mosaicc - a capacity development tool for assessments of climate change impacts on agriculture
TRANSCRIPT
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MOSAICCa Capacity Development Tool for
Multi-disciplinary Assessments of Climate Change Impacts on Agricultureto Support Adaptation Planning
Hideki Kanamaru, Renaud Colmant, and Migena CumaniNRC
16 November, 2015
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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)
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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
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Multi-disciplinary assessmentsDownscaled 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
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• 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
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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
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% 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)
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Number of Dry Days (5-consecutive days with <1 mm of daily rainfall) under MPEH5 GCM
Extreme events2011-2040 vs 1971-2000
Number of Days with Extreme Daily Rainfall exceeding >= 100 mm of daily rainfall under MPEH5 GCM
Dry spells Heavy rainfall
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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
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Precipitation - Mapas de % de cambios para precipitación
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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
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Water balance PREC-PET (map) and Discharge (box plots) for 3 GCMs x 2 emission scenarios
2011-2040
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Changes in discharge by season and agreement among 3 GCMs x 2 emission scenarios
2011-2040 vs 1971-2000
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WABAL
• Crop specific water balance model
• Initially used in crop forecasting (AgroMetShell, FAO)
• Produces various variables such as the Water Satisfaction Index (WSI)
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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.
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• 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
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Rice yield projection - Peru
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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)
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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
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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
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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
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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)
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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
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LANDIS-IIUses
• 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
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LANDIS-IIPnET-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
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Distribution
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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
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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
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Thank you• Info:
– [email protected]– [email protected]– [email protected]– www.fao.org/climatechange/mosaicc
• Partners
Mauro Evangelisti Servizi Informatici
Numerical Ecology of Aquatic Systems
AgroMetShellFAO-MOSAICC is developed in the framework of the EU/FAO Programme on “Improved Global Governance for Hunger Reduction”