Linking Seasonal Forecast To A Crop Yield Model SIMONE SIEVERT DA COSTA DSA/CPTEC/INPE – CNPq HOMERO BERGAMASCHI UFRGS First EUROBRISA.

Slides:

Advertisements

Similar presentations

D. W. Shin, S. Cocke, Y.-K. Lim, T. E. LaRow, G. A. Baigorria, and J. J. OBrien Center for Ocean-Atmospheric Prediction Studies Florida State University,

Advertisements

Impacts of climate on tropical crop production Tom Osborne a Andy Challinor a,b, Tim Wheeler b, Julia Slingo a a Department of Meteorology b Department.

The avoided impacts of climate change on crop production Tom Osborne Thanks to: Simon Gosling, Gillian Fraser, Helen Greatrex, Tim Wheeler, Nigel Arnell.

Dr. Adriana-Cornelia Marica & Alexandru Daniel

Agricultural modelling and assessments in a changing climate

Selected results of FoodSat research … Food: what’s where and how much is there? 2 Topics: Exploring a New Approach to Prepare Small-Scale Land Use Maps.

Simulating Cropping Systems in the Guinea Savanna Zone of Northern Ghana with DSSAT-CENTURY J. B. Naab 1, Jawoo Koo 2, J.W. Jones 2, and K. J. Boote 2,

Challenges for the simulation of crop yields in a changing climate Tim Wheeler Crops and Climate Group

© Crown copyright Met Office 2011 Climate impacts on UK wheat yields using regional model output Jemma Gornall 1, Pete Falloon 1, Kyungsuk Cho 2,, Richard.

Climate-induced changes in erosion during the 21 st century for eight U.S. locations.

Recent S/I Prediction Activities at IRI. IRI Climate Predictability Tool (CPT) Simon Mason.

Enhancing the Scale and Relevance of Seasonal Climate Forecasts - Advancing knowledge of scales Space scales Weather within climate Methods for information.

Translating Climate Forecasts into Agricultural Terms: Advances and Challenges James Hansen, Andrew Challinor, Amor Ines, Tim Wheeler, Vincent Moron presented.

Mechanistic crop modelling and climate reanalysis Tom Osborne Crops and Climate Group Depts. of Meteorology & Agriculture University of Reading.

What are the dominant features of rainfall leading to realistic large-scale yield prediction over West Africa ? Alexis Berg ( LOCEAN-LSCE ), Benjamin Sultan.

Crop yield predictions using seasonal climate forecasts SIMONE SIEVERT DA COSTA DSA/CPTEC/INPE Second EUROBRISA Workshop July.

Some Discussion Points What metrics should be used for comparing observations? What distributional metrics could readily be used? Is anybody playing with.

Workshop on Weather and Seasonal Climate Modeling at INPE - 9DEC2008 INPE-CPTEC’s effort on Coupled Ocean-Atmosphere Modeling Paulo Nobre INPE-CPTEC Apoio:

Dennis P. Lettenmaier Alan F. Hamlet JISAO Center for Science in the Earth System Climate Impacts Group and Department of Civil and Environmental Engineering.

Crop Yield Modeling through Spatial Simulation Model.

Experimental Real-time Seasonal Hydrologic Forecasting Andrew Wood Dennis Lettenmaier University of Washington Arun Kumar NCEP/EMC/CMB presented: JISAO.

Application to the rice production in Southeast Asia Rice Production Research Program Agro-meteorology Division National Institute for Agro-Environmental.

Hydrologic Predictability and Water Year 2009 Predictions in the Columbia River Basin Andy Wood Matt Wiley Bart Nijssen Climate and Water Resource Forecasts.

CGMS/WOFOST model principles

Development of a combined crop and climate forecasting system Tim Wheeler and Andrew Challinor Crops and Climate Group.

Application of seasonal climate forecasts to predict regional scale crop yields in South Africa Trevor Lumsden and Roland Schulze School of Bioresources.

Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss Synthetic future weather time-series at the local scale.

