Details zur Publikation |
| Kategorie | Textpublikation |
| Referenztyp | Zeitschriften |
| DOI | 10.1016/j.wroa.2025.100386 |
Lizenz  |
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| Titel (primär) | Improving algal bloom modelling in eutrophic lakes by calibrating the General Lake Model with satellite remote sensing products |
| Autor | Siebers, M.A.C.; Werther, M.; Werther, D.; Mackay, E.; May, L.; Shatwell, T.; Jones, I.; Blake, M.; Hunter, P.D. |
| Quelle | Water Research X |
| Erscheinungsjahr | 2025 |
| Department | SEEFO |
| Band/Volume | 28 |
| Seite von | art. 100386 |
| Sprache | englisch |
| Topic | T5 Future Landscapes |
| Daten-/Softwarelinks | https://doi.org/10.5285/e404f64c-ddbc-4e3e-8dca-9bea3d68959a https://doi.org/10.5285/f5dc095c-e3f6-4d9e-be80-40e35c298142 https://doi.org/10.5285/2ce166c7-bff2-419b-a17f-0a5d53896416 https://doi.org/10.5285/26ea7dfb-59f2-4dc5-b1ee-e9270167a6a2 https://doi.org/10.5285/2969776d-0b59-4435-a746-da50b8fd62a3 https://doi.org/10.5285/6dd7ac13-c3e3-4156-a0d8-929227dbf9e8 https://doi.org/10.5285/89ff964d-4aed-4b66-8669-71d42c5add52 https://doi.org/10.5285/97256972-e6a5-471c-9236-3a2162075640 |
| Keywords | Algal bloom forecasting; Lake modelling calibration; Earth observation; Conformal prediction |
| 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. |
| dauerhafte UFZ-Verlinkung | https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=31197 |
| Siebers, M.A.C., Werther, M., Werther, D., Mackay, E., May, L., Shatwell, T., Jones, I., Blake, M., Hunter, P.D. (2025): Improving algal bloom modelling in eutrophic lakes by calibrating the General Lake Model with satellite remote sensing products Water Res. X 28 , art. 100386 10.1016/j.wroa.2025.100386 |
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