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MOSAICC
a Capacity Development Tool for
Multi-disciplinary Assessments of Climate
Change Impacts on Agriculture
to Support...
MOSAICC: Modelling System for
Agricultural Impacts of Climate Change
• Need for a tool to facilitate the user experience
b...
Capacity development tool
• By national experts (ministries, universities,
research institutions)
• Using the country’s ow...
Multi-disciplinary assessments
Downscaled climate
projections under
various climate
scenarios
Crop yield
projections
under...
• Different needs
of climate data
among modelers
– Hydrology – on
small grids down to
1km, monthly
– Crop – at station or
...
Statistical downscaling
of climate projections to station level
For the historical period, establish a statistical relatio...
% change in precipitation (A1B, BCM2 model) from 1971-1999 to 2011-2040
BCM2 A1B and A2 Tmin projections aggregated to 79 ...
Number of Dry Days (5-consecutive days with <1
mm of daily rainfall) under MPEH5 GCM
Extreme events
2011-2040 vs 1971-2000...
RCP 4.5 RCP 8.5
CanESM2 15 % 23 %
CNRM-CM5 5 % 10 %
MPI-ESM-MR 10 % 20 %
• Valores de cambios proyectados de precipitación...
Precipitation - Mapas de % de cambios para
precipitación
STREAM – hydrological model
• Empirical model of
surface hydrology ---
from rainfall,
temperature,
evapotranspiration, to
...
Water balance PREC-PET (map) and Discharge
(box plots) for 3 GCMs x 2 emission scenarios
2011-2040
Changes in discharge by season and agreement
among 3 GCMs x 2 emission scenarios
2011-2040 vs 1971-2000
WABAL
• Crop specific water
balance model
• Initially used in crop
forecasting
(AgroMetShell, FAO)
• Produces various
vari...
AQUACROP
• FAO crop water productivity
model to simulate yield
response to water
• Focuses on water
• Uses canopy cover in...
• Climate change makes
differentiated impacts
on provincial yield; some
positive; others negative
• Yields in rainfed area...
Rice yield projection - Peru
DCGE
• Dynamic Computable General
Equilibrium model, developed by
IVM, Free University of Amsterdam
• Model the future evo...
Application of MOSAICC
• Results from MOSAICC form a solid evidence-base
about projected impacts of climate change for
nat...
Advantages
• Participatory approach - facilitate
a collaborative environment for
inter disciplinary study
• Nothing to ins...
Distribution
• Delivered to technical institutions
through:
– Constitution of a working group
– Trainings
– Support to car...
Implementation of MOSAICC
• EU/FAO programme and TCP in Morocco – all
modules
• AMICAF project in the Philippines, Peru
• ...
LANDIS-II
•Developed by Portland State University
•LANDIS-II is a forest landscape simulation model. It simulates how ecol...
LANDIS-II
Uses
• Across large (typically 10,000 - 20,000,000 ha) landscapes.
• Spatial and Temporal Flexibility
– variable...
LANDIS-II
PnET-Succession
• Purdue University, USA
• Assumption 1:
– Ecological models built on phenomenological relations...
Distribution
Main Inputs
Ecoregions input map:
-Temperature
-Precipitation
-Soil
Climate data (by Ecoregion):
-From downscaled and inte...
Main Outputs
Spatial annual maps:
- By species (user choice)
- By interest:
• Biomass
• LAI
• Soil water
• Establishment
G...
Thank you
• Info:
– Hideki.Kanamaru@fao.org
– Renaud.Colmant@fao.org
– Migena.Cumani@fao.org
– www.fao.org/climatechange/m...
MOSAICC - a Capacity Development Tool for Assessments of Climate Change Impacts on Agriculture
MOSAICC - a Capacity Development Tool for Assessments of Climate Change Impacts on Agriculture
MOSAICC - a Capacity Development Tool for Assessments of Climate Change Impacts on Agriculture
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MOSAICC - a Capacity Development Tool for Assessments of Climate Change Impacts on Agriculture

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The MOdelling System for Agricultural Impacts of Climate Change (MOSAICC) is an easily accessible web-based platform which allows users to assess the impacts of climate change on agricultural production.

This presentation provides an overview of the platform, including a description of the different models developed to make projections and results from the project countries.
© FAO: http://www.fao.org

Published in: Education

MOSAICC - a Capacity Development Tool for Assessments of Climate Change Impacts on Agriculture

