Utilizing Geospatial Tools for Assessing Climate Change Vulnerability: A Case Study of the Ratnapura District, Sri Lanka

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M.P.M. Piyasena, S.M.G.Lekana Bandara This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5273082/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Jun, 2025 Read the published version in Environmental Monitoring and Assessment → Version 1 posted 7 You are reading this latest preprint version Abstract This study utilizes geospatial tools to assess the climate change vulnerability of the Ratnapura District, Sri Lanka, by examining three key dimensions: exposure, sensitivity, and adaptive capacity. Ratnapura is particularly prone to climate-related hazards, such as floods, landslides, and droughts, which pose significant threats to its socio-economic stability and environmental health. The assessment employs historical climate data and geographic information to develop exposure maps, while sensitivity is evaluated through an analysis of socio-economic and environmental conditions. Adaptive capacity is measured by examining local institutional frameworks and resource availability. The findings reveal high vulnerability levels, particularly in the Ratnapura and Kalawana Divisional Secretariat (DS) divisions, highlighting the urgent need for targeted adaptation strategies. This study demonstrates the effectiveness of geospatial analysis tools in conducting comprehensive climate vulnerability assessments, providing valuable insights for developing climate-sensitive policies and enhancing disaster risk reduction efforts. The results offer a foundation for local and regional authorities to implement proactive measures to build resilience against climate change impacts. Climate vulnerability assessment exposure sensitivity adaptive capacity climate-related hazards Ratnapura District Sri Lanka Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Introduction Climate change poses significant challenges to ecosystems, economies, and societies worldwide. The Intergovernmental Panel on Climate Change (IPCC) has synthesized substantial evidence indicating that human activities are the primary cause of global warming (IPCC, 2021 ). Recent reports suggest that global temperatures have already increased by approximately 1.1°C above pre-industrial levels, leading to more frequent and severe weather events, including heatwaves, floods, droughts, and storms (IPCC, 2021 ; WMO, 2022 ). The impacts of climate change are multifaceted, affecting various sectors such as agriculture, water resources, and human health. For instance, rising temperatures and altered precipitation patterns have disrupted agricultural productivity, leading to food insecurity in many regions (FAO, 2021 ). Additionally, climate change has exacerbated water scarcity, affecting both the availability and quality of freshwater resources (UN Water, 2021 ). The health sector is also significantly impacted, with increased incidences of heat-related illnesses, vector-borne diseases, and respiratory problems (WHO, 2021 ). Vulnerable populations, including the poor, elderly, and indigenous groups, are disproportionately affected due to limited adaptive capacity and inadequate access to resources (Adger et al., 2009 ; Islam & Winkel, 2017 ). Social determinants such as poverty, lack of education, and inadequate healthcare systems further exacerbate these vulnerabilities (Moss et al., 2021 ). Recent studies emphasize the importance of integrating social, economic, and environmental dimensions in climate vulnerability assessments to develop comprehensive adaptation strategies (Füssel, 2007 ; O’Brien et al., 2019 ). Problem The current vulnerability assessments face challenges in conducting comprehensive vulnerability assessments that incorporate social impact measures of sensitivity and adaptive capacity. There is a significant possibility of increased exposure to disasters due to changes in societal sensitivity and adaptive capacity. However, the lack of comprehensive identification, analysis, and mitigation of these social impacts hampers effective disaster management and resilience-building. Objectives The primary objective of this study is to perform a comprehensive climate vulnerability assessment using geospatial analytical tools, focusing on the dimensions of exposure, sensitivity, and adaptive capacity. This assessment seeks to evaluate the effectiveness of these tools in analyzing climate vulnerability and to generate actionable insights for policy development and decision-making Methodology As the case study area approach was utilized to achieve the objectives of the study. Climate exposure, social economic sensitivity and vulnerability was identified based on the methods described in below and then the vulnerability was identified in each administrative division within the case study area. The following map shows the map of the administrative divisions (named as divisional secretariats) of the selected case study are which is the Rathnapuara district, Sri Lanka. Selected Divisional Secretariats Eheliyagoda Kuruwita Ratnapura Kiriella Imbulpe Balangoda Ayagama Elapatha Pelmadulla Opanayake Nivitigala Weligepola Godakawela Kahawattha Kalawana Embilipitiya Kolonna Exposure Mapping Exposure mapping aims to identify the frequency of hazardous events occurring in each Divisional Secretariat (DS) Division within the Ratnapura District on a decadal basis. To enhance the accuracy of assessing the intensity and frequency of extreme weather events across these divisions, a refined exposure mapping process was employed. This process involved identifying relevant exposure parameters through a comprehensive review of existing literature and expert interviews. Key climate-related hazards—such as floods, droughts, landslides, and anomalies in temperature and precipitation—were identified, and their frequencies were calculated over a ten-year period. The following table shows the data types collected and the methods. Data Collection: Table 1 Data collection for exposure mapping Data Collection Source Period Locations/Details Precipitation Data Department of Meteorology, Sri Lanka 1982–2021 Ratnapura, Lellupitiya, and Udawalawa rainfall stations Climate-Related Hazards Disaster Management Center, Sri Lanka 1982–2021 Frequency and impact level of floods, droughts, and landslides in each DS Divisions within the Sabaragamuwa Province Temperature Data Department of Meteorology, Sri Lanka 1982–2021 Form Rathnapura meteorological station Supplementary Data Field Survey Crowd-Sourced Data: Information from local communities to supplement official records, particularly in under-reported areas. DS Division Boundary data were obtained from the Survey Department Data Validation: Cross-referencing with authoritative sources such as IPCC, FAO, and UN Water reports. Validation through local expert consultations and field surveys. Geospatial Analysis: Analyzing the precipitation data The precipitation data from the three stations were interpolated across the district for each decade from 1982 to 2021. Divisional secretariat boundaries were overlaid using QGIS Zonal Statistics tool to determine decadal rainfall variations within each division. The inverse distance weighted (IDW) method was used for interpolation. The decadal precipitation values were subsequently normalized using the following formula: $$\:{Z}_{10i}=\:\frac{{\mu\:}_{10i-}{\mu\:}_{40i}}{{\sigma\:}_{40}}$$ Where; \(\:{Z}_{10i}\) – Decadal Z score \(\:{\mu\:}_{10i}\) – Mean Precipitation over a Decade for the Divisional Secretary \(\:{\mu\:}_{40i}\) - Mean Precipitation over a 40 years \(\:{\sigma\:}_{40}\) – Standard deviation of the 40 years data Analyzing the Temperature data The temperature data, however, was available from only one weather station, and there was no considerable change during the period of concern, so it was not included in the exposure calculation. Analyzing the Hazard data The decadal frequency of flood, drought, and landslide data for each divisional secretariat were normalized by dividing the maximum frequency associated with each event over the 40 years . Sensitivity Mapping Sensitivity mapping aimed to determine the degree of sensitivity of each DS Division within the Ratnapura District to climate exposure. Parameters related to sensitivity were identified through a comprehensive literature review and expert interviews. These parameters included factors such as population density, poverty levels, infrastructure quality, and ecological conditions. Both qualitative and quantitative data from field surveys, expert interviews, and existing literature were integrated to construct a comprehensive sensitivity map. Following tables shows the data collected and the methods Data Collection: Table 2 Data collection for sensitivity mapping Data Type Sources Data Purpose Socioeconomic Data Department of Census and Statistics, District Secretariat, Divisional Secretariat, statistical agencies − 0–4 age group, Over 65 age categories Identifies vulnerable demographic groups - No. of dependents Measures household economic dependence - Minimum distance to access infrastructure and facilities within 5km Assesses accessibility and mobility to essential services - Major economic activities involved Indicates dependence on climate-sensitive sectors - Income levels and distribution Assesses economic resilience - Literacy rates and education levels Indicates