Adaptation Baselines Through V&A Assessments. Prof. Helmy Eid Climate Change Expert Soil, Water & Environment Res. Institute (SWERI), ARC Giza Egypt Material for : Montreal Workshop 2001. ADAPTATION BASELINES General Recommendations on Adaptation Baselines
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Prof. Helmy Eid
Climate Change Expert
Soil, Water & Environment Res. Institute
(SWERI), ARC Giza Egypt
Material for : Montreal Workshop 2001
General Recommendations on Adaptation Baselines
■ - Baseline (reference). The baseline is any datum against which change
is measured. It might be a “current baseline,” in which case it
represents observable, present-day conditions.
- It also might be a “future baseline,” which is a projected future set of
conditions, excluding the driving factor of interest.
- Alternative interpretations of reference conditions can give rise to
■ Adaptation baseline of policies and measures could be defined as the
set of policies and measures already taken by various concerned
authorities, and NGOs within the frame of the precautionary principle,
to help agriculture, water resources and demand, human health and
coastal zones as well as minimize adverse impacts of warming and sea
dataset and baseline, and this could be done by identifying data
needs and availability and establishing dataset and baselines
■ Identify climatological and sea-level rise that are relevant to
■ Identify non-climatic data required for method development,
calibration and testing (e.g. river flow data, maps of
crop distribution), for methods application (e.g. soil data,
beach profile data, country GDP), and any additional data
(e.g. population density statistics).
■ Assess availability of data; sources, forms, problems of
obtaining data (cost, accessibility, status of data,
documentation, compatibility and uncertainty)
■ Evaluate available data to establish their stability for
selected methods by determining; time resolution,
completeness of records, quality, sites number and their
spatial distribution (for spatial interpolations).
■ Identify stations with a good length of record (ideally 30
years), check data for errors, missing data, clean data,
availability at appropriate time resolution, spatial or
- Daily data can be derived from monthly values by
simple interpolation or using a weather generators.
- Spatial datasets can be developed by tools available
(GIS, and UNUSPLIN).
■ Additional non-climatic data may be required for method
development (calibration and application, specific data
relating to sector and exposure unit will be required
(observed crop phenology and yield, soil data, river
discharge, health statistics, historical changes in relative sea-
A range of climatic and non-climatic data may be required;
geographical, technological, managerial, legislative, economic,
social and political.
■ Interpret data to describe baselines:
Having developed a good quality datasets to complete the
assessment, it is necessary to interpret data for describing climatic
and non-climatic baselines, which
- Need to meet the specific requirements of sector and exposure
- Need to full the requirements of the entire assessment including
■ In any adaptation plan, a survey of adaptation baseline policies,
measures, environmental conditions, available technical tools and
past experience is necessary to ensure suitability of the adaptation
measure to be taken.
■ It could be recommended that a strategic environmental impact
assessment must be carried out for any policy of adaptation and an
environmental impact assessment of any measure.
simple climate models to obtain regional projections of climate change.
(SCENGEN, CLIMPACTS VANDACLIM) are suitable for a multiple sectors
impact assessment and allow the user to explore a wide range of uncertainty
and introduce a time dimension.
■ It is recommended to assess availability of input data for an RCM to
improve climate change scenarios.
■ The use of the process-based models (Simulation models (e.g. DSSAT, COTTAM,
SORKAM, and CROPSYST) is more efficient in the V&A assessments especially
in the agricultural sector.
■ It could be recommended that the use of the cost-benefit models and
the General equilibrium models (Basic Linked System; BLS) as
socioeconomic models is more efficient in the V&A assessments
especially in the agricultural sector. Recardian (Cross sectional)
Model could be used also.
■ Adaptation baselines could be established in the agriculture, water
resources, coastal zones and human health sectors through the
experiences detected from the general current presentation on V&A
■ Improving Assessments of Impacts, Vulnerability and Adaptation
The following are onlythree from high priorities for narrowing gaps
between current knowledge and policymaking needs:
(The IPCC WG II report)
- Quantitative assessment of the sensitivity adaptive capacity and
vulnerability of natural and human systems to climate change.
