Three-dimensional modelling of a Microcystis bloom event in the Swan River estuary, Western Australia

Robson, Barbara; Hamilton, David P.

doi:10.1016/j.ecolmodel.2004.01.006

Cited by 162 publications

(91 citation statements)

References 38 publications

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“…Both DYRESM and ELCOM have been applied widely to investigate the stratification in lakes and drinking water reservoirs and inflow/outflow dynamics of waterbodies (Robson and Hamilton 2003;Hamilton 1999). The most recent version of CAEDYM (v3.3, Hipsey and Hamilton 2008) can also model suspended solids, oxygen and organic and inorganic nutrients (C, N, P and Si), multiple phytoplankton functional groups, zooplankton and fish, benthic biological communities (macroalgae, macrophytes and benthic invertebrates), pathogens, geochemistry (including ions, pH, redox and metals), and sediment oxygen, nutrient and metal fluxes.…”

Section: Caedymmentioning

confidence: 99%

Challenges and opportunities for integrating lake ecosystem modelling approaches

et al. 2010

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A large number and wide variety of lake ecosystem models have been developed and published during the past four decades. We identify two challenges for making further progress in this field. One such challenge is to avoid developing more models largely following the concept of others ('reinventing the wheel'). The other challenge is to avoid focusing on only one type of model, while ignoring new and diverse approaches that have become available ('having tunnel vision'). In this paper, we aim at improving the awareness of existing models and knowledge of concurrent approaches in lake ecosystem modelling, without covering all possible model tools and avenues. First, we present a broad variety of modelling approaches. To illustrate these approaches, we give brief descriptions of rather arbitrarily selected sets of specific models. We deal with static models (steady state and regression models), complex dynamic models (CAEDYM, CE-QUAL-W2, Delft 3D-ECO, LakeMab, LakeWeb, MyLake, PCLake, PROTECH, SALMO), structurally dynamic models and minimal dynamic models. We also discuss a group of approaches that could all be classified as individual based: superindividual models (Piscator, Charisma), physiologically structured models, stage-structured models and traitbased models. We briefly mention genetic algorithms, neural networks, Kalman filters and fuzzy logic. Thereafter, we zoom in, as an in-depth example, on the multi-decadal development and application of the lake ecosystem model PCLake and related models (PCLake Metamodel, Lake Shira Model, IPH-TRIM3D-PCLake). In the discussion, we argue that while the historical development of each approach and model is understandable given its 'leading principle', there are many opportunities for combining approaches. We take the point of view that a single 'right' approach does not exist and should not be strived for. Instead, multiple modelling approaches, applied concurrently to a given problem, can help develop an integrative view on the functioning of lake ecosystems. We end with a set of specific recommendations that may be of help in the further development of lake ecosystem models.

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Section: Caedymmentioning

confidence: 99%

Challenges and opportunities for integrating lake ecosystem modelling approaches

et al. 2010

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“…In one of them (Yoyo model), temporal scales of less than a day were considered, accounting for daily sun light variability, using the static P-E formulation of Peeters and Eilers (1978). Robson and Hamilton (2004) applied a three dimensional, coupled hydrodynamic-ecological model to simulate a Microcystis bloom in an Australian river. Light limitation was based on Steele's static formulation, in the case of freshwater diatoms, and in the saturation type equation described by Webb et al (1974), in the case of other algae.…”

Section: Models (See Text)mentioning

confidence: 99%

Phytoplankton production modelling in three marine ecosystems—static versus dynamic approach

Macedo

Duarte

2006

Ecological Modelling

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Phytoplankton productivity is usually determined from water samples incubated at a number of irradiance levels during several hours. The resultant productivity-irradiance (P-E) curves are then used to estimate local and/or global phytoplankton production. However, there is growing evidence that these curves, referred as static, underestimate phytoplankton photosynthesis to a great deal, by assuming a stable response to light over the incubation period. One of the drawbacks of static P-E curves is the overestimation of photoinhibition.In this work, three one-dimensional vertically resolved models were developed as simply as possible, to investigate differences between static and dynamic phytoplankton productivity in three marine ecosystems: a turbid estuary, a coastal area and an open ocean ecosystem. The results show that, when photoinhibition development time is considered (dynamic model), the primary production estimates are always higher than when calculated with the static model. The quantitative importance of these differences varies with the type of ecosystem and it appears to be more important in coastal areas and estuaries (from 21 to 72%) than in oceanic waters (10%). Thus, these results suggest that primary production estimates, obtained under the assumption of a static behaviour response to light, may underestimate the real values of global phytoplankton primary production. Calculations suggest that the quantitative importance of this underestimation may be larger than the global missing carbon sink.

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“…水温的季节变化是滇池、太湖微囊藻水华季节波动的主要因子 [29][30] . 水温也是湖泊水华预警模型中最重要的参数 [31][32] . 在本实验中, 10℃下微囊藻可以维持 7d 的缓慢生长, 之后生物量快速下降.…”

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Decline of Microcystis aeruginosa FACHB-905 under four stress conditions

Li¹,

Ou²,

Song³

2008

Journal of Lake Sciences

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The present study aimed to evaluate the contribution of stress factors to the decline of Microcystis bloom, also tried to develop some indicator of decline. Four stress conditions including nitrogen starvation, phophorus starvation, dark and low temperature (10℃) were designed. Decrease of biomass and the concomitant change of antioxidant system, including superoxide dismutase (SOD) and catalase (CAT) activities were detected. The result indicated that growth was significantly inhibited under dark and low temperature. Microcystis population declined after 25 days under dark, while declined after only 7 days under low temperature. Compared with the physical stress, nutrition such as nitrogen and phosphorus did not limit the growth effectively in the early stage. Chlorophyll-a decreased as soon as being incubated into nitrogen free BG-11 medium. After being kept in phosphorus free medium for 20 days, chlorophyll-a reached peak, about the twice of the initial concentration, and then decreased at a rate of 0.1mg/(L·d), which was the highest decaying rate among the four factors. It indicated its significance of available phosphorus forMicrocystis survival and decline. According to the results, when phosphorus was limited, or water temperature was decreased to 10℃, cautions should be taken into account for the decrease of dissolved oxygen and possible increase of toxins or odors substances from * 国家高技术研究发展计划 863 项目(2005AA60101005)和中国科学院知识创新工程重要方向项目(KZCX2-YW-426)联合

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Three-dimensional modelling of a Microcystis bloom event in the Swan River estuary, Western Australia

Cited by 162 publications

References 38 publications

Challenges and opportunities for integrating lake ecosystem modelling approaches

Challenges and opportunities for integrating lake ecosystem modelling approaches

Phytoplankton production modelling in three marine ecosystems—static versus dynamic approach

Decline of Microcystis aeruginosa FACHB-905 under four stress conditions

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