Stochastic Disagregation of Monthly Rainfall Data for Crop Simulation Studies Amor VM Ines and James W Hansen International Institute for Climate Prediction.

SECC – CCSP Meeting November 7, 2008 Downscaling GCMs to local and regional levels Institute of Food and Agricultural Sciences Guillermo A. Baigorria

Impact of Climate Change on Flow in the Upper Mississippi River Basin

The Value of ENSO Forecast Information To Dual Purpose Winter Wheat Production In the U.S. Southern High Plains Steve Mauget USDA-ARS Plant Stress & Water.

Developing links with agricultural research centres Andrew Challinor and Tim Wheeler.

Caio A. S. Coelho Supervisors: D. B. Stephenson, F. J. Doblas-Reyes (*) Thanks to CAG, S. Pezzulli and M. Balmaseda.

A General Plant Model. SWAT Model Simulates plant growth through leaf area, light interception, biomass production and stress simulation Water balance,

1 The EUROBRISA Project David Stephenson University of Exeter 1 st EUROBRISA workshop, Paraty, Brazil March 2008.

Downscaling in time. Aim is to make a probabilistic description of weather for next season –How often is it likely to rain, when is the rainy season likely.

Climate Forecasting Unit Prediction of climate extreme events at seasonal and decadal time scale Aida Pintó Biescas.

South Eastern Latin America LA26: Impact of GC on coastal areas of the Rio de la Plata: Sea level rise and meteorological effects LA27: Building capacity.

Zhang Mingwei 1, Deng Hui 2,3, Ren Jianqiang 2,3, Fan Jinlong 1, Li Guicai 1, Chen Zhongxin 2,3 1. National satellite Meteorological Center, Beijing, China.

Scaling up Crop Model Simulations to Districts for Use in Integrated Assessments: Case Study of Anantapur District in India K. J. Boote, Univ. of Florida.

Approaches to Seasonal Drought Prediction Bradfield Lyon CONAGUA Workshop Nov, 2014 Mexico City, Mexico.

Development of a downscaling prediction system Liqiang Sun International Research Institute for Climate and Society (IRI)

Experimental seasonal hydrologic forecasting for the Western U.S. Dennis P. Lettenmaier Andrew W. Wood, Alan F. Hamlet Climate Impacts Group University.

Crop-Weather Models Observed soybean yields (GA yield trials) vs. seasonal rainfall, temperature, simulated yields.

Agriculture and Water Resources Cynthia Rosenzweig and Max Campos AIACC Trieste Project Development Workshop

CLIMATE CHANGE AND PRECIPITATION NEEDS OF WINTER WHEAT Éva Erdélyi, Corvinus University of Budapest Levente Horváth, University of Debrecen

Caio A. S. Coelho, D. B. Stephenson, F. J. Doblas-Reyes (*) and M. Balmaseda (*) Department of Meteorology, University of Reading and ECMWF (*)

Climate impacts on UK wheat yields using regional model output

1 Objective Drought Monitoring and Prediction Recent efforts at Climate Prediction Ct. Kingtse Mo & Jinho Yoon Climate Prediction Center.

Crop yield predictions using seasonal climate forecasts Simone M. S. Costa and Caio A. S. Coelho Instituto Nacional de Pesquisas Espaciais – INPE, São.

Interannual Variability of the Rainy Season in the Tropics of South America Brant Liebmann NOAA-CIRES Climate Diagnostics Center Boulder, Colorado, USA.

Panut Manoonvoravong Bureau of research development and hydrology Department of water resources.

The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology Verification and Metrics (CAWCR)

ISP Meeting, Ouagadougou, 23 Oct 2012 Making Climate Information More Relevant to Smallholder Farmers James Hansen, CCAFS Theme 2 Leader IRI, Columbia.

Sirius wheat simulation model: development and applications Mikhail A. Semenov Rothamsted Research, UK IT in Agriculture & Rural Development, Debrecen,

Copernicus Observations Requirements Workshop, Reading Requirements from agriculture applications Nadine Gobron On behalf Andrea Toreti & MARS colleagues.

