Global Sensitivity of Water Quality Modeling in the Gulf of Finland.
The Gulf of Finland is the most eutrophied water body in the Baltic Sea, which is mainly caused by nutrient loads produced by human activities in its surrounding cities. In order to solve this environmental problem, a computational model based on the understanding the relations between eutrophication, water quality and sediments is needed to forecast the water quality variance in response to the natural and anthropogenic influences. A precise water quality model can be useful to assist the policy making in the Gulf of Finland, and even for the whole Baltic Sea.
Kiirikki model, as one of these models describing the water quality of Baltic Sea in response of water quality variance, is a sediment and ecosystem based model, treating different sub-basins and layers as boxes. This study aims to assess the parameters’ sensitivity level on the scale of the Gulf of Finland. Firstly, the Morris sampling strategy is applied to generate economic OAT (One factor At a Time) samples before screening 50 out of 100 trajectories with distance as large as possible. In order to assess their sensitivity, index and indicator are needed. EE (elementary effect) is adopted to be the assessment index and four core eutrophication indicators from HELCOM 2009a are to be analyzed.
By comparing the (σ,μ) and (σ,μ') plots of each parameters’ EE values (σ is standard deviation, μ is mean value and μ' is the absolute mean value), some parameters are identified as potential sensitive parameter, such as the minimum biomass of cyanobacteria (Cmin), critical point of CO2 flux (CCr), the optimal temperature for detritus phosphorous mineralization (Toptgamma), maximum loss rate of algae (RAmax), optimal temperature for the growth of other algae (ToptmuA), Coefficient for temperature limiting factor for the growth of cyanobacteria (aTmuC), half-saturation coefficient of radiation for cyanobacteria (KIC) and so on. In contrast, the other parameters are ruled out as having very low values in terms of σ, μ and μ'. This is because the feature of Morris sampling strategy makes it easier to achieve high variance of the outputs, resulting into generally higher σ.
Therefore, further investigation with different strategies is needed after the initial screening of the non-sensitive parameters in this study.
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