- Assessment of opportunities to include scientific information on impacts,
vulnerability, and adaptation in decision-making processes.
- Improvement of systems and methods for long term monitoring and understanding.
■ The Egyptian V&A assessment study on the agricultural sector can
be followed in the near countries with similar conditions (an outline
for the case study is included in the current presentation)
To explain ideas in the current presentation on adaptation baselines, the VANDA package developed by
(Warrick et al (1997) in C.E.A.R.S) was selected, followed and combined with local experiences.
In the Vulnerability and Adaptation to Climate Change Assessment studies, the following steps (modules)
have to be carried out:
■ Scoping the assessment.
■ Methods Selection.
■ Dataset and Baselines Development.
■ Testing Methods.
■ Impact Analyses.
■ V&A Synthesis.
General ideas on the V&A assessment package
■ Module I: Scoping the assessment.
Defining the scope of the assessment to identify and carry out the range of tasks and sub-tasks
required to define the scope of a V&A assessment
For the vulnerable sectors in any country, methods selection should be able to:
■ Identify a range of general approaches to V&A assessment.
■ Evaluate and select sector-specific methods.
This module includes two main parts
Part I: General Assessment Approaches
Vulnerability and Adaptation Assessment could be carried out by five general methods.
Temporal analogues and spatial analogues.
Consensus opinion and Surveys of Experts.
Field surveys can involve: Structured and unstructured
interviews and field observations.
Collection of primary data on the response of an exposure
Unit to environmental perturbations through
experimentation. Data can be used in model calibration.
The relationships between climates, biophysical and / or
socio- economic variables are formalized in models.
A. Biophysical (primary) impact models
B. Socio-economic (secondary, tertiary) impact models
C. Integrated models
In V&A assessments studies, two methods are broadly been used and
mentioned in the literature as follows:
1. The Based Linked System (BLS) is a general equilibrium model used in a study
of the effect of climate change on world food supply and agricultural prices
(Rosenzweig et al 1993). The application of this model usually follows
the V&A assessments through modeling as a socio-economic evaluation process.
2. The Recardian model (Cross-sectional approach): The most important
advantage of the (Cross sectional) Recardian approach is its ability to
incorporate efficient private adaptation to climate. Private adaptation involves
changes that farmers would make to tailor operations to the environment
in order to increase profits.
Models range from the very simple to the very complex and include:
■ Emperical-statistical models
■ Biophysical indices
■ Process-based models (simulation models)
Such models can simulate, for example:
■ Crop yields
■ Coastal sediment transport
■ Heat- or cold- induced mortality
Such models often have empirical-statistical components
B. Socio-economic (secondary, tertiary) impact models
Models that evaluate the economic and social consequences arising from
Socioeconomic models include:
■ Cost-benefit models
■ Input-output model.
■ General equilibrium models.
■ Econometric models
■ Partial equilibrium models.
■ Optimization models
Models that combine two or more component models into a single
system in order to allow examination of the connections between
elements such as:
■ Economic activities
■ Climate change and variability
■ Sectoral and cross- sectoral effects
■ Mitigation and adaptation options
■ Economic consequences
Such models vary in their degree of integration, complexity, and
spatial coverage (from local to global).
Two types of these models are:
■ The Idealised Structure of a full Integrated Assessment Model
(IAM) was tabulated by the IPCC WG3 report, p.377.
■ The schematic representation of the VANDACLIM model system
that can assess four sectors (Coastal Resources, Water Resources,
Agriculture and Human health) is described by Warrick et al (1996).
This part aims at:
A.Identifying the range of sector–specific methods and their
characteristics by considering:
■ Data requirements
■ Required expertise/resources
■ Potential to assess adaptation
A summary matrix for generally evaluating methods is useful
B. Evaluation and selection of sector–specific methods for the country.
Considering the appropriateness of each method for application in a specific country, in terms of:
■ The scope of the assessment mentioned before.