  1. 1. MOSAICC a Capacity Development Tool for Multi-disciplinary Assessments of Climate Change Impacts on Agriculture to Support Adaptation Planning Hideki Kanamaru, Renaud Colmant, and Migena Cumani NRC 16 November, 2015
  2. 2. MOSAICC: Modelling System for Agricultural Impacts of Climate Change • Need for a tool to facilitate the user experience by simplifying data processing and simulation runs • Transferable, adaptable (capacity development) • At no cost (freeware)
  3. 3. Capacity development tool • By national experts (ministries, universities, research institutions) • Using the country’s own data • For assessing medium- to long-term climate change impacts on agriculture • To aid climate change adaptation planning
  4. 4. Multi-disciplinary assessments Downscaled climate projections under various climate scenarios Crop yield projections under climate scenarios Simulation of the country’s hydrology and estimation of water resources Economic impact and analysis of policy response at national level Forest productivity changes under climate scenarios Robustness rather than sophistication (minimum input data required, simple), flexibility, wide application, open source
  5. 5. • Different needs of climate data among modelers – Hydrology – on small grids down to 1km, monthly – Crop – at station or on grid or by province, 10-daily – Economics – by province, annual Integration • Server • Spatial database • Web interface
  6. 6. Statistical downscaling of climate projections to station level For the historical period, establish a statistical relationship between station obs and large-scale climate (from reanalysis) -> Apply the statistical model with GCM projections as inputs to derive future climate at station level, daily scale Santander Meteorology group, University of Cantabria
  7. 7. % change in precipitation (A1B, BCM2 model) from 1971-1999 to 2011-2040 BCM2 A1B and A2 Tmin projections aggregated to 79 provinces (2011 - 2040 mean)
  8. 8. Number of Dry Days (5-consecutive days with <1 mm of daily rainfall) under MPEH5 GCM Extreme events 2011-2040 vs 1971-2000 Number of Days with Extreme Daily Rainfall exceeding >= 100 mm of daily rainfall under MPEH5 GCM Dry spells Heavy rainfall
  9. 9. RCP 4.5 RCP 8.5 CanESM2 15 % 23 % CNRM-CM5 5 % 10 % MPI-ESM-MR 10 % 20 % • Valores de cambios proyectados de precipitación: Precipitation - Ensamble de 6 (3 ESMs x 2 RCPs) proyecciones 'plausibles' para Precipitación (promedio de 265 estaciones) Precipitación (an1) – 265 estaciones
  10. 10. Precipitation - Mapas de % de cambios para precipitación
  11. 11. STREAM – hydrological model • Empirical model of surface hydrology --- from rainfall, temperature, evapotranspiration, to the simulation of river runoff and water availability in large river basins. IVM, Free University of Amsterdam and WaterInsight
  12. 12. Water balance PREC-PET (map) and Discharge (box plots) for 3 GCMs x 2 emission scenarios 2011-2040
  13. 13. Changes in discharge by season and agreement among 3 GCMs x 2 emission scenarios 2011-2040 vs 1971-2000
  14. 14. WABAL • Crop specific water balance model • Initially used in crop forecasting (AgroMetShell, FAO) • Produces various variables such as the Water Satisfaction Index (WSI)
  15. 15. AQUACROP • FAO crop water 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, sugar beet, quinoa, soybean etc.
  16. 16. • Climate change makes differentiated impacts on provincial yield; some positive; others negative • Yields in rainfed areas will be more negatively affected than irrigated areas, both in the A1B and A2 scenarios at the BCM2 and CNCM3 climate models Rainfed rice yield change 2011-2040 vs 1971-2000
  17. 17. Rice yield projection - Peru
  18. 18. 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)
  19. 19. Application of MOSAICC • Results from MOSAICC form a solid evidence-base about projected impacts of climate change for national climate change adaptation planning – Which regions are more affected than other regions • by temperature increase or precipitation increase/decrease? • by crop/forest productivity changes? • by river flow changes, and irrigation potential? • Best suited for sub-national scale assessment and national aggregation. Not for exploring best adaptation options at local scale, but for identifying areas/crops/basins that require adaptation intervention
  20. 20. Advantages • Participatory approach - facilitate a collaborative environment for inter disciplinary study • Nothing to install (web browser) • Remote access • Easy data exchange • Low computing time • No data format or unit conversion • Data tracking down the flow
  21. 21. Distribution • Delivered to technical institutions through: – Constitution of a working group – Trainings – Support to carry out an integrated impact study • As a component of a project, or on its own
  22. 22. Implementation of MOSAICC • EU/FAO programme and TCP in Morocco – all modules • AMICAF project in the Philippines, Peru • AMICAF-SSC in Indonesia, Paraguay – except for economy module • CSA and NAP projects in Malawi, Zambia – climate and crop (MOSAICC-basic)
  23. 23. LANDIS-II •Developed by Portland State University •LANDIS-II is a forest landscape simulation model. It simulates how ecological processes including succession, seed dispersal, disturbances, and climate change affect a forested landscape over time. Forestry Model Selection
  24. 24. LANDIS-II Uses • Across large (typically 10,000 - 20,000,000 ha) landscapes. • Spatial and Temporal Flexibility – variable time steps for each process – variable spatial resolution and extent • Built for Collaboration – on-line database of extensions – open-source extensions – well documented – flexible model architecture
  25. 25. LANDIS-II PnET-Succession • Purdue University, USA • Assumption 1: – Ecological models built on phenomenological relationships and behavior of the past are “Not robust enough under novel conditions” Gustafson, 2013 ; Williams et al., 2007 • Assumption 2: – Process-based models have “More robust predictions under novel conditions” Cuddington et al. 2013; Gustafson, 2013  PnET process-based model integrated in LANDIS-II as succession process
  26. 26. Distribution
  27. 27. Main Inputs Ecoregions input map: -Temperature -Precipitation -Soil Climate data (by Ecoregion): -From downscaled and interpolation Initial communities: -Input map -List species age cohorts by Initial Site Classes Species parameters: -Longevity -Sexual maturity -Seeding distance -Foliar characteristics -Shade and Fire tolerance Values have already been given to most of the parameters (applied for categories of species) Disturbances: -Harvest -Fire -Wind
  28. 28. Main Outputs Spatial annual maps: - By species (user choice) - By interest: • Biomass • LAI • Soil water • Establishment Graphs and tables : - For all the species • Total Biomass • LAI (m2) • Establishment • Soil water • CC impacts • Disturbance impacts • Harvested wood
  29. 29. Thank you • Info: – Hideki.Kanamaru@fao.org – Renaud.Colmant@fao.org – Migena.Cumani@fao.org – www.fao.org/climatechange/mosaicc • Partners Mauro Evangelisti Servizi Informatici Numerical Ecology of Aquatic Systems AgroMetShell FAO-MOSAICC is developed in the framework of the EU/FAO Programme on “Improved Global Governance for Hunger Reduction”

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