MOSAICC: An inter-disciplinary system of models to evaluate the impact of climate change on agriculture

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MOSAICC: An inter-disciplinary system of models to evaluate the impact of climate change on agriculture, By Francois Delobel and Oscar Rojas ,Land and Water Days in Near East & North Africa, 15-18 December 2013, Amman, Jordan

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  • ORIGINE:
    FAO WB etude d’impact des changements climatiques sur l’agriculture marocaine
    50 cultures
    Problemes rencontrés: formattage, grand nombre de simulations, temps de calcul, quantité de données générées.
  • ORIGINE:
    FAO WB etude d’impact des changements climatiques sur l’agriculture marocaine
    50 cultures
    Problemes rencontrés: formattage, grand nombre de simulations, temps de calcul, quantité de données générées.
  • MOSAICC: An inter-disciplinary system of models to evaluate the impact of climate change on agriculture

    1. 1. MOSAICC: An inter-disciplinary system of models to evaluate the impact of climate change on agriculture Francois Delobel and Oscar Rojas Amman, 15 Dec 2013
    2. 2. • Downscaled climate projection from SDSM • Impacts on Crop Yields (rainfed and irrigated) • Hydrological and economic impacts also evaluated (WB)
    3. 3. • 1 GCM (HadCM3) • 2 scenarios (A2, B2) • 4 time slices (2000, 2030, 2050, 2080) • 6 agroecological zones • 50 Crops = Huge amount of data generated = Huge time processing (including parametrization) Replication? Transferability?
    4. 4. Concept • Need for a tool to facilitate the user experience by simplifying data processing and simulation runs • Include additional models • Transferable (capacity reinforcement) • At no cost (freeware)
    5. 5. Concept MOSAICC: Modelling System for Agricultural Impacts of Climate Change •Capacity development tool for •Assessing climate change impacts on agriculture at national level (trends) •By national experts (ministries, universities, research institutions) •Using own data •In a perspective of decision support
    6. 6. Economic impact and analysis of policy response at national level Crop yield projections under climate scenarios Concept Simulation of the country’s hydrology and estimation of water resources Downscaled climate projections under various climate scenarios
    7. 7. Model selection • Expert consultation (Jan 2010) • Robustness rather than sophistication (low data input, commonly available), flexibility, wide application, open source • 1 Statistical Downscaling tool, 2 crop models, 1 Hydrological model and 1 Economic model
    8. 8. Statistical Downscaling Portal
    9. 9. Statistical Downscaling Portal • Created for the ENSEMBLE project by the Santander Meteorology group, University of Cantabria • Methods: Analogs, weather typing, regression, neural networks • Cross validation • 8 ESM from CMIP5
    10. 10. STREAM • Developed by IVM, Free University of Amsterdam and WaterInsight • Conceptual empirical hydrological model. • Core: a GIS-based rainfall runoff model which enables the simulation of river discharges and water availability in large river basins.
    11. 11. STREAM • Extensions: – Dams; – Data input check and calculation (from DEM) – Automatic calibration
    12. 12. WABAL • Crop specific water balance model • Initially used in crop forecasting (AgroMetShell, FAO) • Produces various variables such as the Water Satisfaction Index (WSI)
    13. 13. AQUACROP • FAO cropwater productivity model to simulate yield response to water • Focuses on water • Uses canopy cover instead of leaf area index • Balances simplicity, accuracy and robustness • Planning tool • Calibrated for cotton, maize, potato, tomato, wheat, rice, surgar beet, quinoa, soybean etc.
    14. 14. AQUACROP
    15. 15. Yield projection calculation • The crop model is used to the yield variations due to the weather conditions • A yield function (regression model) is established between recorded yields and model outputs • The yield function is applied to projected weather conditions to obtain crop yield projections • Possible use of scenarios on technological progress (not modelled)
    16. 16. DCGE • Dynamic Computable General Equilibrium model, developed by IVM, Free University of Amsterdam • Model the future evolution of the national economy of a country and the changes induced by variations of crop yields under climate change scenarios. • Generic, adaptable to local conditions (production factors, activities, commodities, consumer types etc) according to the data availability • Requires the assemblage of a social accounting matrix (SAM)
    17. 17. DCGE
    18. 18. Utilities • Interpolation (kriging, AURELHY) • Growing season beginning and length • ET0 calculation • Definition of study area (GIS tool) • DEM processing for hydrological modelling
    19. 19. AURELHY • Topography-based interpolation method (Meteo France) • Combines predictions from regression models based on “landscape variables” and kriging • Able to reproduce effects of landforms on local climates (Foehn etc)
    20. 20. AURELHY
    21. 21. Integration • Server • Spatial database • Web interfaces (user profiles, work modes, experiment definition and management, data management) • Shell (data preparation, experiment execution, output storage)
    22. 22. Integration IPCC GCM Low resolution projections Server Climate Historical weather data Modellers interface Downscaled climate projections Historical crop yield statistics Historical water use statistics Crop characteristics Crops Hydrology Yield projections Water resources projections Soil data Technological progress scenarios Current state of economy Economy Macroeconomic scenarios Economic impacts Historical discharge data Soil and Land use data Dam characteristics End-user interface
    23. 23. Interfaces • Home page – log-in
    24. 24. Interfaces • Functions (utilities and models)
    25. 25. Interfaces • Data management
    26. 26. Interfaces • Experiment management
    27. 27. Advantages • • • • • • • Participatory approach Remote access Nothing to install (web browser) Easy data exchange Low computing time No data format or unit conversion Data tracking down the flow
    28. 28. Decision support • Relevance of simulations and modelisation – Scenario testing (climate, varieties, crop management, water use, demography, policies etc.) – Facilitate understanding of processes at stake – Very suitable for climate change studies • Limitations: – Reduced reality, non comprehensive, under assumptions – Uncertainties
    29. 29. Decision support • “Essentially, all models are wrong, but some are useful” (G. Box, statistician) • Data quality: garbage in = garbage out • Not to be taken alone!
    30. 30. Distribution • Delivered to technical institutions through: – Constitution of a working group – Trainings – Support to carry out an integrated impact study • Operational in the Philippines and Morocco • Foreseen: Niger, Peru, Guatemala
    31. 31. Demo • Morocco server http://81.192.163.58/
    32. 32. Thank you for your attention • Info: – www.fao.org/climatechange/mosaicc – MOSAICC@fao.org • Partners Mauro Evangelisti Servizi Informatici Numerical Ecology of Aquatic Systems AgroMetShell
    33. 33. Thank you for your attention • Welcome to Climate Smart Agriculture stand

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