■ Expertise and resources available
■ Data availability
■ Availability of methods
A country-specific matrix for evaluation and selection of method(s) is useful.
- To identify data needs and availability
- To establish datasets and baselines required for the assessment of
adaptation options in different sectors.
PART 1: Identify Data Needs, Availability and Suitability
There are a several important tasks that need to be completed to facilitate development of datasets.
■ Identification of data needs
■ Assessment of data availability
■ Evaluation of available data
■ Identification of data needs
■ Identify climatological and sea level rise data that are
relevant to selected methods
■ Identify non-climatic data requirements for method development, calibration
and testing (e.g. river flow data, maps of crop distribution).
■ Identify non-climatic data requirements for method
application (e.g. soils data, beach profile data, country GDP)
■ Identify any additional data (e.g. population density
statistics) required for a synthesis of results
Identify potential sources for data. These might include:
■ Government Agencies
■ Institutions, such as Universities
■ International Agencies such as WMO, WHO, and FAO
Data may be in the term of:
■ Publications or unpublished reports
■ Digested or hard-copy records
■ Maps, aerial photographs, satellite images
Problems in Obtaining Data
■ Accessibility (there may be institutional rules governing
release of data).
■ Status of the data (a lot of data remains undigitised and
■ Compatibility between different data types (e.g. time
period, location, resolution).
■ Identification of the uncertainties and research gaps.
The available data need to be examined to establish their suitability for the selected assessment methods,
■ time resolution of climate data (whether daily or
monthly) for required variables
■ completeness of records, including length of record and
number of missing values
■ quality of the data
■ the number of sites and their spatial distribution
(important for identifying interpolation of data if
PART 2: Develop the Baseline Climate Dataset
Having obtained access to the required data and carried out an evaluation, it is necessary to:
■ Identify stations with a good length of record (ideally 30 years)
■ Check data for errors, missing values, anomalies and
■ Clean data, where feasible, and format correctly
■ Ensure data are available at the appropriate time resolution
■ Spatial or temporal interpolation of data may be required
Data may not be available at the required time and space
Various methods and tools are available for dealing with such
■ Daily data can be derived from monthly values by using
a simple interpolation or by using a weather generator
(WGEN, WM, CLIMGEN, CWG.etc).
■ Spatial datasets can be developed using tools available
within Geographical Information System (GIS), or tools
such as ANUSPLIN (commonly known as the
METHODS FOR TEMPORAL AND SPATIAL INTERPOLATION
Additional non-climatic data may be required for method
development, calibration, testing, and application and
for: Interpretation and synthesis of results
Specific data relating to the sector and exposure unit under
examination will be required
■ observed crop phenology and yield data
■ soils data
■ river discharge data
■ health statistics
■ historical changes in relative sea level
FOR INTERPETATION & SYNTHESIS OF RESULTS
A range of non-climatic data may be required, including:
■ geographical: (land use or communications).
■ technological: (pollution control, water regulation).
■ managerial: (forest rotation, fertilizer use).
■ legislative: (water-use quotas, air quality standards).
■ economic: (income levels, commodity prices).
■ social: (population, diet).
■ political: (levels and styles of decision making).
Having developed good quality datasets in order to complete the
assessment, it is necessary to:
■ Interpret data for describing climatic and non-climatic
■ These need to meet the specific requirements of the
sector and exposure unit (s) being examined with the
selected method (s).
■ Additionally they need to fulfill the requirements of the
entire assessment, taking account of cross-sectoral
To assess predictive capability of the methods under present –day and
possible future conditions; the following three tasks have to be carried out:
■ Validate and/or test sensitivity
■ Evaluate uncertainties of the method
■ Determine whether model calibration or selection of a new method is
■ Standard practices for testing methods
■ Expert judgment
■ What is Scenarios:
- A scenario is a coherent, internally consistent, and plausible
description of a possible future state of the World (IPCC, 1994).
- It is not a forecast; each scenario is one alternative image of how
the future can unfold.
