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This portion of the website summarizes the AIACC projects' methods for impacts, adaptation, and vulnerability assessment, includes definitions of the concepts used, and provides examples from the AIACC projects. This page also synthesizes the climate scenarios’ data and methodologies for developing regional climate scenarios by each AIACC project. The purpose is to build a framework for the synthesis of the results of the AIACC program in order to provide summaries for policy-makers.

This section will be updated to include descriptions of the different methodologies and to provide examples from all the AIACC projects. Last update 20 June 2003.


The following table provides a summary of the methods for impacts, adaptation, and vulnerability assessment used by each project.

N Project Methods for VA
1 AF04 Decision Support Systems
2 AF07 n.a.
3 AF14 Sustainable Livelihoods, Indicators
4 AF20 n.a.
5 AF23 Decision Support Systems
6 AF38 Decision Support Systems
7 AF42 Sustainable Livelihoods
8 AF47 Cost-Benefit Analysis
9 AF90 Indicators
10 AF91 Indicators, Multi-criteria Analysis
11 AF92 Indicators
12 AS06 Decision Support Systems, Disaster Analysis
13 AS07 Decision Support Systems, Disaster Analysis
14 AS12 Decision Support Systems
15 AS21 Indicators
16 AS25 Multi-criteria Analysis
17 LA06 Disaster Analysis
18 LA26 Indicators, Decision Support Systems
19 LA27 Decision Support Systems
20 LA29 Indicators
21 LA32 Indicators
22 SIS06 Multi-criteria Analysis
23 SIS09 Sustainable Livelihoods, Decision Support Systems
24 SIS90 Decision Support Systems


Exposure is the nature and degree to which a system is exposed to significant climatic variations.

Exposure Unit is an activity, group, region, or resource that is subjected to climatic stimuli.

Impacts (to climate) are the consequences of climate change on natural and human systems.

Potential Impacts are all impacts that may occur given a projected change in climate, without considering adaptation.

Residual Impacts are the impacts of climate change that would occur after adaptation.

Coping Range is the variation in climatic stimuli that a system can absorb without producing significant impacts. The coping range can be regarded as the adaptive capacity of a system to deal with current variability.

Sensitivity is the degree to which a system is affected, either adversely or beneficially, by climate-related stimuli (change or extremes).

Adaptation refers to all those responses to climate change that may be used to reduce vulnerability or to actions designed to take advantage of new opportunities that may arise as a result of climate change.


Vulnerability = Sensitivity - Autonomous Adaptive Capacity - Planned Adaptation

Current vulnerability = Current impacts – Coping range

Future vulnerability = Future impacts – Planned Adaptation – Coping range

Adaptive capacity is the ability of a system to adjust to climate change, including climate variability and extremes, to moderate potential damages, to take advantage of opportunities, or to cope with the consequences. Adaptive capacity to climate change refers to both the ability inherent in the coping range and the ability to move or expand the coping range with new or modified adaptations. Initiatives to enhance adaptive capacity would expand the coping range.

Adaptive capacity may be considered as the policy response to an impact as a function of the socio-political characteristics of a location (ability to cope), e.g. defined through wealth (GDP), democracy, etc. An adaptive capacity index may be based on variables that are scenario dependant and geographically-explicit.


Method AIACC Project
Indicators AF14, AF90, AF91, AF92, AS21, LA26, LA29, LA32
Sustainable Livelihoods AF14, AF42, SIS09
Cost-Benefit Analysis AF47
Decision Support Systems AF04, AF23, AF38, AS06, AS07, AS12, LA26, LA27, SIS09, SIS90
Multi-criteria Analysis AF91, AS25, SIS06
Disaster Analysis AS06, AS07, LA06
 EXAMPLES > Indicators
LA26: Indicators

Project: Impact of Global Change on the Coastal Areas of the Rio De La Plata: Sea Level Rise and Meteorological Effects.

Urban Vulnerability to Climate Change
Vicente Barros and Claudia Natenzon

Large areas of Buenos Aires city suffer frequent floods. Climate Change is increasing the frequency and the area covered by floods caused by storm tides in the Rio de la Plata and the risk of permanent flood in coastal areas (Figure 1). IPCC scenarios of sea level rise and climate are used to develop scenarios of the main forcings of the Plata water level.

