Development of a combined crop and climate forecasting system - PowerPoint PPT Presentation

Tim Wheeler and Andrew Challinor

t.r.wheeler@rdg.ac.uk

Crops and Climate Group

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

general circulation model

crop model

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

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)

Simple correlations betweenrainfall and crop yield

Seasonal rainfall and groundnut yields for all India.

Time trend removed. rainfall yield

District level groundnut yields (kg ha-1)

Mean of 1966 - 1990

Data source: ICRISAT

Sub-divisional level seasonal rainfall (JJAS, cm)

Mean of 1966 - 1990

Data source: IITM

• 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)

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

1975

Total rainfall: 394mm

Model: 1059 kg/ha

Obs: 1360 kg/ha

1981

Total rainfall 389mm

Model: 844 kg/ha

Obs: 901 kg/ha

Andhra

Pradesh

Gujarat

• 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.

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)

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

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.

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

• 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

Crops ‘growing’ in HadAM3

All-India groundnut yield (red) with simulated mean yield (black) and spatial standard deviation (grey shading).

TAMSAT

Teo Chee-Kiat

David Grimes

• 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