capacity to understand and respond to warnings - Estimate Headcount index Assesses overall capacity to respond to disasters - Social capital and community networks Reflects social resilience and community support structures Environmental Conditions Department of Census and Statistics, District Secretariat, Divisional Secretariat, Environmental agencies - Estate Population Indicates population density and land use - Household Land Consumption (Hectares) Assesses land use pattern - Biodiversity and ecosystem health Evaluates environmental sensitivity to climate change - Water resources (availability and quality) Assesses sensitivity of local water resources to climate change - Soil quality and erosion risk Indicates agricultural and environmental vulnerability Conditions of Housing Department of Census and Statistics, Disaster Management Center, Divisional Secretariat, Housing agencies - Number of Housing Units and Household Provides data on exposure and density - Housing Units and Principal Material of Wall (e.g., Mud, Cadjans, Palmyrah, etc.) Assesses structural vulnerability to disasters - Number of Occupied Housing Units (hut & shanty) Indicates physical vulnerability - Proximity to emergency services (hospitals, fire stations, police) Measures accessibility to emergency response services - Building age and maintenance conditions Reflects the likelihood of damage in extreme weather events Sensitivity data Analysis To assess the sensitivity of each DS Division within the Ratnapura District, each identified parameter was normalized to ensure comparability across different units and scales. The normalization process involved dividing the value of each parameter by the maximum value observed for that parameter across all DS Divisions within the province. This standardization facilitated the integration of various data types into a unified sensitivity index. The normalized values were then incorporated into the sensitivity map, allowing for a spatial analysis of sensitivity variations over each decade. This approach highlighted the relative sensitivity of each DS Division to climate exposure, accounting for changes in demographic, socioeconomic, environmental, and housing conditions over time. The formula for normalization was: $$\:N{V}_{ij}=\frac{P{V}_{ij}}{MP{V}_{j}}$$ Where: \(\:N{V}_{ij}=\) Normalized value for parameter j in DS Division I \(\:P{V}_{ij}=\) Observed value for parameter j in DS Division I \(\:MP{V}_{ij}=\:\) Maximum observed value for parameter j across all DS Division Adaptive Capacity Mapping Adaptive capacity mapping assessed the degree of adaptability of each DS Division to climate exposure. Parameters for adaptive capacity were also determined through a review of the literature and expert interviews. In cases where specific data were unavailable, proxies were used. Following table shows the data types collected and the method; Table 3 Data Collection for Adaptive Capacity Data Type Sources Data Purpose Health Indicators Department of Census and Statistics, District Secretariat, Divisional Secretariat, statistical agencies - Number of hospitals Indicates availability and access to healthcare services - Number of Public Health Inspectors (PHIs) and Midwives Reflects local health workforce and capacity for preventive care and maternal health - Availability of emergency services (ambulances, emergency rooms) Assesses capacity for rapid response to health emergencies Educational Indicators Department of Census and Statistics, District Secretariat, Divisional Secretariat, statistical agencies - Student-to-teacher ratio Reflects quality of education and capacity for knowledge dissemination - Number of schools Measures access to educational facilities - Literacy rates and education levels Indicates community awareness and understanding of climate risks Environmental Conditions Department of Census and Statistics, District Secretariat, Divisional Secretariat, statistical agencies - Per capita land use and consumption (Hectares) Assesses sustainable land use practices and dependency on natural resources - Availability and quality of natural resources (water, soil, etc.) Reflects the resilience of local ecosystems to climate change Governance Indicators Department of Census and Statistics, District Secretariat, Divisional Secretariat, and electoral agencies - Number of registered electors Indicates citizen engagement and participation in governance - Presence of local governance structures and community organizations Reflects capacity for local decision-making and policy implementation - Existing disaster management policies and programs Assesses preparedness and institutional capacity to respond to climate impacts Economic Indicators Department of Census and Statistics, District Secretariat, Divisional Secretariat, and economic agencies - Income levels and employment rates Measures economic resilience and diversification of livelihood sources - Access to financial services (banks, microfinance, insurance) Assesses financial capacity for recovery and adaptation - Availability of social safety nets (e.g., welfare programs, subsidies) Reflects the capacity to support vulnerable populations during climate-related events Adaptive capacity data Analysis To evaluate the adaptive capacity of each DS Division within the Ratnapura District, each parameter was normalized to facilitate comparability across different units and scales based on the above equation. The normalized values were then incorporated into an adaptive capacity map, highlighting variations in adaptive capacity for each DS Division over each decade. This method enabled the identification of areas with higher or lower adaptive capacities, reflecting their ability to respond to and recover from climate impacts. Vulnerability Index Calculation A comprehensive vulnerability index for each DS division was calculated using the formula: $$\:V=E+S-A$$ ; Where V- Climate change vulnerability, E – Exposure, S-Sensitivity, and A -Adaptive Capacity A vulnerability map for the district was prepared, highlighting the vulnerability index for each DS division. This map aids in identifying the most vulnerable areas. Validation and Verification The final vulnerability map was validated through ground-truthing data from 2022 and consultations with local stakeholders, including government agencies, community leaders, and experts in climate science and disaster management. Robustness checks and sensitivity analyses were conducted to confirm the reliability and accuracy of the vulnerability assessment. Results and discussion Exposure mapping - Precipitation Figure 3 depicts the decadal change in accumulated precipitation for the Divisional Secretariats of the Ratnapura District over four decades: 1982–1991, 1992–2001, 2002–2011, and 2012–2021, derived from the data above. The maps use varying shades of blue to represent different precipitation levels, with darker shades indicating higher accumulated precipitation in millimeters. The legend specifies the precipitation ranges, which span from 3,216 to 41,813 units. This visual representation allows for a comparative analysis of precipitation changes over the 40-year period within the district. The analysis of decadal precipitation patterns in the Ratnapura district reveals a consistent concentration of high precipitation in the northern regions over the past four decades, particularly in Eheliyagoda, Kururvita, Ratnapura, Kiriella, Ayagama, Elapatha, Pellmadulla and Nivitigala. Additionally, there has been a gradual expansion of moderate precipitation areas towards the south. In contrast, the southeastern regions have experienced a noticeable decline in precipitation, indicating potential drying trends, especially in Embilipitiya, Kolonna, and Weligepola. Over the four decades, there is also a discernible trend of decreasing decadal accumulated precipitation across the entire district. These evolving patterns hold significant implications for water resources, agriculture, and local climate resilience. Exposure mapping – Temperature The map above in Fig. 4 shows how the average decadal temperature of DS Divisions in the Ratnapura District has changed. However, since the decadal mean temperature has not significantly changed during the period, this value was not considered in the calculation of climate vulnerability. The lack of significant variation suggests that temperature, while important, may not have been a major factor contributing to climate vulnerability in this region during the specified period, leading to its exclusion from the vulnerability assessments. Exposure mapping - Extreme events: Following Fig. 5 shows the decadal change of the frequency of the flood, droughts and landslides in Divisional Secretaries with the Sabaragamuwa province from 1982–2021. The Fig. 5 left illustrates a dynamic pattern of flood frequency across the province, with consistent flood-prone areas in the central and southwestern regions and an increasing trend in the northeastern regions over time. The peak in flood activity during 2002–2011 suggests heightened flood risk during that period, especially in Ratnapura, Balangoda and Pelmadulla, followed by a slight reduction in the most recent decade. This analysis underscores the importance of ongoing flood risk assessment and adaptive management strategies to mitigate future flood impacts. The Fig. 5 middle illustrates a concerning trend of increasing drought frequency in the province, particularly