Region (CMR). 20% of land of Colombo Metropolitan Region (CMR) has been identified as low lying areas. As well as, it is the main economic core area of the entire country and one third of population of Sri Lanka are also living in CMR. Housing plots are very micro level spatial economic units which can be mapped as real world situation in their particular household together with its surrounding environmental condition However, most of low income families have been living in the marginal low- lying areas with risk of natural disaster. Even if restricted, controlled and managed the conversion of low laying areas by regulatory bodies, it has rising the individual household in Low lying areas and they act as converter of low-lying areas. In this study, it has been observed increasing of large amount of houses on low lying areas by satellite imagery based analysis. Due to rising of large amount individual houses on low-lying areas, it is difficult to force the current law and regulations. However above particular individual land conversion process is slow even they captured the low land rapidly. To observe that stability condition in each individual land plot is quiet difficult context. Because grass roots level household information is needed to gathering. Therefore, in this study, it is attempt to carry out grass root level survey in a highest problematic area. A sample village has been selected using GIS techniques to carry out the questionnaire survey and 140 houses have been interviewed to obtain the primary data. Housing plots has been demarcated using GIS Techniques and public participatory mapping. Following behavior variables have been evaluated and mapped using GIS. Housing type (dependent variable), Living time in each plot, Permanent trees in the plot, Public participatory approach of household, Alcoholic and smoking habit of head of household. GIS map- combine tools have been applied for final GIS analysis to check the relation of above variables with housing stability. KEY WORDS: Low Lying Areas, Land Conversion, Living with Risk, Housing emerging Reduce Exposure to Reduce Risk 58

SPATIAL ANALYSIS AND GEOGRAPHIC INFORMATION TO REDUCE RISK IN COMMUNITY; NONG PLING MODEL Ladachart Taepongsorat1, Choosak Nithikathkul1, Pipat Reungsaeng2, Bangon Changsap3, Supaporn Wannapinyosheep3, Pissamai Homchumpa1, Anothai Trivanich2 and Chalobol Wongsawad4 1Faculty of Medicine, Mahasarakham University, Mahasarakham Province, Thailand 2Faculty of Science, Khon Kaen University, Khon Kaen Province, Thailand 3Faculty of Science and Technology, Huachewchalermprakiet University, Samut Prakan Province, Thailand 4Faculty of Science, Chiang Mai University, Chiang Mai Province, Thailand E-mail: [email protected] . ABSTRACT The attraction of uncooked food for natives in regions endemic for soil trasmitted parasites generates continuing public health problems, more than 115 countries by war, natural disaster, disease and poverty. Current reports indicate that spatial analysis and geographically information of parasitic infections with the potential to reduce cause disease are found in community areas, which are under the supervision of the Faculty of Medicine, Mahasarakham University. This present study was conducted for surveillance characteristics of parasitic infection refers to a parasitic worm.In mobile health service by Faculty of Medicine, Mahasarakham Province, Data regarding the demographic information and parasitic health situation characteristics of the population were thought to be useful in the development of a strategy to reduce risk to reduce exposure for control and eradicate infections cost-effectively. During the community health service period (October, 2011 – March, 2012) , the total population was 122 ( Age range of the population was from 10 till 70, he highest education level was bachelor degree). The majority of parasitic zoonosis infections were food-borne parasitic zoonosis. The parasite associated with Cholangiocarcinoma is Opisthorchis viverrini. It was found in 2 stool samples [2 cases, 1.64 percent]. Base on this study overall, a raised standard of personal and public hygiene would reduce transmission. Education of food consumption (eating) behaviour in health security would reduce infection. Improved education in eating and cooking behaviors therefore remains a crucial tool in the control of liver fluke infections. These educational programs should target individuals and communities at risk. Further sociological studies are recommended to elucidate risk behaviors in the transmission of opisthorchiasis and other intestinal parasites. KEY WORDS: Opisthorchis viverrini, community, Mahasarakham University THE ROLE OF GEO-SPATIAL TOOLS IN RICE SUPPLY CHAIN MANAGEMENT DURING DISASTERS Suby Anthony Asian Institute of Technology, Thailand, E-mail: [email protected] ABSTRACT: Around seventy percent of the total rice exported is produced in South-East Asia. A disaster in this area seriously disrupts and weakens the supply of rice to other parts of the world and 2011 floods showcased the same challenge. Another significant rice contributing country which is amongst the top five rice exporting countries in the world is Pakistan, but in 2010 its rice exports were reduced because of the extensive floods throughout much of the country. Gathering accurate, more up-to-date and timely information for better decision making in situations like these have been huge challenges for both government and private organizations. These concerns can be addressed by the help of geo-spatial tools. These tools are unaffected by extreme environmental conditions and are perfect for disaster relief assistance, container tracking, asset visibility and tracking, remote monitoring and complete supply chain management The study will also show that regional integration capacity of an economy plays an important role in stabilizing the global supply chain by filling the gaps. The paper is a Reduce Exposure to Reduce Risk 59

combination of primary and secondary data gathering, with secondary data as basis for building the empirical part of the research. Primary data was collected from the ministry of commerce of concerned countries. KEY WORDS: Supply chain, Geo-Spatial tools, Regional integration. GEOINFORMATION TECHNOLOGY FOR EFFICIENT INFRASTRUCTURE MANAGEMENT - ASSESSMENT OF GEOTECHNICAL PROPERTY VARIATIONS IN FIVE SOILS FROM SRI LANKA P.L. Dharamapriya1 and H.A.H. Jayasena2 Department of Geology, Faculty of Science, University of Peradeniya, Peradeniya 20400, Sri Lanka E-mail: [email protected], [email protected] ABSTRACT Sustainable development of infrastructure is a key to planned urban environments in more modern global economies driven by industrialization. For efficient management of design and construction of such infrastructure projects, prior assessment of geotechnical property variations is valuable and could drive to reduce number of field and laboratory tests. Our study aims at assessing geotechnical relationships for residual, alluvial, marshy, coastal, and compacted soils within the wet and the dry zones of Sri Lanka. Plastic limit (PL), liquid limit (LL), shrinkage limit (SL), plasticity index (PI), liquidity index (LI), compression index (Cc), swell potential (SP), activity, natural moisture content (NMC) and SPT-N values, were either extracted from reports or deduced from formulas and graphs. Wet zone Marshy soils (My_W) show highest statistical means for PL(23.43%), LL(39.90%), PI(15.60%), SL(19.50%), LI(0.96), Cc(0.210) and NMC(35.16%), representing poor engineering properties. Whereas, wet zone residual soils (Re_W) represent wide range with direct correlation to the parent material. CH, OH, CL and SM soil groups for some My_W display high expansive, while, CL, Pt/CL, OL, SC, SM in My_W, CL and SC in Re_W and CL in Dry Zone Alluvial Soils (Al_D) are medium expansive. The higher SPT-N values were recorded in the upper 6.00m and between 12.00-16.00m for Shell of Dry Zone Earth Dams (SED_D), whereas Al_D represent higher N values for 6.00-12.00m zone. Except for marshy soils, no vertical variation for plasticity was observed. Since high to medium swell potential and unsaturated zone are encountered within the upper 1.50m, attention should pay to the My_W. LL and PI of My_W were strongly correlated (R2 = 0.83) so that PI% = -1.91 + (0.46*LL%) could be used to calculate PI. The results provide baseline geotechnical property variations for the five soils, so that planning could be improved and time and cost for investigations could be cut down. KEY WORDS: Geotechnical property, Soils, wet and the dry zones, Statistical mean, Sri Lanka Reduce Exposure to Reduce Risk 60

Technical Session-3 [Hall A]: Climate Change & Disaster / Drought Climate Change Risk Assessment Over the Shadegan Wetland- Iran 62 Halime Etemadi, Mohammad Sharifikia, Seyede Zahra Samadi, Abbas Esmaeili 68 Sari, Afshin Danekar 77 Regional Scale Assessment of Rangeland Degradation and Its Key Drivers using 83 Remotely Sensed Vegetation Net Primary Productivity Data Ranjani Wasantha Kulawardhana, Sorin C Popescu, Rusty A Feagin, Matt C. 83 Reeves, Robert A. Washington-Allen 84 84 Drought Monitoring in Rajasthan, India using Geographic Information System and 85 Remote Sensing 85 K.Rajendram, N. R Patel Role of Climate on The Glacier Dynamics and Water Resources In the Indian Himalayas AL. Ramanathan, Anurag Linda, Jose George Pottakkal, Virender Singh and Parmanand Sharma Assessment Of Uncertainty In Climate Change Predictions For Koshi River Basin, Nepal Using Multi-Model Ensembles Anshul Agarwal, Mukand S. Babel and Shreedhar Maskey Climate Change Vulnerability Mapping: a Statistics-Based Approach Nandana Mahakumarage, Nayana Mawilmada Drought Mitigation by using Geoinformatics as a Tool: A Case study in Karainagar DS Division S. Yoharajan Impact of Dry Spell in Disaster Management - Forecasting Aspects S. C. Mathugama, T. S. G. Peiris Developing Maps for Hazard, Vulnerability and Risk of Drought in Sri Lanka: An Approach for Indexing Time Series Statistics in Hydrological, Agricultural And Socio- Economic Perspectives RanjithPremalal De Silva, B.V.R. Punyawardena Reduce Exposure to Reduce Risk 61

CLIMATE CHANGE RISK ASSESMENT OVER THE SHADEGAN WETLAND- IRAN Halime Etemadi, Mohammad Sharifikia, Seyede Zahra Samadi, Abbas Esmaeili Sari and Afshin Danekar 1Dept. Environmental Science- Tarbiat Modares University, Tehran, Iran, @: [email protected] 2Dept. Remote Sensing -Tarbiat Modares University, Tehran, Iran, @: [email protected] 3Cardiff University, England, @:[email protected] 4Dept. Environmental Science- Tarbiat Modares University, Tehran, Iran: @:[email protected] 5Dept. Environmental Science- University of Tehran, Tehran, Iran @: [email protected] ABSTRACT Wetlands are one of the most important ecosystems with the highest ecological value in the world where exposed with the adverse effects of climate change. The present study deal on future climatic risk over the Shadegan Wetland, which is the largest international wetland of Iran covers an area about 4000 Km2 as well as creates a suitable habitat for a number of migrating waterfowls. In other, for risk assessment in this area we applied LARS-WG downscaling method for 2025 horizon, Generated daily minimum temperature increase between 0.12 and 1.4°C in cold season and decrease by between o.17°C and 0.37°C in warm season. In Future spring and summer Maximum temperature will reduce 0.20 to 5.53°C and will raise 0.18 to 0.73 °C in winter. According to results Precipitation in next decade will decrease in winter, spring and autumn and will increase in summer. This result indicated the wetland will expose to risk of significant water level reduction subsequently expected to significantly decrease the abundance of marble duck in coming period Due to reduce in suitable overwintering habitat of aquatic migratory bird and loss of spawning. KEY WORDS: Shadegan Wetland, Climate change, LARS-WG downscaling Method, water resource risk assessment 1. MANUSCRIPT resolution are developed in the future, the 1.1 General Instructions downscale techniques might be still needed for Climate appears to be generally changeable evaluating the impact of climate change on precipitation and temperature during the last half of environment because the spatial resolution of GCMs the 20th century particularly in arid and semi-arid remains quite coarse and it has no accurate for regions. Regions with arid and semi-arid climates environment studies. In the other hands the outputs could be sensitive even to insignificant changes in of these models don't have local and temporal climatic characteristics. Understanding the adequate accuracy for climate change impacts relationships among the climate variables, and studying on environmental systems. Therefore anthropogenic effects are important for the downscaling method can be use as a reliable sustainable management of environmental resources technique for projection of future climatic variables. in these regions. This study relies on one statistical downscaling model, based on observed data; LARS-WGS define the global average temperature will rise by about relationships between the large-scale variable data, 3.6°C, with a range of 1.5– 4.5°C, depending on the derived either from climate model outputs or model used (IPCC, 2001) The climatic response to observations, and local-scale surface conditions. the enhanced greenhouse effect is calculated by means of highly complex General Circulation LARS-WG is a stochastic weather generator which Models (GCMs). GCM simulations of local climate can be used for the simulation of weather data at a at individual grid points are often poor especially single site (Racsko et al, 1991; Semenov et al, 1998; when the area has complex topography (Means et Semenov and Brooks, 1999), under both current and al., 1999). Even if global climate models of high Reduce Exposure to Reduce Risk 62