This is done using two hydrodynamic numerical models:

  • A two dimensional and vertically integrated model that permits simulating water level under different conditions.
  • A three dimensional model to simulate the salinity front within the Rio de la Plata.

Scenarios of water level provide the input to assess present and future risks on the area potentially subject to permanent or transient floods. The area affected by a 5 m sea level rise is indicated in Figure 2. This cote results from the projected maximum water level at Buenos Aires coast under the combined forcing of the mean sea level rise of the next hundred years, wind storms and the largest observed stream flows of the two main tributaries of the La Plata River.

Social vulnerability is studied from present social conditions under “normal” and “stressed” situations. Once the population that can be affected in a direct way is estimated, the following steps are developed:

  • Quantitative approach: Indicators (specific for each case, that are also available and free of charge) about demography, life conditions, work, production and consumption
  • Quantitative approach: a Social Vulnerability Index, as a preliminary identification of administrative units with high Social Vulnerability.
  • Qualitative approach: Land use; inventory of goods, service and infrastructure in exposition.
  • Qualitative approach: Dimensions of the Social Vulnerability (economics, ideological cultural, policies/institutional) in case studies.

Figure 1. Rio de la Plata and Buenos Aires

Figure 2. Area affected by a 5 m sea level rise.

LA29: Indicators

Project: Integrated Assessment of Social Vulnerability and Adaptation to Climate Variability and Change Among Farmers in Mexico and Argentina

Adaptive Capacity of Farmers to Climate Variability and Change in Central Mexico
Carlos Gay and Marta Vinacour

In order to assess the adaptive capacity of farmers to climatic variability and change, our project is basing its methodology on the MESMIS [1] and on recent research on social vulnerability in central Mexico [2]. The MESMIS framework was originally developed to evaluate the sustainability of natural resource management in rural areas. Analytically, we consider that adaptive capacity is determined by the interaction of social, economic, institutional and environmental processes that combine to affect farmers’ decisions at the moment that they face climatic risk and change.

Adaptive capacity (AC) refers to farmers’ ability to recognize present and future climatic risks, respond to and cope with risk (through reorganization of activities, investments, resource allocation, etc.) in order to minimize risk of future negative consequences. Such capacity has been defined as being related to specific sustainability attributes of that system, and for our project we are using the following [3]: a) access to resources (AR) that are critical to preparing for and recovering from climatic events, and are identified together with stakeholder; b) flexibility (FL), which reflects the capacity of a system to return to an equilibrium state after being affected and depends on access to resources and system diversity; and c) stability (ST), which refers to the frequency of both climatic and non-climatic shocks and degree of uncertainty affecting the decision-making environment of the system. Both consistent resource access and flexibility contribute to a systems´ stability, that refers to the system’s property of being able to sustain itself. Thus, all this attributes are inter-related and together determine a system’s adaptive capacity. Moreover, as our project interest is with agricultural producers’ adaptive capacity, and this capacity is highly differentiated within the sector according to the type of farms we consider and the different physical sensitivities of production to climatic risk, we are expressing adaptive capacity as follows:



i = 1, 2,..., n and represents specific climatic events.
j = 1, 2,…, m and represents different type of producers.
k = 1,2,..., w and represents particular geographical zones to be considered.
c = whether an agricultural productive unit or an agricultural producer’s livelihood.

Methodologically, assessing agricultural producers’ adaptive capacity to adverse climatic events rests on the idea that no single set of indicators can be determined in advance for analyzing any of its attributes, but rather the indicators should be defined through both an analysis of existing literature on the systems of study, and collaborative research with actors in the system to determine the critical points that characterize each attribute, specific to the system. In our case, this means extensive consultations both with sector experts and with different classes of farmers in order to identify the appropriate variables that best represent the attributes of interest for each group and sector. Many of the indicators identified in the project (see table below) may also be considered indicators of rural sustainability, and as such, we hope that although we cannot address all aspects of sustainability in our vulnerability research, we will provide a basis from which necessary connections between future social development and future vulnerability can be made.

