While oceanic and coastal acidification has gained increased attention,long-term pH trends and their drivers in large freshwater systems remain poorly understood.The Laurentian Great Lakes are the world’s largest fre...While oceanic and coastal acidification has gained increased attention,long-term pH trends and their drivers in large freshwater systems remain poorly understood.The Laurentian Great Lakes are the world’s largest freshwater system,and in many ways resemble marine ecosystems.However,unlike the open ocean and coastal waters where pH has declined due to rising atmospheric CO_(2),no significant pH trends have been observed in the Laurentian Great Lakes,despite significant ecosystem changes driven partly by the invasion of dreissenid mussels.This study examined 41 years of field observations from Lake Michigan to investigate the long-term carbonate chemistry dynamics.Observational results revealed substantial declines in both total alkalinity(TA)and dissolved inorganic carbon(DIC)over the four decades.Mussel shell calcification emerged as the primary mechanism behind these declines,accounting for 97%and 47%of the observed changes in TA and DIC,respectively,lowering water column pH by 0.24 units.Elevated carbon accumulation in soft mussel tissues,coupled with long-term changes in the air-water pCO_(2)gradient during summer,significantly contributed to long-term DIC variations,explaining 18%and 28%of the lake-wide DIC loss.These two mechanisms also resulted in an overall pH increase of 0.09 and 0.12 units,largely offsetting the calcification-driven pH decrease.These findings bridge a gap in acidification research for large freshwater systems and provide valuable insights for comprehensive lake-wide management strategies.展开更多
With all the improvement in wave and hydrodynamics numerical models, the question rises in our mind that how the accuracy of the forcing functions and their input can affect the results. In this paper, a commonly used...With all the improvement in wave and hydrodynamics numerical models, the question rises in our mind that how the accuracy of the forcing functions and their input can affect the results. In this paper, a commonly used numerical third-generation wave model, SWAN is applied to predict waves in Lake Michigan. Wind data are analyzed to determine wind variation frequency over Lake Michigan. Wave predictions uncertainty due to wind local effects are compared during a period where wind has a fairly constant speed and direction over the northern and southern basins. The study shows that despite model calibration in Lake Michigan area, the model deficiency arises from ignoring wind effects in small scales. Wave prediction also emphasizes that small scale turbulence in meteorological forces can increase prediction errors by 38%. Wave frequency and coherence analysis show that both models can predict the wave variation time scale with the same accuracy. Insufficient number of meteorological stations can result in neglecting local wind effects and discrepancies in current predictions. The uncertainty of wave numerical models due to input uncertainties and model principals should be taken into account for design risk factors.展开更多
Lake Michigan, the sixth largest freshwater lake in the world by surface area, was utilized as a water body for assessment. Field data collected at sampling sites throughout the lake in an intensive monitoring effort ...Lake Michigan, the sixth largest freshwater lake in the world by surface area, was utilized as a water body for assessment. Field data collected at sampling sites throughout the lake in an intensive monitoring effort were utilized for evaluation of the distribution of sediment measurements. An assessment of sediment nutrient and carbon measurements within Lake Michigan was completed to recognize strata resulting from the hydrodynamics of the system. Nonparametric comparison tests revealed that significant differences exist between measurements of sediment nutrients and organic carbon in the lake using strata based upon water column depth (all results demon-strated a p < 0.05, α = 0.05). Cross-validation analysis was applied to the field-collected samples, revealing that large errors occur when estimating sediment flux of carbon or nutrients at a given location in the lake without considering stratification of the distributions of these measurements. Errors in estimating sediment concentrations of nutrients and carbon specific to a location in the lake demonstrated a statistically significant increase when stratification of sediment measurements wasn’t employed among sites. For example, distributions of errors in estimating all nutrients and organic carbon concentrations, whereby distance squared inverse interpolation methods were applied, demonstrated a statistically significant increase in absence of stratification (all p < 0.001, α = 0.05). These results have implications for characterization, monitoring, and modeling sediment and water interaction as related to eutrophication, as well as to contaminant exposure and bioaccumulation for chemicals within Lake Michigan and large water bodies where stratification of the sediment based upon physics of the system exists.展开更多
In this paper,the Particle Swarm Optimization(PSO)algorithm is employed to deal with the Adaptive Network based Fuzzy Inference System(ANFIS)model drawbacks in prediction of wind-driven waves.In the ANFIS model select...In this paper,the Particle Swarm Optimization(PSO)algorithm is employed to deal with the Adaptive Network based Fuzzy Inference System(ANFIS)model drawbacks in prediction of wind-driven waves.In the ANFIS model selection of fuzzy IF-THEN rules structure and numbers is not an automatic process.In addition,in the ANFIS model extraction of fuzzy antecedent and consequent parameters is a gradient-based method which makes the answer susceptible to entrap in local optima.To cope with the ANFIS deficiencies,herein the PSO algorithm is coupled with the wave predictor FIS models in three viewpoints to optimize fuzzy subtractive clustering parameters,i.e.radii of clustering and quash factor,and the antecedent and consequent parameter of fuzzy IF-THEN rules.At first viewpoint,two PSO algorithms are used to optimize fuzzy subtractive clustering parameters and fuzzy IF-THEN rule parameters.In the second viewpoint,a PSO algorithm is used to optimize subtractive clustering parameters while the ANFIS model is used to tune the fuzzy IF-THEN rule parameters.In the third viewpoint,only a PSO algorithm is used to optimize the subtractive clustering parameters along with fuzzy IF-THEN rule parameters.Gathered data sets by National Data Buoy Center(NDBC)at Lake Michigan are used to evaluate the developed models for prediction of wave parameters including significant wave height and peak spectral period.Results indicate the efficiency of PSO algorithm to improve the ANFIS model accuracy.展开更多
基金Supported by the National Natural Science Foundation of China(No.43277051)the Key Laboratory of Integrated Regulation and Resources Development of Shallow Lakes of Ministry of Education(No.B230203006).
