How morphology shapes the parameter sensitivity of lake ecosystem models

Andersen, Tobias Kuhlmann; Bolding, Karsten; Nielsen, Anders; Bruggeman, Jorn; Trolle, Dennis

doi:10.1016/j.envsoft.2020.104945

Cited by 19 publications

(12 citation statements)

References 45 publications

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“…We performed a sensitivity analysis using the parsac software to determine what parameters to include in the calibration of the Selmaprotbas model (Bruggeman and Bolding 2020;Andersen et al 2021). Parsac applies the Sensitivity Analysis Library (Herman and Usher 2017) in Python.…”

Section: Sensitivity Analysismentioning

confidence: 99%

Drivers of phytoplankton responses to summer wind events in a stratified lake: A modeling study

Mesman

Ayala

Goyette

et al. 2022

Limnology & Oceanography

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Extreme wind events affect lake phytoplankton by deepening the mixed layer and increasing internal nutrient loading. Both increases and decreases in phytoplankton concentration after strong wind events have been observed, but the precise mechanisms driving these responses remain poorly understood or quantified. We coupled a one-dimensional physical model to a biogeochemical model to investigate the factors regulating short-term phytoplankton responses to summer wind events, now and under expected warmer future conditions. We simulated physical, chemical, and biological dynamics in Lake Erken, Sweden, and found that strong wind could increase or decrease the phytoplankton concentration in the euphotic zone 1 week after the event, depending on antecedent lake physical and chemical conditions. Wind had little effect on phytoplankton concentration if the mixed layer was deep prior to wind exposure. Higher incoming shortwave radiation and hypolimnetic nutrient concentration boosted phytoplankton concentration, whereas higher surface water temperatures decreased concentrations after wind events. Medium-intensity wind events resulted in more phytoplankton than high-intensity wind. Simulations under a future climate scenario did not show marked differences in the way wind events affect phytoplankton concentration. These findings help to better understand how wind impacts vary as a function of local environmental conditions and how climate warming and changing extreme weather dynamics will affect lake ecosystems.

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Section: Sensitivity Analysismentioning

confidence: 99%

Drivers of phytoplankton responses to summer wind events in a stratified lake: A modeling study

Mesman

Ayala

Goyette

et al. 2022

Limnology & Oceanography

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“… 31 , 45 , 74 , 75 For these methods, modeling plays a complementary role, allowing not only prediction of spatial and temporal distribution of nutrients but also simulation of processes responsible for excessive biomass growth in surface waters. Although this problem is well discerned for lakes and reservoirs, 14 − 18 attempts to verify and apply numerical models to analyze nutrient loads and chlorophyll “a” concentrations in flowing surface waters are still scarce. 27 , 28 , 75 , 76 This is mainly due to the multiplicity and complexity of processes taking place and the limited amount of monitoring data in such systems.…”

Section: Discussionmentioning

confidence: 99%

Biomass Production Potential in a River under Climate Change Scenarios

Orlińska-Woźniak

Szalińska

Jakusik

et al. 2021

Environ. Sci. Technol.

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Excessive production of biomass, in times of intensification of agriculture and climate change, is again becoming one of the biggest environmental issues. Identification of sources and effects of this phenomenon in a river catchment in the space–time continuum has been supported by advanced environmental modules combined on a digital platform (Macromodel DNS/SWAT). This tool enabled the simulation of nutrient loads and chlorophyll “a” for the Nielba River catchment (central-western Poland) for the biomass production potential (defined here as a TN:TP ratio) analysis. Major differences have been observed between sections of the Nielba River with low biomass production in the upper part, controlled by TN:TP ratios over 65, and high chlorophyll “a” concentrations in the lower part, affected by biomass transport for the flow-through lakes. Under the long and short-term RCP4.5 and RCP8.5 climate change scenarios, this pattern will be emphasized. The obtained results showed that unfavorable biomass production potential will be maintained in the upper riverine sections due to a further increase in phosphorus loads induced by precipitation growth. Precipitation alone will increase biomass production, while precipitation combined with temperature can even enhance this production in the existing hot spots.

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“…To determine what parameters to include in the calibration of the Selmaprotbas model (biogeochemistry), we performed a sensitivity analysis using the ParSAC software (Bruggeman and Bolding 2020;Andersen et al 2021), which applies the Sensitivity Analysis Library (SALib, Herman and Usher 2017) in Python. All parameters in the Selmaprotbas model, scaling factors for inflow discharge and concentrations, and the five calibrated parameters in GOTM were included in the sensitivity analysis (totalling 55 parameters), and responses in simulated mean values of ammonium, nitrate, phosphate, total phosphorus, chlorophyll-a, and oxygen were assessed.…”

Section: Sensitivity Analysismentioning

confidence: 99%

“…We followed a density-based delta-sensitivity method (Borgonovo 2007), which has been described by Andersen et al (2021) for the GOTM-FABM-PCLake model. First, a Latin hypercube sampling (McKay et al 2000) was done to generate a number of parameter sets equal to 200 times the number of parameters.…”

Section: Sensitivity Analysismentioning

confidence: 99%

“…A 95-percent confidence interval around the sensitivity values was obtained by 100 resamples using a bootstrapping approach (Plischke et al 2013). To distinguish sensitive from insensitive parameters, we introduced a dummy parameter in the sensitivity analysis (Andersen et al 2021). If a parameter's sensitivity value fell within the 95-percent confidence interval of the dummy for all variables (ammonium, nitrate, phosphate, chlorophyll-a, oxygen), that parameter was excluded from the calibration.…”

Section: Sensitivity Analysismentioning

confidence: 99%

See 1 more Smart Citation

Drivers of phytoplankton responses to summer storms in a stratified lake: a modelling study

Mesman¹,

Ayala²,

Goyette³

et al. 2022

Preprint

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Extreme wind events affect lake phytoplankton amongst others by deepening the mixed layer and increasing internal nutrient loading. Both increases and decreases of phytoplankton biomass after storms have been observed, but the precise mechanisms driving these responses remain poorly understood or quantified. In this study, we coupled a one-dimensional physical model to a biogeochemical model to investigate the factors regulating short-term phytoplankton responses to summer storms, now and under expected high-temperature conditions. We simulated physical, chemical and biological dynamics in Lake Erken, Sweden, to take advantage of a long-term time series that we used to calibrate our model. We found that wind storms could increase or decrease the phytoplankton concentration one week after the storm, depending on antecedent lake physical and chemical conditions. Storms had little effect on phytoplankton biomass if the mixed layer was deep prior to storm exposure. Higher incoming shortwave radiation and hypolimnetic nutrient concentration boosted growth, whereas higher surface water temperatures decreased phytoplankton concentration after storms. Medium-intensity wind speeds resulted in more phytoplankton biomass after storms than high-intensity wind. Simulations under a future climate scenario did not show marked differences in the way wind affects phytoplankton growth following storms. Our study shows that storm impacts on lake phytoplankton are complex and likely to vary as a function of local environmental conditions.

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How morphology shapes the parameter sensitivity of lake ecosystem models

Cited by 19 publications

References 45 publications

Drivers of phytoplankton responses to summer wind events in a stratified lake: A modeling study

Drivers of phytoplankton responses to summer wind events in a stratified lake: A modeling study

Biomass Production Potential in a River under Climate Change Scenarios

Drivers of phytoplankton responses to summer storms in a stratified lake: a modelling study

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