Poster Presentation AMOS Annual Meeting and International Conference on Tropical Meteorology and Oceanography

The implication of spatial interpolated climate data on biophysical modelling in agricultural systems (#1041)

De Li Li Liu 1 2 , Fei Ji 3 , bin Wang 1 , Cathy Waters 4 , Puyu Feng 1 , Rebecca Darbyshire 5
  1. New South Wales Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga, New South Wales, Australia
  2. Climate Change Research Centre, University of New South Wale, Sydney, New South Wales, Australia
  3. Department of Planning and Environment, New South Wales Office of Environment and Heritage, NSW, Australia
  4. Orange Agricultural Institute, NSW Department of Primary Industries, Orange, NSW, Australia
  5. NSW DPI, Queanbyyan, New South Wales, Australia

Spatial modelling of agricultural production has been more frequently analysed to assist with regional and long-term planning. Typically, point-scale crop models are used to construct these analyses with researchers using various sources of input data, including observed and interpolated gridded climate data. Understanding the implication of data choice on production estimates is crucial to understand the consequences of selecting methodological approaches for agricultural model outputs. In this study, we compared observed data (SILO PPD) and gridded data (SILO gridded) of a commonly used Australian climate dataset. We assessed the consequences of the differences in climate variables (biases) to the modelled outputs. Our results showed that the major differences between gridded and observed climate datasets were for rainfall variables. Interpolated gridded data tended to have larger rainfall frequency and smaller rainfall intensity, leading to lower surface runoff and higher soil evaporation that caused less plant water uses and less nitrogen uptake, and ultimately resulted in biases in simulated crop biomass and yield. In addition, the results indicated that agricultural models implemented with gridded data could produce similar overall model outputs as those driven by observed climate data, but can result in more uncertainties in simulated spatial outputs. This is particularly evident in regions with high rainfall and extreme values. Our results showed that applying agricultural models with observed data and interpolating these results spatially may be the optimal approach to minimise the biases in production modelling outputs and reduce computing time and storage.