文摘While oceanic and coastal acidification has gained increased attention,long-term pH trends and their drivers in large freshwater systems remain poorly understood.The Laurentian Great Lakes are the world’s largest freshwater system,and in many ways resemble marine ecosystems.However,unlike the open ocean and coastal waters where pH has declined due to rising atmospheric CO_(2),no significant pH trends have been observed in the Laurentian Great Lakes,despite significant ecosystem changes driven partly by the invasion of dreissenid mussels.This study examined 41 years of field observations from Lake Michigan to investigate the long-term carbonate chemistry dynamics.Observational results revealed substantial declines in both total alkalinity(TA)and dissolved inorganic carbon(DIC)over the four decades.Mussel shell calcification emerged as the primary mechanism behind these declines,accounting for 97%and 47%of the observed changes in TA and DIC,respectively,lowering water column pH by 0.24 units.Elevated carbon accumulation in soft mussel tissues,coupled with long-term changes in the air-water pCO_(2)gradient during summer,significantly contributed to long-term DIC variations,explaining 18%and 28%of the lake-wide DIC loss.These two mechanisms also resulted in an overall pH increase of 0.09 and 0.12 units,largely offsetting the calcification-driven pH decrease.These findings bridge a gap in acidification research for large freshwater systems and provide valuable insights for comprehensive lake-wide management strategies.
文摘With all the improvement in wave and hydrodynamics numerical models, the question rises in our mind that how the accuracy of the forcing functions and their input can affect the results. In this paper, a commonly used numerical third-generation wave model, SWAN is applied to predict waves in Lake Michigan. Wind data are analyzed to determine wind variation frequency over Lake Michigan. Wave predictions uncertainty due to wind local effects are compared during a period where wind has a fairly constant speed and direction over the northern and southern basins. The study shows that despite model calibration in Lake Michigan area, the model deficiency arises from ignoring wind effects in small scales. Wave prediction also emphasizes that small scale turbulence in meteorological forces can increase prediction errors by 38%. Wave frequency and coherence analysis show that both models can predict the wave variation time scale with the same accuracy. Insufficient number of meteorological stations can result in neglecting local wind effects and discrepancies in current predictions. The uncertainty of wave numerical models due to input uncertainties and model principals should be taken into account for design risk factors.
文摘Lake Michigan, the sixth largest freshwater lake in the world by surface area, was utilized as a water body for assessment. Field data collected at sampling sites throughout the lake in an intensive monitoring effort were utilized for evaluation of the distribution of sediment measurements. An assessment of sediment nutrient and carbon measurements within Lake Michigan was completed to recognize strata resulting from the hydrodynamics of the system. Nonparametric comparison tests revealed that significant differences exist between measurements of sediment nutrients and organic carbon in the lake using strata based upon water column depth (all results demon-strated a p < 0.05, α = 0.05). Cross-validation analysis was applied to the field-collected samples, revealing that large errors occur when estimating sediment flux of carbon or nutrients at a given location in the lake without considering stratification of the distributions of these measurements. Errors in estimating sediment concentrations of nutrients and carbon specific to a location in the lake demonstrated a statistically significant increase when stratification of sediment measurements wasn’t employed among sites. For example, distributions of errors in estimating all nutrients and organic carbon concentrations, whereby distance squared inverse interpolation methods were applied, demonstrated a statistically significant increase in absence of stratification (all p < 0.001, α = 0.05). These results have implications for characterization, monitoring, and modeling sediment and water interaction as related to eutrophication, as well as to contaminant exposure and bioaccumulation for chemicals within Lake Michigan and large water bodies where stratification of the sediment based upon physics of the system exists.
文摘In this paper,the Particle Swarm Optimization(PSO)algorithm is employed to deal with the Adaptive Network based Fuzzy Inference System(ANFIS)model drawbacks in prediction of wind-driven waves.In the ANFIS model selection of fuzzy IF-THEN rules structure and numbers is not an automatic process.In addition,in the ANFIS model extraction of fuzzy antecedent and consequent parameters is a gradient-based method which makes the answer susceptible to entrap in local optima.To cope with the ANFIS deficiencies,herein the PSO algorithm is coupled with the wave predictor FIS models in three viewpoints to optimize fuzzy subtractive clustering parameters,i.e.radii of clustering and quash factor,and the antecedent and consequent parameter of fuzzy IF-THEN rules.At first viewpoint,two PSO algorithms are used to optimize fuzzy subtractive clustering parameters and fuzzy IF-THEN rule parameters.In the second viewpoint,a PSO algorithm is used to optimize subtractive clustering parameters while the ANFIS model is used to tune the fuzzy IF-THEN rule parameters.In the third viewpoint,only a PSO algorithm is used to optimize the subtractive clustering parameters along with fuzzy IF-THEN rule parameters.Gathered data sets by National Data Buoy Center(NDBC)at Lake Michigan are used to evaluate the developed models for prediction of wave parameters including significant wave height and peak spectral period.Results indicate the efficiency of PSO algorithm to improve the ANFIS model accuracy.