future climate conditions. These data are in the form a suitable habitat for a number of migrating of daily time-series for a suite of climate variables, waterfowls, which fly to this area from north namely, precipitation (mm), maximum and Europe, Canada and Siberia in autumn. In this minimum temperature (°C) and solar radiation study, data is collected, from three meteorological (MJm-2day-1). Stochastic weather generators are stations as they have sufficient record length as used in a wide range of studies such as environment required by LARS-WG, Ramhormoz (R), Mashahr management, hydrological application, agricultural harbor (M), Behbahan (B) (table1). The daily risk assessment (Semenov and Barrow, 1997). It has rainfall and temperatures data of R and M stations is been tested for diverse climates and found better available for 1986 to 2005(20 years).The observed than some other generators e.g WGEN, WGEN uses data was obtained from Iranian Meteorological simple standard distributions, whereas LARS-WG Organization (www.irimet.net). Daily precipitations tends to use the more flexible semi-empirical as well as daily maximum and minimum distributions. In the results of testing the generators temperature data were defined as predicted variables over the 18 diverse sites, with LARS-WG able to for the downscaling experiments. Climatic variables match the observed data much better than WGEN corresponding to the future climate change scenario (Semenov et al., 1998). A study by Semenov (2008) for the study area are extracted from the ECHO-G has tested the skill of the LARS-WG stochastic GCM output is from Hamburg University and weather generator to simulate extreme weather Southern Korea - A2 Emission Scenario - with events at 20 locations with diverse climates, and has 2/8*2/8digree of resolution (300*300 Km at a grid shown its ability to model rainfall extremes with point which is closest to the study area. Data is reasonable skill. The weather generator has been extracted for one distinct periods that covering a 30 examined in varied climates in Asia, Europe and years period between 2010 and 2039). New Zealand, North America the results has shown The weather generator had the power to reproduce 3. METHOD most of the characteristics of the observed data reliability at each site (Qian et al., 2004). 3.1 Stochastic Weather Generator The goal of this study is to study one statistical LARS-WG is based on the series weather generator downscaling model then applied its downscaled described in semenov et al. (1998). results in the future impact studies. In fact, the ultimate goal of downscaling approach is to Calibration and Validation of LARS-WG: generate an estimate of meteorological variables corresponding to a given scenario of future climate LARS-WG model calibration consists of calculating so these research meteorological variables will be the relevant statistical parameters for each used as a basis for any climate change impact meteorological variable from the observed historical assessments. The objective of this study is to assess data. These parameters or the once modified based future climate change in an international wetland on future climate change scenario are then used to due to one downscaling model using global stochastically generate realistic climate data circulation model (GCM) simulated predictors corresponding to the present and future climate instead of the observed NCEP predictors. This scenario, respectively. For the first set of analysis provides some indication of how experiment, mean of observed daily precipitation as downscaling model will generate of future climatic well as daily maximum and minimum temperatures variables on the GCM outputs. at the stations of Maroona-Jarrehi catchment for the period 1986–2005 were used to extract the statistical 2. STUDY AREA AND DATA SOURCE parameters of the current climate. For precipitation, these parameters consist of monthly histogram The study Area includes the Jarrahi Basin in intervals and frequency of events in each interval for southwest of Iran. The area of Jarrahi Basin is dry and wet spell lengths, as well as precipitation approximately 24310 km2; it is characterized by a amounts. On the other hand, temperature is modeled Mediterranean climate consisting of hot and dry in LARS-WG by using Fourier series which can be summers and mild and rainy winters. Jarrahi River constructed with parameters such as mean value, network consists of two large reservoirs (Maroon amplitude of the sine and cosine curves and phase and Jarrehi) which are the main freshwater supplier angle. Both maximum and minimum temperatures of Shadegan Wetland, the largest international are modeled more accurately by considering wet wetland of Iran covers about 4000 Km2 and creates and dry days separately; therefore, the temperature parameters for wet and dry days are derived separately. Reduce Exposure to Reduce Risk 63

Table1. Characteristic of meteorological study stations Station current period Geographical characteristic Station Type Behbahan 1991 - 2005 ELEVATION LATITUDE LONGITUDE SYNOPTIC Ramhormoz 1986 - 2005 SYNOPTIC Mahshahr 1986 - 2005 313/0 'N36◦30 'E14◦50 SYNOPTIC /5105 'N16 ◦31 'E36◦ 49 6/2 30◦33'N 'E9◦49 Figure 1. Location map of Shadegan Wetland in Maroon-Jarrahi catchment The weather generator also uses parameters 4. RESULTS corresponding to average autocorrelation values for 4.1 maximum and minimum temperatures in the minimum and maximum temperature derived from 2025 horizon observed weather data. After the observed weather The result of simulation Monthly means Tmin for data is analyzed in this way, the derived statistical 2010-2039 period with LARS model are represented parameters are used to generate synthetic weather in Table 2. According to result, Future Tmin data representing the current climate. To get a generated with LARS indicated that Tmin will raise representative statistics of the synthetic data, a 30- 0.56, 0.45 and 1.4°C in winter at M, R and B year data was generated for each climate variable stations and 0.24 and 0.12°C in autumn at R and B considered. Finally, statistical tests are performed to stations respectively also future spring and summer see if the data generated by the weather generator Tmin will reduce 0.17, 0.37, 0.3°C and 0.3, 0.31, comes from the same population as the observed 0.25°C at M, R and B stations respectively. ones. Generated Tmin with LARS model increase by between 0.12 and 1.4°C in cold season and decrease by between o.17°C and 0.37°C in warm season. In three stations, downscaling method suggests increases Tmin for cold season and decreases Tmin for warm season for 2010-2039 periods. Reduce Exposure to Reduce Risk 64

90 60 80 70 60 50 40 30 20 10 0 JAN FEB MAR APR MAY JUNE JULY AUG SEP OCT NOV DEC observed Ecog (1961-1990) Ecog (2010-2039) precipitation (mm) 50 Precipitation (mm) 40 30 20 10 0 Spring Summer Atumn Winter Observed Ecog (1961-1990) Ecog (2010-2039) Figure 2: Monthly mean daily precipitation (a) and seasonally mean daily precipitation (b) (observed and model) for the current and future periods in mean of 3 station. Table2. Monthly means daily Tmin of Observed and 2010-2039 simulated LARS Station Mahshahr Ramhormoz Behbahan Month OBS LARS %change OBS LARS January OBS LARS %change 8.369 8.473 1.2 7.034 7.871 %change February 8.12 9.451 9.481 0.3 7.565 10.49 11.9 March 9.413 8.738 7.6 12.93 14.17 9.6 10.68 11.12 38.7 April 13.32 18.43 18.32 -0.6 16.36 15.82 4.1 May 18.95 10.38 10.3 24.28 23.26 -4.2 21.77 21.38 -3.3 June 23.45 27.74 27.76 0.1 25.42 25.43 -1.8 July 25.83 13.42 0.8 30.24 29.71 -1.8 28.20 27.52 0.0 August 28.41 29.77 29.12 -2.2 27.32 26.69 -2.4 September 27.84 18.28 -3.5 25.95 26.18 0.9 22.52 23.07 -2.3 October 23.48 21.47 20.26 -5.6 17.71 17.63 2.4 November 19.62 23.04 -1.7 14.62 14.86 1.6 12.07 12.03 -0.5 December 13.38 10.35 12.06 16.5 8.538 9.027 -0.3 9.644 26.38 2.1 5.7 27.6 -2.9 27.01 -3.0 24.2 3.1 19.13 -2.5 13.98 4.5 9.365 -2.9 Table3. Monthly means daily Tmax of Observed and 2010-2039 simulated LARS Station Mahshahr Ramhormoz Behbahan Month LARS %change OBS LARS January OBS 18.47 7.9 OBS LARS %change 17.27 18.82 %change February 17.11 20.89 3.8 19.77 20.39 9.0 March 20.12 24.90 0.3 17.09 17.62 3.1 23.9 23.85 3.1 April 24.83 31.55 -1.6 31.56 31.36 -0.2 May 32.05 38.71 -1.3 19.84 19.73 -0.6 38.48 38.01 -0.6 June 39.22 43.51 -0.6 43.43 42.75 -1.2 July 43.78 44.58 -1.3 24.4 26 6.6 44.77 44.75 -1.6 August 45.19 44.11 -1.1 44.62 44.02 0.0 September 41.03 -0.7 31.89 31.45 -1.4 40.99 41 -1.3 October 44.6 35.35 0.0 35.17 34.82 0.0 November 41.33 27.03 2.0 39.44 38.27 -3.0 -1.0 December 35.35 19.15 -2.5 26 26.14 0.5 26.49 44.31 43.91 -0.9 19.84 19.94 0.5 19.65 45.98 45.73 -0.5 45.61 45.03 -1.3 41.96 26.18 -37.6 35.72 33.99 -4.8 26.56 26.71 0.6 19.78 21.92 10.8 Future Tmax generated with LARS indicated that Tmax will reduce 0.42, 0.67, 0.45°C and 0.46, 5.53, Tmax in months of APR, MAY, JUN, JUL, AUG, 0.20°C at M, R and B stations respectively also SEP, OCT will decrease at all three station and the Tmax will raise 0.73, 0.67 and 0.70°C in winter at rest of months will raise. Maximum increasing is 1.5 M, R and B stations and 0.01 and 0.18°C in autumn in JAN at B station and maximum decreasing is 15.7 at M and R stations respectively. A comparsion of in SEP at R station. Future spring and summer two downscaling method demonstrated, both models Reduce Exposure to Reduce Risk 65

