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

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)

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

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

• 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

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

% 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)

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

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

Precipitation - Mapas de % de cambios para precipitación

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

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 variables such as the Water Satisfaction Index (WSI)

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.

• 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

Rice yield projection - Peru

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)

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

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

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

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)

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

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

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

Distribution

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

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

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

AgroMetShellFAO-MOSAICC is developed in the framework of the EU/FAO Programme on “Improved Global Governance for Hunger Reduction”