Vulnerability to climate change refers to the degree to which a system, community, or region is susceptible to, and unable to cope with, the adverse impacts of climate change. Analyzing this vulnerability is a critical step in climate change assessment and adaptation planning. Various methods have been developed to assess vulnerability, integrating biophysical, social, economic, and institutional dimensions. These methods generally combine qualitative and quantitative approaches and often employ a combination of data collection, modeling, and participatory techniques to provide a comprehensive understanding of vulnerability. One widely used method for analyzing vulnerability is the indicator-based approach. This method involves identifying specific indicators that represent exposure, sensitivity, and adaptive capacity, which are the three main components of vulnerability. Exposure refers to the degree to which a system is exposed to climate-related hazards, sensitivity measures the extent to which a system is affected by climate stressors, and adaptive capacity indicates the ability to cope or adjust to these impacts. Indicators may include factors such as population density in flood-prone areas, agricultural dependency on rainfall, income levels, access to healthcare, and institutional capacity. Data for these indicators are often collected from statistical databases, surveys, and remote sensing technologies. Once the indicators are identified, they are normalized and weighted, and a composite vulnerability index is calculated. This approach allows policymakers to compare vulnerability across regions or sectors and prioritize adaptation strategies. However, a limitation of this method is that it may oversimplify complex social-ecological systems, and the selection and weighting of indicators can sometimes be subjective. Another method is the model-based approach, which involves using computational or simulation models to project the impacts of climate change on various sectors and assess vulnerability under different scenarios. Models can range from simple crop yield projections to sophisticated integrated assessment models that link climate, economic, and social data. For example, hydrological models can simulate river flows and flood risks under future climate scenarios, while crop models can estimate changes in agricultural productivity due to altered temperature and precipitation patterns. Model-based approaches allow for scenario analysis, helping stakeholders understand potential future vulnerabilities under different climate pathways. They are particularly useful for planning long-term adaptation strategies. However, they require high-quality data, technical expertise, and assumptions about future socio-economic and climate conditions, which can introduce uncertainties. Participatory methods are also crucial in vulnerability assessment because they incorporate local knowledge and stakeholder perspectives. These methods involve engaging communities, local governments, and other stakeholders in identifying climate risks, prioritizing vulnerabilities, and evaluating adaptive capacity. Participatory rural