Tmax rise in cold season and reduce in warm season decrease the abundance of marble duck in coming for 2010-2039 periods. period then Significant seasonal stresses could occur due to the associated lowering of water levels. 4.2 Precipitation Wetland plants are vulnerable to climate change Fig 2a,b. Maximum and minimum different of because of the delicate balance between the rainfall, observed and modeled sum precipitation defined temperature and evapotranspiration that is critical to 19.8 mm and 0.02 mm in the months of January and their physiology functions (Dawson et al., 2003). July respectively. LARS-WG model has downscaled The dominant plant community in this wetland is precipitation closer to observed data in most of sedge (Cyperaceae), typha (Typhaceae) reed months except January, February and March. (Graminaceae) that was seen in the north and fresh Different of observed and modeled precipitation in water wetland. Decrease in water level due to cold season is due to present both of Somoum and increasing evaporation and decreasing precipitation Mediterranean air mass in study region. Maximum in winter significantly will reduce vegetation observed precipitation occurred in the month of wetland consequently reduce in suitable January and minimum value happened in the month overwintering habitat of aquatic migratory bird and of June July August and September. Downscaling loss of spawning. Stream flows affecting ecosystem projection exhibited precipitation in the summer productivity with reducing summer water season was less and minimum different of observed availability and water quality might be decrees. and modeled Precipitation related to this season. Consistent with previous studies (Larson, 1995; The main reason of that is continuous stability of Poiani and Johnson, 1991; Withey and Kooten, Azor near tropical zone in the summer which 2011) changes in wetlands are more sensitive to making climate very hot and intolerable in the temperature changes than to changes in summers. Further more Fig 14 b exhibits that precipitation. Decreasing precipitation and precipitation in coming decade will decrease 9.5 increasing temperature in winter will causes of mm, 1.12 mm, 7.13 mm in winter, spring and raising salinity and extension of salty water habitats autumn and will increase 0.11 mm in summer. in Shadegan area. Also in the cold months of the year increase in temperature will expose to danger 5. DISCUSSION of significant reduction of water level in Shadegan Wetlands are one of the most important ecosystems wetland. with the highest ecological value in the world. The consequences of climate change have the potential REFERENCES to significantly affect all the environmental ecosystems. According to the output of this Dawson, T.P., Berry, P.M. and Kampa, E., 2003. research, climate change effects are predicted to Climate change impacts on freshwater wetland become more noticeable in coming decade in habitats. Journal of Nature Conservation,11 Shadegan wetland. According to results (1), 25–30. Precipitation in coming decade will decrease in winter, spring and autumn and will increase in IPCC, 2001. Climate Change 2001: Impacts, summer in all three stations. Since Changes in Adaptation and Vulnerability. Contribution of precipitation will alter water level also Wetland Working Group II to the Third Assessment ecosystems depend on water levels, this Report of the Intergovernmental Panel on phenomenon is likely to have a significant impact Climate Change, J.J. McCarthy, O.F. Canziani, on these habitats and associated species. Shadegan N.A. Leary, D.J.Dokken and K.S.White, Eds., wetland is the largest global habitat of marble duck Cambridge University Press, Cambridge.1032 (Marmaronetta angustirostris). The presence of this pp. species and other migratory overwintering species in Shadegan wetland strongly depend on the Larson, D. 1995. Effects of climate on numbers of continuous existence of water resulting from winter northern prairie wetlands. Climatic Change, floods so that the abundance of marble duck has 30, 169–180. exponential relationship with water level in January (sima and Tajrishi, 2006). Due to decrease of Means, L. O., Bogardi, I., Giorgi, F., Matyasovszky, precipitation and increase of temperature I. and Paleecki, M. 1999. Comparison of consequently increase of evaporation in winter climate change scenarios generated from especially in January expected to significantly regional climate model experiments and statistical downscaling. Journal of Geophysical Research, 104, 6603-6621. Reduce Exposure to Reduce Risk 66

Poiani, K. A. and Johnson, C.W. 2011.Global Semenov, M. A. and Brooks, R. J. 1999. Spatial warming and prairie wetlands. Bioscience interpolation of the LARS-WG stochastic 1991; 41: 611–618. weather generator in Great Britain. Climate Research, 11, 137-148. Qian, B. D., Gameda, S., Hayhoe, H., De Jong, R. and Bootsma, A. 2004. Comparison of LARS- Semenov, M. A. Brooks, R. J. Barrow, E. M. and WG and AAFC-WG stochastic weather Richardson, C. W. 1998. Comparison of the generators for diverse Canadian climates. WGEN and LARS-WG stochastic weather Climatic Research. 26 (3), 175–191. generators for diverse climates. Climate Research, 10: 95–107. Racsko, P., Szeidl, L. and Semenov, M. 1991. A serial approach to local stochastic weather Sima, S. and Tajrishi, M. 2006. Water allocation for models. Ecological Modelling, 57, 27-41. wetland environmental water requirements: the case of shadegan wetland, Jarrahi catchment, Semenov, M. A. 2008. Simulation of extreme Iran. Proc, seventh International Confreres of weather events by a stochastic weather Civil Engineering, Tehran Iran, 356-362. generator. Climate Research, 35(3), 203-212. Withey, P. Kooten, G. C. V. The effect of climate Semenov, M. A. and Barrow, E. M. 1997. Use of a change on optimal wetlands and waterfowl stochastic weather generator in the management in Western Canada. Ecological development of climate change scenarios. Economics, 70, 798–805. Climatic Change, 35, 397-414. Reduce Exposure to Reduce Risk 67

REGIONAL SCALE ASSESSMENT OF RANGELAND DEGRADATION AND ITS KEY DRIVERS USING REMOTELY SENSED VEGETATION NET PRIMARY PRODUCTIVITY DATA. Ranjani Wasantha Kulawardhana1, Sorin C Popescu1, Rusty A Feagin1, Matt C. Reeves3, and Robert A. Washington-Allen1,2, 1. Department of Ecosystem Science and Management, Texas A&M University, 2138 TAMU, College Station, Texas 77843-2138, USA, [email protected], [email protected], [email protected], washington- [email protected], 2. Department of Geography, University of Tennessee, 304 Burchfiel Bldg, Knoxville, TN 37996, 3. USDA-Forest Service Rocky Mountain Research Station, Forestry Sciences Laboratory (FoSL), 800 E. Beckwith Ave, Missoula, Montana, 59801, USA, [email protected] ABSTRACT: Land degradation implies the reduction of the resource potential of the landscape through different processes. Several studies have attempted to estimate the extent of land degradation at local to global scales. However, the extent of global dryland degradation is still unknown with estimates ranging from 10 – 80%. This uncertainty is due to the inability to accurately monitor phenomena such as livestock grazing and/or climatic events at appropriate spatial and temporal scales. Time series analysis of factors that drives or act as indicators of degradation could help assess the extent of dryland degradation. Net primary productivity (NPP) is considered as an important component of the global carbon budget and is used as an indicator of ecosystem function. It is also considered to be an ecological indicator of degradation. Within rangeland environments, livestock grazing and climate variability play a major role in determining productive capacity of the land. Furthermore, increased availability of historical archives of satellite data at larger spatial and longer temporal scale has made it possible to assess varying levels of degradation at difeferent scales. Consequently, the purpose of this study was to understand rangeland degradation patterns in terms of the response of remotely sensed NPP to commercial grazing and climatic drivers over the period from 1982 to 2009. Specific objectives were to: 1) understand spatial patterns and temporal trends in rangeland degradation over US drylands as reflected in vegetation NPP; and to 2) explore the impacts of precipitation variability and commercial livestock grazing on rangeland vegetation productivity and thus on degradation potential at state level using data on Texas rangelands. In this study, A US national land cover map of rangeland vegetation and the aridity index were combined to define US and Texas rangelands. An 8-km pixel resolution predicted NPP dataset from 1982 to 2009 was defined to this national rangeland extent as well as the extent of the state of Texas. Time series of spatial maps of annual precipitation and livestock distribution at the county-level were defined to the extent of Texas. These datasets of drivers were analysed with respect to Texas rangeland NPP. We found that, at the national-level, rangeland productivity increases along an aridity gradient; i.e. NPP decreases as the level of aridity increases. Over the period 1982 to 2009, US rangeland NPP shows a significant (p = 0.05, r2=0.36) net carbon gain of 0.256gC m-2year-1 that is driven primarily by the dry sub-humid and semi-arid regions that showed net carbon gains of 1.19 gC m-2 yr-1, and 1.88 gC m-2 yr-1, respectively. These results are consistent with the findings of past studies concerning C gains of drylands. However, in drylands this net carbon gain could also be suggestive of increased woody encroachment. At the Texas spatial scale, aggregate time series analysis indicates a significant linear relationship between NPP and mean annual precipitation (p=0.05, r2=0.37). Livestock grazing did not show significant relationship with NPP. KEY WORDS: livestock grazing, NPP, remote sensing, US rangelands Reduce Exposure to Reduce Risk 68

1. INTRODUCTION NPP is the net amount of solar energy converted to plant organic matter through photosynthesis. It is The extent of dryland degradation is unknown with the remaining fraction of biomass produced after estimates ranging from 10 – 80% (Lund, 2007). accounting for various energy losses during cellular This range of uncertainty is mainly due to lack of respiration and maintenance processes. NPP is an monitoring at appropriate spatial and temporal important component of the global carbon budget scales for their conditions as well as for the impacts and is an indicator of ecosystem function or of natural and human induced disturbances. provisioning service. It is also considered to be an ecological indicator of degradation. It can be Drylands account for almost half of the earth‘s directly assessed by using different field terrestrial land surface and may play an important measurements. However, these methods are not role in the global carbon (C) cycle (Knapp et. al., cost effective across large areas whereas coarse to 1998). From the standpoint of C cycling, drylands, medium resolution remotely sensed images have have received less research attention than forested served as an efficient and cost effective source of ecosystems. However, drylands have been information for the estimation of NPP and other identified as an ecosystem that is susceptible to aspects of the carbon cycle (e.g., Xiao et al. 2008). anthropogenic land degradation, a process defined as desertification (UNCCD, 2004). Thus, In this study, we used a 28 year remotely sensed identifying early signs of desertification could help NPP dataset to understand rangeland degradation alleviate possible consequences of human patterns in terms of the response of remotely disturbances in these managed ecosystems. sensed NPP to commercial grazing and climatic drivers. The objectives of this study were to: 1) Central to the definition of desertification adopted understand spatial patterns and temporal trends in by the United Nations is the concept of reduced rangeland degradation over US drylands as productive potential of the land (UNCOD, 1977). reflected in vegetation NPP; and to 2) explore the Hence, monitoring long term trends in rangeland impacts of precipitation variability and commercial vegetation productivity could help in assessing livestock grazing on rangeland vegetation changes in their health and condition. Further, productivity and thus on degradation potential at understanding the impacts of environmental state level using data from US state, Texas.. disdurbances and land use interventions on vegetation productivity and thus degradation could 2. METHODS help land managers to adjust their management practices as a measure to alleviate long-term loss of 2.1 Study area lands productive capacity. In the long run, understanding of spatial and temporal patterns of This study was focused on the rangelands, a land changes in these environments will help decision use type where subsistence and commercial makers to implement appropriate policies towards livestock grazing occur, within the extent of US sustaining these lands. drylands (Figure 1). For the definition of drylands, our study follows the World Atlas of However, the lack of consistent data to monitor Desertification (Middleton and Thomas, 1997). NPP particularly at larger spatial (regional to They defined drylands globally using a set global) and longer temporal (> 15 year) scales have threshold of the aridity index (AI) that is the ratio prevented our ability to detect early signs of long of mean annual precipitation (MAP) to mean terms trends in these landscape processes. Further, annual potential evapotranspiration (MAPET). AI lack of understanding of the impacts of natural values lower than 1 indicate an annual moisture processes and human disturbances limits our ability deficit and for drylands, the upper threshold is to implement appropriate management practices to 0.65. This indicates areas in which MAPET is at sustain their productivity levels and thus economic least ~1.5 times greater than MAP. We derived AI potential. Within rangeland environments, dataset for the contiguous US using MAP and commercial and subsistence grazing, fire and MPET data developed by Trabucco et al. (2009) climate variability play major roles in determining and available for free download at CGIAR data productive capacity of the land. archive (http://csi.cgiar.org/Aridity/). A wide variety of satellite based remote sensing We defined rangelands, the land area used for techniques have been used to identify and monitor grazing by commercial livestock, using 2001 vegetation conditions over larger areas. While the National Land Cover Dataset (NLCD, Homer et. methods used in these studies vary widely, the vast al., 2007) available for download from the data majority of these studies heavily depend on two archives of Multi-Resolution Land Characteristics data products: Normalized Difference Vegetation Consortium (MRLC) data archive Index (NDVI), and NPP. (http://www.mrlc.gov/nlcd01_data.php). Two land cover classes; Grassland/Herbaceous and Reduce Exposure to Reduce Risk 69

