Bayesian calibration and uncertainty quantification for a large nutrient load impact modelZenodoZenodoZenodo
Nutrient load simulators are large, deterministic, models that simulate the hydrodynamics and biogeochemical processes in aquatic ecosystems. They are central tools for planning cost-efficient actions to fight eutrophication since they allow scenario predictions on impacts of nutrient load reduction...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Ecological Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954124005181 |
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| _version_ | 1832595420353658880 |
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| author | Karel Kaurila Risto Lignell Frede Thingstad Harri Kuosa Jarno Vanhatalo |
| author_facet | Karel Kaurila Risto Lignell Frede Thingstad Harri Kuosa Jarno Vanhatalo |
| author_sort | Karel Kaurila |
| collection | DOAJ |
| description | Nutrient load simulators are large, deterministic, models that simulate the hydrodynamics and biogeochemical processes in aquatic ecosystems. They are central tools for planning cost-efficient actions to fight eutrophication since they allow scenario predictions on impacts of nutrient load reductions to, e.g., harmful algal biomass growth. Due to being computationally heavy, the uncertainties related to these predictions are typically not rigorously assessed though. In this work, we developed a novel Bayesian computational approach for estimating the uncertainties in predictions of the Finnish coastal nutrient load model FICOS. First, we constructed a likelihood function for the multivariate spatio-temporal outputs of the FICOS model. Then, we used Bayesian optimization to locate the posterior mode for the model parameters conditional on long term monitoring data. After that, we constructed a space-filling design for FICOS model runs around the posterior mode and used it to train a Gaussian process emulator for the (log) posterior density of the model parameters. We then integrated over this (approximate) parameter posterior to produce probabilistic predictions for algal biomass and chlorophyll a concentration under alternative nutrient load reduction scenarios. Our computational algorithm allowed for fast posterior inference and the Gaussian process emulator had good predictive accuracy within the highest posterior probability mass region. The posterior predictive scenarios showed that the probability to reach the EU’s Water Framework Directive objectives in the Finnish Archipelago Sea is generally low even under large load reductions. |
| format | Article |
| id | doaj-art-b85c08328d4741efafaa02510c004874 |
| institution | Kabale University |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-b85c08328d4741efafaa02510c0048742025-01-19T06:24:42ZengElsevierEcological Informatics1574-95412025-03-0185102976Bayesian calibration and uncertainty quantification for a large nutrient load impact modelZenodoZenodoZenodoKarel Kaurila0Risto Lignell1Frede Thingstad2Harri Kuosa3Jarno Vanhatalo4Department of Mathematics and Statistics, Faculty of Science, University of Helsinki, P.O. Box 68, 00014, Helsinki, Finland; Corresponding author.Finnish Environment Institute, Latokartanonkaari 11, 00790, Helsinki, FinlandDepartment of Biological Sciences, University of Bergen, Postboks 7803, 5020, Bergen, NorwayFinnish Environment Institute, Latokartanonkaari 11, 00790, Helsinki, FinlandDepartment of Mathematics and Statistics, Faculty of Science, University of Helsinki, P.O. Box 68, 00014, Helsinki, Finland; Organismal and Evolutionary Biology Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, P.O. Box 65, 00014, Helsinki, FinlandNutrient load simulators are large, deterministic, models that simulate the hydrodynamics and biogeochemical processes in aquatic ecosystems. They are central tools for planning cost-efficient actions to fight eutrophication since they allow scenario predictions on impacts of nutrient load reductions to, e.g., harmful algal biomass growth. Due to being computationally heavy, the uncertainties related to these predictions are typically not rigorously assessed though. In this work, we developed a novel Bayesian computational approach for estimating the uncertainties in predictions of the Finnish coastal nutrient load model FICOS. First, we constructed a likelihood function for the multivariate spatio-temporal outputs of the FICOS model. Then, we used Bayesian optimization to locate the posterior mode for the model parameters conditional on long term monitoring data. After that, we constructed a space-filling design for FICOS model runs around the posterior mode and used it to train a Gaussian process emulator for the (log) posterior density of the model parameters. We then integrated over this (approximate) parameter posterior to produce probabilistic predictions for algal biomass and chlorophyll a concentration under alternative nutrient load reduction scenarios. Our computational algorithm allowed for fast posterior inference and the Gaussian process emulator had good predictive accuracy within the highest posterior probability mass region. The posterior predictive scenarios showed that the probability to reach the EU’s Water Framework Directive objectives in the Finnish Archipelago Sea is generally low even under large load reductions.http://www.sciencedirect.com/science/article/pii/S1574954124005181Bayesian inferenceUncertainty quantificationBayesian optimizationGaussian processEcological simulatorPrediction |
| spellingShingle | Karel Kaurila Risto Lignell Frede Thingstad Harri Kuosa Jarno Vanhatalo Bayesian calibration and uncertainty quantification for a large nutrient load impact modelZenodoZenodoZenodo Ecological Informatics Bayesian inference Uncertainty quantification Bayesian optimization Gaussian process Ecological simulator Prediction |
| title | Bayesian calibration and uncertainty quantification for a large nutrient load impact modelZenodoZenodoZenodo |
| title_full | Bayesian calibration and uncertainty quantification for a large nutrient load impact modelZenodoZenodoZenodo |
| title_fullStr | Bayesian calibration and uncertainty quantification for a large nutrient load impact modelZenodoZenodoZenodo |
| title_full_unstemmed | Bayesian calibration and uncertainty quantification for a large nutrient load impact modelZenodoZenodoZenodo |
| title_short | Bayesian calibration and uncertainty quantification for a large nutrient load impact modelZenodoZenodoZenodo |
| title_sort | bayesian calibration and uncertainty quantification for a large nutrient load impact modelzenodozenodozenodo |
| topic | Bayesian inference Uncertainty quantification Bayesian optimization Gaussian process Ecological simulator Prediction |
| url | http://www.sciencedirect.com/science/article/pii/S1574954124005181 |
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