in Kolonna, Godakawela, and Embilipitiya. While there was a temporary reduction in drought events during 2002–2011, the last decade (2012–2021) shows a significant intensification of drought conditions, with some areas experiencing up to five droughts. This highlights the urgent need for effective drought management and climate adaptation strategies to mitigate the impact of future droughts. The Fig. 5 right illustrates a notable increase in landslide frequency within the district over time, with the southwestern regions experiencing the most significant surge during the 2002–2011 period. While there is a reduction in landslide activity in the most recent decade, the frequency remains higher than in earlier periods, indicating a persistent landslide risk. This pattern highlights the importance of implementing effective landslide mitigation and management strategies to protect vulnerable regions from future landslide events. Climate exposure map of the Ratnapura District Based on the above normalized precipitation and hazard frequency data, the following map (Fig. 6 ) illustrates the divisional-wise decadal change in exposure for the Ratnapura District from 1982 to 2021. The above maps illustrate a significant increase in the exposure of the Ratnapura district to hazards over the four decades, with the highest levels observed during the 2002–2011 and 2012–2021 periods. The southwestern and central regions, particularly the areas of Godakawela, Weligepola, and Pelmadulla, are notably vulnerable. This vulnerability is a reflection of the combination of high hazard frequency and increasing precipitation in these areas. These trends underscore the urgent need for proactive risk management and climate adaptation measures to reduce exposure and enhance the resilience of vulnerable communities in the district. The below map in Fig. 7 shows the average exposure map for the Ratnapura District, based on the above data; Based on the above the exposure map covering a span of forty years, two Divisional Secretariat Divisions, Ratnapura and Kalawana, emerge as areas with high exposure to climate change impacts. This heightened exposure is attributed to factors such as their geographical location, land characteristics, and a history of climate-related events. Conversely, the Divisional Secretariat Divisions of Ambilipitiya and Weligepola show relatively low exposure. These areas may experience lower vulnerability to climate change impacts, potentially due to their distinct geographical features or historical weather patterns that have mitigated such risks. Climate Sensitivity map of the Ratnapura District The following Fig. 8 shows divisional-wise sensitivity capacity in the Ratnapura District for 2021. The above map highlights significant spatial variations in vulnerability across the region. Embilipitiya and Ratnapura emerge as the most sensitive, likely necessitating targeted interventions to mitigate risk and enhance resilience. In contrast, Kiriella and Opanayake show lower sensitivity, which may indicate fewer vulnerabilities or better capacity to manage risks. This map is a crucial tool for guiding policy decisions and resource allocation to strengthen the overall resilience of the district. Adaptive map The following Fig. 9 shows the divisional-wise adaptive capacity for the Ratnapura District in 2021 shows significant variation across the district. The map dempstrate Balangoda, Ratnapura, Imbulpe, and Embilipitiya DS divisions have higher adaptive capacity, indicating better resilience to environmental and socio-economic challenges. In contrast, Kiriella, Elapatha, Kahawatta, and Opanayake have lower adaptive capacity, suggesting a need for focused development efforts to enhance resilience. These patterns are crucial for informed policy-making and resource allocation to improve the overall adaptive capacity and resilience of the Ratnapura District. Climate vulnerability map Following Fig. 10 shows the degree of vulnerability of each DS division of the Ratnapura District. Th valunarabilitye map of the Ratnapura District highlights significant variations in vulnerability across its DS divisions. The central and western regions, particularly Ratnapura and Pelmadulla, are the most vulnerable, whereas the northeastern and southeastern divisions, such as Weligepola and Embilipitiya, are the least vulnerable. These insights are crucial for guiding targeted interventions, resource distribution, and policy-making to enhance resilience and reduce vulnerability across the district. However, there are certain limitations associated with the accuracy of the findings. The density of weather data collection was insufficient, which directly affects the reliability of the results related to climate exposure. Additionally, the accuracy of sensitivity and adaptation-related data for each DS division needs to be verified against third-party information. Validation The Fig. 11 below presents the validation map for the Ratnapura District, illustrating the count of people affected by floods, droughts, and landslides in 2022 at the divisional secretariat level. To assess the validity of this research, the distribution of affected populations due to floods, droughts, and landslides in 2022 was analyzed across the relevant divisional secretariats. However, due to the unavailability of updated sensitivity and adaptive capacity data for 2022, direct validation of these specific factors was challenging. According to the climate change vulnerability map, the Ratnapura Divisional Secretariat exhibits high vulnerability and has experienced significant impacts from climate-related hazards. The Pelmadulla area also shows increased vulnerability, with an expanding affected area in the Elapatha Divisional Secretariat. In contrast, the Weligepola and Embilipitiya Divisional Secretariats, which were identified as having lower vulnerability, have been comparatively less affected by climate-related hazards. This correspondence suggests that the levels of vulnerability identified in the vulnerability maps are aligned with the actual impacts observed in these areas. Overall, a comparison between the climate change vulnerability map and the validation map reveals both consistencies and some deviations in the level of impact experienced, indicating the general accuracy of the geospatial-based vulnerability assessment tools. Conclusion The study highlights the urgent need for targeted climate adaptation strategies in the Ratnapura District, especially in high-risk areas like Ratnapura and Pelmadulla. The increasing frequency of floods, droughts, and landslides, combined with varying levels of sensitivity and adaptive capacity, underscores the complex challenges the district faces in mitigating the impacts of climate change. The findings offer valuable insights for policymakers, stressing the importance of integrating climate considerations into disaster risk reduction and adaptation planning to strengthen the district's overall resilience using geospatial analysis tools. Enhancing infrastructure, social services, and adaptive capacity in the most vulnerable areas is crucial to reducing the district's overall vulnerability to climate change. Recommendations To build resilience against climate-related risks in the Ratnapura District, a multi-faceted approach is necessary. This should include enhancing early warning systems for floods, landslides, and droughts by leveraging modern technologies to provide timely and accurate alerts. Comprehensive flood management plans, including infrastructure improvements, are vital for mitigating flood risks. Similarly, reducing landslide risks requires conducting geotechnical surveys and implementing measures such as slope stabilization. Drought mitigation efforts should focus on sustainable water management strategies and promoting drought-resistant crops. Strengthening social resilience is equally important, which can be achieved by enhancing educational programs on disaster preparedness and investing in healthcare facilities. Furthermore, developing and implementing both regional and national climate change adaptation plans is crucial, as is ensuring that climate change considerations are fully integrated into relevant policies and development frameworks. Together, these strategies provide a robust foundation for reducing the district's vulnerability to climate change and enhancing overall resilience. Declarations Funding Statement I confirm that no external funding was utilized for this research project Author Contribution N.M.P.M. Piyasena identified the research problem, developed the methodology, and was responsible for writing the manuscript. S.M.G.L. Bandara collected the data and performed the data analysis. Data Availability The data will be given upon request References Adger, W. N., Dessai, S., Goulden, M., Hulme, M., Lorenzoni, I., Nelson, D. R., et al. (2009). "Are there social limits to adaptation to climate change?" Climatic Change, 93(3–4), 335–354. https://doi.org/10.1007/s10584-008-9520-z FAO (2021). The State of Food Security and Nutrition in the World 2021: Transforming Food Systems for Food Security, Improved Nutrition, and Affordable Healthy Diets for All . Food and Agriculture Organization of the United Nations. Available online. Füssel, H.-M. (2007). "Vulnerability: A Generally Applicable Conceptual Framework for Climate Change Research." Global Environmental Change, 17(2), 155–167. https://doi.org/10.1016/j.gloenvcha.2006.05.002 IPCC (2021). Climate Change 2021: The Physical Science Basis . Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., et al. (eds.)]. Cambridge University Press. https://doi.org/10.1017/9781009157896 Islam, S. N., & Winkel, J. (2017). Climate Change and Social Inequality . United Nations Department of Economic and Social Affairs (DESA) Working Paper No. 152. Available online. Moss, R. H., et al. (2021). Building a Climate-Resilient Economy and Society: Proceedings of the Symposium on Climate Resilience Research and Practice . National Academies Press. Available online. O’Brien, K., Eriksen, S., Inderberg, T. H., & Sygna, L. (2019). Climate Adaptation and Development: Transforming Paradigms and Practices . Routledge. Available online. UN Water (2021). Summary Progress Update 2021: SDG 6 — Water and Sanitation for All . United Nations. Available online. WHO (2021). COP26 Special Report on Climate Change and Health: The Health Argument for Climate Action . World Health Organization. Available online. WMO (2022). State of the Global Climate 2022 . World Meteorological Organization. Available online. Weis, S. W. M., Agostini, V. N., Roth, L. M., Gilmer, B., Schill, S. R., Knowles, J. E., & Blyther, R. (2016). "Assessing vulnerability: an integrated approach for mapping adaptive capacity, sensitivity, and exposure." Climatic Change, 136(3–4), 615–629. https://doi.org/10.1007/s10584-016-1642-0 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 19 Jun, 2025 Read the published version in Environmental Monitoring and Assessment → Version 1 posted Editorial decision: Revision requested 14 Jan, 2025 Reviews received at journal 06 Jan, 2025 Reviewers agreed at journal 10 Dec, 2024 Reviewers invited by journal 06 Dec, 2024 Editor assigned by journal 21 Nov, 2024 Submission checks completed at journal 21 Nov, 2024 First submitted to journal 16 Oct, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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2","display":"","copyAsset":false,"role":"figure","size":57976,"visible":true,"origin":"","legend":"\u003cp\u003eFlow Chart\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5273082/v1/c88026b9991921800b08f733.jpg"},{"id":71890805,"identity":"ddc05549-c31a-41ad-a7d1-fe1f061e0e1c","added_by":"auto","created_at":"2024-12-19 13:04:19","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":99805,"visible":true,"origin":"","legend":"\u003cp\u003eDecadal accumulative DS level precipitation map\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5273082/v1/7e36677bd108c71381613dd2.jpg"},{"id":71890267,"identity":"c8e8f785-59c0-4d22-a431-bf65ed05b17c","added_by":"auto","created_at":"2024-12-19 12:56:19","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":94182,"visible":true,"origin":"","legend":"\u003cp\u003eDecadal mean temperature of the Ratnapura District\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5273082/v1/c3ac0415aba1d8ae2c624c5d.jpg"},{"id":71889009,"identity":"a0a736ee-6b24-4891-8815-46f549940d71","added_by":"auto","created_at":"2024-12-19 12:48:19","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":222309,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlood (left), Drought (middle), and Landslides (right) decal change maps\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5273082/v1/f37bc3fdaa0d330ae031f9b8.jpg"},{"id":71890807,"identity":"a9ae7ea2-b1a6-4468-bfa8-a798ce8dfee1","added_by":"auto","created_at":"2024-12-19 13:04:20","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":16275,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eClimate exposure map in decadal change in Ratnapura District\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5273082/v1/2d8fc945f23347bfea5b21ee.jpg"},{"id":71889004,"identity":"983e51da-8310-4a94-834c-7abf68aae0f9","added_by":"auto","created_at":"2024-12-19 12:48:19","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":21457,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAverage Exposure \u0026nbsp;map in Ratnapura District\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5273082/v1/e745abb5346c1db7f68c443a.jpg"},{"id":71889007,"identity":"ee8f71ec-f923-4c7e-9bb9-35c3d9fe36bf","added_by":"auto","created_at":"2024-12-19 12:48:19","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":16613,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSensitivity map in Ratnapura District\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5273082/v1/4e10b9723acc5b0f00eb1e56.jpg"},{"id":71890269,"identity":"45fcebb8-faab-43bc-ab05-da28f4616fb6","added_by":"auto","created_at":"2024-12-19 12:56:19","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":13687,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdaptation Capability Map\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5273082/v1/c415c601972f57631948faa7.jpg"},{"id":71889010,"identity":"11217238-419a-4d0a-a5e3-88a64eb1fc2d","added_by":"auto","created_at":"2024-12-19 12:48:19","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":14364,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eClimate vulnerability map\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5273082/v1/39b0456903a14bbe38722387.jpg"},{"id":71889012,"identity":"84503e78-e76a-4c08-a797-039f0e7b42b4","added_by":"auto","created_at":"2024-12-19 12:48:19","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":24165,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eValidation map\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5273082/v1/6b9fb1ab1fd80b3953640b1b.jpg"},{"id":85231301,"identity":"4deea0d1-9d11-433c-8983-4aa5a19d4872","added_by":"auto","created_at":"2025-06-23 16:05:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1779653,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5273082/v1/40f70b36-3cff-4027-b622-55039a588a0e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Utilizing Geospatial Tools for Assessing Climate Change Vulnerability: A Case Study of the Ratnapura District, Sri Lanka","fulltext":[{"header":"Introduction","content":"\u003cp\u003eClimate change poses significant challenges to ecosystems, economies, and societies worldwide. The Intergovernmental Panel on Climate Change (IPCC) has synthesized substantial evidence indicating that human activities are the primary cause of global warming (IPCC, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Recent reports suggest that global temperatures have already increased by approximately 1.1\u0026deg;C above pre-industrial levels, leading to more frequent and severe weather events, including heatwaves, floods, droughts, and storms (IPCC, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; WMO, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe impacts of climate change are multifaceted, affecting various sectors such as agriculture, water resources, and human health. For instance, rising temperatures and altered precipitation patterns have disrupted agricultural productivity, leading to food insecurity in many regions (FAO, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Additionally, climate change has exacerbated water scarcity, affecting both the availability and quality of freshwater resources (UN Water, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The health sector is also significantly impacted, with increased incidences of heat-related illnesses, vector-borne diseases, and respiratory problems (WHO, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eVulnerable populations, including the poor, elderly, and indigenous groups, are disproportionately affected due to limited adaptive capacity and inadequate access to resources (Adger et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Islam \u0026amp; Winkel, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Social determinants such as poverty, lack of education, and inadequate healthcare systems further exacerbate these vulnerabilities (Moss et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Recent studies emphasize the importance of integrating social, economic, and environmental dimensions in climate vulnerability assessments to develop comprehensive adaptation strategies (F\u0026uuml;ssel, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; O\u0026rsquo;Brien et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eProblem\u003c/h3\u003e\n\u003cp\u003eThe current vulnerability assessments face challenges in conducting comprehensive vulnerability assessments that incorporate social impact measures of sensitivity and adaptive capacity. There is a significant possibility of increased exposure to disasters due to changes in societal sensitivity and adaptive capacity. However, the lack of comprehensive identification, analysis, and mitigation of these social impacts hampers effective disaster management and resilience-building.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eThe primary objective of this study is to perform a comprehensive climate vulnerability assessment using geospatial analytical tools, focusing on the dimensions of exposure, sensitivity, and adaptive capacity. This assessment seeks to evaluate the effectiveness of these tools in analyzing climate vulnerability and to generate actionable insights for policy development and decision-making\u003c/p\u003e \u003c/div\u003e"},{"header":"Methodology","content":"\u003cp\u003eAs the case study area approach was utilized to achieve the objectives of the study. Climate exposure, social economic sensitivity and vulnerability was identified based on the methods described in below and then the vulnerability was identified in each administrative division within the case study area.