Year: 2025
Type: preprint
Abstract: Near-term, iterative ecosystem forecasts with scenarios representing alternate management decisions have high potential for providing valuable insights on how different decisions may impact future ecological conditions. For freshwater ecosystems, which are often managed for multiple objectives, scenario-based forecasting has yet to be commonly applied for water quality management, despite a rich history in water quantity forecasting (e.g., floods, droughts). We applied a near-term, iterative water quality 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 a series of multi-gate barrages to optimize multiple 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 the lake managers to generate automated 1-30 day-ahead barrage operation scenario forecasts to inform management decisions. Over ~2 years, forecasts were able to accurately predict lake water level, water temperature, and salinity and outperformed null baseline models up to 10-30 days ahead. Alternative barrage management scenarios were particularly important during drier hydrological conditions and resulted in substantive changes to lake level and export of salt from the lake. In-situ lake temperatures and salinity were less sensitive to barrage operations and generally more dependent on variability in lake inflow discharge. Overall, our study helps provide a framework for anticipatory decision-making to meet water quantity and quality targets in a highly-managed system increasingly experiencing climate stressors.
Language: English
Subfield: Ocean Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Awards: Global Centers Track 2: Building the Global Center for Forecasting Freshwater Futures, NEON RCN: The Ecological Forecasting Initiative RCN: Using NEON-enabled near-term forecasting to synthesize our understanding of predictability across ecological systems and scales, Collaborative Research: URoL:ASC: Applying rules of life to forecast emergent behavior of phytoplankton and advance water quality management, Between a hot place & hypoxia: Quantifying fish-kill risk in inland rivers
OpenAlex ID: W4414498604
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