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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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adaptation baselines through v a assessments

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



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

multiple baselines.

■ 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

level rise.


■ It is recommended that the V&A assessments need to develop

dataset and baseline, and this could be done by identifying data

needs and availability and establishing dataset and baselines

as follows:

■ Identify climatological and sea-level rise that are relevant to

studied method(s).

■ 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).


■ Develop the baseline climate dataset:

■ 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

temporal interpolation.

- Daily data can be derived from monthly values by

simple interpolation or using a weather generators.

- Spatial datasets can be developed by tools available


■ 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-



■ Interpret results and Synthesis:

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

cross-sectoral dependencies.

■ 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.


■ The use of linked model approach uses GCM results and results from

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.

■ Scenarios

■ Impact Analyses.

■ Adaptation.

■ 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


■ Module II: Methods Selection.

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.

2.Expert Surveys

Consensus opinion and Surveys of Experts.

3.Field Surveys

Field surveys can involve: Structured and unstructured

interviews and field observations.

4. Experimentation

Collection of primary data on the response of an exposure

Unit to environmental perturbations through

experimentation. Data can be used in model calibration.

5. Modeling

The relationships between climates, biophysical and / or

socio- economic variables are formalized in models.


The major types of model for impact assessment include:

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.


A.     Biophysical (primary) impact models

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

■ Rainfall-runoff

■ 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

biophysical impacts.

Socioeconomic models include:

■ Cost-benefit models

■ Input-output model.

■ General equilibrium models.

■ Econometric models

■ Partial equilibrium models.

■ Optimization models


C. Integrated 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:

■ Advantages

■ Disadvantages

■ 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.

These include:

■ 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


■ Assessment of data availability

Identify potential sources for data. These might include:

■ Government Agencies

■ Institutions, such as Universities

■ International Agencies such as WMO, WHO, and FAO

■ NGOs

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

■ Cost

■ Accessibility (there may be institutional rules governing

release of data).

■ Status of the data (a lot of data remains undigitised and


■ Documentation.

■ Compatibility between different data types (e.g. time

period, location, resolution).

■ Identification of the uncertainties and research gaps.


■ Evaluation of available data

The available data need to be examined to establish their suitability for the selected assessment methods,

by determining:

■ 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


■ Spatial datasets can be developed using tools available

within Geographical Information System (GIS), or tools

such as ANUSPLIN (commonly known as the

Hutchinson method)


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

Examples include:

■ observed crop phenology and yield data

■ soils data

■ river discharge data

■ health statistics

■ historical changes in relative sea level


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



Module IV: Testing the Methods

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


Helpful Techniques:

■ Standard practices for testing methods

■ Expert judgment


Module V: Scenario Development

■ 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.


■ Other environmental factors, which is a catch-all for a range of no

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

V&A assessment.

■ Use selected methods to create scenarios of future climate and sea-

level change and of future environmental and socio-economic



1.     Socioeconomic baselines:

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.


Socioeconomic baselines (Cont.)

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


2.    Climate Scenarios:

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

socioeconomic conditions

■ 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


3. Identify types of climate and sea-level change scenarios

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

Model-based scenarios:

- 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 –

atmosphere interactions


Types of GCM output

Two types of perturbation experiment have been conducted with GCMs:

-   Equilibrium experiments

-   Transient experiments

Linked-based Approach:

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.

This approach:

·Allows the user to explore a wide range of uncertainties.

·Introduces a time dimension


Select and apply methods for developing climate and sea-level change scenario


Criteria for Evaluation & Types of Climate Change Scenarios (Tool)

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).

Baselines Climatologies

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


Module VI: Assess Future Impacts

  • Module Goal:
  • To apply the selected methods, baselines and scenarios to determine and evaluate
  • the impacts of climate change on selected sectors
  • 1. Determine the Impacts of Climate Change
  • Several steps need to be completed, including:
  • ·Establishment of the base for comparison
  • ·Application of selected methods with relevant baselines data
  • ·Application of selected methods with chosen scenarios
  • ·Presentation of results
  •   2. Interpret the Results
  • The range of model and scenario uncertainties should be considered
  • Consider Uncertainties
  • There may be uncertainties arising from:
  • - Differences between models or in model assumptions
  • - These differences need to be accounted for in the
  • assessment by further application of methods
  • - Impact analysis is an iterative process

