The following methods, tools, and data sources are employed to assess exposure comprehensively, each with strengths depending on the context, geographic scale, and specific climate hazards under consideration.

Geospatial Analysis 
Geographic Information Systems (GIS) are widely used for overlaying the extent of specific climate-related hazards (zones affected by drought, floods, cyclones, dust storms, wildfires etc.)  with the spatial distribution of exposed assets, populations, and infrastructure. Thus, GIS maps provide visual insights into areas with high exposure to climatic risks.
Remote sensing offers very versatile tools and scalable data products for monitoring land use, vegetation cover, population density, and infrastructure placement. Remote sensing is particularly useful in large or remote areas for mapping exposure of particular ecosystems to climate-induced hazards, for example exposure of mangrove forests to storm surges, or natural forests and tree plantations to heat waves.
Digital Elevation Models (DEMs) have an important role in identifying low-lying areas where infrastructure, populations, or ecosystems might be vulnerable to flooding and sea-level rise.

Geospatial tools are often combined with specialized environmental models in hazard-specific  exposure assessments. For example, hydrological models simulating flood extent and frequency, combined with GIS data on infrastructure and population, can provide detailed assessments of flood exposure. Agrometeorological and crop yield models, relying on meteorological, soil and land use data, can predict how drought might affect crops and water supply.

Statistical and Indicator-Based Assessments
Exposure indicators are quantitative metrics of exposure based on factors like population density, asset location, economic activity, or ecosystem types in hazard-prone areas. For example, the number of people and houses within low-lying areas, designated as flood-prone, can serve as an indicator of exposure to flooding.
Composite indices, such as the Climate Risk Index (see below), combine multiple indicators, such as fatalities and damages, to provide an overall exposure score for specific countries or regions, enabling comparative analysis across different areas.

Climate Risk Index

Description The Climate Risk Index (CRI) evaluates and ranks the level of exposure of countries to extreme weather events, such as storms, floods, and heatwaves, highlighting the countries most affected. The CRI was compiled and published annually by Germanwatch, an NGO focusing on sustainable development and climate policy. The last report was issued for year 2021 containing weather loss events for 2019 and the period 2000-2019.   The CRI is based on the following indicators related to extreme climate- and weather-related events: (1) Number of deaths (in absolute terms); (2) Number of deaths per 100,000 inhabitants; (3) Economic losses: in absolute terms, expressed in US$ as losses in Purchasing Power Parity; (4) Economic losses: relative, as a percentage of GDP. Calculation Ranking is used in each of these categories as a normalization technique. The weights assigned to each indicator are: (1) 1/6, (2) 1/3, (3) 1/6, (4) 1/3. For example, in the CRI for 2000-2019, Bangladesh ranks 9th in indicator (1), 37th in indicator (2), 13th in indicator (3), and 37th in indicator (4). The overall CRI score is then calculated as: CRI score = (9 x 1/6) + (37 x 1/3) + (13 x 1/6) + (37 x 1/3) = 28.33 . Only six countries have a lower CRI score, hence BanglaDesh ranks 7th in the table of countries most affected by extreme weather events in the period 2000-2019.   Evaluation The CRI relies on actual historical data from widely recognized sources, such as the Munich Re’s NatCatSERVICE, ensuring that its analysis is firmly rooted in real-world events. Covering a large number of countries, it enables comparisons of climate risks across regions and over time. By focusing on the direct impacts of extreme weather events—such as fatalities and economic losses—it provides a tangible measure of the consequences of climate-related disasters. This approach highlights the disproportionate burden of climate impacts on vulnerable countries, raising awareness among policymakers, the media, and the public about the urgency of climate action and the unequal challenges faced by developing nations. However, by concentrating solely on extreme weather events and their direct impacts, such as economic losses and fatalities, the CRI does not account for the broader spectrum of climate risks, including biodiversity loss, desertification, and other long-term effects.

Data Collection and Surveys
Population and housing census data provide detailed information on the locations of people and assets, which can be overlaid with hazard maps to assess exposure levels. Surveys can further capture data on household assets and livelihoods, offering insights into the exposure of specific socioeconomic groups.
Community-based vulnerability assessments, involving direct engagement with local communities to identify assets, infrastructure, and resources provide locally specific exposure information. These point locations may offer representative sampling sites to be integrated into broader-scale analyses.

Spatial-Temporal Modeling
Dynamic models forecast how exposure evolves over time due to urbanization, population growth, and climate change. For instance, urban expansion models can predict future exposure to floods as cities grow into flood-prone areas.
Exposure assessments frequently integrate climate model outputs (e.g., from CMIP6) to simulate future scenarios, such as temperature increases or altered precipitation patterns. These scenarios help assess how exposure to specific risks may intensify under different climate change projections

Integration with Socioeconomic Data
Estimating the value of assets and economic activities in hazard-prone areas adds an economic perspective to exposure assessment. Examples are the economic cost of infrastructure at risk from flooding or the value of agricultural production in drought-prone zones. Demographic and health data can be used to identify vulnerable populations within hazard areas, such as elderly populations in heatwave-prone urban areas.