Abstract
Abstract. Statistical seasonal forecasts of 3-month streamflow totals are released in Australia by the Bureau of Meteorology and updated on a monthly basis. The forecasts are often released in the second week of the forecast period, due to the onerous forecast production process. The current service relies on models built using data for complete calendar months, meaning the forecast production process cannot begin until the first day of the forecast period. Somehow, the bureau needs to transition to a service that provides forecasts before the beginning of the forecast period; timelier forecast release will become critical as sub-seasonal (monthly) forecasts are developed. Increasing the forecast lead time to one month ahead is not considered a viable option for Australian catchments that typically lack any predictability associated with snowmelt. The bureau's forecasts are built around Bayesian joint probability models that have antecedent streamflow, rainfall and climate indices as predictors. In this study, we adapt the modelling approach so that forecasts have any number of days of lead time. Daily streamflow and sea surface temperatures are used to develop predictors based on 28-day sliding windows. Forecasts are produced for 23 forecast locations with 0–14- and 21-day lead time. The forecasts are assessed in terms of continuous ranked probability score (CRPS) skill score and reliability metrics. CRPS skill scores, on average, reduce monotonically with increase in days of lead time, although both positive and negative differences are observed. Considering only skilful forecast locations, CRPS skill scores at 7-day lead time are reduced on average by 4 percentage points, with differences largely contained within +5 to −15 percentage points. A flexible forecasting system that allows for any number of days of lead time could benefit Australian seasonal streamflow forecast users by allowing more time for forecasts to be disseminated, comprehended and made use of prior to the commencement of a forecast season. The system would allow for forecasts to be updated if necessary.
Highlights
The Australian Bureau of Meteorology operates a statistical seasonal streamflow forecasting service to assist water management agencies in making informed decisions about water management strategies in the season ahead
The results demonstrate that mean skill, averaged across all forecast locations and seasons, decreases monotonically for lead times from 0 to 14 days and the trend continues to 21-day lead time
Compared with forecasts with 0-day lead time, the mean reduction in continuous ranked probability score (CRPS) skill scores in skilful cases is approximately 4 pp (Fig. 9), which is likely to be tolerated by forecast users in exchange for earlier forecast release
Summary
The Australian Bureau of Meteorology (the bureau) operates a statistical seasonal streamflow forecasting service to assist water management agencies in making informed decisions about water management strategies in the season ahead. The forecasts help reduce the uncertainty in seasonal flow volumes to be expected and provide users with more certainty in decision making. Water authorities use forecasts to assist decisions on water allocation and water restrictions, manage river operations, schedule environmental watering, develop water transfer strategy and provide water order advice. They use forecasts to guide future storage levels, help decide on releases, produce water allocation outlooks to inform water markets and manage risks at construction sites along rivers. State government agencies use forecasts to make decisions about environmental monitoring of streams, schedule irrigation, assess flood
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