Shrub/Scrub (NLCD classes 52 and 71, Systems. This dataset is available for download at respectively) were used as the rangeland extent. US Geological Survey (USGS) National Atlas data Within these two land cover classes, we further archive (http://nationalatlas.gov/mld/wildrnp.html). eliminated the area that is not allowed for Dryland and rangeland extents were then commercial grazing by excluding wilderness areas intersected to extract the grazable rangeland area of as defined by US National Wilderness Preservation US. Land uLsaend/ulsaen/ ldancdocvoveerr CClalassss Figure 1: The study area: Rangelands of contiguous US. [AI is defined as the ratio of mean annual precipitation (MAP) to mean annual potential evapotranspiration (MAPET)] 2.2 Data processing and analysis Annual livestock data are available for download at To understand the dynamics of US rangeland US Department of Agriculture‘s National productive capacity, we analyzed spatial and Agricultural Statistics Service (NASS) data archive temporal patterns of remotely sensed NPP data (http://www.nass.usda.gov/). County level goat and within an area that covered 17 western states sheep numbers were downloaded from NASS for (figure 1). However, to understand the impacts of Texas. Grazing cattle number estimates for Texas potential degradation drivers, precipitation and were obtained from the US Forest Service. Both livestock grazing, we studied this relationship at data sets were acquired for the period from 1982 to the spatial scale of the state of Texas. 2010, though goat data only started to be available in the mid-1990s. Livestock numbers were 2.2.1 NPP, Precipitation and Livestock data converted to county level forage demand maps for The most recent NPP data product released by Zao Texas. This conversion followed the Animal Unit and Running, (2010) was used as the primary NPP (AU) definitions and the forage demand dataset. This data were available only for the period requirements specified by Holechek (1988) and from 2000 – 2009. For the period from 1982 to was based on the assumption of 6 months grazing 1999, we used 8km rangeland NPP data predicted period during a year. Estimates of forage demand based on a pixel based least squares regression were then converted to LFP that is defined as the model developed using NOAA AVHRR GIMMS total annual livestock forage demand per unit area. annually integrated NDVI (ΣNDVI – independent The LFP unit area is the rangeland extent or variable) and MODIS NPP (dependent grazeable area within each Texas county, thus we variable,Kulawardhana et al, unpublished data). assumed uniform distribution of livestock over rangelands within each county. Gridded 4-km pixel resolution precipitation maps for the period from 1982 to 2009 were obtained 2.2.2: Spatial patterns and temporal trends in from the PRISM (Parameter-elevation Regressions US rangeland NPP on Independent Slopes Model) climate group data archives (http://www.prism.oregonstate.edu/, Di We analyzed the spatial variability of NPP over 4 Luzio et al. 2008). dryland sub types, i.e., Hyper-arid, arid, semi-arid, and dry sub-humid using the mean 8-km predicted Reduce Exposure to Reduce Risk 70

US rangeland NPP map (Figure 2, Table 1). To Where: Xi = NPP (or MAP) for ith year, and examine the temporal variability and trends within these 4 regions, we used least squares linear Xmean = 28 year mean NPP (or MAP) regression analysis by defining time and rangeland mean NPP as independent and dependent variables Mean LFP maps were produced to examine spatial respectively. Mean rangeland NPP statistics for variations in grazing impacts over Texas each region were extracted from NPP layers of rangelands. each year. The coefficient of determination (r2) provides a measure of the magnitude of the positive Least squares linear regression analyses were or negative trend and the slope (ß), if significant, performed on mean NPP anomalies (dependent will indicate the rate of carbon gain or loss in variable) and mean annual precipitation and LFP rangelands. Further, descriptive statistics for NPP (independent variables) of each year to examine data on four dryland sub regions were evaluated to their relationships. identify rangeland productivity variation along the aridity gradient. Maps of mean rangeland NPP, annual precipitation and LFP were evaluated to understand their spatial 2.2.3: Impacts of livestock grazing and patterns over Texas rangelands. precipitation variability on Texas rangelands as reflected in NPP 3. RESULTS AND DISCUSSION In following sections, we first discuss spatial To identify inter annual variations in rangeland patterns and temporal trends in rangeland NPP as NPP and precipitation, and to understand any reflected in predicted and MODIS estimated NPP possible linkages among them, we evaluated during the period 1982 to 2009. We then evaluate percent anomalies of NPP and annual precipitation possible linkages between NPP, precipitation and for each year. These datasets were derived for the livestock grazing using data on Texas rangelands. extent of Texas rangelands. Calculations were performed for each pixel using equation 1. 3.1 Spatial patterns in US rangeland NPP Analysis of rangeland mean annual NPP data % anomaly = Xi  X mean *100 revealed an increasing gradient of NPP from the X mean west towards the east (figure 2). This spatial pattern of variation was evident in all years during the Equation 1 study period. Reduce Exposure to Reduce Risk 71

Figure 2: Spatial variation in 28 year means annual NPP Mean rangeland NPP was 179gCm-2year-1± 110 *SD = Standard deviationNPP (gC/m^2/year) gCm-2year-1 with a range from 19 to 790 gCm- 2year-1. These remotely-sensed estimates of 3.2 Temporal trends in rangeland NPP at rangeland NPP are consistent with independent national and state level, for Texas NPP estimates from field studies (Leith, 1973; Least squares linear regression analysis was Noy-Meir, 1973; Whittaker and Likens, 1973; and performed on mean NPP values of US rangelands Ludwig 1987). Whittaker and Likens (1973) and the four dryland sub-types to determine the reported mean NPP of 70 gCm-2year-1for desert direction, magnitude (r2), and rate of NPP gain or scrub lands. Results of similar studies by Noy-Meir loss (ß). US rangelands showed a significant (p = (1973), Leith (1973) and Ludwig (1987) however 0.01, r2 = 0.36) positive trends with a net carbon reported different estimates of NPP for desert gain (ß) of 0.26gC m-2year-1 while Texas grasslands and scrublands with mean NPP values rangelands did not reveal significant (p =0.05) of 30 to 300 gCm-2year-1, 10 to 250 gCm-2year-1 and trends in NPP (figure 3). This increasing trend in 30 - 600 gCm-2year-1respectively. The increased rangeland NPP at national scale is consistent with variability over these desert grasslands (at the scale the observed global ―greening‖ of drylands of this study) could account for the greater (Langanke et al. 2012) and the findings of Nemani variability between these estimates. In a recent et al. (2003) and Zhao and Running (2010) study based on remote sensing estimates of NPP, concerning the net carbon gain of the northern Cao et. al. (2004) reported global dryland NPP in hemisphere from 1982 to 2009. the range of 703 ± 44 gCm-2year-1. According to the global estimates of MEA (2005), rangeland and . cropland land uses jointly account for nearly 90% of area of dryland areas. Hence, NPP estimates 450 reported by Cao et al. (2004) can be considered as fairly good estimates for rangelands within dryland 400 ecosystems. Consequently, long term records of satellite based estimates of rangeland NPP used in 350 R² = 0.0074 this study reasonably reflect the vegetation 300 condition of US rangelands. 250 Mean rangeland NPP revealed larger variations across four dryland sub types. NPP values decrease 200 along the aridity gradient from sub-humid to hyper- arid areas (table 1). This could explain the greater 150 y = 1.3569x - 2499.4 impact of climatic parameters (i.e. temperature and 100 R² = 0.36 precipitation) which determine the level of moisture stress, on vegetation productivity. 50 Table1: Variation and change in mean rangeland 0 NPP across an aridity index represented by four 1982 1985 1988 1991 1994 1997 2000 2003 2006 2009 dryland sub- types of US. US TX NPP (gCm-2year-1) Dryland sub type Figure 3: Rangeland NPP trends at national and Texas state level Mean (1982 to 2009) SD* Among 4 dryland sub types, NPP trends were Hyper-arid 95 28 significant (p= 0.05) for the semi-arid and dry sub- humid regions only (Table 2). However, these Arid 115 47 trends were of very low magnitude (r2 = 0.25 and 0.22 respectively). Net carbon gains were  = 1.19 Semi-arid 218 114 gC m-2 and  = 1.89 gC m-2, respectively (Table 2, Figure 4). Relatively drier hyper-arid and arid Dry sub humid 370 117 zones showed no net carbon gain or loss as compared to the two other zones (figure 3, and table 2). In general, rangeland NPP of all 4 dryland sub types showed very high levels of inter-annual variations over the period 1982 to 2009. However, , the levels of inter-annual variability in rangeland NPP over relatively drier hyper-arid and arid zones were low as compared to two other zones (figure 4, and table 1). Reduce Exposure to Reduce Risk 72

Dryland sub type Adjusted r2 F Significance for increased carbon sequestration ability in these rangelands under the conditions of sufficient Hyper-arid 0.08 0.077 moisture availability. This could also suggest increased woody encroachment, a result of Arid 0.01 0.367 degradation in US rangelands at national scale. However, for complete understanding of the Semi-arid 0.25 0.004 changes in these rangelands,and their spatial variability across regions, it is important to perform Dry sub humid 0.22 0.006 similar . analyses at local scale using detailed data at finer scale. Table 2: NPP trends across 4 dryland sub-types: 3.3 Impacts of livestock grazing and least squares linear regression results for US precipitation on Texas rangelands as reflected in rangelands over four dryland sub types. NPP Two main drivers of vegetation productivity in drylands are water availability and land use/ management, particularly grazing. Grazing results the removal or consumption of vegetation biomass. In order to evaluate the impacts of climate and human induced activities on vegetation trends, long term records of precipitation and livestock numbers were analyzed. In this paper we discuss only the findings from Texas rangelands. Figure 4: Inter-annual variations in rangeland mean 3.3.1 Linkages between rangeland NPP and NPP across four dryland sub types. precipitation Slightly increasing trends in rangeland NPP over The PRISM data indicate that MAP for Texas two dryland sub types could indicate the possibility rangelands is 551mm with a range of 225 – 1034 mm that increases from west towards the east. This a) b) spatial pattern of precipitation variation follows the same pattern as of NPP (figure 4). Figure 4: Mean NPP (a), and Mean Annual precipitation (MAP,b) maps for Texas rangelands 73 Reduce Exposure to Reduce Risk

