Developing scenario-based, near-term iterative forecasts to inform water management

Near-term, iterative ecosystem forecasts with scenarios representing alternate management decisions have high potential for providing valuable insights on how decisions may impact future ecological conditions. Scenario-based forecasts may be particularly useful for informing the decision-making around freshwater ecosystems managed for multiple objectives. We applied a near-term, iterative forecasting system at Lake Alexandrina, Australia, which is located at the nexus of one of the world’s largest catchments and a protected coastal wetland. Lake Alexandrina is highly managed through multi-gate barrages to optimize catchment, lake, and outflow targets, providing an unparalleled opportunity to investigate the value of scenario-based probabilistic forecasting for decision-makers. We co-developed the forecasting system with managers to generate automated 1-30 day-ahead barrage operation scenario forecasts. Over ∼2 years, forecasts accurately predicted lake water level, water temperature, and salinity and outperformed null baseline models up to 10-30 days ahead. Alternative barrage scenarios were particularly important during drier hydrological conditions and resulted in substantive changes to lake level and salt export from the lake. Lake temperatures and salinity were less sensitive to barrage operations and generally more dependent on variability in lake inflow discharge. Overall, our study provides a framework for forecasting lake water quantity and quality variables for anticipatory decision-making.