Meteorology 485 Long Range Forecasting Friday, February 13, 2004.

Judith Curry James Belanger Mark Jelinek Violeta Toma Peter Webster 1

VERIFICATION OF A DOWNSCALING SEQUENCE APPLIED TO MEDIUM RANGE METEOROLOGICAL PREDICTIONS FOR GLOBAL FLOOD PREDICTION Nathalie Voisin, Andy W. Wood and.

EVALUATION OF A GLOBAL PREDICTION SYSTEM: THE MISSISSIPPI RIVER BASIN AS A TEST CASE Nathalie Voisin, Andy W. Wood and Dennis P. Lettenmaier Civil and.

GLAM-wheat modelling in China Sanai Li Supervisors: Prof. Tim Wheeler, Dr Andrew Challinor Prof. Julia Slingo, Crops and Climate Group.

Teleconnection Patterns and Seasonal Climate Prediction over South America The Final Chapter??? Tércio Ambrizzi and Rosmeri P. da Rocha University of São.

Long-lead streamflow forecasts: 2. An approach based on ensemble climate forecasts Andrew W. Wood, Dennis P. Lettenmaier, Alan.F. Hamlet University of.

Caio A. S. Coelho, S. Pezzulli, M. Balmaseda (*), F. J. Doblas-Reyes (*) and D. B. Stephenson Forecast calibration and combination: A simple Bayesian approach.

South Asian Climate Outlook Forum (SASCOF-5) (Pune, India, April 2014) Country Presentation-Maldives Zahid Director Climatology Maldives Meteorological.

Tushar Sinha Assistant Professor

GPC CPTEC: Seasonal forecast activities update

With special thanks to Prof. V. Moron (U

University of Washington Center for Science in the Earth System

N. Voisin, J.C. Schaake and D.P. Lettenmaier

Presentation transcript:

Linking Seasonal Forecast To A Crop Yield Model SIMONE SIEVERT DA COSTA DSA/CPTEC/INPE – CNPq HOMERO BERGAMASCHI UFRGS First EUROBRISA Workshop March 2008 –Paraty, RJ - Brazil

Talk Outline  Aim  Study region  Motivation  Crop model calibrated for South America  Methods (Bias Correction)  Results  Future Direction

Aim Produce maize crop yield prediction based on climate information (seasonal forecasts). Crop yield model Climate Forecast National Statistics Maize Grain Yield Source: IBGE

The Area of Study Rio Grande Do Sul State (RS) “Long River of South” 27.2°- 29.8°S/51.2°- 56.0°W

About Maize in RS… After USA and China, Brazil is the main maize producer in the entire world, and RS is the second greatest producer nationally (IBGE, 2006). Sowing Date: Sep/Oct Harvest: Feb Crop cycle ~130 days

Santa Rosa Passo Fundo Main producer region (all shaded area in the map) Bergamaschi et al Motivation: Crop and Climate Relationship Correlation btw obs.maize yield and rainfall Data Source: crop yield (IBGE) rainfall (INMET, FEPAGRO)

Standardised rainfall and yield anom. Rainfall in all cycle (~ days) 0.56 Rainfall in 0-30 days after tasselling Bergamaschi et al. 2008

Can crop model to reproduce the interannual variability of maize yield? Source: IBGE

SOIL WATER TRANSPIRATION BIOMASS LEAF CANOPY ROOT SYSTEM Water Stress Transpiration Efficiency YIELD Development Stage Yield is a time varying fraction of Biomass Outputs Yield Gap Parameter Schematic diagram of GLAM (adapted from crop and climate group webpage-Reading) Crop model: General Large Area Model Challinor et al., 2003 GLAM is a processed based crop model, which simulates soil water budget, crop plant phenology, canopy growth, root growth, aerial dry mass and grain yield

Crop model: GLAM adapted to RS  GLAM were initially tested for groundnut yield across India (Challinor et al., 2003), and it was adapted to simulate maize yield for RS (Bergamaschi et.al., 2008 in preparation.).

