Spatial scale Most crop models initially developed at field scale. Use experimental station met data. GCM output downscaled to provide local met data. Few models developed for operation at larger spatial scales
General Large Area Model for annual crops Correlation coefficient for detrended groundnut yield and rainfall in India. Relationship between yield and rainfall variability observed at large spatial scales. Implies a potential to model at such scales, leading to the development of GLAM. Relatively simple model due to problems associated with parameter estimation
Temporal resolution Crop simulation models require daily data Interpolated/downscaled from monthly means Sub-seasonal variability important - timing of events important e.g. flowering
1975 Total rainfall: 394mm Yield = 1360 kg/ha 1981 Total rainfall 389mm Yield = 901 kg/ha Groundnut crop in Andhra Pradesh, India Crop model able to capture difference between years
Use of reanalysis by crop simulation models Extremely limited use of reanalysis for crop modelling CRUTS2.1 - monthly 0.5° global, 1901-2002 [pre, tmp, tmx, tmn, dtr, vap, cld, wet, frs] station data and/or downscaling commonly used Sole example Challinor et al (2005) Simulation of Crop Yields Using ERA-40: Limits to Skill and Nonstationarity in Weather-Yield Relationships J. Applied Met. 44,516-531.
Indian summer monsoon rainfall Fractional difference between ERA-40 and IITM data in (a) mean and (b) standard deviation of JJAS precipitation. (c) The correlation in JJAS precipitation between the two datasets. Challinor et al (2005) Despite a general over-prediction of tropical precipitation in ERA-40, JJAS rainfall tends to be lower than observations.
Indian summer monsoon rainfall Challinor et al (2005) Fractional difference between ERA-40 and IITM in the mean (1966-89) number of JJAS days with precipitation in the ranges shown. (c) The difference in days (ERA-40 -IITM) between the mean monsoon onset over the period of 1966-89. ERA-40: too many light rain-days, too few heavy rain-days. Skill in simulating onset varies
Seasonality The ERA-40 and IITM seasonal cycle of precipitation for two grid cells in India. Bars show the 1966-89 mean, and whiskers show one standard deviation. Challinor et al (2005)
Crop simulations Challinor et al (2005) Comparison between simulated (control run) and observed yields for the period of 1966-89: ratio of simulated to observed (a) mean and (b) standard deviation, and (c) correlation between simulated and observed yields. Best performance in NW India where climate signal is strongest
Crop simulations Challinor et al (2005) Fractional changes in the rmse in yield, from the control run baseline, for (a) bias correction B1, (b) bias correction B2, Mean bias correction Interannual bias correction Indication of potential importance of climate model improvement for crop applications
Conclusion Limited use of reanalysis data as driving data for mechanistic crop models. Reanalysis can fill the gaps where observational data is sparse. - Interannual variability (climatology in CRUTS) - Finer temporal resolution When used, can provide an indication of maximum skill attainable from GCM-crop model forecasting system. - Biases in reanalysis limits skill of crop forecasts - Indicates benefit of climate model improvement
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