Development of a structurally dynamic model for forecasting the effects of restoration of Lake Fure, Denmark

Gürkan, Zeren; Zhang, J.; Jørgensen, S.E.

doi:10.1016/j.ecolmodel.2006.03.006

Cited by 71 publications

(25 citation statements)

References 22 publications

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“…For example, Gurkan et al (2006) developed a structurally dynamic model to assess the effects of restoration on Lake Fure, Denmark. Liu et al (2008) used a Bayesian approach in the model to estimate effects of nutrient reduction.…”

Section: Introductionmentioning

confidence: 99%

Modeling the effects of external nutrient reductions on algal blooms in hyper-eutrophic Lake Taihu, China

Tang

Acharya

2016

Ecological Engineering

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a b s t r a c tHarmful algal blooms in Lake Taihu, China, are getting plagued due to excess nutrients loads. Despite several recent strict nutrient management strategies, especially to reduce the external nutrient loads, Lake Taihu still receives large amounts of nutrients from its tributaries. As a result, frequency and intensity of algal blooms have not abated as expected. In this study, a three-dimensional eutrophication model was used to identify the limiting factors for algal growth and the effect of external nutrient loads reduction on water quality. The modeling results showed that the limiting factor for the dominant algae (cyanobacteria) depended on seasonality and location. Water temperature affected algal growth in winter, while light was important for deeper water columns in all seasons. Phosphorus (P) and Nitrogen (N) impacted algal growth from spring to autumn. Results showed that Chlorophyll a (Chl-a) concentrations only decreased about 38%, 26% and 24% in Meiliang Bay, Zhushan Bay and Northwest Zone, respectively when implementing high nutrient reduction scenario (external N and P inputs reduced up to 80% and 50%). External nutrient reduction for both N and P is a prudent and likely most practical solution for reducing bloom potentials and overall trophic state of this currently hypereutrophic system. However, achieving observable algal control is not likely in the short term and other ecological restoration tools will be required to battle with the legacy of several decades of nutrient over-enrichment.Published by Elsevier B.V.

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Section: Introductionmentioning

confidence: 99%

Modeling the effects of external nutrient reductions on algal blooms in hyper-eutrophic Lake Taihu, China

Tang

Acharya

2016

Ecological Engineering

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“…Seasonal growth of phytoplankton is strongly influenced by environmental factors and nutritional status of water bodies, especially in tropical regions, where these factors show distinct variation with seasons (Paerl et al 2001). Different models using various physico-chemical variables have been developed to predict the algal bloom events in different type of water bodies (Maier and Dandy 1997;Robson and Hamilton 2004;Gurkhan et al 2006;Onderka 2007).…”

Section: Discussionmentioning

confidence: 99%

Predicting Phytoplankton Growth and Dynamics in Relation to Physico-chemical Characteristics of Water Body

Sharma¹,

Mohan

Kumar

2009

Water Air Soil Pollut

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In recent times, frequency and severity of algal blooms has increased significantly. To control their expansion, it is essential to identify the factors responsible for blooming of waters. Nutrient overenrichment (mainly due to anthropogenic activities) and environmental factors (including the climate change) are considered the major catalyst for onset, proliferation, and development of blooms. A eutrophic subtropical water reservoir (Durgakund Pond, Varanasi) was sampled for 2 years (2000)(2001)(2002), and data were obtained on various physico-chemical factors such as temperature, dissolved oxygen, pH, phosphorus (PO 4 − and other P forms), nitrogen (NO 3 − , NO 2 − , and NH + 4 ), chloride, iron, and phytoplankton biomass in terms of chlorophyll a, total protein, and carbohydrate content. Linear regression models that developed indicated that phosphorous, nitrogen, and iron had maximal impact on phytoplankton growth. Physical factors, though vital, had an indirect effect in facilitating the interaction among various nutrients and their availability.

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“…In past decades, water quality modeling has been developed to quantify the lake's response to external loading (Chapra, 1997;Arhonditsis et al, 2004;Cerco and Noel, 2004;Scavia et al, 2006;Zou et al, 2009;Wang et al, 2012) and expanded to evaluate the effectiveness of restoration efforts and future decision making (Sagehashi et al, 2000;Gurkan et al, 2006;Martins et al, 2008;Norton et al, 2009;Hu et al, 2010). The importance of modeling in water quality restoration analysis cannot be overemphasized, and omitting such analysis in real-world practices can have very costly consequences (Vieira and Lijklema, 1989;Ahmad et al, 2001;Lung, 2001;National Research Council, 2001;Pelley, 2003;Arhonditsis et al, 2006;Liu et al, 2008).…”

Section: Introductionmentioning

confidence: 98%

Quantitative evaluation of lake eutrophication responses under alternative water diversion scenarios: A water quality modeling based statistical analysis approach

Liu¹,

Wang²,

Hu³

et al. 2014

Science of The Total Environment

120

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Development of a structurally dynamic model for forecasting the effects of restoration of Lake Fure, Denmark

Cited by 71 publications

References 22 publications

Modeling the effects of external nutrient reductions on algal blooms in hyper-eutrophic Lake Taihu, China

Modeling the effects of external nutrient reductions on algal blooms in hyper-eutrophic Lake Taihu, China

Predicting Phytoplankton Growth and Dynamics in Relation to Physico-chemical Characteristics of Water Body

Quantitative evaluation of lake eutrophication responses under alternative water diversion scenarios: A water quality modeling based statistical analysis approach

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