Bayesian modelling of algal mass occurrences—using adaptive MCMC methods with a lake water quality model

Malve, Olli; Laine, Marko; Haario, Heikki; Kirkkala, Teija; Sarvala, Jouko

doi:10.1016/j.envsoft.2006.06.016

Cited by 80 publications

(45 citation statements)

References 12 publications

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“…Insufficient site characterization and temporal fluctuations render various parameters in (1) uncertain. The data reported in [4,5] suggest that over the summer, temperature T , global irradiance I, outflow rate q out , and predatory loss f i C z typically exhibit much smaller variation than the fluctuations of nutrients. Consequently, we treat the total nutrients contents (P tot and N tot ) as random functions of time t and assume the remaining parameters to be deterministic.…”

Section: Problem Formulationmentioning

confidence: 99%

“…This model is generalized to account for the temporal evolution of n algae groups with biomass concentrations c i (t) (i = 1,...,n) in a lake of volume V and average depth h. Then the model [4] consists of a system of n coupled ODEs,…”

Section: Problem Formulationmentioning

confidence: 99%

“…To be concrete, we base our analysis on a model of the growth of four competing algae groups: Diatoms, Chrysophyceae, nitrogen-fixing cyanobacteria, and minor species [4]. This model is generalized to account for the temporal evolution of n algae groups with biomass concentrations c i (t) (i = 1,...,n) in a lake of volume V and average depth h. Then the model [4] consists of a system of n coupled ODEs,…”

Section: Problem Formulationmentioning

confidence: 99%

“…A number of recent studies [3][4][5][6] employed a probabilistic framework to quantify parametric uncertainty in predictions of HABs. These analyses are typically based on simplifying assumptions and rely on the ensemble variance of concentrations to quantify predictive uncertainty.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Stochastic Forecasting of Algae Blooms in Lakes

Wang

Tartakovsky

2012

Modelling and Simulation in Fluid Dynamics in Porous Media

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We consider the development of harmful algal blooms (HABs) in a lake with uncertain nutrients inflow. To quantify the impact of this uncertainty on predictions of the concentrations of various algae groups, we explore two alternative approaches based on the Fokker-Planck equation and PDF methods. Both approaches quantify predictive uncertainty by deriving deterministic equations for joint probability density functions of the algae concentrations. As an example, we study the impact of uncertain initial concentration and inflow-outflow ratio on the evolution of cyanobacteria (the blue-green algae).

show abstract

Section: Problem Formulationmentioning

confidence: 99%

Section: Problem Formulationmentioning

confidence: 99%

Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Stochastic Forecasting of Algae Blooms in Lakes

Wang

Tartakovsky

2012

Modelling and Simulation in Fluid Dynamics in Porous Media

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“…Kuo et al (2008) used dynamic programming in a model for eutrophication management in Taiwan, and Burger et al (2008) modeled the relative importance of nutrient loads and phytoplankton biomass in a shallow lake. To estimate the effect of nutrients and grazing zooplankton on phytoplankton, Malve et al (2007) applied a Bayesian model for algal mass within a lake water-quality model. Elliott et al (2007) used a phytoplankton community model and a lake physical model to simulate the phytoplankton community of Lake Erke, Sweden.…”

Section: Introductionmentioning

confidence: 99%

Phytoplankton and Zooplankton Modeling in the Chahnimeh Man-Made Reservoir by Means of Issadm

Monfared¹

2013

Appl Ecol Env Res

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Abstract. The increase of phytoplankton and zooplankton in the lakes and reservoirs that supply water to downstream regions is an important environmental issue. In this paper, concentrations of phytoplankton and zooplankton in Chahnimeh reservoir are predicted using an Integrated Seasonal Separate AdvectionDiffusion Model (ISSADM). Chahnimeh reservoir is a seminatural deep hole that is stagnant in some part of the reservoir for most of the year. ISSADM is a three-dimensional numerical model based on a finitevolume solution that was developed to predict water quality in lakes and reservoirs affected by seasonal rivers. The model is capable of simulating water-quality parameters, as well as phytoplankton and zooplankton concentrations. To verify the model, field measurements and experiments conducted during a one-year period were compared with model results, demonstrating good agreement. The model is appropriate for predicting phytoplankton and zooplankton concentrations as well as eutrophication processes in similar, seasonal, aquatic environments.

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Parameter optimization, sensitivity, and uncertainty analysis of an ecosystem model at a forest flux tower site in the United States

Huang

Yan

2014

J Adv Model Earth Syst

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Ecosystem models are useful tools for understanding ecological processes and for sustainable management of resources. In biogeochemical field, numerical models have been widely used for investigating carbon dynamics under global changes from site to regional and global scales. However, it is still challenging to optimize parameters and estimate parameterization uncertainty for complex process-based models such as the Erosion Deposition Carbon Model (EDCM), a modified version of CENTURY, that consider carbon, water, and nutrient cycles of ecosystems. This study was designed to conduct the parameter identifiability, optimization, sensitivity, and uncertainty analysis of EDCM using our developed EDCM-Auto, which incorporated a comprehensive R package-Flexible Modeling Framework (FME) and the Shuffled Complex Evolution (SCE) algorithm. Using a forest flux tower site as a case study, we implemented a comprehensive modeling analysis involving nine parameters and four target variables (carbon and water fluxes) with their corresponding measurements based on the eddy covariance technique. The local sensitivity analysis shows that the plant production-related parameters (e.g., PPDF1 and PRDX) are most sensitive to the model cost function. Both SCE and FME are comparable and performed well in deriving the optimal parameter set with satisfactory simulations of target variables. Global sensitivity and uncertainty analysis indicate that the parameter uncertainty and the resulting output uncertainty can be quantified, and that the magnitude of parameter-uncertainty effects depends on variables and seasons. This study also demonstrates that using the cutting-edge R functions such as FME can be feasible and attractive for conducting comprehensive parameter analysis for ecosystem modeling.

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Bayesian modelling of algal mass occurrences—using adaptive MCMC methods with a lake water quality model

Cited by 80 publications

References 12 publications

Stochastic Forecasting of Algae Blooms in Lakes

Stochastic Forecasting of Algae Blooms in Lakes

Phytoplankton and Zooplankton Modeling in the Chahnimeh Man-Made Reservoir by Means of Issadm

Parameter optimization, sensitivity, and uncertainty analysis of an ecosystem model at a forest flux tower site in the United States

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