Transcript Adaptation Baselines Through V&A Assessments Prof. Helmy Eid Climate Change Expert Soil, Water & Environment Res.

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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- 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.
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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:
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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).
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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.
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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 sealevel.
■
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
unit.
- 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
methodologies.
■
■ Improving Assessments of Impacts, Vulnerability and Adaptation
The following are only three 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.
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Assessment of opportunities to include scientific information on impacts,
vulnerability, and adaptation in decision-making processes.
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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)
Introduction
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:
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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.
1. Analogues
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:
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Emperical-statistical models
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Biophysical indices
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Process-based models (simulation models)
Such models can simulate, for example:
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Crop yields
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Coastal sediment transport
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Rainfall-runoff
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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:
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Cost-benefit models
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Input-output model.
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General equilibrium models.
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Econometric models
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Partial equilibrium models.
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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:
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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:
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The Idealised Structure of a full Integrated Assessment Model
(IAM) was tabulated by the IPCC WG3 report, p.377.
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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).
Part II: METHODS EVALUATION
This part aims at:
A. Identifying the range of sector–specific methods and their
characteristics by considering:
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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:
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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.
Module III: DATASETS AND BASELINES
DEVELOPMENT
Objectives
- 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:
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Identification of data needs
Assessment of data availability
Evaluation of available data
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Identification of data needs
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Identify climatological and sea level rise data that are
relevant to selected methods
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Identify non-climatic data requirements for method development, calibration
and testing (e.g. river flow data, maps of crop distribution).
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Identify non-climatic data requirements for method
application (e.g. soils data, beach profile data, country GDP)
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Identify any additional data (e.g. population density
statistics) required for a synthesis of results
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Assessment of data availability
Identify potential sources for data. These might include:
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Government Agencies
Institutions, such as Universities
International Agencies such as WMO, WHO, and FAO
NGOs
Data may be in the term of:
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Publications or unpublished reports
Digested or hard-copy records
Maps, aerial photographs, satellite images
Problems in Obtaining Data
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Cost
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Accessibility (there may be institutional rules governing
release of data).
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Status of the data (a lot of data remains undigitised and
uncleaned).
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Documentation.
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Compatibility between different data types (e.g. time
period, location, resolution).
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Identification of the uncertainties and research gaps.
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Evaluation of available data
The available data need to be examined to establish their suitability for the selected assessment methods,
by determining:
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time resolution of climate data (whether daily or
monthly) for required variables
completeness of records, including length of record and
number of missing values
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quality of the data
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the number of sites and their spatial distribution
(important for identifying interpolation of data if
required)
PART 2: Develop the Baseline Climate Dataset
Having obtained access to the required data and carried out an evaluation, it is necessary to:
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Identify stations with a good length of record (ideally 30 years)
Check data for errors, missing values, anomalies and
discontinuities
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
METHODS FOR TEMPORAL AND SPATIAL INTERPOLATION
Data may not be available at the required time and space
resolution.
Various methods and tools are available for dealing with such
situations.
■
Daily data can be derived from monthly values by using
a simple interpolation or by using a weather generator
(WGEN, WM, CLIMGEN, CWG.etc).
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Spatial datasets can be developed using tools available
within Geographical Information System (GIS), or tools
such as ANUSPLIN (commonly known as the
Hutchinson method)
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
FOR METHOD DEVELOPMENT CALIBRATION, TESTING AND APPLICATION
Specific data relating to the sector and exposure unit under
examination will be required
Examples include:
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observed crop phenology and yield data
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soils data
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river discharge data
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health statistics
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historical changes in relative sea level
FOR INTERPETATION & SYNTHESIS OF RESULTS
A range of non-climatic data may be required, including:
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geographical: (land use or communications).
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technological: (pollution control, water regulation).
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managerial: (forest rotation, fertilizer use).
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legislative: (water-use quotas, air quality standards).
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economic: (income levels, commodity prices).
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social: (population, diet).
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political: (levels and styles of decision making).
INTERPRET DATA TO DESCRIBE BASELINES
Having developed good quality datasets in order to complete the
assessment, it is necessary to:
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Interpret data for describing climatic and non-climatic
baselines
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These need to meet the specific requirements of the
sector and exposure unit (s) being examined with the
selected method (s).
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Additionally they need to fulfill the requirements of the
entire assessment, taking account of cross-sectoral
dependencies.
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:
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Validate and/or test sensitivity
Evaluate uncertainties of the method
Determine whether model calibration or selection of a new method is
necessary.
Helpful Techniques:
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Standard practices for testing methods
Expert judgment
Module V: Scenario Development
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What is Scenarios:
A scenario is a coherent, internally consistent, and plausible
description of a possible future state of the World (IPCC, 1994).