Mean NPP for Texas rangelands was 289 gC m-2 the western part of Texas during the drought years yr-1.± 118, thus, Texas rangelands showed greater while relatively higher values were observed for productivity as compared to US rangelands (179 the eastern part of Texas with extremely higher gC m-2 yr-1.± 110. Our data also revealed a very values during wet years. high variability in spatial and inter-annual variations of rangeland NPP (figures 4 & 5) . Precipitation and NPP showed greater inter-annual Extremely low values of NPP were reported from variability. This was reflected very clearly in NPP and precipitation anomalies (Figure 5). 50% Anomalies 0 1982 -50 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 NPP Precip Figure 5: Net Primary Productivity (NPP) and precipitation anomalies for Texas rangelands over the period 1982 to 2009 Any persistent deviations from the long term year lag effect of precipitation on the rangeland average would also indicate long term trends in the vegetation productivity was evident in the data data, yet both precipitation and NPP anomalies did (figure 4). This was even clearer during the not show any consistent trends over time. However, transition from wet to dry years. Rangeland over the last decade, % anomalies in NPP and vegetation productivity shows immediate increase precipitation were greater as compared to that of following a wet year while an immediate decline in the period prior to 2000 (figure 4). Precipitation productivity was not clearly evident following a and NPP anomalies also reflect greater fluctuations dry year. This also indicates the level of resilience over the period 1982 to 2009. of these rangeland ecosystems. Relationship between mean annual precipitation level LFP estimates for grazed cattle, sheep and and rangeland NPP anomalies was significant goat. Mean forage demand for Texas rangelands (p=0.05, r2 = 0.37) for the period 1982 to 2009. was 54.9 g m-2year-1 ± 91.2 g m-2year-1 with a range Thus, reveals a closer linkage between them. A 1 from 0 – 1300 g m-2year-1 . In general, mean LFP values showed a gradient from west to east which 3.3.2 Impacts of livestock grazing on rangeland follows the gradient of rangeland mean NPP and productivity precipitation (figures 6 & 4). Impacts of livestock grazing on rangeland vegetation productivity were analyzed using county Reduce Exposure to Reduce Risk 74

Figure 6: Mean LFP (1982 – 2009) variations over Texas rangelands Mean LFP did not show significant relationship to hemisphere from 1982 to 2009. This is also precipitation analomalies (p=0.05). Thus, findings suggestive of increased woody encroachment in US of this study does not indicate livestock grazing as rangelands, a result of rangeland degradation at the a driver of rangeland productivity. However, it is national scale. important to consider the two main limitations in our livestock data; particularly, discontinuities in At the Texas spatial scale, aggregate time series livestock data availability (i.e. data gaps from 1987 analysis indicates a significant linear relationship – 1992 and 1982 – 1992 for sheep and goat between mean annual precipitation (p=0.05, numbers), and inability to access data on livestock r2=0.37) and NPP anomalies. Livestock grazing, distribution within each county. Due to the did not show significant relationship with NPP. unavailability of data on animal distribution, on the assumption of uniform animal distribution, we However, for the majority of the area, the effect of equally distributed county level livestock forage livestock grazing was underestimated due to demand estimates over the rangeland extent of each limitations in data availability and as a result of the county. Thus, , our LFP estimates may have scale of a analysis. Thus, for detailed underestimated the grazing impacts on rangeland characterizations at smaller scale and policy related vegetation. Particularly, these LFP estimates may issues, it is important to verify both data and not have captured localized impacts of heavy findings using location specific data at finer spatial grazing under high stocking rates and does not resolution. account for areas with decreased rangeland condition or health. Thus, incorporation of finer ACKNOWLEDGEMENTS scale livestock distribution data in to LFP estimates This research was funded by the United States would alter these findings. Department of Agriculture-Forest Service Grant # 07-CS-11221611-341 to R.W-A. Two fellowship 3.4: Conclusions grants; 1) Tom-Slick Senior Graduate Fellowship from the College of agriculture and Life Sciences We found that, at the national-level, rangeland of Texas A & M, and 2) Schlumberger Faculty for productivity decreases along an aridity gradient; Future Fellowship from Schlumberger foundation i.e. NPP decreases as the level of aridity increases. that covered academic expenses of the first author Over the period 1982 to 2009, US rangeland NPP are also greatly acknowledged. shows a net carbon gain of 0.256gC m-2year-1 that is driven primarily by relatively less arid, dry sub- REFERENCES humid and semi-arid regions. These finding are Cao, M., Prince, S. D., Small, J. and Goetz, S. J., consistent with the observed global ―greening‖ of drylands (Langanke et al. 2012) and the findings of 2004, Remotely sensed Inter-annual variations Nemani et al. (2003) and Zhao and Running (2010) and trends in terrestrial net primary concerning the net carbon gain of the northern productivity 1981–2000. Ecosystems, 7, 233 – 242. Reduce Exposure to Reduce Risk 75

DiLuzio, M., Johnson, G.L., Daly, C., Eischeid, United Nations Conference on Desertification J.K. and Arnold, J.G., 2008, Constructing (UNCOD), 1977, Desertification: Its causes retrospective gridded daily precipitation and and consequences, (Pergamon, Oxford). temperature datasets for the conterminous United States. Journal of Applied Whittaker, R. and Likens, G. E., 1973, Primary Meteorology and Climatology, 47, 475-497. production: The biosphere and man. Human Ecology, 1, 357 – 369. Holechek, J.L., 1988, An approach to setting the stocking rate. Rangelands, 10, 10-14. Zhao, M. and Running, S. W., 2010, Drought- induced reduction in global terrestrial net Knapp, A. K., Conard, S. L. and Blair,J. M.,1998, primary production from 2000 through 2009. Determinants of soil CO2 flux from a sub- Science, 329, 940 humid grassland: effect of fire and fire history. Ecological Applications, 8, 760–770. Langanke, T., Fensholt, R., Rasmussen, K., Reenberg, A. Prince, S.D., Scholes, B., Tucker, C., Bao Le, Q. Bondeau, A., Eastman, R, Epstein, H., Gaughan, A.E., Hellden, U., Mbow, C., Olsson, L., Paruelo, J., Schweitzer, C., Seaquist, J., and Wessels, K., 2012, Global dryland greenness. Trends, drivers and policy implications, GLP Report No. 6. GLP-IPO, Copenhagen. Leith, I., 1973, Primary production: Terrestrial ecosystems. Human Ecology, 1, 303 – 332. Millennium Ecosystem Assessment (MEA). (2005), Dryland systems. Ecosystems and human well-being: current state and trends, (Washington, D. C.). Ludwig, J.A., 1986, Primary production variability in desert ecosystems. Pattern and Process in Desert Ecosystems, edited by W. G. Whitford ( Albuquerque, New Mexico). Middleton, N and Thomas, D., 1997, world atlas of desertification, 1997, (London, UK) Nemani, R., Keeling, C., Hashimoto, H., Jolly, W., Piper, S., Tucker, C., Myneni, R., and Running S., 2003, Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science, 300, 1560-1563. Noy-Meir, I., 1973, Desert Ecosystems: Environment and producers. Annual Review of Ecology and Systematics, 4, 25-51. Reeves, M.C., Winslow, J.C., and Running, S.W., 2001, Mapping weekly rangeland vegetation productivity using MODIS algorithms. Journal of Range Management, 54, A90- A105. UNCCD (United Nations Convention to Combat Desertification), 2004, United Nations Publication- A/AC-241/27. Trabucco, A., and Zomer, R.J., 2009, Global Aridity Index (Global-Aridity) and Global Potential Evapo-Transpiration (Global-PET) Geospatial Database. CGIAR Consortium for Spatial Information. Published online, at: http://www.csi.cgiar.org/ Reduce Exposure to Reduce Risk 76

DROUGHT MONITORING IN RAJASTHAN, INDIA USING GEOGRAPHIC INFORMATION SYSTEM AND REMOTE SENSING K.Rajendram1 and N.R Patel2 1Department of Geography, Eastern University, Chenkalady, 30350, Sri Lanka e-mail: [email protected] 2Agriculture and Soil Division, Indian Institute of Remote Sensing, No4, Kalidas Road, Dehra Dun, India. e-mail:[email protected] ABSTRACT: Rainfall of Rajasthan is variable, seasonal, and unevenly distributed resulting frequent occurrence of droughts, aridity and degradation of vegetation growth. Frequent occurrences of droughts in Rajasthan have resulted in significant economical losses, ecological degradation and environmental deterioration. The objectives of the study are to assess potential of long-term time series of vegetation index from Advanced Very High Resolution Radiometer (AVHRR) as descriptor of drought, to study the spatial and temporal pattern of drought and to analyze vegetation trends using long-tem NDVI from AVHRR for drought and desertification monitoring. The NOAA- AVHRR 15 days composites of GIMMS NDVI for 21 years from1983, monthly rainfall data for the same 21-years period over 102 rain gage stations and the crop statistics were used in this study. To monitor the drought and vegetation greenness variability annual, seasonal and monthly NDVI and its mean, standard deviation, and NDVI Anomaly Index (NAI), integral NDVI (iNDVI) and rainfall ratio were computed in pixel level. To study the spatial and temporal pattern of meteorological drought condition, Standardized Precipitation Index (SPI) were computed at different time scale for all 102 rain gauge stations and then SPI results were interpolated. The spatial pattern SPI during drought years are having higher negative anomaly from July to September in greater part of Rajasthan. In 2000 drought, an extreme value of SPI reaches up to -3.69 in September and –2.84 in August, which exhibits the severity of drought condition. The trend and linear association between NAI and SPI showed that there is a significant strong high positive correlation in October in all agro-ecological zones in Rajasthan. The correlation between iNDVI (biomass) and rainfall reveal that, the relationship is relatively strong (>0.5) in the hyper arid and arid districts (i.e Jaisalmer, Bikaner, Churu), The trends of integral NDVI/RF ratio results suggest that about 35 % of study area has experienced decreasing trends of vegetation growth, ongoing degradation, and desertification process. Keywords: AVHRR, Vegetation, Drought, NDVI, NAI. Introduction closely associated with climate variability. Vegetation and drought monitoring allows agro-climatic planners As vulnerability to drought has increased globally, to evaluate the effectiveness of their planning and greater attention has been directed to reduce the risk management actions in order to develop or through mitigation measures. The economic, social, and progressively modify their management practices. environmental costs and losses associated with drought are of great concern in India particularly in desert state Drought is one of the worst natural calamities that of Rajasthan. The agricultural activities are chiefly affect in desert states of Rajasthan recurrently and influenced by the weather and climate. Rajasthan's create innumerable problems immediately or over the economy is mainly based on agriculture. About 80 time as the economy experiences the adverse impacts. percent of the population lives in rural areas and is The availability of water in the Rajasthan is frequently dependent on farming. Rainfall of Rajasthan is variable, affected by the failure monsoon and its greater seasonal, and unevenly distributed resulting frequent variability on both space and time scales resulting in occurrence of droughts, aridity and degradation of prolonged drought. For example, in the year 2002, the vegetation growth. This Variable climatic condition state of Rajasthan was in the grip of a severe drought. exerts dramatic impact on vegetation and crop All 32 districts (41,000 villages) in the state were production. The success or failure of crop growth mainly in arid and semi-arid regions of Rajasthan is Reduce Exposure to Reduce Risk 77

