Adaptation Baselines Through V

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Adaptation Baselines Through VA Assessments

• Prof. Helmy Eid
• Climate Change Expert
• Soil, Water Environment Res. Institute
• (SWERI), ARC Giza Egypt
• Material for Montreal Workshop 2001

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ADAPTATION BASELINES 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.
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It is recommended that the VA 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).
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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.
  - 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- level.
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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.
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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 VA 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 VA 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 VA methodologies.
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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. - 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 VA 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)
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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   Scoping the assessment.
Methods Selection. Dataset and Baselines
Development. Testing Methods. Scenarios
Impact Analyses. Adaptation. VA
Synthesis.    General ideas on the VA 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 VA assessment
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Module II Methods Selection.   For the
vulnerable sectors in any country, methods
selection should be able to Identify a
range of general approaches to VA 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.  
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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 VA
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 VA 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.
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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-be
nefit models Input-output model. General
equilibrium models. Econometric
models Partial equilibrium models. Optimizat
ion models
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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).
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Part II METHODS EVALUATION   This part aims
at   A.    Identifying the range of
sectorspecific 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 sectorspecific 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.
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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 Identification of data
needs Assessment of data availability Evalua
tion 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
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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
uncleaned).   Documentation.   Compat
ibility between different data types (e.g. time
period, location,
resolution).   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   time resolution of climate
data (whether daily or
monthly) for required variables complet
eness 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
required)   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 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
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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). 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
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FOR METHOD DEVELOPMENT CALIBRATION, TESTING AND
APPLICATION 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   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). manageri
al (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).
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INTERPRET DATA TO DESCRIBE BASELINES   Having
developed good quality datasets in order to
complete the assessment, it is necessary to
  Interpret data for describing climatic and
non-climatic baselines   These need
to meet the specific requirements of the
sector and exposure unit (s) being examined with
the selected method (s).   Additional
ly they need to fulfill the requirements of the
entire assessment, taking account of
cross-sectoral dependencies.
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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 necessary.   Helpful
Techniques Standard practices for testing
methods Expert judgment
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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.
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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.   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.
Objectives To identify the different methods
for generating scenarios of future
change Evaluate and select methods for
developing scenarios for use in a VA
assessment. Use selected methods to create
scenarios of future climate and sea- level
change and of future environmental and
socio-economic baselines.
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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.  
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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
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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
sectors? -   Representation of
uncertainties?  
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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
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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
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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
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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 Regional validation
Antiquity  
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• 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

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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
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Task 3. Evaluate and Recommend Evaluation of
National Objectives Economic efficiency Risk
avoidance Environmental protection Equity Re
gional 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 VA 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.
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Steps of Vulnerability and Adaptation Assessment
Socio-economic scenario
Experiments/ Technology options
Develop scenarios
MAGICC
Climatic Data in DSSATSModel Format
Select GCM
SCENGEN
DSSAT
Impact Assessment
Adaptation Options
Monthly Climatic Data
Daily Climatic Data
CLIMATE DATA GENERATOR
Other Simulation models developed in Crystal Ball
Experiments/ Technology options
Socio-economic scenario
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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).
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STRUCTURE OF DSSAT (Deterministic Model)
Crops File .CUL, .SPE, .ECO
Soil File .SOL
Weather File .WTH
Experiment File .eeX
DSSAT v35
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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)

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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
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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 water-
related specific characteristics.
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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.
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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.
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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

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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).

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VA Studies Areas in Egypt

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VALIDATION OF DSSAT FOR WHEAT

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Validation of DSSAT for Maize
Predicted Grain Biomass Yields at Sakha (Obs.
and Sim.).
21
16
Obs.
Yield (thousand kg/ ha)
Sim.
11
6
1
Giza 2
TWC 310
Pioneer
Giza 2
TWC 310
Pioneer
Grain Yield
Biomass Yield
Maize Validation Test.
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SOYBEAN MODEL VALIDATION
Soybean Seed Yield
Sim. and Obs. Seed Yield (t/ ha).
4
3
Seed Yield (t/ ha)
2
1
Gem.
Giza
Region
Sim.
Obs.
Simulated and Observed Seed Yield
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IMPACT ON MAIZE YIELD
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IMPACT ON MAIZE ET
TWC 310 Maize ET (mm) in 2050
Compared to Base ET at Shandaweel.
1000
900
800
ET (mm)
700
600
500
Base
CCCM
GFD3
GF01
Climate Change Scenarios
330 ppm CO2
555 ppm CO2
Maize ET Change in Year 2050
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VA 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

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Coastal
Malaria
Sea Level
Maize Yield
Discrage
Flooding
Water Balance
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IMPACT OF CLIMATE CHANGE ON PRODUCTIVITY
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IMPACT OF CC ON EVAPOTRANSPIRATION
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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.

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ADAPTATION OPTIONS

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ADAPTATION OPTIONS

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