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Uncertainties of different weather data input on three multi-models simulations of yield and water use

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HAL Id: hal-01581579

https://hal.archives-ouvertes.fr/hal-01581579

Submitted on 3 Jun 2020

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Uncertainties of different weather data input on three

multi-models simulations of yield and water use

Davide Cammarano, Keith B. Matthews, Dave Miller, Mike Rivington, Gianni

Bellocchi

To cite this version:

Davide Cammarano, Keith B. Matthews, Dave Miller, Mike Rivington, Gianni Bellocchi. Uncertainties

of different weather data input on three multi-models simulations of yield and water use. International

Crop Modelling Symposium - iCROP2016, Mar 2016, Berlin, Germany. �hal-01581579�

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International Crop Modelling Symposium

15-17 March 2016, Berlin

214

Uncertainties of different weather data input on three multi-models

simulations of yield and water use

D. Cammarano1*– M. Rivington1– K. B. Matthew1– D. G. Miller1–G. Bellocchi2 1

The James Hutton Institute, Craigiebuckler and Invergowrie, Aberdeen and Dundee, AB15 8QH and DD2 5DA, UK;

2

Grassland Ecosystem Research Unit, French National Institute of Agricultural Research, 5 Chemin de Beaulieu, 63039 Clermont-Ferrand, France.

* Corresponding author: Davide Cammarano, Email: davide.cammarano@hutton.ac.uk Introduction

One of the sources of uncertainty in simulating the plausible impacts of climate change on crop production is the usefulness of the weather projections. Climate model outputs provide both biased and uncertain representations of observed data, and therefore there will be errors related with the use of the projections. Building on previous research about the link between weather data type, sources with known biases, and multiple crop model errors, we investigated the complexities in using climate model projections representing different spatial scales within climate change impacts and adaptation studies.

Materials and Methods

Five weather data sets were used in this study: i) observed weather data (1960-1990) from The British Atmospheric Data Centre (BADC, 2006); ii) the HadRM3 initial realisation original hindcast (OrH); iii) the OrH downscaled using the bias correction (BC) method of Rivington et al., (2008) (DsH); the HadRM3 estimates for the SRES A2 (medium-high GHG emissions) original future projections for 2070-2100 (OrF); iv) the OrF data downscaled using the BC method (DsF). The three crop simulation models used for this study were the APSIM, CropSyst, and DSSAT models to represent a generic spring barley crop at 12 UK sites. A single reference sandy loam soil was used for the simulations across the 12 sites. A spring barley cultivar was calibrated using data from barley variety trials.

Results and Discussion

The results of running three spring barley simulation models using observed weather data and original and downscaled RCM data, has shown that attention is required in interpreting model outputs because misleading conclusions could be drawn from results where original climate model projection estimates are used in modelling studies of future impacts. The OrH rainfall had a very different impact on crop model daily patterns of evapotranspiration and water stress index. Using the results of a single crop model would also be a limiting factor in studying projected climate impacts on agricultural production.

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International Crop Modelling Symposium

15-17 March 2016, Berlin

215 a) Re la tiv e Ch an ge (% ) -60 -40 -20 0 20 40 60 CS AP DS

Anthesis Maturity Yield Cumulative

Evapotranspiration b)

Days after sowing

0 20 40 60 80 100 120 140 0.0 0.2 0.4 0.6 0.8 1.0 W at er S tre ss In de x CS DS AP

Figure 1. (a) Relative change between BADC and OrH respect for CropSyst (CS), Apsim (AP), and DSSAT (DS)

and (b) daily water stress index for one weather station using the OrH as input to the models.

The three crop models used were very close in simulating phenology, with variability existing in simulating yield and evapotranspiration. Although these three models are not like climate models, and when initial conditions and other inputs are given correctly they should be able to simulate similar yields and growth rates. However, the interacting effects of the way their processes are modelled and the way similar equations describing growth are parameterized will cause variability between models.

Conclusions

Based on the results of this study, we argue that the types of errors manifesting themselves due to weather data source in crop model estimates will also occur in other types of environmental models (ecological, hydrological, etc.). The lessons learned from the behaviour of the crop models can be informative to these other types of models. Though not tested here, it would seem logical that other types of downscaling (i.e. statistical or weather generators) and other bias correction methods, would also have a similar form of impact.

Acknowledgements

The results of this research were obtained within an international research project named “FACCE MACSUR – Modelling European Agriculture with Climate Change for Food Security, a FACCE JPI knowledge hub”. The authors would like to thank the Scottish Government’s Rural and Environment Science and Analytical Services Division (RESAS) and the meta-programme Adaptation of Agriculture and Forests to Climate Change (AAFCC) of the French National Institute for Agricultural Research (INRA) for their funding support of this research. Thanks to Mr. K. Marsh at the BADC for processing the HadRM3 model data and to the Meteorological Office and Hadley Centre for permission to use their data.

References

BADC (2006). British Atmospheric Data Centre. http://badc.nerc.ac.uk/home/index.html

Rivington, M., Miller, D., Matthews, et al., (2008). Downscaling Regional Climate Model estimates of daily precipitation, temperature and solar radiation data. Climate Research, 35: 181-202.

Figure

Figure 1. (a) Relative change between BADC and OrH respect for CropSyst (CS), Apsim (AP), and DSSAT (DS)  and (b) daily water stress index for one weather station using the OrH as input to the models

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