declared drought-affected by the government and 45 Fig. 1: Location of Study Area million people (out of 56 million of state population) in the state were in need of relief, food, fodder and water continent. Hielkema et.al. (1986) and Cristina Milesi shortages had become acute. In the year 2000, drought et.al. (2004) investigated the relationship between was prevalent in 31 districts in the state and among NDVI and climatic variables in arid-and semi arid these 25 districts ware affected severely. Nearly 33.04 regions. All these studies describe generally a good million people and 39.97 million cattle were affected. correlation between rainfall and NDVI. More than 75% crop lands were under severe drought condition. All this has caused loss of livelihood leading In India, considerable amounts of works have been to mass migration in search of employment. These done on vegetation and drought; however the Remote frequent droughts are emphasis the need for more Sensing and GIS based drought related studies are research. Fig.1, show the location of study area. fewer. Recently Bhuiyan, Singh and Kogan (2006) analyzed the drought dynamics in the Aravalli region of Remote sensing techniques have a potential for Rajasthan using different indices based on the remote predicting, monitoring and assessing the vegetation and sensing data. In this study SPI has been used to quantify drought. The NOAA–Advanced Very High Resolution the precipitation deficit. The spatial and temporal Radiometer (AVHRR) based vegetation indices are drought dynamics during monsoon and non- monsoon widely accepted as good indicators for monitoring seasons have been carried out through drought index climatic variations and droughts (Seiler, Kogan et. al. map and GIS environment. Ramesh et al.(2003), 2006). In recent years significant studies have been analyzed drought in India using NOAA–AVHRR carried out on vegetation and droughts in different parts satellites data. Recently, this technique has been of the world by various scholars based on NOAA- improved by converting NDVI with radiation measured AVHRR data. Ichii et.al. (2002) studied the relationship in one of the thermal channels and converting between NDVI and climatic variables on a global scale brightness temperature into the VCI and TCI. These using AVHRR data. Some of applications were based indices are being used for estimation of vegetation on vegetation condition index (VCI), Temperature health and monitoring drought. Condition Index (TCI), and/or Anomaly Vegetation Index (AVI) which extracted from AVHRR derived NDVI and brightness temperature data (Wang, Pengxin; Gong, Jianya; Li, Xiaowen, 2001). Richard Poccard (1998) studied the NDVI sensitivity to seasonal and intra-annual rainfall variations in Southern Africa. Fuller analyzed (1998) the Trends in NDVI time series and their relation to rangeland and crop production in Senegal, West Africa. Liu and Kogan (1996) analyzed the regional drought using the vegetation condition index in South American Data and Methodology From the AVHRR long-term monthly temporal coverage firstly, the continental file of Eurasia was The remotely sensed data product is acquired from the downloaded and the Indian region was extracted and NOAA-AVHRR of Global Inventory Modeling and then all images were re-projected into Albers Equal Mapping Studies (GIMMS) from the University of Area Projection and then Rajasthan was extracted. To Maryland Global Land Cover Facility Data Distribution monitor the trend of vegetation and droughts, annual, Centre. The NOAA-AVHRR 15 days value composites seasonal and monthly NDVI and its mean, maximum, of monthly NDVI for 21years from January 1983 to standard deviation and NDVI Anomaly Index (NAI), December 2003 are used. The composite data has integral NDVI (iNDVI) and rainfall ratio were spatial resolution of 8 km in Albert Equal Area Conic computed for each year and each month in pixel level. projection using the Clarke 1866 ellipsoid. Mean To derive NAI the following formula has been used. monthly rainfall data for 102 rain gauge stations in Rajasthan from India Meteorological Department and NAI = NDVI-NDVI  /NDVI Equation-1 the Department of revenue for the period of 21 years (1983-2003) were collected and used. The locations of rain gauge stations are shown in Fig.2. Reduce Exposure to Reduce Risk 78

Where, NDVI and NDVI signify the NDVI mean Fig. 2: Rain Gauge Station and standard deviation respectively. suggested by Guttman in 1998 as type III distribution. Then correlation between the long-term iNDVI and Gamma distribution with three parameters ,,  seasonal rainfall has been tested. Then correlation (respectively mean, standard deviation and skewness ) between NAI and SPI has been tested using AVHRR- is given as follows: GIMMS 15 day composites (15a and15b), These 21 years monthly images were re-scaled to obtain NDVI values ranging +1 to 1 by using the Scale factor = 0.001. Standardized Precipitation Index (SPI) and drought The National Drought Mitigation Center introduced the Standardized Precipitation Index (SPI) to monitor rainfall anomaly and drought conditions. The SPI computation for any place is based on the long-term precipitation record for a chosen period. SPIs were  1  x   p 1   x   x       F   e -Equation-2  p  Where,  x   , p>2 and  p is incomplete gamma function of p. To study the rainfall anomaly and meteorological seasonal rainfall and the SPI results were interpolated droughts the SPI has been computed for 1, 2, and 3 - using weighted inverse distance method. Drought is month time scale for all 102 rain gauge stations in then studied from the normalized rainfall series in Rajasthan for long-term monthly rainfall data. To study accordance with the SPI criteria (Table-1). the spatial pattern of rainfall and drought scenario, Seasonal rainfall anomaly has been computed from Table-1: Drought Classification (modified) 1983-2003 for growing season (June to October). In order to test the strength of linear association between SPI Values Categorization SPI versus NAI, firstly zonal attribute statistics (District wise mean) has been computed based on the -0.5 and above No drought interpolated SPI and NAI raster image in each pixel level using Arc GIS software. These correlations -0.5 to -0.7 Abnormally Dry provide climatic influence on vegetation and crop production -.0.8 to -1.2 Moderate drought -1.3 to -1.5 Severe drought -1.6 to -1.9 Extreme drought -2 and less Exceptional drought Source: U.S. Drought Monitoring Mitigation Centre Results and Discussion Pattern of vegetation in a region depends primarily on cultivators have been facing in desert regions like its hydro-climatic environment. Rainfall is the Rajasthan state. Many studies have clearly predominant climatic variable for the growth of demonstrated that there is a direct relationship between vegetation. The variability of precipitation creates the rainfall and vegetation growth. The good vegetation worst drought hazards, especially in arid and semi-arid cover really depends on availability of water. In climates, where slight departure from the mean may be Rajasthan, the rainfall occurs mostly in the southwest the critical factor in the crop failure. A consequence of monsoon season. The average seasonal rainfall of high rainfall variability is perhaps the high risk that Rajasthan is 490mm (1983-2003). However rainfall Reduce Exposure to Reduce Risk 79

varies spatially and temporally. The variability of hyper arid districts of Jaisalmer, Bikaner and arid rainfall is indeed greater in the Northwestern hyper arid districts of Barmer, Jodhpur western arid and eastern semi arid plains than in the Southeastern humid and sub humid plains of Rajasthan. Fig.3:Seasonal Rainfall Fig. 4: Mean NDVI The spatial distribution of mean seasonal rainfall and mean NDVI in the Rajasthan is shown in the Fig.3 & 4 respectively. The average seasonal rainfall corresponds well with NDVI. The seasonal rainfall varies spatially from 125mm to 1465mm. Generally the southern districts of Jhalawar, Baran, Kota, Banswara, Chittargarh and Bundi comparatively has higher rainfall and vegetation signals rather than the northwestern Spatial Pattern of NAI, SPI and Drought SPI and NAI for selected drought years of 2000 and negative SPI attains up to –2.02 in September and –1.67 2002 and the normal years of 2001 and 2003 have been in August. presented to illustrate the pattern of rainfall anomaly, droughts and greenness variability of vegetation. Fig.5 Fig 5: 3-Month SPI for Drought(D) and Normal(N) Years shows the spatial pattern of 3-month SPI for the month D‐2000 from June to October for drought years 2000 and 2002 respectively. It can be observed during these drought N‐2001 years the higher negative SPI anomaly were noticed D‐2002 during July, August, September and October in greater N‐2004 part of Rajasthan. In the month of June comparatively anomaly was less. These drought years SPI anomalies Fig-6: Monthly NDVI Anomaly Index(NAI) for Drought(D) and Normal(N) Years highly correspond with NAI. Negative NDVI anomaly D-2000 had widespread during July and August in most part of N-2001 these areas during the drought years of 2000 and 2002 (Fig. 6). In the normal year (2001and 2003) NDVI D-2002 anomalies and SPI anomalies were positive, however N-2003 after withdrawal of monsoon SPI anomalies as well as NDVI anomalies are gradually increasing. This shows NAI that rainfall has great impact on vegetation greenness. This study reveals that SPI correspond well with vegetations greenness. Temporal Pattern of NAI and SPI and Drought The Long-term monthly NAI trend demonstrates the temporal pattern of greenness variability over the years. It further reveals during drought years vegetation greenness declines and vise-versa in normal years. In 2000 drought, an extreme value of SPI reaches up to - 3.69 in September and –2.84 in August, which exhibits the severity of drought condition. Generally the station belongs to desert districts of Jaisalmer, Bikaner, Churu having extreme negative SPI during drought years and moderate in normal years. During 2000 drought Relationship between NAI and SPI correlations had revealed in the month of October in all agro-ecological zones and in September in hyper arid & The correlation analysis signified that the relationship arid plains (r = 0.967, at 98% level), Tarai (r = 0.799) between the NAI and 3-month SPI during kharif and semi arid (r = 0.725) plains. In the month of growing season in different agro-climatic zones of Rajasthan (Table-2). Significantly strong high positive Reduce Exposure to Reduce Risk 80

October strong correlation is noticed (r = 0.95) in Table 2 Correlation between NAI & SPI irrigated plain and flood plain (r =0.812). In June and September significantly positive correlations had Agro-climate detected in the hyper arid & arid plain, irrigated plain, transitional plain and Semi arid plain. It clearly showed zone N JUL AUG SEP OCT precipitation and NAI response. SPI having the dominant influence on NAI, variability of vegetation Hyper Arid & growth highly associated with SPI. During the month of Arid Plain 20 -0.572** 0.754** 0.967** 0.6768* July and August negative correlations were detected in the entire agro-climatic zone due to lesser seasonal Semi Arid Plain 16 -0.877** -0.843* 0.725** 0.779** vegetation growth and low vigor of crop conditions (low Lai). Irrigated Plain 8 -0.637* -0.832* 0.434* 0.950** Transitional Plain 24 -0.713** 0.840** 0.799** 0.600** Flood Plain 16 -0.662** 0.847** -0.186 0.812** Humid Plain 28 -0.431 0.783** -0.143 0.568** Sub Humid Plain 16 -0.310 -0.819 0.560 0.711** ** Significant at 0.01 level* Significant at 0.05 level Rainfall-integrated NDVI (iNDVI) Relationship The NDVI integrals covering the entire growing presented a relative stability.The districts of Jaisalmer, season from June to October were computed in order Jodhpur, Naguar, Sikar, Bikaner, Churu, Jhunjhunun to assess the seasonal vegetation growth. The net and northwestern part of Barmer seem to have been annual increase of biomass or net primary production the most affected by land degradation. is a measure of the production of ecosystem. This quantity bears a direct relationship to photosynthesis Fig.7: iNDVI/RF Relationship and NDVI is strongly correlated with both, particularly in arid lands. The ratio of primary production to rainfall iNDVI /RF is a better parameter to characterize arid and semi arid region like Rajasthan. The correlation between iNDVI (biomass) and rainfall were computed (Fig.7) and reveal that the rainfall- iNDVI relationship is relatively strong (>0.5) in the hyper arid and arid districts of Jaisalmer, Bikaner, Churu, Barmer and Jodpur. Thus, rainfall controls a large part of the spatial and temporal variation in biomass at regional scale, it is clear that, at the local scale, there is considerable variation in the response of vegetation to rainfall. The results show about 35 % of study area has experienced decreasing trends in the iNDVI/RF ratio that may reflect possible degradation of the vegetation growth, and therefore ongoing desertification process. About 45% of the study area Conclusion during drought years are higher negative anomaly The NOAA-AVHRR 15-day composites of GIMMS during July to October in greater part of Rajasthan. have great potential resources for monitoring the During year 2000 drought, an extreme value of vegetation, droughts and crop performance. Present 3month-SPI reaches up to -3.69 in September and – study demonstrates that the spatial and temporal 2.84 in August, this exhibits the severity of drought variations of NDVI are closely linked with condition. The correlations between iNDVI and precipitation anomaly and there is strong association rainfall results reveal that, the iNDVI and rainfall between 3-month SPI and NAI. SPI and NAI for relationship is relatively strong in the hyper arid and selected drought and normal years show the role of arid districts. The trends of integral NDVI/RF ratio rainfall on vegetation growth and greenness results suggest that about 35 % of study area has variability. Generally the southern districts of experienced decreasing trends of vegetation growth, Jhalawar, Baran, Kota, Banswara, Chittargarh and ongoing degradation, and desertification process. Bundi comparatively have higher rainfall and Between NAI and SPI significantly strong high vegetation signals rather than the North-western hyper positive correlations had revealed in October in all arid districts. The spatial patterns of 3month-SPI Reduce Exposure to Reduce Risk 81

