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Development of a combined crop and climate forecasting system. Tim Wheeler and Andrew Challinor t.r.wheeler@rdg.ac.uk. Crops and Climate Group. A combined crop and climate forecasting system Report from: ‘Food Crops in a Changing Climate’. Linking climate information to crop models.

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development of a combined crop and climate forecasting system
Development of a combined crop and climate forecasting system

Tim Wheeler and Andrew Challinor

t.r.wheeler@rdg.ac.uk

Crops and Climate Group

a combined crop and climate forecasting system report from food crops in a changing climate

A combined crop and climateforecasting systemReport from:‘Food Crops in a Changing Climate’

linking climate information to crop models
Linking climate informationto crop models

general circulation model

crop model

At what scale should information pass between crop and climate models?

development of a combined crop climate forecasting system
Development of a combinedcrop / climate forecasting system

Fully coupled crop-climate simulation

Find spatial scale of weather-crop relationships

Osborne (2004)

Challinor et. al. (2003)

Ensemble

methods

Climate

change

(Challinor et al, 2005b,c)

(2005c,d)

Crop modelling at the working spatial scale

Hindcasts with observed weather data

Challinor et. al. (2004)

(Challinor et al, 2004)

and reanalysis

(Challinor et al, 2005a)

slide5

Simple correlations betweenrainfall and crop yield

Seasonal rainfall and groundnut yields for all India.

Time trend removed. rainfall yield

patterns of seasonal rainfall and yield of groundnut in india
Patterns of seasonal rainfall and yield of groundnut in India

District level groundnut yields (kg ha-1)

Mean of 1966 - 1990

Data source: ICRISAT

patterns of seasonal rainfall and yield of groundnut in india7
Patterns of seasonal rainfall and yield of groundnut in India

Sub-divisional level seasonal rainfall (JJAS, cm)

Mean of 1966 - 1990

Data source: IITM

g eneral l arge a rea m odel for annual crops glam
General Large Area Modelfor Annual Crops (GLAM)
  • Aims to combine:
    • the benefits of more empirical approaches (low input data requirements, validity over large spatial scales) with
    • the benefits of a process-based approach (e.g. the potential to capture intra-seasonal variability, and so cope with changing climates)
  • Uses a Yield Gap Parameter to account for the impact of differing nutrient levels, pests, diseases, non-optimal management to simulate farm yields

Challinor et. al. (2004)

slide9

1200

National Yield Statistics

)

1100

-1

GLAM simulation

1000

900

800

Groundnut yield (kg ha

700

600

500

400

1965

1970

1975

1980

1985

1990

Year

Hindcasts of groundnut yield for

all India using GLAM

capturing the effects of intra seasonal variability
Capturing the effects ofintra-seasonal variability

1975

Total rainfall: 394mm

Model: 1059 kg/ha

Obs: 1360 kg/ha

1981

Total rainfall 389mm

Model: 844 kg/ha

Obs: 901 kg/ha

using era40 reanalysis data

Andhra

Pradesh

Gujarat

Using ERA40 reanalysis data
  • Gujarat: bias correction of climatological mean rainfall works well
    • Correlation with observed yields 0.49  0.60
  • Andhra Pradesh: simulated mean yield < observed, variability >> observed
    • Incorrect seasonal cycle (both mean and variability) though Jun and Sept good. This is harder to correct.
using probabilistic climate forecasts
Using probabilistic climate forecasts

Model average

63 ensemble members

713 kg ha-1

Observed

775 kg ha-1

Use of DEMETER multi-model ensemble for groundnut yield in Gujarat, 1998 from Challinor et al (2005)

probabilistic forecasting of crop failure
Probabilistic forecasting of crop failure
  • The number of ensemble members predicting yield below a given threshold is an indication of probability of occurrence
  • Found predictability in crop failure
slide14

The impact of water and temperature stress at flowering under climate change

Hadley Centre PRECIS model, A2 (high emission) scenario

1960-1990

2071-2100

1 = no

impact

0 = max.

impact

Groundnut

  • Current risk is dominated by water stress; in the future climate run temperature stress dominates in the north.
variety response to temperature stress alone under climate change
Variety response to temperature stress alone under climate change

Hadley Centre PRECIS model, A2 (high emission) scenario 2071-2100

Number of years when the total number of pods setting is below 50%.

Sensitive variety

Tolerant variety

an integrated approach to climate impact assessments
An integrated approach to climate impact assessments
  • Crops can modify their own environment
    • The water cycle and surface temperatures vary according to land use
  • Integrate biological and physical modelling
    • By working on common spatial scale
    • By fully coupling the models
fully coupled crop climate simulation
Fully coupled crop-climate simulation

Crops ‘growing’ in HadAM3

using satellite estimates of rainfall
Using satellite estimates of rainfall

TAMSAT

Teo Chee-Kiat

David Grimes

conclusions
Conclusions
  • A combined crop and climate modelling system has been developed and tested for the current climate.
    • It shows skill in seasonal hindcasts and with climate ensembles
    • It has been used to study crop responses to climate change
    • Can be fully coupled to a GCM, and driven by satellite data