Module VII: Adaptation

  •  Module Goal: To identify classify and evaluate adaptation options
  • Tasks
  • 1.Identify and classify options
  • 2.Screen
  • 3.Evaluate and recommend
  • Task 1. Identify and classify options
  • Adaptation – deal with effects of climate change
  •   ■ reduce adverse impacts
  • ■ enhance opportunities
  • Task 1: Types of Adaptive Response
  •  - Autonomous adjustments
  • - Adaptation options
  • Task 1: A Broad Classification
  • ■ Bear (accept or absorb losses)
  • ■ Share (distribute losses, e.g. flood insurance)
  • ■ Prevent (modify human systems, e.g. flood plain regulation)
  • ■ Protect (modify physical systems, e.g. embankments)
  • Task 2: Screening criteria include:
  • ■ Incorporate climate change into planning and long-term decisions
  • ■ Improve flexibility because climate change impacts are uncertain
  • ■ Effective in conjunction with non-climate stressors
  • ■ Benefits in the absence of climate change
  • ■ Culturally acceptable
  • ■ Politically feasible

Task 3. Evaluate and Recommend

Evaluation of National Objectives

■ Economic efficiency

■ Risk avoidance

■ Environmental protection

■ Equity

■ 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

using criteria.


Steps of Vulnerability and Adaptation Assessment

Socio-economic scenario

Experiments/ Technology options

Develop scenarios


Select GCM

Climatic Data in DSSATSModel Format



Impact Assessment

Adaptation Options

Monthly Climatic Data

Daily Climatic Data


Other Simulation models developed in Crystal Ball

Experiments/ Technology options

Socio-economic scenario


Crop Models.

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).


STRUCTURE OF DSSAT (Deterministic Model)

Crops File

*.CUL, *.SPE, *.ECO

Soil File




Experiment File





  • Develop database for climatic data (and climate data generator)
  • Develop database for soil parameters
  • Develop database for crop parameters
    • Wheat : (Short Management Crop), (Long MC)
    • Soybean: (Short MC), (Long MC)
    • Maize : (Short MC), (Long MC)

Climatic, Climate Change Scenarios

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.

sensitivity criteria
Sensitivity Criteria
  • Very sensitive (VS): 25% change in parameter values results in more than 25% change in outputs
  • Sensitive (S): 25% change in parameter values results in 15-25% change in outputs
  • Less sensitive (SS): 25% change in parameter values results in 5-15% change in outputs
  • not sensitive (NS): 25% change in parameter values results in 0-5% change in outputs
level of sensitivity
Level of Sensitivity
  • Parameters which are sensitive and very sensitive are:
    • Soil: parameters which are related to soil water availability
    • Crops: Phenology parameters, in particular for active vegetative phase, seed filling phase and leaves growth, size of leaves (LAI).

V&A Studies Areas in Egypt

  • WINDOWS\Desktop\3.bmp.BMP

Validation of DSSAT for Maize

Predicted Grain & Biomass Yields at Sakha (Obs. and Sim.).




Yield (thousand kg/ ha)





Giza 2

TWC 310


Giza 2

TWC 310


Grain Yield Biomass Yield

Maize Validation Test.

soybean model validation

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.




ET (mm)








Climate Change Scenarios

330 ppm CO2

555 ppm CO2

Maize ET Change in Year 2050

v a for other sectors using simple models stochastic models
V&A for Other Sectors using simple models (Stochastic Models)
  • Develop climatic Data generator in Crystal ball
  • Modify parameters of climatic data generator according to emission scenario or GCM model used in analysis
  • Develop simple models which relate climatic factors with response of particular sectors to the climate or more complex models which relate climatic factors and other factors with response of the sectors to the factors



Sea Level

Maize Yield



Water Balance

adaptation using team
  • Adaptation using the EPA's TEAM model (Tool for Environmental Assessment and Management):
  • The multi-criteria approach was used to evaluate different strategies using multiple aspects or evaluation attributes. The TEAM model (Susan 1996) was used in the present study. Socio-economic adaptation strategy evaluation in the present approach is based on a quantitative base through the farm income and a qualitative one, i.e. food security, industrial/employment, water demand, food culture and chemical usage.
adaptation options
  • WINDOWS\Desktop\1.bmp.bmp
adaptation options1
  • WINDOWS\Desktop\3.bmp.BMP