1. Flexibility (FL)  
  • Diversity of agricultural system
Diversity of seeds available and used; number of crops planted
  • Income diversity
Diversity of income sources (agriculture, livestock, off-farm and non-farm)
  • Resource base (endowments) (biological, physical, human, social, financial)
Water supply; soil quality and diversity; land tenure, size, distribution; financial capital; education and age; material equipment and machinery ; animals
Tendencies and changes in the above  
2. Stability (ST)  
  • Exposure to/ impact of market risk
Variability in input and output prices and availability
  • Exposure to/ impact of climatic risk
Main climatic impacts; agriculture and cattle losses
  • Degree of variability/change in rural economy
Migration; land sales, land rental
Tendencies and changes in the above  
3. Resource Access (AR)  
  • Access to financial resources
Formal and informal credit
  • Participation in support programs
Technology transfer; technical assistance
  • Participation in social programs
Emergency welfare programs, social services

[1] Masera y López-Ridaura (2000).
[2] Eakin (2002).
[3] We recognize that these are not the only attributes of adaptive capacity, but for the purpose of our research we are using those identified by Eakin (2002), which explained adaptive capacities of smallholder farmers in central Mexico.

EXAMPLES > Sustainable Livelihoods
AF14: Sustainable Livelihoods

Project: Environmental Strategies for Increasing Human Resilience in Sudan: Lessons for Climate Change Adaptation in North and East Africa

Sustainable Livelihoods in Sudan
Balgis M. Osman and Nagmeldin Goubti

Sustainable livelihood assessment is intended to generate an understanding of the role and impact of a project on enhancing and securing local people’s livelihoods.

The AIACC AF14 project will use the DFID model outlined below, and the notion of the five capitals (natural, physical, human, social and financial), albeit loosely, in order to capture perceptions of resilience in the data collection process.

DFID’s Sustainable Livelihoods Framework:

The project involves four separate case studies – each involving travel, fieldwork, data collection, analysis and writing. Commissioned experts will conduct the research and writing with the support and close collaboration of the project partners. It is expected that case study reports will be prepared in such a way that their results can be compared and synthesized into a set of project findings. Given this, it is clearly critical that each case study researcher closely follows a carefully designed research protocol, as outlined below.

It is anticipated that each case study will require modest adaptations of the project’s data collection methods outlined below, in order to suite the unique community context. The goal in developing and using this instrument is to identify the richness of a community’s experience and avoid reductionism, while at the same time, gathering comparable information. Nevertheless, it is expected that the main components of our approach will be applied. Case study research is divided into four main stages, as outlined below:

  • Background/preparation
  • Fieldwork
  • Policy analysis
  • Progress reporting
  • Synthesis

EXAMPLES > Cost-Benefit Analysis
[Under Construction]
EXAMPLES > Decision Support Systems

Building Capacity to Assess Impact of Climate Change/Variability and Develop Adaptive Responses for the Mixed Crop/Livestock Production Systems in the Argentinean and Uruguayan Pampas

Information and Decision Support Systems for Agriculture and Climate
Agustin Gimenez and Walter Baethgen

The agricultural community involved in research, development and production activities in developing countries is facing two primary challenges. Firstly, the drive for increasing agricultural productivity in a sustainable basis, i.e., increased output per unit area of land in ways that minimize damage to the environment and the natural resource base. The second concern is the growing awareness that the constricted information flows between farmers, researchers, extension workers, policy makers, and agribusiness personnel are a serious impediment to achieve sustainable development. The difficulties raised by these concerns, coupled with the complexity of most agroecosystems, the necessity of taking a long term view of biophysical processes, and the limited research resources available, all contribute to support the notion that new tools and methodologies are required to study the problems of sustainable production, suggest solutions, and assess their impacts at the farm level.