\u003c/p\u003e \u003cp\u003eThe following map shows the map of the administrative divisions (named as divisional secretariats) of the selected case study are which is the Rathnapuara district, Sri Lanka.\u003c/p\u003e \u003cp\u003eSelected Divisional Secretariats\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e Eheliyagoda\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eKuruwita\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eRatnapura\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eKiriella\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eImbulpe\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eBalangoda\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eAyagama\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e Elapatha\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ePelmadulla\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eOpanayake\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eNivitigala\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eWeligepola\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eGodakawela\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eKahawattha\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eKalawana\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eEmbilipitiya\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eKolonna\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eExposure Mapping\u003c/h3\u003e\n\u003cp\u003eExposure mapping aims to identify the frequency of hazardous events occurring in each Divisional Secretariat (DS) Division within the Ratnapura District on a decadal basis. To enhance the accuracy of assessing the intensity and frequency of extreme weather events across these divisions, a refined exposure mapping process was employed. This process involved identifying relevant exposure parameters through a comprehensive review of existing literature and expert interviews. Key climate-related hazards\u0026mdash;such as floods, droughts, landslides, and anomalies in temperature and precipitation\u0026mdash;were identified, and their frequencies were calculated over a ten-year period. The following table shows the data types collected and the methods.\u003c/p\u003e\n\u003ch3\u003eData Collection:\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eData collection for exposure mapping\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eData Collection\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLocations/Details\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrecipitation Data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDepartment of Meteorology, Sri Lanka\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1982\u0026ndash;2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRatnapura, Lellupitiya, and Udawalawa rainfall stations\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClimate-Related Hazards\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDisaster Management Center, Sri Lanka\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1982\u0026ndash;2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFrequency and impact level of floods, droughts, and landslides in each DS Divisions within the Sabaragamuwa Province\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature Data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDepartment of Meteorology, Sri Lanka\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1982\u0026ndash;2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eForm Rathnapura meteorological station\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSupplementary Data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eField Survey\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCrowd-Sourced Data: Information from local communities to supplement official records, particularly in under-reported areas.\u003c/p\u003e \u003cp\u003eDS Division Boundary data were obtained from the Survey Department\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eData Validation:\u003c/h3\u003e\n\u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eCross-referencing with authoritative sources such as IPCC, FAO, and UN Water reports.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eValidation through local expert consultations and field surveys.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGeospatial Analysis:\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eAnalyzing the precipitation data\u003c/h2\u003e \u003cp\u003eThe precipitation data from the three stations were interpolated across the district for each decade from 1982 to 2021. Divisional secretariat boundaries were overlaid using QGIS Zonal Statistics tool to determine decadal rainfall variations within each division. The inverse distance weighted (IDW) method was used for interpolation. The decadal precipitation values were subsequently normalized using the following formula:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{Z}_{10i}=\\:\\frac{{\\mu\\:}_{10i-}{\\mu\\:}_{40i}}{{\\sigma\\:}_{40}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere;\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{Z}_{10i}\\)\u003c/span\u003e \u003c/span\u003e \u0026ndash; Decadal Z score\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{\\mu\\:}_{10i}\\)\u003c/span\u003e \u003c/span\u003e \u0026ndash; Mean Precipitation over a Decade for the Divisional Secretary\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{\\mu\\:}_{40i}\\)\u003c/span\u003e \u003c/span\u003e - Mean Precipitation over a 40 years\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{40}\\)\u003c/span\u003e \u003c/span\u003e \u0026ndash; Standard deviation of the 40 years data\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eAnalyzing the Temperature data\u003c/h3\u003e\n\u003cp\u003eThe temperature data, however, was available from only one weather station, and there was no considerable change during the period of concern, so it was not included in the exposure calculation.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAnalyzing the Hazard data\u003c/h2\u003e \u003cp\u003eThe decadal frequency of flood, drought, and landslide data for each divisional secretariat were normalized by dividing the maximum frequency associated with each event over the 40 years .\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity Mapping\u003c/h2\u003e \u003cp\u003eSensitivity mapping aimed to determine the degree of sensitivity of each DS Division within the Ratnapura District to climate exposure. Parameters related to sensitivity were identified through a comprehensive literature review and expert interviews. These parameters included factors such as population density, poverty levels, infrastructure quality, and ecological conditions. Both qualitative and quantitative data from field surveys, expert interviews, and existing literature were integrated to construct a comprehensive sensitivity map. Following tables shows the data collected and the methods\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eData Collection:\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eData collection for sensitivity mapping\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eData Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSources\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eData\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePurpose\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSocioeconomic Data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDepartment of Census and Statistics, District Secretariat, Divisional Secretariat, statistical agencies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;0\u0026ndash;4 age group, Over 65 age categories\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIdentifies vulnerable demographic groups\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e- No. of dependents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMeasures household economic dependence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e- Minimum distance to access infrastructure and facilities within 5km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAssesses accessibility and mobility to essential services\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e- Major economic activities involved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIndicates dependence on climate-sensitive sectors\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e- Income levels and distribution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAssesses economic resilience\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e- Literacy rates and education levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIndicates capacity to understand and respond to warnings\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e- Estimate Headcount index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAssesses overall capacity to respond to disasters\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e- Social capital and community networks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReflects social resilience and community support structures\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEnvironmental Conditions\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDepartment of Census and Statistics, District Secretariat, Divisional Secretariat, Environmental agencies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e- Estate Population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIndicates population density and land use\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e- Household Land Consumption (Hectares)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAssesses land use pattern\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e- Biodiversity and ecosystem health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEvaluates environmental sensitivity to climate change\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e- Water resources (availability and quality)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAssesses sensitivity of local water resources to climate change\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e- Soil quality and erosion risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIndicates agricultural and environmental vulnerability\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eConditions of Housing\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDepartment of Census and Statistics, Disaster Management Center, Divisional Secretariat, Housing agencies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e- Number of Housing Units and Household\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProvides data on exposure and density\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e- Housing Units and Principal Material of Wall (e.g., Mud, Cadjans, Palmyrah, etc.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAssesses structural vulnerability to disasters\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e- Number of Occupied Housing Units (hut \u0026amp; shanty)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIndicates physical vulnerability\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e- Proximity to emergency services (hospitals, fire stations, police)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMeasures accessibility to emergency response services\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e- Building age and maintenance conditions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReflects the likelihood of damage in extreme weather events\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity data Analysis\u003c/h2\u003e \u003cp\u003eTo assess the sensitivity of each DS Division within the Ratnapura District, each identified parameter was normalized to ensure comparability across different units and scales. The normalization process involved dividing the value of each parameter by the maximum value observed for that parameter across all DS Divisions within the province. This standardization facilitated the integration of various data types into a unified sensitivity index.\u003c/p\u003e \u003cp\u003eThe normalized values were then incorporated into the sensitivity map, allowing for a spatial analysis of sensitivity variations over each decade. This approach highlighted the relative sensitivity of each DS Division to climate exposure, accounting for changes in demographic, socioeconomic, environmental, and housing conditions over time.\u003c/p\u003e \u003cp\u003eThe formula for normalization was:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:N{V}_{ij}=\\frac{P{V}_{ij}}{MP{V}_{j}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:N{V}_{ij}=\\)\u003c/span\u003e \u003c/span\u003e Normalized value for parameter \u003cem\u003ej\u003c/em\u003e in DS Division \u003cem\u003eI\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:P{V}_{ij}=\\)\u003c/span\u003e \u003c/span\u003e Observed value for parameter \u003cem\u003ej\u003c/em\u003e in DS Division \u003cem\u003eI\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:MP{V}_{ij}=\\:\\)\u003c/span\u003e \u003c/span\u003eMaximum observed value for parameter \u003cem\u003ej\u003c/em\u003e across all DS Division\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eAdaptive Capacity Mapping\u003c/h2\u003e \u003cp\u003eAdaptive capacity mapping assessed the degree of adaptability of each DS Division to climate exposure. Parameters for adaptive capacity were also determined through a review of the literature and expert interviews. In cases where specific data were unavailable, proxies were used. Following table shows the data types collected and the method;\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eData Collection for Adaptive Capacity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eData Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSources\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eData\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePurpose\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHealth Indicators\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDepartment of Census and Statistics, District Secretariat, Divisional Secretariat, statistical agencies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e- Number of hospitals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIndicates availability and access to healthcare services\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e- Number of Public Health Inspectors (PHIs) and Midwives\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReflects local health workforce and capacity for preventive care and maternal health\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e- Availability of emergency services (ambulances, emergency rooms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAssesses capacity for rapid response to health emergencies\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational Indicators\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDepartment of Census and Statistics, District Secretariat, Divisional Secretariat, statistical agencies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e- Student-to-teacher ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReflects quality of education and capacity for knowledge dissemination\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e- Number of schools\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMeasures access to educational facilities\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e- Literacy rates and education levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIndicates community awareness and understanding of climate risks\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEnvironmental Conditions\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDepartment of Census and Statistics, District Secretariat, Divisional Secretariat, statistical agencies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e- Per capita land use and consumption (Hectares)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAssesses sustainable land use practices and dependency on natural resources\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e- Availability and quality of natural resources (water, soil, etc.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReflects the resilience of local ecosystems to climate change\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGovernance Indicators\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDepartment of Census and Statistics, District Secretariat, Divisional Secretariat, and electoral agencies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e- Number of registered electors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIndicates citizen engagement and participation in governance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e- Presence of local governance structures and community organizations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReflects capacity for local decision-making and policy implementation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e- Existing disaster management policies and programs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAssesses preparedness and institutional capacity to respond to climate impacts\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEconomic Indicators\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDepartment of Census and Statistics, District Secretariat, Divisional Secretariat, and economic agencies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e- Income levels and employment rates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMeasures economic resilience and diversification of livelihood sources\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e- Access to financial services (banks, microfinance, insurance)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAssesses financial capacity for recovery and adaptation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e- Availability of social safety nets (e.g., welfare programs, subsidies)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReflects the capacity to support vulnerable populations during climate-related events\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eAdaptive capacity data Analysis\u003c/h2\u003e \u003cp\u003eTo evaluate the adaptive capacity of each DS Division within the Ratnapura District, each parameter was normalized to facilitate comparability across different units and scales based on the above equation.