Authors: Rakhshinda Bano – University of Vermont
Title: What Limits Our Forecasts? Input Variable Sensitivity in 30-Day HABs Predictions
Presentation Type: Poster
Abstract: Harmful Algal Blooms (HABs) pose significant environmental and economic challenges, particularly in sensitive ecosystems like Lake Champlain, where their impacts can disrupt aquatic health and local livelihoods. In this context, effective seasonal forecasting becomes crucial, enabling stakeholders to anticipate bloom events and implement timely mitigation and management strategies. This study employs a process-based modelling approach to simulate the seasonal dynamics of HABs. It integrates hydrological forecasts from the National Water Model and meteorological data to drive the AEM3D lake ecosystem model, capturing nutrient loading and bloom formation. The study focuses on two key case study areas: St. Albans Bay and Missisquoi Bay (located in the U.S. part of Lake Champlain). Additionally, a sensitivity analysis is conducted to quantify the elasticity of HAB forecasts to varying hydrological and meteorological inputs, evaluating the responsiveness of predictions to input perturbations. This analysis provides insights into the drivers of forecast variability and supports the development of more resilient seasonal prediction systems for HABs. Early warning and prediction of HABs can empower policymakers to implement timely interventions, thereby mitigating environmental degradation, economic losses, and health impacts associated with HABs.