The agricultural sector in developing countries is confronting great changes determined by industrialization and modernization, eliminated subsidies, environmental constraints, land use conflicts, biotechnology, and increased overall risk. In this context the availability, accessibility, and application of relevant agricultural information is of high priority for policy makers, farmers, technicians, and researchers. Often the availability of information in the developing world is not a major limitation: most countries have adequate research systems generating relevant information. Moving the relevant information from research centers to extension agents, policy makers and farmers has been a more frequent limitation. On the other hand, in many developing countries stakeholders have increasingly easy access to information systems through different channels including the Internet. Difficulties in these cases are often caused by the massive amount of information and the lack of tools for its analysis and prioritization that results in a lack of adequate application.

Furthermore, tools are currently available for farmers, government planners, and decision makers in general that allow them to obtain and analyze massive quantities of information and assist them in the planning and decision making processes. Examples of such tools include among many others: simulation models, expert systems, remote sensing, geographic information systems (GIS), global positioning systems (GPS). However, the main constraint to effectively use this information is the typical complexity involved in the application of such tools and the consequent need for training.

Responding to this challenge, since the early 1990s the International Soil Fertility and Agricultural Development Center (IFDC), the National Agricultural Research Institutes of Uruguay (INIA), Argentina (INTA) and Brazil (Embrapa), established a workgroup to develop applications of the Information and Decision Support Systems (IDSS) approach. Since then this group (IDSS-Uruguay) has been conducting activities in collaboration with NASA, NOAA, US-EPA, the International Research Institute for Climate Prediction (IRI), the Australian group APSRU, the Joint Research Center (JRC) of the European Commission, and with several Universities frm the USA (e.g., Colorado, Columbia, Nebraska, Georgia).

Description of the IDSS

The IDSS approach (Figure 3) is based on the linking and integration of: (a) maps and associated databases (soils, weather, land use, political divisions, market centers, routes, rivers); (b) National and regional statistics (production, socioeconomic, demographic); (c) prices of inputs and products; (d) remotely sensed acquired information (crops, pastures, natural resources, climate); (e) simulation models of crop, pasture and forest growth, development and production; (f) a decision support system specifically designed for agro-technology transfer; (g) probabilistic seasonal climate forecasts; (h) climate change scenarios (GCMs, RCMs and statistical methods); (i) methods for land use evaluation and for defining land use feasibility classes; (j) simulation models of soil carbon and nutrient dynamics and soil erosion; (k) tools for agricultural applications of global positioning systems (GPS); and (l) geographic information systems (GIS) to process and analyze maps and databases and to generate information that can be easily understandable and applied by agricultural stakeholders.

The main objective of the IDSS workgroup is:

To develop applications of modern tools for obtaining, processing and analyzing information to improve agricultural planning and development and to optimize the use of the natural resource base. Work is conducted at different spatial scales (National, regional, farmer groups/communities, individual farms) and generates results in simple formats that are easily understandable and applicable by different types of agricultural stakeholders (government agencies, International organizations, NGOs, agribusinesses, farmer organizations, individual farmers).

Examples of activities established by the IDSS workgroup in Latin America

  • Assist governments in the prioritization of aid in emergency situations (e.g., droughts, floods, frosts)
  • Develop applications of seasonal climate forecasts (e.g. based on El Niño) to improve agricultural planning and decision-making
  • Assess the vulnerability of agricultural production systems to current climate variability and to possible future scenarios of climate change
  • Identify agricultural production systems best adapted to current climate variability and to possible future scenarios of climate change
  • Study the environmental impacts resulting of changes in land use (e.g., introducing annual crops in natural rangelands, deforestation, water contamination)
  • Establish greenhouse gas inventories in the agricultural and forestry sectors and explore opportunities in the emerging Carbon Market (Kyoto Protocol) in those sectors
  • Establish agroecological zones and land use feasibility classes for different agricultural activities (summer/ winter crops, pastures, forests)
  • Assess variability and risk in agricultural production to support rural credit and rural insurance programs
  • Establish crop and pasture yield forecasts

Figure 3. Structure of the Information Decision Support System (IDSS)

EXAMPLES > Multi-criteria Analysis

SIS06: Multi-criteria Analysis

Project: The Threat of Dengue Fever - Assessment of Impacts and Adaptation to Climate Change in Human Health in the Caribbean

Analytic Methods for Evaluating Risk of Dengue in the Caribbean
Anthony Chen and Samuel Rawlins

The project integrates several statistical techniques to evaluate the relative, absolute, and attributable risk of dengue [1]:

Analysis of Variance (Anova) to evaluate the association between climate, mosquito densities, dengue cases, seasonal changes etc.