\u003c/p\u003e \u003cp\u003eThe normalized values were then incorporated into an adaptive capacity map, highlighting variations in adaptive capacity for each DS Division over each decade. This method enabled the identification of areas with higher or lower adaptive capacities, reflecting their ability to respond to and recover from climate impacts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eVulnerability Index Calculation\u003c/h2\u003e \u003cp\u003eA comprehensive vulnerability index for each DS division was calculated using the formula:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:V=E+S-A$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e; Where V- Climate change vulnerability, E \u0026ndash; Exposure, S-Sensitivity, and A -Adaptive Capacity\u003c/p\u003e \u003cp\u003eA vulnerability map for the district was prepared, highlighting the vulnerability index for each DS division. This map aids in identifying the most vulnerable areas.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eValidation and Verification\u003c/h2\u003e \u003cp\u003eThe final vulnerability map was validated through ground-truthing data from 2022 and consultations with local stakeholders, including government agencies, community leaders, and experts in climate science and disaster management. Robustness checks and sensitivity analyses were conducted to confirm the reliability and accuracy of the vulnerability assessment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results and discussion","content":"\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eExposure mapping - Precipitation\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e depicts the decadal change in accumulated precipitation for the Divisional Secretariats of the Ratnapura District over four decades: 1982\u0026ndash;1991, 1992\u0026ndash;2001, 2002\u0026ndash;2011, and 2012\u0026ndash;2021, derived from the data above. The maps use varying shades of blue to represent different precipitation levels, with darker shades indicating higher accumulated precipitation in millimeters. The legend specifies the precipitation ranges, which span from 3,216 to 41,813 units. This visual representation allows for a comparative analysis of precipitation changes over the 40-year period within the district.\u003c/p\u003e \u003cp\u003eThe analysis of decadal precipitation patterns in the Ratnapura district reveals a consistent concentration of high precipitation in the northern regions over the past four decades, particularly in Eheliyagoda, Kururvita, Ratnapura, Kiriella, Ayagama, Elapatha, Pellmadulla and Nivitigala. Additionally, there has been a gradual expansion of moderate precipitation areas towards the south. In contrast, the southeastern regions have experienced a noticeable decline in precipitation, indicating potential drying trends, especially in Embilipitiya, Kolonna, and Weligepola. Over the four decades, there is also a discernible trend of decreasing decadal accumulated precipitation across the entire district. These evolving patterns hold significant implications for water resources, agriculture, and local climate resilience.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eExposure mapping \u0026ndash; Temperature\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe map above in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows how the average decadal temperature of DS Divisions in the Ratnapura District has changed. However, since the decadal mean temperature has not significantly changed during the period, this value was not considered in the calculation of climate vulnerability. The lack of significant variation suggests that temperature, while important, may not have been a major factor contributing to climate vulnerability in this region during the specified period, leading to its exclusion from the vulnerability assessments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eExposure mapping - Extreme events:\u003c/h2\u003e \u003cp\u003eFollowing Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the decadal change of the frequency of the flood, droughts and landslides in Divisional Secretaries with the Sabaragamuwa province from 1982\u0026ndash;2021.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e left illustrates a dynamic pattern of flood frequency across the province, with consistent flood-prone areas in the central and southwestern regions and an increasing trend in the northeastern regions over time. The peak in flood activity during 2002\u0026ndash;2011 suggests heightened flood risk during that period, especially in Ratnapura, Balangoda and Pelmadulla, followed by a slight reduction in the most recent decade. This analysis underscores the importance of ongoing flood risk assessment and adaptive management strategies to mitigate future flood impacts.\u003c/p\u003e \u003cp\u003eThe Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e middle illustrates a concerning trend of increasing drought frequency in the province, particularly in Kolonna, Godakawela, and Embilipitiya. While there was a temporary reduction in drought events during 2002\u0026ndash;2011, the last decade (2012\u0026ndash;2021) shows a significant intensification of drought conditions, with some areas experiencing up to five droughts. This highlights the urgent need for effective drought management and climate adaptation strategies to mitigate the impact of future droughts.\u003c/p\u003e \u003cp\u003eThe Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e right illustrates a notable increase in landslide frequency within the district over time, with the southwestern regions experiencing the most significant surge during the 2002\u0026ndash;2011 period. While there is a reduction in landslide activity in the most recent decade, the frequency remains higher than in earlier periods, indicating a persistent landslide risk. This pattern highlights the importance of implementing effective landslide mitigation and management strategies to protect vulnerable regions from future landslide events.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eClimate exposure map of the Ratnapura District\u003c/h2\u003e \u003cp\u003eBased on the above normalized precipitation and hazard frequency data, the following map (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) illustrates the divisional-wise decadal change in exposure for the Ratnapura District from 1982 to 2021.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe above maps illustrate a significant increase in the exposure of the Ratnapura district to hazards over the four decades, with the highest levels observed during the 2002\u0026ndash;2011 and 2012\u0026ndash;2021 periods. The southwestern and central regions, particularly the areas of Godakawela, Weligepola, and Pelmadulla, are notably vulnerable. This vulnerability is a reflection of the combination of high hazard frequency and increasing precipitation in these areas. These trends underscore the urgent need for proactive risk management and climate adaptation measures to reduce exposure and enhance the resilience of vulnerable communities in the district.\u003c/p\u003e \u003cp\u003eThe below map in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows the average exposure map for the Ratnapura District, based on the above data;\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on the above the exposure map covering a span of forty years, two Divisional Secretariat Divisions, Ratnapura and Kalawana, emerge as areas with high exposure to climate change impacts. This heightened exposure is attributed to factors such as their geographical location, land characteristics, and a history of climate-related events.\u003c/p\u003e \u003cp\u003eConversely, the Divisional Secretariat Divisions of Ambilipitiya and Weligepola show relatively low exposure. These areas may experience lower vulnerability to climate change impacts, potentially due to their distinct geographical features or historical weather patterns that have mitigated such risks.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eClimate Sensitivity map of the Ratnapura District\u003c/h2\u003e \u003cp\u003eThe following Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e shows divisional-wise sensitivity capacity in the Ratnapura District for 2021.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe above map highlights significant spatial variations in vulnerability across the region. Embilipitiya and Ratnapura emerge as the most sensitive, likely necessitating targeted interventions to mitigate risk and enhance resilience. In contrast, Kiriella and Opanayake show lower sensitivity, which may indicate fewer vulnerabilities or better capacity to manage risks. This map is a crucial tool for guiding policy decisions and resource allocation to strengthen the overall resilience of the district.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eAdaptive map\u003c/h2\u003e \u003cp\u003eThe following Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e shows the divisional-wise adaptive capacity for the Ratnapura District in 2021 shows significant variation across the district.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe map dempstrate Balangoda, Ratnapura, Imbulpe, and Embilipitiya DS divisions have higher adaptive capacity, indicating better resilience to environmental and socio-economic challenges. In contrast, Kiriella, Elapatha, Kahawatta, and Opanayake have lower adaptive capacity, suggesting a need for focused development efforts to enhance resilience. These patterns are crucial for informed policy-making and resource allocation to improve the overall adaptive capacity and resilience of the Ratnapura District.