- Scenarios often require additional information (e.g. about baseline
conditions) more than results of projection as a raw material.
Type of Scenarios:
The types of scenarios include scenarios of:
■ Socioeconomic factors, which are the major underlying anthropogenic
cause of environmental change and have a direct role in conditioning
the vulnerability of societies and ecosystems to climatic variations
and their capacity to adapt to future changes.
■ Land use and land cover, which currently are undergoing rapid
change as a result of human activities.
climate changes in the natural environment (e.g. CO2 concentration,
and fresh water availability) that are projected to occur in the future
and could substantially modify the vulnerability of a system or activity
to impacts from climate change.
■ Climate, which is the focus of the IPCC and underpins most impact
■ Sea-level, which generally is expected to rise relative to the land (with
some regional expectations) as a result of global warming-posing a
threat to some low-lying coasts and islands.
■ To identify the different methods for generating scenarios of future
■ Evaluate and select methods for developing scenarios for use in a
■ Use selected methods to create scenarios of future climate and sea-
level change and of future environmental and socio-economic
T he socioeconomic baseline describes the present or future state
of all nonenvironmental factors that influence an exposure unit.
The factors may be :
geographical (land use or communications),
technological (pollution control, water regulation),
managerial (forest rotation, fertilizer use),
Legislative (water use quotes, air quality standards),
economic (income levels, commodity prices),
social (population, diet), or
political (levels and styles of decision making).
Scenarios need to be:
possible (i.e. not violate known constraints such as land acreage);
plausible (i.e., in line with current expectations); and
interesting (e.g., a scenario that projects a bright future without
problems is appealing but not necessarily.
Variables needed for scenarios in some sectors are
■ Population growth and Economic growth for General secors.
■ Land use, water use, food demand, atmospheric composition & deposition,
agricultural policies (incl. International trade), adaptation capacity (economic, technological, institutional) for Agriculture
■ Water use for agriculture, domestic, industrial, and energy sector for Water Resources
■ Population density, economic activity, land use and adaptation capacity (economic,technological, institutional) for Coastal zones
■ Food and water accessibility and quality, health care (incl. base), demographic structure, urbanization and (economic, technological, institutional) for Human health
Climate Scenario; refers to a plausible future climate, and a climate change
scenario, which implies the difference between some plausible future climate
and the present-day climate, through the terms are used interchangeably
in the scientific literature.
Tasks needed for scenario development
1. Apply criteria to guide scenario development
A number of factors need to be considered. Is the scenario appropriate for the:
- Scope of the assessment (including methods and data)?
- Selected time horizons?
- Time and space resolution of selected method?
- Available expertise, resources and data?
- Need for consistency, both within and between impacts
- Representation of uncertainties?
2. Develop future baselines in absence of climate change
■ Baselines are required for both future environmental and
■ These baselines serve as the reference against which
impacts of future climate change are measured
Approaches to future baselines development
In the absence of existing projections, future baselines may have
to be constructed.
Some broad approaches are:
■ Trend extrapolations
■ Model-based projections
■ Expert judgment
Three types can be identified:
(Analogue, Synthetic and Model-based scenarios).
Analogues: (Instrumental and Palaeoclimatic analogues)
Synthetic:(Involve the incremental adjustment of the baselines climate
- Direct use of GCM output and - Linked model approach
GCMs estimates are uncertain because of, inter alia:
■ Inadequate projections of future patterns of radiative forcing
■ Coarse spatial resolution
■ Simplified representation of sub-grid scale processes and surface –
Two types of perturbation experiment have been conducted with GCMs:
- Equilibrium experiments
- Transient experiments
A linked model approach uses GCM results and results from simple climate models
to obtain regional projections of climate change. The main steps involve:
·Standardizing output from GCMs to derive patterns of change per degree
of global warming
·Scaling the patterns by output from simple global climate models.
·Applying the climate changes to the baseline climatology.
·Allows the user to explore a wide range of uncertainties.