  • Regression analysis to determine changes in time, space, etc.
  • Evaluating epidemiologic association to answer the following questions: Could the association have been observed by chance?; Could the association be due to bias?; Could other confounding variables have accounted for observed relationship?; To whom does this association apply?; Does the association represents a cause and effect relationship?
  • Analysis of causation to answer the following questions: Is there a logical time relationship?; Is there a large relative risk?; Can we demonstrate a dose-response relationship?; Is it reversible?; Is it consistently found to be present in different study sites?; Is it consistent for various study designs?; Is it biologically plausible?

[1] Relative risk is a measure of the extent to which those exposed to a risk factor are likely to get a disease compared with the non-diseased general population. Absolute risk is the incidence rate for a group exposed to a risk factor. Attributable risk is the difference in the incidence of a disease between the exposed (diseased) and non-exposed (non-diseased) groups.

EXAMPLES > Disaster Analysis

LA06: Disaster Analysis

Project: Assessment of Impacts and Adaptation Measures for the Water Resources Sector Due to Extreme Events Under Climate Change Conditions in Central America

Economic Impact of Hydrometeorological Disasters in Costa Rica
Walter Fernandez and Maxx Campos

The main objective is to determine the economic impact of disasters originated by hydrometeorological events, on the economy of Costa Rica during 1996-2001. The types of disasters covered under this study include: droughts, floods, river overflows, heat waves, rainy winds, rain related accidents and landslides. These include 84% of the total of natural disasters documented in the country during that same period of time.

For the events documented in each year of the period, the following aspects were valued:

  • Cost of relief (food and supplies, aid, equipment and removal of debris, etc.)
  • Value of homes damaged or destroyed
  • Value of reconstruction and/or repair of public schools, hospitals and clinics
  • Cost of repair of meters of length of damaged roads
  • Value of damaged bridges and aqueducts
  • Value of losses in agriculture hectares of damaged crops and other measures officially reported
  • Cost of medical attention to victims and evacuees
  • Cost of relocation of families
  • Value of death and missing persons based on the working years potentially lost of those individuals under 65 years

The series show a decreasing trend after 1998 (Figures 4 and 5), due in part to the reduction of deaths, and because of the reduction of the intensity of the events considered.

The study findings show that the average annual value of the damages caused by hydrometeorological disasters was 57,498,036,184 colones, the currency in Costa Rica (US $ 146,305435.5), and represent a 1.15% of the GDP of Costa Rica. Considering the estimate of losses in agricultural crops, in relation to the total national agricultural production (agricultural, silvicultural and fisheries GDP), the annual average represents 1.5% of the agricultural GDP.

The direct values (medical attention to victims, compensation of agricultural losses) reach an annual average of 18,778,124,609 colones (US$ 47,781487.5), which represent a 3% of the central government annual revenues. This amount is what the considered hydrometeorological events should require for its relief and compensation for damages and infrastructure.

The estimated values for investment in roads, bridges, aqueducts and damaged or destroyed buildings and facilities, represent a high opportunity cost for the country’s economic development options and social welfare. This amount is more or less, the equivalent to the investment in 30 new rural aqueduct systems, 157 bridges, near 425 Km of road systems, and more than 50 rural school buildings of three rooms each. Therefore, this values are appropriate indicators of the benefits foregone by the country in social and economic investments, in exchange of replacing the investments damages or destroyed by a recurrent hydrometeorological events in the five year period.

For those disasters whose magnitudes require an official declaration for state of emergency, a regulation plan is required. This entails appointing executing agencies (governmental entities, local governments, and local organizations) and developing an investment plan. The National Commission for Emergencies (CNE) is responsible for the accounting of the executed funds in the items described in the investment plan for each event. Such accounting is systematized since 1998, and the average annual expenditures amount to 1,586,904587 colones (US$ 4,037925.5), which represent only 17.3% of the total reported damages (Table 1).

