Improving algal bloom modelling in eutrophic lakes by calibrating the General Lake Model with satellite remote sensing products

Abstract

Accurate forecasting of algal blooms in lakes can support effective freshwater management. However, observational datasets for calibrating and validating algal bloom forecasting models such as the General Lake Model - Aquatic Eco Dynamics (GLM-AED) are often scarce, which impedes robust model calibration and forecasting ability. Satellite remote sensing can help fill these gaps by offering high-frequency, large-scale measurements of phytoplankton chlorophyll-a concentration (mg m-3), but satellite chl-a products often carry high uncertainty. Here we introduce a novel approach to quantify uncertainty in satellite chl-a based on conformal prediction, with the aim of integrating robust chlorophyll-a products into GLM-AED. Using Sentinel-2 imagery from two eutrophic lakes in the UK, Esthwaite Water and Loch Leven, we obtain remotely sensed chlorophyll-a with low systematic signed percentage bias (-1.22 % and 0.38) and moderate median symmetric accuracy (15.87 and 43.02 %) using Polymer atmospheric correction. We effectively flag potentially uncertain chlorophyll-a estimates (coverage factor: 75.6 - 81 %). Integrating the screened remotely sensed chlorophyll-a estimates improved GLM-AED algal bloom forecasts by 50 % in Loch Leven and 13 % in Esthwaite Water, with the greater improvement in Loch Leven attributed to its higher initial model errors. In contrast, incorporating unscreened chlorophyll-a estimates into GLM-AED increases validation errors on average by 32 %. Our findings show that process-based model predictions can substantially benefit from incorporating additional satellite-derived chlorophyll-a estimates. At the same time, they highlight a crucial need for robust uncertainty quantification to support downstream applications such as algorithm validation, biological monitoring in data-scarce regions, and water management decision-making. Moreover, because conformal prediction is model-agnostic and satellite-derived chlorophyll-a products are globally accessible, our study paves the way for large-scale, well-calibrated bloom forecasting through process-based models.