Seasonal climate forecasts and decision support systems for drought prone agriculture: a case study based on the development and application of the Rainman climate analysis software

Abstract

This paper considers the role of decision support systems to apply seasonal climate information in agriculture by documenting the development and application of the Australian Rainman computer package as a case study. Rainman aims to develop knowledge and skills for managing climate variability in agriculture by analysing effects of the El Nino / Southern Oscillation (ENSO) on rainfall to derive probability-based seasonal climate forecasts. The two main seasonal forecast tools used in Rainman are the Southern Oscillation Index (SOI) and an index of Sea Surface Temperature (SST). The Rainman version 4 prototype is due for release and has improved seasonal forecast analyses and capacity for world-wide mapping of seasonal rainfall information at district and regional scales. There has also been interest in applying seasonal forecast technology to water supplies and irrigation systems and has led to developing the StreamFlow supplement for analysis of streamflow and run-off data. A central principle used in developing Rainman has been to include only seasonal forecast methods that have been well established and accepted by the scientific community and national organisations with responsibility in seasonal climate forecasting. Thus, the participative process to define and review Rainman has been an important element in the development of Rainman as a decision support product. Peer review is a necessary part of the quality assurance process in developing decision support systems. In communicating knowledge of risk we have found that cumulative probability distributions work well for scientists. However, in communicating with the farming community, other ways of expressing risk have been more effective such as frequency plots, pie charts, box plots and time series. Rainman analyses follow accepted scientific conventions by applying several statistical tests to seasonal forecasts so that: (a) users have some guidance regarding the statistical reliability of the forecast information, and (b) duty of care is discharged in providing forecast information to users. The Rainman case study shows that software is an effective way to provide people with climatic information because it can be detailed but easy to use, comprehensive and locally relevant. Learning to use ENSO information is maximised by combining “hands-on” learning with the software with participation in a workshop where people share ideas and experiences. Benefits of using Rainman include improved knowledge and skills about the variable climate and seasonal climate forecasts, enhanced agriculture and resource management decisions, and reduced climate risk exposure.