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It is not a forecast; each scenario is one alternative image of how
the future can unfold.
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Scenarios often require additional information (e.g. about baseline
conditions) more than results of projection as a raw material.
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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.
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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.
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Climate, which is the focus of the IPCC and underpins most impact
assessments.
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.
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Objectives:
To identify the different methods for generating scenarios of future
change
Evaluate and select methods for developing scenarios for use in a
V&A assessment.
Use selected methods to create scenarios of future climate and sealevel change and of future environmental and socio-economic
baselines.
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:
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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
sectors?
Representation of uncertainties?
2.
Develop future baselines in absence of climate change
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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
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Approaches to future baselines development
In the absence of existing projections, future baselines may have
to be constructed.
Some broad approaches are:
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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
Examples: SCENGEN, CLIMPACTS, SIMUSCEN, VANDACLIM
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
Consider:
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Regional validation
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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
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reduce adverse impacts
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enhance opportunities
Task 1: Types of Adaptive Response
- Autonomous adjustments
- Adaptation options
Task 1: A Broad Classification
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Bear (accept or absorb losses)
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Share (distribute losses, e.g. flood insurance)
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Prevent (modify human systems, e.g. flood plain regulation)
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Protect (modify physical systems, e.g. embankments)
Task 2: Screening criteria include:
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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
Objective:
Prepare a comprehensive, interpretive, and communicative
synthesis of major findings and key conclusions
Tasks
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.
Socio-economic
scenario
Steps of Vulnerability and Adaptation Assessment
Experiments/
Technology
options
MAGICC
Daily
Climatic
Data
Other
Simulation
models
developed in
Crystal Ball
Adaptation Options
Monthly
Climatic
Data
Climatic
Data in
DSSATS
Model
Format
Impact Assessment
Select GCM
CLIMATE DATA GENERATOR
SCENGEN
Develop
scenarios
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)
Weather File
*.WTH
Crops File
*.CUL, *.SPE, *.ECO
Experiment File
*.eeX
DSSAT v35
Soil File
*.SOL
DSSAT Model
• 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
SOIL FILE
Typical soils at Sakha, Giza and Shandaweel
are described elsewhere.
The description of the soils in the crop
models includes texture, albedo, and waterrelated specific characteristics.
CROP GENETIC
COEFFICIENTS
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.
EXPERIMENTAL
DATA
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
• 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
• 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 WHEAT
Validation of DSSAT for Maize
Predicted Grain & Biomass Yields at Sakha (Obs. and Sim.).
Yield (thousand kg/ ha)
21
16
Obs.
Sim.
11
6
1
Giza 2
TWC 310
Pioneer
Grain Yield
Giza 2
TWC 310
Biomass Yield
Maize Validation Test.
Pioneer
SOYBEAN MODEL
VALIDATION
Soybean Seed Yield
Sim. and Obs. Seed Yield (t/ ha).
Seed Yield (t/ ha)
4
3
2
1
Gem.
Giza
Region
Sim.
Obs.
Simulated and Observed Seed Yield
IMPACT ON MAIZE YIELD
Grain Yield (t/ ha).
Maize Grain Yield Changes (t/ ha) under
330 & 555 ppm CO2 in Egypt.
11
9
7
5
3
1
Sakha (TWC 310)
Giza (SC 10)
Shandaweel (TWC 310)
Region
Base 330
CCCM 330
Climate Change Scenarios
CCCM 555 GFD3 330
GFD3 555
GF01 330
GF01 555
S imulated Maize Yield under GCMs.
IMPACT ON MAIZE ET
TWC 310 Maize ET (mm) in 2050
Compared to Base ET at Shandaweel.
1000
ET (mm)
900
800
700
600
500
Base
CCCM
GFD3
Climate Change Scenarios
330 ppm CO2
555 ppm CO2
Maize ET Change in Year 2050
GF01
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
Coastal
Sea Level
Maize Yield
Malaria
Discrage
Flooding
Water Balance
IMPACT OF CLIMATE
CHANGE ON PRODUCTIVITY
Change in crop productivity (ton/ fad) (deficit or excess) in
Egypt by the year 2050 due to climate change.
3.5
Crop Productivity (t/ fad)
3
2.5
2
1.5
1
0.5
0
Wheat
Maize
Cotton
Sorghum
Barley
Crop
Base yield (t/ fad)
Yield in 2050
Rice
Soybean
IMPACT OF CC ON
EVAPOTRANSPIRATION
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 OPTIONS
• WINDOWS\Desktop\3.bmp.BMP