+ Site Statistics
References:
52,654,530
Abstracts:
29,560,856
PMIDs:
28,072,755
+ Search Articles
+ Subscribe to Site Feeds
Most Shared
PDF Full Text
+ PDF Full Text
Request PDF Full Text
+ Follow Us
Follow on Facebook
Follow on Twitter
Follow on LinkedIn
+ Translate
+ Recently Requested

Eutrophication risk assessment using Bayesian calibration of process-based models: Application to a mesotrophic lake



Eutrophication risk assessment using Bayesian calibration of process-based models: Application to a mesotrophic lake



Ecological Modelling 208(2-4): 215-229



We introduce the Bayesian calibration of process-based models to address the urgent need for robust modeling tools that can effectively support environmental management. The proposed framework aims to combine the advantageous features of both mechanistic and statistical approaches. Models that are based on mechanistic understanding yet remain within the bounds of data-based parameter estimation can accommodate rigorous and complete error analysis. The incorporation of mechanism improves the confidence in predictions made for a variety of conditions, while the statistical methods provide an empirical basis for parameter estimation and allow for estimates of predictive uncertainty. Our illustration focuses on eutrophication modeling but the proposed methodological framework can be easily transferred to a wide variety of disciplines (e.g., hydrology, ecotoxicology, air pollution). We examine the advantages of the Bayesian calibration using a four state variable (phosphate-detritus-phytoplankton-zooplankton) model and the mesotrophic Lake Washington (Washington State, USA) as a case study. Prior parameter distributions were formed on the basis of literature information, while Markov chain Monte Carlo simulations provided a convenient means for approximating the posterior parameter distributions. The model reproduces the key epilimnetic temporal patterns of the system and provides realistic estimates of predictive uncertainty for water quality variables of environmental interest. Finally, we highlight the benefits of Bayesian parameter estimation, such as the quantification of uncertainty in model predictions, optimization of the sampling design of monitoring programs using value of information concepts from decision theory, alignment with the policy practice of adaptive management, and expression of model outputs as probability distributions, that are perfectly suited for stakeholders and policy makers when making decisions for sustainable environmental management.

(PDF emailed within 0-6 h: $19.90)

Accession: 020995005

Download citation: RISBibTeXText

DOI: 10.1016/j.ecolmodel.2007.05.020


Related references

Influence of the drainage basin on the eutrophication of the a mesotrophic lake piaseczno and dis eutrophication of the pond lake bikcze poland. Acta Hydrobiologica 18(1): 23-52, 1976

Bayesian calibration of process-based forest models: bridging the gap between models and data. Tree Physiology 25(7): 915-927, 2005

Do higher data frequency and Bayesian auto-calibration lead to better model calibration? Insights from an application of INCA-P, a process-based river phosphorus model. Journal of Hydrology 527: 641-655, 2015

Predicting the frequency of water quality standard violations using Bayesian calibration of eutrophication models. Journal Of Great Lakes Research: 4, 698-720, 2008

Graphical models and Bayesian domains in risk modelling: application in microbiological risk assessment. Preventive Veterinary Medicine 110(1): 4-11, 2013

Integration of Bayesian analysis for eutrophication prediction and assessment in a landscape lake. Environmental Monitoring and Assessment 187(1): 4169, 2015

Calibration of crash risk models on freeways with limited real-time traffic data using Bayesian meta-analysis and Bayesian inference approach. Accident; Analysis and Prevention 85: 207-218, 2016

Toward a Bayesian procedure for using process-based models in plant breeding, with application to ideotype design. Euphytica 207(3): 627-643, 2016

Assessment of lake eutrophication recovery: the filtering trajectory method (FTM) and its application to Dianchi Lake, China. Environmental Monitoring and Assessment 191(6): 360, 2019

Non-chemical Risk Assessment for Lifting and Low Back Pain Based on Bayesian Threshold Models. Safety and Health at Work 8(2): 206-211, 2017

Application of mathematical models to the eutrophication process. Pages 16-30 1968, 1968

Eutrophication process Alkaline phosphatase activity of microplankton in the moroccan mesotrophic reservoir. Revue des Sciences de l'Eau 15(1): 51-62, 2002

Development of Lake Ladoga ecosystem models: Modeling of the phytoplankton succession in the eutrophication process. I. Ecological Modelling 165(1): 49-77, 1 July, 2003

Bayesian approach for the calibration of models: application to an urban stormwater pollution model. Water Science and Technology 47(4): 77-84, 2003

Bayesian variable selection for Gaussian process regression: Application to chemometric calibration of spectrometers. Neurocomputing 73(13-15): 2718-2726, 2010