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Adaptation Baselines Through V&A Assessments

Adaptation Baselines Through V&A Assessments

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Adaptation Baselines Through V&A Assessments

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  1. 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

  2. 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.

  3. ■ 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).

  4. ■ 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.

  5. ■ 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.

  6. ■ 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.

  7. ■ 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)

  8. 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. ■ 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

  9. ■ 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.

  10. 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.

  11. 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

  12. 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).

  13. Part II: METHODS EVALUATION 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.

  14. 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 ■ 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

  15. ■ 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. ■ Compatibility between different data types (e.g. time period, location, resolution). ■ Identification of the uncertainties and research gaps.

  16. ■ 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 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

  17. 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

  18. 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). ■ 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).

  19. 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). ■ Additionally they need to fulfill the requirements of the entire assessment, taking account of cross-sectoral dependencies.

  20. 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

  21. 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.

  22. ■ 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 V&A assessment. ■ Use selected methods to create scenarios of future climate and sea- level change and of future environmental and socio-economic baselines.

  23. 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.

  24. 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

  25. 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?

  26. 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

  27. 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

  28. 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

  29. 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

  30. 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

  31. 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

  32. 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.

  33. Steps of Vulnerability and Adaptation Assessment Socio-economic scenario Experiments/ Technology options Develop scenarios MAGICC Select GCM Climatic Data in DSSATSModel Format SCENGEN DSSAT Impact Assessment Adaptation Options Monthly Climatic Data Daily Climatic Data CLIMATEDATAGENERATOR Other Simulation models developed in Crystal Ball Experiments/ Technology options Socio-economic scenario

  34. 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).

  35. STRUCTURE OF DSSAT (Deterministic Model) Crops File *.CUL, *.SPE, *.ECO Soil File *.SOL WeatherFile *.WTH Experiment File *.eeX DSSATv35

  36. 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)

  37. 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

  38. 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.

  39. 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.

  40. 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.

  41. 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

  42. 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).

  43. V&A Studies Areas in Egypt • WINDOWS\Desktop\3.bmp.BMP

  44. VALIDATION OF DSSAT FOR WHEAT

  45. 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.

  46. 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

  47. IMPACT ON MAIZE YIELD

  48. 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

  49. 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