Forecasting agricultural drought: the Australian Agricultural Drought Indicators

Abstract

Drought is a recurrent and significant driver of stress on agricultural enterprises in Australia. Historically, rainfall indices have been used to identify drought and inform government responses. However, rainfall indicators may not fully reflect agricultural or economic drought conditions and are a lagging indicator. To address these shortcomings, the AADI (Australian Agricultural Drought Indicators) was recently developed to monitor and forecast drought for upcoming seasons using biophysical and agro-economic models, including crop yields, pasture growth and farm profit, at ĝ1/4 5 km2 resolution. Here, we evaluate the skill of drought indicator forecasts driven by the ACCESS-S2 dynamical global climate model over a hindcast period from 1990-2018. Analysis of the AADI hindcasts finds that antecedent landscape conditions significantly enhance predictive skill for crop yields, pasture growth and farm profit across a financial year. As lead time shortens from 12 to 3 months, forecast confidence increases: median farm profit skill rises from 43 % at 12 months to 67 % and 73 % at 6 and 3 months, respectively, whilst median farm profit biases remain below 2 % across all lead times, with high reliability indicating a well-calibrated ensemble, making the forecasts highly suitable for risk management and decision-making. Forecasts for wheat, sorghum, and pasture are also skilful and reliable in ensemble spread, although residual biases can occur (e.g. up to 20 % for sorghum) and suggests further system refinements are needed. Analysis of historical events under both dry and wet conditions demonstrated the AADI system's ability to identify drought-impacted areas with increased confidence up to 6 months earlier than rainfall deficits.