The estimates respond to the data base available in different sources. Its important to consider that such information is not generated in a systematic or continuous fashion. Due to this condition, the estimates are based in indirect criteria and logical assumptions. This suggests the need to develop a database that enables authorities and researchers to have an accurate information of what hydrometeorological events may imply in economic terms and to what it forgoes when disasters are recurrent.

Figure 4. Number of Events and Valuation of Total Damage

Figure 5. Damages by hydrometeorological events as percentage of GDP

Table 1. Coverage of expenditure in repair and reconstruction as part of the total direct cost (in 2003 colones). *Adjusted for inflation to 2003 current colones.
Year Expenditure Direct costs* % coverage
1,998 1,783,640,654 27,796,080,588 6.42%
1,999 1,303,013,364 9,655,023,425 13.50%
2,000 1,135,753,469 3,598,794,108 31.56%
2,001 458,245,383 2,583,279,245 17.74%
Average     17.30%

Sources of information and databases: La Red, 2002.
Base de datos Des inventar, versión 5.4.1. 30 enero.

Office for Disaster Assistance (OFDA) USAID

AS06: Disaster Analysis

Project: Potential Impacts of Climate Change and Vulnerability and Adaptation Assessment for Grassland Ecosystem and Livestock Sector in Mongolia

Disaster Analysis in Mongolia
Punsalmaa Batima

Following the principle indicated in “An Adaptation Policy Framework” the Mongolia project is concerned with climate variability and extreme events. In the past impact and vulnerability in livestock sector and country socio-economy was more measurable when occur natural disasters caused by meteorological extreme events. Other reason to study extremes is that the government highlights prevention and mitigation of risk of natural disasters like Dzud, drought, wind- and snowstorm that have affected country socio-economy, human lives and livelihood of thousands of households since 1999.

The main objectives of case study are to:

  • Quantify extreme meteorological event and livestock sector components
  • Logical definition and formula of interrelationships between components
  • Quantified assessment of hazard, vulnerability and risk
  • Compare assessment results with actual losses
  • Evaluate recommendations and adaptation policy options

We studied two cases with different in temporal scale:

  • Dzud disaster, which is a complex and continues process with losses of millions of animals, serious disruption of agriculture and country socio-economy, affect households’ income, and increase unemployment and poverty. Usually it covers big area and starts in summer with drought, hot wave, continued for autumn, winter and spring with extreme cold, heavy snowfall and harsh windstorm. Recovery of losses is continued for years. Mongolia has been under Dzud pressure since 1999. Mitigation measures are more important.
  • Windstorm disaster, which is a complex, but sudden process, covers big area with high danger spots, affects human lives, animal losses, environment, infrastructure, houses and shelters. Warning and preparedness measures are important.

The case study of Dzud was done in 2001 within UNDP funded project MON/00/302 to meet government requirement to have a recommendation for development of policy on prevention and mitigation of natural disaster. As the study yielded with the recommendation, basing on it the government of Mongolia developed a Programme “Assisting to protect livestock against drought and Dzud disaster” and now implementing it with state fund financing. International organizations and donors starting to support some development projects that address local capacity building to prevent and mitigate natural disasters. As an example of success story of research work and its implementation this case can be useful for climate change study in three ways:

  1. For development of methodology to assess impacts, vulnerability, and link with government short- and mid-term objectives
  2. For development of recommendations and policies
  3. For development of tactics to work with government and other stakeholders and deliver outputs of research work (recommendation and adaptation policy) to them to achieve immediate follow-up reaction with real activities and financing mechanisms

The case study includes the following components:

  1. To test a simplified methodology for relative assessment of hazard, vulnerability and risk which is designed to rank administrative units by level of facing danger. It is to be as the first step toward to assessment of vulnerability of livestock sector under non-linear, extreme events of climate.
  2. To assess early warning and emergency system of Mongolia on the example of windstorm disaster and develop a recommendation for further improvement.

The first component is for climate change study, while the other is required for Disaster Management Board of Mongolia and the launching by government and UNDP project “Strengthening of disaster Mitigation and Management System in Mongolia”. Such integration could happen as the result of close collaboration of climate change researchers with disaster management authorities and international organization.


climate scenarios table

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