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

Siebers, Maud A.C. ORCID: https://orcid.org/0000-0003-4553-691X; Werther, Mortimer ORCID: https://orcid.org/0000-0002-0775-9285; Odermatt, Daniel ORCID: https://orcid.org/0000-0001-8449-0593; Mackay, Eleanor ORCID: https://orcid.org/0000-0001-5697-7062; May, Linda ORCID: https://orcid.org/0000-0003-3385-9973; Shatwell, Thomas ORCID: https://orcid.org/0000-0002-4520-7916; Jones, Ian; Blake, Matthew ORCID: https://orcid.org/0009-0009-5419-9338; Hunter, Peter D.. 2025 Improving algal bloom modelling in eutrophic lakes by calibrating the General Lake Model with satellite remote sensing products. Water Research X, 28, 100386. 12, pp. 10.1016/j.wroa.2025.100386

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Abstract/Summary

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.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.1016/j.wroa.2025.100386
UKCEH and CEH Sections/Science Areas:	Environmental Pressures and Responses (2025-)
ISSN:	2589-9147
Additional Information:	Open Access paper - full text available via Official URL link.
Additional Keywords:	algal bloom forecasting, lake modelling calibration, earth observation, conformal prediction
NORA Subject Terms:	Ecology and Environment Electronics, Engineering and Technology Hydrology
Date made live:	05 Aug 2025 14:05 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/540012

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