Calibration GLAM were based on observational data (soil and crop phenology). UFRGS, Eldorado do Sul Site, Brazil. Muller et al., 2005

Input data to GLAM: SOIL WATER TRANSPIRATION BIOMASS LEAF CANOPY ROOT SYSTEM Water Stress Transpiration Efficiency YIELD Development Stage Yield is a time varying fraction of Biomass Outputs Yield Gap Parameter Daily data required: Solar Radiation Min. Temperature Max. Temperature Rainfall Schematic diagram of GLAM (adapted from crop and climate group webpage-Reading)

Maize Grain yield Estimative using GLAM and observed weather data Observed weather data from meteorological site (P, T, Rad.) FEPAGRO, INMET GLAM OBS (IBGE)

Can GLAM be used to do crop prediction with daily seasonal forecast data?

Seasonal weather data:11 ensemble member ECMWF (single grid point)

Seasonal weather data into crop model: Forecast issued Sep Daily Precipitation (a grid point) - 11 ensemble member from ECMWF model - first month of each forecasts initialized in Sep, Oct, Nov, Dec, Jan, Feb. (RS crop cycle) Rad. & Temp. Observation Daily mean climatology for wet and dry days (1998 – 2005)

ECMWF Daily Climatology ( ) for crop cycle Mean Rainfall (mm/day) sep oct nov dec jan fev month obs Ensemble mean Indiv. Member

Monthly Mean Rainfall R : R(mm d -1 ) = I(mm wd -1 ) x f(wd d -1 ) intensity frequency d=day wd=wet day (Ines and Hansen, 2006) Rainfall decomposition:

Intensity (mm/wd) Frequency (wd/d) sep oct nov dec jan fev month sep oct nov dec jan fev month Obs. Indiv. Member Emsemble Mean

Methods - Bias Correction of daily GCM: -Frequency (wd day-1) – wd = wet day F(p gcm =0) F(p obs =0) c) P 0 – used to truncate the GCM distribution=mean frequency of rainfall abv p 0 matches the obsv. Rainfall. a) b) Daily rainfall (mm) observation GCM Ines and Hansen (2006) Cumulative distribution function

F(p i ) b) p gcm p’ gcm Methods - Bias Correction of daily GCM: -intensity (mm wd-1) Ines and Hansen (2006) observation GCM

Methods - Bias Correction of daily GCM: -Multiplicative Shift

sep oct nov dec jan fev month sep oct nov dec jan fev month sep oct nov dec jan fev month Mean Rainfall (mm/day) mult. shift uncorrected Bias correction Obs. Indiv. Member Ensemble Mean

sep oct nov dec jan fev month sep oct nov dec jan fev month sep oct nov dec jan fev month Intensity (mm/wd) mult. shift uncorrected Bias correction Obs. Indiv. Member Ensemble Mean

sep oct nov dec jan fev month sep oct nov dec jan fev month sep oct nov dec jan fev month frequency (wd/d) mult. shift uncorrected Bias correction Obs. 11 GCM Mem. Mean GCM

Hindcast of Yield– Main producer region DATA INPUT FORECAST: OBS = observed weather data UNC = uncorrected seasonal forecast MS = seasonal forecast corrected using multiplicative shift IF = seasonal forecast, intensity & frequency correction method

Future Direction Statistical downscaling – to take advantage of regional of seasonal forecast skill (spatial calibration). Use of weather generator – to reproduce daily data (temporal disaggregation). Use of space-temporal downscaled daily prediction into crop model. Compared skill of different crop yield forecast approach (grid point and spatial downscaled data).

Thanks: Andrew Challinor (The University of Leeds-UK) Caio Coelho (CPTEC- Brazil) Homero Bergamaschi (UFRGS, Brazil) Tim Wheeler (The University of Reading-UK) Jim Hansen (IRI – USA) Walter Baethgen (IRI – USA)