Forecasting Chlorophyll-a in the Murray–Darling Basin Using Remote Sensing
2025
Ming Li | Klaus Joehnk | Peter Toscas | Luis Riera Garcia | Huidong Jin | Tapas K. Biswas
Reliable forecasts of large-scale chlorophyll-a (Chl-a) levels one week ahead in the Murray&ndash:Darling Basin are essential for water resources management, as increasing Chl-a levels in water bodies indicate possible harmful algal blooms, a serious threat for freshwater security. A lack of high-resolution data in space and time is a major constraint for delivering early warnings. To address data scarcity, we developed a forecasting model integrating remote sensing data and time-series modelling. Using in situ Chl-a measurements from Murray&ndash:Darling Basin water bodies, we locally recalibrated a two-band ratio algorithm, namely the Normalized Difference Chlorophyll Index (NDCI), from Sentinel-2 data to derive Chl-a levels. The recalibrated model significantly improved the accuracy of high Chl-a estimates in our dataset after mitigating data heteroscedasticity. Building on these improved satellite-derived Chl-a estimates, we developed a time-series model for forecasting weekly Chl-a levels including quantification of forecast uncertainty through prediction intervals. The developed model, validated at eight sites for 2021&ndash:2022 data, performed well at shorter lead times, showing R2 = 0.41 and RMSE = 8.1 &mu:g/L for overall performance at a one-week lead time. The prediction intervals generally aligned well with nominal levels, demonstrating their reliability. This study provides a valuable tool for the water managers/decision-makers to issue early warnings of algal blooms in the Murray&ndash:Darling Basin.
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