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eClimate vulnerability map\u003c/h2\u003e \u003cp\u003eFollowing Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e shows the degree of vulnerability of each DS division of the Ratnapura District.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTh valunarabilitye map of the Ratnapura District highlights significant variations in vulnerability across its DS divisions. The central and western regions, particularly Ratnapura and Pelmadulla, are the most vulnerable, whereas the northeastern and southeastern divisions, such as Weligepola and Embilipitiya, are the least vulnerable. These insights are crucial for guiding targeted interventions, resource distribution, and policy-making to enhance resilience and reduce vulnerability across the district.\u003c/p\u003e \u003cp\u003eHowever, there are certain limitations associated with the accuracy of the findings. The density of weather data collection was insufficient, which directly affects the reliability of the results related to climate exposure. Additionally, the accuracy of sensitivity and adaptation-related data for each DS division needs to be verified against third-party information.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003eValidation\u003c/h2\u003e \u003cp\u003eThe Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e below presents the validation map for the Ratnapura District, illustrating the count of people affected by floods, droughts, and landslides in 2022 at the divisional secretariat level.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo assess the validity of this research, the distribution of affected populations due to floods, droughts, and landslides in 2022 was analyzed across the relevant divisional secretariats. However, due to the unavailability of updated sensitivity and adaptive capacity data for 2022, direct validation of these specific factors was challenging.\u003c/p\u003e \u003cp\u003eAccording to the climate change vulnerability map, the Ratnapura Divisional Secretariat exhibits high vulnerability and has experienced significant impacts from climate-related hazards. The Pelmadulla area also shows increased vulnerability, with an expanding affected area in the Elapatha Divisional Secretariat.\u003c/p\u003e \u003cp\u003eIn contrast, the Weligepola and Embilipitiya Divisional Secretariats, which were identified as having lower vulnerability, have been comparatively less affected by climate-related hazards. This correspondence suggests that the levels of vulnerability identified in the vulnerability maps are aligned with the actual impacts observed in these areas.\u003c/p\u003e \u003cp\u003eOverall, a comparison between the climate change vulnerability map and the validation map reveals both consistencies and some deviations in the level of impact experienced, indicating the general accuracy of the geospatial-based vulnerability assessment tools.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe study highlights the urgent need for targeted climate adaptation strategies in the Ratnapura District, especially in high-risk areas like Ratnapura and Pelmadulla. The increasing frequency of floods, droughts, and landslides, combined with varying levels of sensitivity and adaptive capacity, underscores the complex challenges the district faces in mitigating the impacts of climate change. The findings offer valuable insights for policymakers, stressing the importance of integrating climate considerations into disaster risk reduction and adaptation planning to strengthen the district's overall resilience using geospatial analysis tools. Enhancing infrastructure, social services, and adaptive capacity in the most vulnerable areas is crucial to reducing the district's overall vulnerability to climate change.\u003c/p\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003eRecommendations\u003c/h2\u003e \u003cp\u003eTo build resilience against climate-related risks in the Ratnapura District, a multi-faceted approach is necessary. This should include enhancing early warning systems for floods, landslides, and droughts by leveraging modern technologies to provide timely and accurate alerts. Comprehensive flood management plans, including infrastructure improvements, are vital for mitigating flood risks. Similarly, reducing landslide risks requires conducting geotechnical surveys and implementing measures such as slope stabilization. Drought mitigation efforts should focus on sustainable water management strategies and promoting drought-resistant crops. Strengthening social resilience is equally important, which can be achieved by enhancing educational programs on disaster preparedness and investing in healthcare facilities. Furthermore, developing and implementing both regional and national climate change adaptation plans is crucial, as is ensuring that climate change considerations are fully integrated into relevant policies and development frameworks. Together, these strategies provide a robust foundation for reducing the district's vulnerability to climate change and enhancing overall resilience.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding Statement\u003c/h2\u003e \u003cp\u003eI confirm that no external funding was utilized for this research project\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eN.M.P.M. Piyasena identified the research problem, developed the methodology, and was responsible for writing the manuscript. S.M.G.L. Bandara collected the data and performed the data analysis.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data will be given upon request\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdger, W. N., Dessai, S., Goulden, M., Hulme, M., Lorenzoni, I., Nelson, D. R., et al. 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(2016). \"Assessing vulnerability: an integrated approach for mapping adaptive capacity, sensitivity, and exposure.\" Climatic Change, 136(3\u0026ndash;4), 615\u0026ndash;629. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10584-016-1642-0\u003c/span\u003e\u003cspan address=\"10.1007/s10584-016-1642-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Climate vulnerability assessment, exposure, sensitivity, adaptive capacity, climate-related hazards, Ratnapura District, Sri Lanka","lastPublishedDoi":"10.21203/rs.3.rs-5273082/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5273082/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study utilizes geospatial tools to assess the climate change vulnerability of the Ratnapura District, Sri Lanka, by examining three key dimensions: exposure, sensitivity, and adaptive capacity. Ratnapura is particularly prone to climate-related hazards, such as floods, landslides, and droughts, which pose significant threats to its socio-economic stability and environmental health. The assessment employs historical climate data and geographic information to develop exposure maps, while sensitivity is evaluated through an analysis of socio-economic and environmental conditions. Adaptive capacity is measured by examining local institutional frameworks and resource availability. The findings reveal high vulnerability levels, particularly in the Ratnapura and Kalawana Divisional Secretariat (DS) divisions, highlighting the urgent need for targeted adaptation strategies. This study demonstrates the effectiveness of geospatial analysis tools in conducting comprehensive climate vulnerability assessments, providing valuable insights for developing climate-sensitive policies and enhancing disaster risk reduction efforts. The results offer a foundation for local and regional authorities to implement proactive measures to build resilience against climate change impacts.\u003c/p\u003e","manuscriptTitle":"Utilizing Geospatial Tools for Assessing Climate Change Vulnerability: A Case Study of the Ratnapura District, Sri Lanka","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-19 12:48:14","doi":"10.21203/rs.3.rs-5273082/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-01-15T02:25:26+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-06T12:13:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"184510547016911889534146593356740957100","date":"2024-12-10T22:39:00+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-12-06T23:55:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-21T11:30:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-21T11:28:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Monitoring and Assessment","date":"2024-10-16T06:21:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"6e172387-efb8-4dfc-9624-8514da7de069","owner":[],"postedDate":"December 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-23T15:59:38+00:00","versionOfRecord":{"articleIdentity":"rs-5273082","link":"https://doi.org/10.1007/s10661-025-14220-1","journal":{"identity":"environmental-monitoring-and-assessment","isVorOnly":false,"title":"Environmental Monitoring and Assessment"},"publishedOn":"2025-06-19 15:57:12","publishedOnDateReadable":"June 19th, 2025"},"versionCreatedAt":"2024-12-19 12:48:14","video":"","vorDoi":"10.1007/s10661-025-14220-1","vorDoiUrl":"https://doi.org/10.1007/s10661-025-14220-1","workflowStages":[]},"version":"v1","identity":"rs-5273082","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5273082","identity":"rs-5273082","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}