agro-ecological zones. The SPI vs NAI show a agro-climatic zones which corresponding with negative correlation in the early growing season and maximum leaf area index (LAI). strong positive correlation in late growing season in all Fuller, D. O., 1998, Trends in NDVI time series and Refeerences their relation to rangeland and crop production in Senegal, 1987-1993, International Journal of Bhuiyan,C. Singh,R.P., and Kogan, F.N., 2006. Remote Sensing,19, (10), 2013 – 2018. Monitoring drought dynamics in the Aravalli region (India) using different indices based on Guttman, N.B., 1998, Comparing the Palmer drought ground and remote sensing data, 8, 289-302. severity index and standardized precipitation index, Journal of the American Water Resources Cristina Milesi et.al., 2004. Climate Variability, Association, 34, 113-121. Vegetation productivity and people at risk, Global International Journal of Remote Sensing,17,(14), and Planetary change, xx, 1-11. 2761-2782. Hielkema,J.U. et. al., 1986. Rainfall and vegetation Ramesh P. Singh, Sudipa Roy and F. Kogan, monitoring in the Savana zone of the demogratic 2003,Vegetation and temperature condition – republic of Sudan using the NOAA advance indices from NOAA AVHRR data for drought very high resolution radiometer, International monitoring over India, International Journal of Journal of Remote Sensing, 7, 1499-1513. Remote Sensing,24, 4393 – 4402 Ichii, K. and Yamaguchi, Y., 2002. Global Richard,Y and Poccard,I., 1998. Statistical study of Correlation for NDVI and Climatic Variables and NDVI sensitivity to seasonal and intra-annual NDVI trends: 1982-1990. rainfall Liu, W, Kogan, FN, 1996. Monitoring regional Wang, Pengxin; Gong, Jianya; Li, Xiaowen, 2001. drought using the vegetation condition index, Vegetation-Temperature Condition Index and Its Application for Drought Monitoring, Geometrics variations in Southern Africa, International Journal of and Information Science of Wuhan University, Remote Sensing, 19, (15), 2907–2920. 26,(5), 412-417. Seiler, Kogan et. al., 2006. Seasonal and interannual responses of the vegetation and production of crops in Cordoba-Argentina assessed by AVHRR derived vegetation indices, Advances in Space Research, 39, 89-94. Reduce Exposure to Reduce Risk 82

ROLE OF CLIMATE ON THE GLACIER DYNAMICS AND WATER RESOURCES IN THE INDIAN HIMALAYAS” AL. Ramanathan1, Anurag Linda1, Jose George Pottakkal1, Virender Singh1 and Parmanand Sharma2 1.School of Environmental Sciences, Jawaharlal Nehru University, New Delhi-110067, India 2. NCAOR, GOA, E-mail*: [email protected] Abstract Glaciers are sensitive indicators of limate change and changes in their mass balance in response to climatic fluctuations results in variations in river water flows that affect the livelihood and fauna and flora of the region. Influence of climate change on glacier plays an important role in the estimation of future water resources in the high Himalyan mountain basins of the world of which the Himalayan river basins cater to the greatest population centers of the world. Himalayan rivers water resources are influenced by snow, ice melt and also monsoonal precipitation. Some of the glacier fall s on the monsoon and monsoon –arid transition zone. The climate change may be playing a major role in the melt water discharge of these glacier which ultimately feeds rivers like Ganges, Indus and other river system. Mass balance balance studies carried out in Himalayas are examined here to get an insight into their role in water resource management. The temperature, snow fall and rain fall data has been evaluated for their influence on the melt water discharge. ASSESSMENT OF UNCERTAINTY IN CLIMATE CHANGE PREDICTIONS FOR KOSHI RIVER BASIN, NEPAL USING MULTI-MODEL ENSEMBLES Anshul Agarwal1, Mukand S. Babel2 and Shreedhar Maskey2 1,2Water Engineering and Management, Asian Institute of Technology, PO Box 4, Klong Luang, Pathumthani 12120, Thailand, E-mail: [email protected] 1, [email protected] 2 3UNESCO-IHE Institute for Water Education, PO Box 3015, 2601 DA Delft, The Netherlands E-mail: [email protected] 3 ABSTRACT Uncertainty of climate change prediction was evaluated for 18 climate change scenarios projected by six GCMs (ECHAM5, HadCM3, IN-CM3, IPSL-CM4, GFDL-CM2.1 & CCSM3) and three emission scenarios (A1B, A2 & B1) for Koshi river basin, Nepal. The use of multi model ensembles allows assessment of the range of uncertainty in predictions of climate. Large scale climate data from GCM was downscaled using weather generator LARS-WG to represent the future climate at basin scale. A large number of future climate data from many GCMs and scenarios induced a range of uncertainty for future climate predictions. Model simulated the baseline period (1971-2000) climate quite satisfactorily for all the stations of Koshi basin. Change in future climate is represented for three future periods 2011-30 (2020s), 2046-65 (2055s) and 2080-99 (2090s). Two physiographic regions of Koshi basin were considered for this study; Hills (700-1500m above sea level) and mountains (1500-2700m above sea level). Most of the GCMs show an increase in average annual precipitation for all future periods. As per A1B scenario precipitation will increase in hills by 4.87, 16.50 & 11.69% by 2020s, 2050s and 2090s respectively. A2 scenario indicated an increase of 2.22, 8.69 & 17.71% while B1 indicated an increase of 2.56, 6.52 & 8.55% by 2020s, 2050s and 2090s respectively in Hills of Koshi basin. For A1B scenario highest increase is predicted during 2050s, while 2080s shows a slight decrease compared to 2050s precipitation. Inter model variability during each time horizon and for all seasons shows the uncertainty linked with GCMs predictions. All projections indicate an increase in average annual tmax for both the zones of Koshi basin. Considering all GCM projections this increase will vary from 0.59-1.22°C during 2020s, 1.38- 2.98 °C during 2050s & 1.47-5.27 °C during 2080s for Hills. Scenario A1B represents the highest increase during the periods 2020s and 2050s, while during 2090s scenario A2 represents the highest increase. KEY WORDS: GCM, Downscaling, Uncertainty, LARS-WG, Koshi basin Reduce Exposure to Reduce Risk 83

CLIMATE CHANGE VULNERABILITY MAPPING: A STATISTICS-BASED APPROACH Nandana Mahakumarage and Nayana Mawilmada TheTMS Company, 11/2 Wijeyamangalarama Road, Kohuwala, Nugegoda, Sri Lanka E-mail: NandanaMahakumburage, [email protected] ABSTRACT In 2010, the Asian Development Bank (ADB) and the Government of Sri Lanka undertook an initiative to develop the country’s first National Climate Change Adaptation Strategy. The primary goal of the initiative was to assess how to adapt key sectors in the economy and safeguard Sri Lanka’s national interest against climate change threats. The strategic planning exercise involved a groundbreaking GIS vulnerability mapping exercise, which for the first time in Sri Lanka enabled an assessment of the scale and spatial distribution of potential climate change vulnerabilities across key sectors in the economy. The United Nations Framework Convention on Climate Change (UNFCCC) defines climate change vulnerability as a function of the character, magnitude and rate of climate variation and its effects to which a system is exposed, its sensitivity, and its adaptive capacity. The authors, as part of the project team, built on the UNFCCC definitions of climate change vulnerability to develop a robust statistics-based methodology for analyzing and mapping climate change vulnerability in key sectors. The methodology developed, which involves developing sector-specific vulnerability indices based on a composite analysis of exposure, sensitivity, and adaptive capacity has broad based applicability in climate change adaptation and disaster risk reduction planning. DROUGHT MITIGATION BY USING GEOINFORMATICS AS A TOOL: A CASE STUDY IN KARAINAGAR DS DIVISION S. Yoharajan Centre for Information Resource Management, Northern Provincial Council E-mail: [email protected] ABSTRACT Jaffna district is within the dry zone in Sri Lanka. The climatic event of Drought affects the Jaffna district like some other parts in this country. The Divisional Secretariat division of Karainagar which is situated in the Jaffna district of the Northern Province also faces the effects of the dry weather. The population of 10,564 persons who live in this area are facing immense difficulties in obtaining the supply of drinking water and water for other requirements and this difficulty is continuing for a long time. The drinking water that is required by the people of the area is drawn through wells which are situated in private lands and temple lands. The water for various purposes are met from conservation of rain water in 14 tanks which are located in this division. But during the dry season the wells will run dry and the tanks too run dry. Therefore, the people will face immense difficulties in finding water during the dry season. In the background of the seasonal effect during the dry months, the study attempts to propose suitable measures in accordance with the Geoinformatics and their usage as a tool. Tube wells could be provided in areas where they are necessary by demarcating the existing wells and the respective sites where the population density is very high. By using satellite images the underground water resources could be identified. At the same time the storm water channels could be identified and these images could be used to construct small tanks to conserve more rain water for cultivation and domestic use of the people. By undertaking meaningful development activities with the help of Geoinformatics in the small islet of Karainagar, it would be possible to provide facilities relating to supply of water to the people of the area throughout the year. Reduce Exposure to Reduce Risk 84