·Introduces a time dimension
Examples: SCENGEN, CLIMPACTS, SIMUSCEN, VANDACLIM
Select and apply methods for developing climate and sea-level change scenario
According to the goal of scenario, the method of scenario could be selected (if it is
Analogue, Model-based GCM, Model-based Linked or Synthesis).
A popular climatological baseline period is a 30-year “normal period” as defined by the
WMO. The current WMO normal period is 1961-1990, which provides a standard
reference for many impact studies.
The final climate change scenarios should be built using three or more GCM (i.e
HadCM2, ECHAM4 and CSIRO9), no less than two scenarios of GHG emissions
(IS92a, IS92d and/or Kyotoa1) and a system like MAGICC/SCENGEN. It also
includes the creation of the climate baseline (the optimum will be with a national
coverage and a spatial resolution no less than 0.5 latitude degrees for the period
1961-90. However, it could also be used 1971-2000).
If the Approach Concerns GCMs
■ Regional validation ■ Antiquity
Evaluation of National Objectives
■ Economic efficiency
■ Risk avoidance
■ Environmental protection
■ Regional development
·Module VIII: Synthesis of Findings into a National Report
Prepare a comprehensive, interpretive, and communicative
synthesis of major findings and key conclusions
1.Outline the format for sectoral reporting
2.Explain cross-sectoral themes or interactions
3. Prepare the final report
The initial three steps for improving future
V&A studies would be:
1. The standardisation of methods within each region;
2. The improvement of vulnerability studies; and,
3. The development of adaptation options that could be evaluated
Experiments/ Technology options
Climatic Data in DSSATSModel Format
Monthly Climatic Data
Daily Climatic Data
Other Simulation models developed in Crystal Ball
Experiments/ Technology options
Crop yields and water requirements were
estimated with the CERES models included
in DSSAT2.5 and (DSSAT3 1995).
The DSSAT3 crop models include the option
of simulating changes in crop photosynthesis
and water consumptive use (ET) that result
from changes in atmospheric CO2. COTTAM
model was used to simulate cotton yield under
0, +2 and +4 °C (Jackson et al 1988).
*.CUL, *.SPE, *.ECO
Daily maximum and minimum temperatures,
precipitation, and solar radiation for Sakha
(1975 to 1995), Giza (1960 to 1989), and
Shandaweel (1965 to 1994) were used.
Climate change scenarios for each site were created
Combining output of three equilibrium General
Circulation Models (GISS, GFDL, UKMO for studies
up to 1995 and CCCM, GFD3, GF01 for studies of 1996)
with the daily climate data for each site
Typical soils at Sakha, Giza and Shandaweel
are described elsewhere.
The description of the soils in the crop
models includes texture, albedo, and water-
related specific characteristics.
Genetic coefficients were generated for all crops.
COTTAM model was validated as well without
creating genetic coefficients through Crop Model
Validation.The CERES models for wheat, barley,
sorghum, rice and maize were validated with local
agronomic experimental data for Centric Delta
(Sakha) and Middle Egypt (Giza). SORGO
and GRO models were validated for soybean.
CERES Wheat and Maize were revalidated.
The first step is to calibrate and validate the models
with local agronomic experimental data for a
set of sites representative of major Egyptian agricultural
regions (Eid 1994, and Eid et al 1996).
Next, simulations with observed climate provide a
baseline. Then, crop model simulations were run with
a suite of climate change scenarios.
Finally, farm-level adaptations are tested to characterize
possible adjustments to climate change.
Predicted Grain & Biomass Yields at Sakha (Obs. and Sim.).
Yield (thousand kg/ ha)
Grain Yield Biomass Yield
Maize Validation Test.
Soybean Seed Yield
Sim. and Obs. Seed Yield (t/ ha).
Seed Yield (t/ ha)
Simulated and Observed Seed Yield
TWC 310 Maize ET (mm) in 2050
Compared to Base ET at Shandaweel.
Climate Change Scenarios
330 ppm CO2
555 ppm CO2
Maize ET Change in Year 2050