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

Transcription

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 Downscaled climate projection from SDSM Impacts on Crop Yields (rainfed and irrigated) Hydrological and economic impacts also evaluated (WB)

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 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 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 Economic impact and analysis of policy response at national level Concept Simulation of the country s hydrology and estimation of water resources Crop yield projections under climate scenarios Downscaled climate projections under various climate scenarios

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 Statistical Downscaling Portal

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 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 STREAM Extensions: Dams; Data input check and calculation (from DEM) Automatic calibration

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 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 AQUACROP

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 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 DCGE

18 Utilities Interpolation (kriging, AURELHY) Growing season beginning and length ET 0 calculation Definition of study area (GIS tool) DEM processing for hydrological modelling

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 AURELHY

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 Integration Modellers interface IPCC GCM Low resolution projections Historical weather data Historical crop yield statistics Crop characteristics Soil data Crops Climate Downscaled climate projections Hydrology Server Historical water use statistics Historical discharge data Soil and Land use data Technological progress scenarios Yield projections Water resources projections Dam characteristics Current state of economy Macroeconomic scenarios Economy End-user interface Economic impacts

23 Home page log-in Interfaces

24 Interfaces Functions (utilities and models)

25 Data management Interfaces

26 Interfaces Experiment management

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 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 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 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 Morocco server Demo

32 Thank you for your attention Info: Partners Mauro Evangelisti Servizi Informatici Numerical Ecology of Aquatic Systems AgroMetShell

33 Thank you for your attention Welcome to Climate Smart Agriculture stand