IMPACT OF DRY SPELL IN DIASTER MANGMENT - FORECASTING ASPECTS S C Mathugama and T S G Peiris Department of Mathematic, University of Moratuwa, Katudedha, Moratuwa, Sri Lanka E-mail: [email protected] ABSTRACT Observational evidence indicates that global climate changes have significantly affected a diverse set of natural and human systems and activities in many countries and consequently the global community is facing the impact of such natural disasters. Longer dry spells is one of the recurrent feature of the natural disaster in the dry zone of Sri Lanka. The unpredictable pattern of dry spells have already caused significant damages to the livelihood of people and the economy of the country. A review on statistical anlysis on dry spells by Mathugama and Peiris (2011) showed that no studies were reported to predict the starting date or length of dry spells. This research was therefore initiated to explore the possibility of forecasting starting period and the length of the four longest dry spells within a year ('critical dry spells - CDS') in the selected five locations in the DL1 agro-ecological region in Sri Lanka. There is a significant correlations (p<0.05) among starting dates of successive critical dry spells, but such association was not found for the length of the CDS. Log regression models and weighted regression models were developed to forcast the starting dates of second, third and fourth critical dry spells separately for all locations All the models and all parameters were significant (p<0.005) and the models were tested for an independant set of data. However, a model was not able develop for the starting date of the first CDS. Critical dry spell length series is very complicated due to structural and behavioral changes influenced by climate and also not equally spaced. Two new types of non linear models were developed using existing bilinear models. First one is based on normal non linear model with component with additive error and then add bilinear terms to the model. The second new approach was to add an exogeneous input variable to the bilinear model. The results obtained in this study will helpful to minimize unexpected damage due to droughts and will help effective and efficient planning in disasters management. DEVELOPING MAPS FOR HAZARD, VULNERABILITY AND RISK OF DROUGHTIN SRI LANKA: AN APPROACH FOR INDEXING TIME SERIES STATISTICS IN HYDROLOGICAL, AGRICULTURAL AND SOCIO- ECONOMIC PERSPECTIVES RanjithPremalal De Silva and B.V.R. Punyawardena Uwa-Wellassa University, Badulla, Sri Lanka, E-mail: [email protected] Senior Research Officer, Natural Resources Management Center, Department of Agriculture, Sri Lanka ABSTRACT Under a changing and variable climate, the risk of drought is increasing worldwide. Sri Lanka has no exception of it. Drought or extreme negative rainfall anomalies are experienced in Sri Lanka and almost all locations of the island have a potential vulnerability to drought occurrences. Historical and legendary accounts show that even country’s wettest region, Southwestern part of the central hills have had severe droughts in the past.Increased drought risk arises from increased likelihood of drought plus increased vulnerability to drought of different spatial units of the country. This is attributed to natural and anthropogenic forcing including human behavior and decisions. Hence scientists and policy makers now believe that crisis management strategies in drought vulnerable regions of a country should focus on risk management techniques to reduce drought risk and the impact of future drought events. Under such situation it is of paramount importance to evaluate the spatial and temporal variability of drought proneness of Sri Lanka (drought risk profile) for Sri Lanka so that mitigation strategies could be effectively undertaken without wasting the limited resources. This study made an attempt to Reduce Exposure to Reduce Risk 85

evaluate the drought risk by combining 14 rainfall related indices namely, (i) Total rainfall deficit per year,(ii) Highest total consecutive deficit, (iii) Total number of months with deficit per year, (iv) Highest consecutive number of months with deficit, (v) Deficit over excess, (vi) Highest deficit within a month, (vii) Number of months per year with rainfall less than 30 mm, (viii)Highest number of consecutive months where rainfall is less than 30 mm,(ix) Average rainfall of months where rainfall is less than 30 mm, (x) Lowest average rainfall where rainfall is less than30 mm in consecutive months, (xi) Total deficit rainfall less than 30 mm, (xii)Mean annual daily deficit, (xiii) Mean annual non rainy days, and (xiv) Mean number of days per year where rainfall is less than or equal 1mm. These indices were derived from daily and monthly data series to develop a common drought indicator using factor analysis for 46 agro-ecological regions. This indicator is named as Hazard index and used for mapping drought risk profile of the country. In the assessment of vulnerability, the degree of exposure to drought was analyzed to cover hydrological drought both in terms of surface and ground water availability, agricultural drought and socio economic drought at the Divisional Secretariat (DS) Division level. The indices developed for Availability and utilization of surface water sources are include (i) Total tank surface area, (ii) Total length of rivers, (iii) Watershed area density, (iv) Total command area and indices for Availability of ground water sources are (i) Aquifer area, and (ii) Total number of tube wells. Agricultural drought assessment are derived from (i) Cultivable paddy per person, (ii) Irrigation based weighted paddy extent and (iii) Percentage of highland farming population. Socio-economic component includes the indices derived on (i) Total population,(ii) Population below poverty line, (iii) Number of families having less than three meals per day and(iv) Percentage of entrepreneurs. The composite indicator is named as Vulnerability Index for mapping drought risk profile of the country. The drought hazard and vulnerability index is combined to develop the Potential drought risk map of Sri Lanka. Reduce Exposure to Reduce Risk 86

Technical Session-4 [Hall B]: Disaster Risk Reduction & Policy Development of GIS Based Disaster risk Assessment System for Decision Making 88 Tae Sung Cheong, Waon Ho Yi 93 98 Natural Disaster Management T.M.N. Peiris, D.G.Fernando 103 109 GIS tools and Approaches for Mainstreaming Disaster Risk Reduction into Local 118 Development Plans; A Case Study from Ambalanthota Nandana Mahakumarage, L.D.C.B. Kekulandala, Buddika Hapuarachchi, 118 Vajira Hettige Mainstreaming GIS in Natural Disaster Management – A Case Study of Pakistan 2010 Super Floods Muhammad Ali GIS Based Model for Geo-hazard Assessments in Mountainous Areas of Pakistan; A case study of Hundur Village Shareef Hussain Development of Autonomous Multi Agent Systems for Qualitative Risk Assessment in Disaster Management D.S. Kalana Mendis, Asoka S. Karunananda, Udaya Samaratunga, Uditha Rathnayake Implementation of Disaster Management Institutions in Baluchistan, Pakistan Syed Ainuddin, Jayant Kumar Routray Reduce Exposure to Reduce Risk 87

DEVELOPMENT OF GIS BASED DISASTER RISK ASSESMENT SYSTEM FOR DECISION MAKING Tae Sung Cheong & Waon Ho Yi Research Engineer, National Disaster Management Institute, E-mail: [email protected] & Professor, Kwangwoon University, E-mail: [email protected] ABSTRACT: In recent decades, around the world, catastrophic disasters such as hurricane Katrina, cyclone Nargis have occurred frequently causing a massive loss of life and negative long-term social, economic and environmental consequences. Out of damage from natural disasters in recent decade, more than 60% is due to typhoon in Korea. Timely information analysis and warning dissemination is main issues on typhoon related disaster risk management, also information sharing between organizations or communities and integrating various formatted information from different systems are very important. NEMA developed National Disaster Management System (NDMS) as a comprehensive nationwide GIS based disaster information analysis system to assess disaster risk and support decision making. In the GIS based disaster risk management system, monitoring data, simulation results and images information from CCTV and satellite are integrated and analyzed for assessing compound disaster risk and supporting timely decision making. The warning massages are disseminated by Cell Broadcasting Service (CBS), caption system using exclusive intranet, PC, cellular phone, FAX, messenger and twitter to people who being on dangerous area. NEMA hosted 4th Asian Ministerial Conference on Disaster Risk Reduction (AMCDRR) in Incheon, Korea on 25-28 October 2010 with the special partnership of UNISDR to share disaster information and technology at country and global level. To follow the action plan of 4th AMCDRR, NEMA has been developing a global platform as a platform of the platforms for sharing information and transferring technology related to Disaster Risk Reduction (DRR) and Climate Change Adaptation (CCA). KEY WORDS: Risk Assessment, Decision Making, Climate change Adaptation, Disaster Risk Management Introduction environmental threats and challenges to water issues such as storm, water scarcity, water Climate change led to increase of natural disaster sanitation, water quality, floods and droughts. For and enormous damage of life and property. Korea, the annual damage was about 1.8 billion Especially, the cities in small stream regions are USD and the annual recovery cost was about 3.0 becoming more susceptible and vulnerable to billion USD from water related disasters during last disasters due to the rapid paced urbanization, 10 years from 2000 to 2009. Out of damages from climate change and imprudent environmental natural disasters in recent decade, more than 60% is degradation. The world is being faced with due to typhoon in Korea shown in Figure 1. (a) Number of Events (b) Damages (M USD) (c) Recovery (M USD) Figure 1: Record of Natural Disasters in Korea from 2000 to 2009 National Emergency Management Agency Institute for Disaster Prevention (NIDP) developed (NEMA) has been developing the National Disaster GIS based disaster information analysis system Management System (NDMS) as a comprehensive through information sharing and analyzing with nationwide information system to support disaster GIS based information based on IT technology to management processes in terms of prevention, analysis and assesses the complex and compound preparation, response and recovery. As disaster disasters risk. aspects show complex and compound, integrated disaster information analysis is issued. National Reduce Exposure to Reduce Risk 88

To reduce disaster risk and adapt climate change in solution. It is not easy to link or combine systems Asia and the Pacific region, NEMA has developing because system has different data format but data the Platform of Information and Technology for as an output or input of system and model to Climate Change Adaptation (CCA) and Disaster analysis is easy to collect control in a system. Risk Reduction (DRR) by 2015 as an international cooperation system for sharing information and For combining and integrating the information in a technology related to DRR and CCA. The global system, it is important to make a standard format to platform is one of the action plans decided in the access and integrate the information conveniently. 4th Asian Ministerial Conference on Disaster Risk Using the geographical definition, it is possible to Reduction (AMCDRR) and is need to establish make combine and integrate the information in a tangible and practical strategies for DRR to share system. Also national level network system is information and technologies for CCA. needed to share the information in real time in anywhere which is very important for urban flood Disaster Information Analysis System risk management. To reduce major disaster risk due to climate and environmental changes, National Now, as disaster aspects show complex and Emergency Management Agency (NEMA) has compound disasters, integrated disaster information been developing National Disaster Management analysis is very important. For example, the System (NDMS) as a comprehensive nationwide information needed for urban flood disaster risk information system to support disaster management management are i) the hydrologic and hydraulic processes in terms of prevention, preparation, characteristics of the river basin, ii) strategy for risk response and recovery. management such as land-use planning, stream code, building code and guideline for enhancement NEMA collect all disaster management related of river basin, iii) utilities such as natural drainage information form 34 agencies of Korea for one- system within an urban area, iv) urban flood risk sight disaster information services which statistics, management policy and v) the economic, political, resources, risk and disaster information are shared socio-cultural and ecological environment of the and transferred (National Institute for Disaster flood prone area. Prevention, 2010). NDMS services information as follows: i) real time monitoring information such as These information are not provided by one institute river stage, flow, wind speed, rainfall, dam water or one government organization but by various level, CCTV images and satellite images shown in institutes and government organizations who they Figure 2, ii) statistical information analyzed by provide information collected from monitoring and period and disaster type, iii) resources information measuring, determined or estimated from such as emergent recovery equipments, relief integrated analyzing or/and modeling for their own goods, refugee facilities, iv) localized risk objectives. For the disaster risk management, these information such as forest fire risk map, landslide all information are used for sharing, analyzing or risk map, flood risk map and wind related disaster integrating to create new knowledge, estimate for risk map, v) real-time disaster information such as warning and support decision making. The main flood, typhoon, heavy rain, landslide, earthquake, issue is how can collect these information to share forest fire and vi) other information such as media, and analysis. Linking among database systems of communication and special weather report. various institutes and government organizations is Figure 2: CCTV Information Sharing through NDMS 89 Reduce Exposure to Reduce Risk