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

MOSAICC:An inter-disciplinary system of models

to evaluate the impact of climate change on agriculture

Francois Delobel and Oscar RojasAmman, 15 Dec 2013

• Downscaled climate projection from SDSM

• Impacts on Crop Yields (rainfed and irrigated)

• Hydrological and economic impacts also evaluated (WB)

• 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?

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)

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

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

Concept

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

Statistical Downscaling Portal

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

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.

STREAM

• Extensions:– Dams;– Data input check

and calculation (from DEM)

– Automatic calibration

WABAL

• Crop specific water balance model

• Initially used in crop forecasting (AgroMetShell, FAO)

• Produces various variables such as the Water Satisfaction Index (WSI)

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.

AQUACROP

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)

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)

DCGE

Utilities

• Interpolation (kriging, AURELHY)• Growing season beginning and length• ET0 calculation

• Definition of study area (GIS tool)• DEM processing for hydrological modelling

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)

AURELHY

Integration

• Server• Spatial database• Web interfaces (user profiles, work modes,

experiment definition and management, data management)

• Shell (data preparation, experiment execution, output storage)

IntegrationClimate

Historical weather

dataDownscaled

climate projections

HydrologyCrops

Economy

IPCC GCMLow resolution

projections

Historical discharge

data

Water resourcesprojections

Historical water usestatistics

Historical crop yieldstatistics

Yield projections

Current stateof economy

Macroeconomicscenarios

Economic impacts

Crop characteristics

Soil data

Technological progressscenarios

Soil and Land use

data

Dam characteristics

Modellersinterface

End-userinterface

Server

Interfaces

• Home page – log-in

Interfaces• Functions (utilities and models)

Interfaces• Data management

Interfaces• Experiment management

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

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

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!

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

Demo

• Morocco serverhttp://81.192.163.58/

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

Thank you for your attention

• Welcome to Climate Smart Agriculture stand