Simulating water quality and ecological status of Lake Vansjø, Norway, under land-use and climate change by linking process-oriented models with a Bayesian network

Abstract

Excess nutrient inputs and climate change are two of multiple stressors affecting many lakes worldwide. Lake Vansjø in southern Norway is one such eutrophic lake impacted by blooms of toxic blue-green algae (cyanobacteria), and classified as moderate ecological status under the EU Water Framework Directive. Future climate change may exacerbate the situation. Here we use a set of chained models (global climate model, hydrological model, catchment phosphorus (P) model, lake model, Bayesian Network) to assess the possible future ecological status of the lake, given the set of climate scenarios and storylines common to the EU project MARS (Managing Aquatic Ecosystems and Water Resources under Multiple Stress). The model simulations indicate that climate change alone will increase precipitation and runoff, and give higher P fluxes to the lake, but cause little increase in phytoplankton biomass or changes in ecological status. For the storylines of future management and land-use, however, the model results indicate that both the phytoplankton biomass and the lake ecological status can be positively or negatively affected. Our results also show the value in predicting a biological indicator of lake ecological status, in this case, cyanobacteria biomass with a BN model. For all scenarios, cyanobacteria contribute to worsening the status assessed by phytoplankton, compared to using chlorophyll-a alone.

Coupling Water Column and Sediment Biogeochemical Dynamics: Modeling Internal Phosphorus Loading, Climate Change Responses, and Mitigation Measures in Lake Vansjø, Norway

“…Catchment hydrology and loads of suspended sediments, particulate P, and dissolved species were compiled previously (Couture et al, ), using daily measurements of flow and biweekly measurements of water chemistry over a 30‐year period (1 January 1983 to 31 December 2013) measured at the gauging station at Hogfoss (Station 3.22.0.1000.1; Norwegian Water Resources and Energy Directorate). Lake water chemistry and temperature data were provided by the Vansjø‐Hobøl monitoring program, conducted by the Norwegian Institute for Bioeconomy Research and by the Norwegian Institute for Water Research (Haande et al, ).…”

Applications of Bayesian Networks as Decision Support Tools for Water Resource Management under Climate Change and Socio-Economic Stressors: A Critical Appraisal

“…A high proportion of these studies have sought to understand climatic and/or non-climatic drivers and their impacts on water-related systems and to evaluate the performance of management options under these changing conditions. Effects of numerous climatic drivers have been considered, including sea-level rise on water supply management (e.g., [2]), and on water quality management (e.g., [49]); precipitation and temperature on groundwater management (e.g., [63,64,67]), on reservoir management (e.g., [135]), on water supply management (e.g., [3,132]), on water quality management (e.g., [27,43,45] and on nutrient management (e.g., [119,126]) and precipitation on water supply and demand management (e.g., [18,127,130,131]). Similarly, the non-climatic drivers that have been considered, have included effects of population growth on water supply and demand management (e.g., [128]), and on water quality management (e.g., [41]); crop production changes in irrigation system management (e.g., [96,102]); population growth and agricultural production on water supply and demand management (e.g., [128]); agricultural production on irrigation water management (e.g., [93][94][95]100]), on water supply management (e.g., [85]), and on groundwater management (e.g., [67]); changes in domestic use and in agricultural and industrial production on water supplies and demand management (e.g., [127]).…”

Long-term influence of climate and experimental eutrophication regimes on phytoplankton blooms

“…The response of lakes to concurrent drivers of eutrophication can be explored using 91 process-oriented models (Couture et al 2018; Page et al 2018;Janssen et al 2019). A variety of 92 lake ecosystem models exist that include physical processes and nutrient dynamics, varying in 93 modeling approach, spatial dimensions, and complexity of process representation (Robson 94 2014).…”