ORCID Profile
0000-0002-1028-9413
Current Organisation
University of Adelaide
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Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Elsevier BV
Date: 06-2002
Publisher: Elsevier BV
Date: 07-1995
Publisher: Springer International Publishing
Date: 2018
Publisher: Elsevier BV
Date: 04-2008
Publisher: Elsevier BV
Date: 11-2017
DOI: 10.1016/J.HAL.2017.09.003
Abstract: An early warning scheme is proposed that runs ensembles of inferential models for predicting the cyanobacterial population dynamics and cyanotoxin concentrations in drinking water reservoirs on a diel basis driven by in situ sonde water quality data. When the 10- to 30-day-ahead predicted concentrations of cyanobacteria cells or cyanotoxins exceed pre-defined limit values, an early warning automatically activates an action plan considering in-lake control, e.g. intermittent mixing and ad hoc water treatment in water works, respectively. Case studies of the sub-tropical Lake Wivenhoe (Australia) and the Mediterranean Vaal Reservoir (South Africa) demonstrate that ensembles of inferential models developed by the hybrid evolutionary algorithm HEA are capable of up to 30days forecasts of cyanobacteria and cyanotoxins using data collected in situ. The resulting models for Dolicospermum circinale displayed validity for up to 10days ahead, whilst concentrations of Cylindrospermopsis raciborskii and microcystins were successfully predicted up to 30days ahead. Implementing the proposed scheme for drinking water reservoirs enhances current water quality monitoring practices by solely utilising in situ monitoring data, in addition to cyanobacteria and cyanotoxin measurements. Access to routinely measured cyanotoxin data allows for development of models that predict explicitly cyanotoxin concentrations that avoid to inadvertently model and predict non-toxic cyanobacterial strains.
Publisher: Elsevier BV
Date: 07-2020
Publisher: Elsevier BV
Date: 12-2001
Publisher: Elsevier BV
Date: 09-2001
DOI: 10.1016/S0160-4120(01)00095-2
Abstract: This paper discusses the application of genetic algorithms to the construction of rule-based models. Water quality time series will be explored to extract predictive rules for algal blooms in freshwater lakes. The hypertrophic Japanese Lake Kasumigaura is used to demonstrate the technique.
Publisher: Wiley
Date: 22-03-2017
DOI: 10.1002/RRA.3141
Publisher: Springer Berlin Heidelberg
Date: 2003
Publisher: Elsevier BV
Date: 12-2021
Publisher: Elsevier BV
Date: 03-2013
Publisher: Elsevier BV
Date: 04-2019
DOI: 10.1016/J.SCITOTENV.2019.01.286
Abstract: Quantifying the water quantity and quality variations resulting from human induced activities is important for policy makers in view of increasing water scarcity and water pollution. Simple models can be robust tools in estimating the runoff from catchments, but do they also sufficiently reflect complex physio-chemical processes required for spatially-explicit simulation of soil-water interactions, and the resulting pollutant responses in catchments? Do these models respond sensitive to the impacts of different land use change representations? These questions are considered by applying the semi-distributed process-based catchment models SWAT and SOURCE to the Sixth Creek catchment in South Australia. Both models used similar data whereas inputs for SOURCE were generated from land-use based Functional Units (FUs), while FUs for SWAT were based on land use, soil and slope combinations. After satisfying calibration of both models for the outlet station of the catchment, the simulated flow by SOURCE produced high goodness of fit metrics, while nutrient loads simulated by SWAT were more realistic. Both models benefitted from using locally available Potential Evapotranspiration data for calibrating the hydrology. Scenarios of intensified land uses by two models showed more credible results for sediment and nutrient loads with the static approach when simulating the linear rather than the non-linear land use changes. The study has shown that informing decisions on the hydrology at catchment scale is well suited to less-complex models, whereas decisions on impact of land use change on water quality in catchments are better suited by models with process descriptions for soil-water interactions.
Publisher: Elsevier BV
Date: 11-2006
Publisher: Elsevier BV
Date: 05-2022
DOI: 10.1016/J.HAL.2022.102229
Abstract: The Lake Suwa (Japan) has a history of non-N-fixing Microcystis blooms. Lake Kinneret (Israel) experienced multiannual periods of sole domination by the dinoflagellate Peridinium gatunense and periods dominated seasonally by P. gatunense or cyanobacteria. Extensive studies have been carried out in both lakes regarding the role of dissolved inorganic nitrogen and phosphorus as drivers of primary productivity. There is growing evidence that dissolved organic nitrogen (DON) compounds also influence not only biomass and structure of phytoplankton communities but also microcystin production. This study focuses on relationships of DON with: (1) population dynamics of Microcystis spp. and concentrations of microcystins in Lake Suwa, and (2) population dynamics of P. gatunense as well as N- and non-N-fixing cyanobacteria in Lake Kinneret. Modelling results for historical data of Lake Suwa by means of the hybrid evolutionary algorithm HEA revealed that the prediction of abundances of four Microcystis species and concentrations of cyanotoxins achieved higher coefficients of correlation when DON/DIN-ratios were included as drivers. Population dynamics of P. gatunense in Lake Kinneret appeared to have a strong inverse relationships with DON/DIN-ratios reflected by inferential models of HEA with higher coefficients of correlation when driven by DON/DIN-ratios. When DON/DIN-ratios were included as drivers, models of Microcystis spp. in Lake Kinneret performed higher coefficients of determination compared to models of N-fixing cyanobacteria. The study highlights the need to consider DON for improved understanding and management of population dynamics of cyanobacteria and dinoflagellates in freshwater lakes.
Publisher: Elsevier BV
Date: 04-2018
Publisher: Elsevier BV
Date: 11-2006
Publisher: Elsevier BV
Date: 2008
Publisher: CSIRO Publishing
Date: 2005
DOI: 10.1071/MF04237
Abstract: Many streams and wetlands have been affected by increasing salinity, leading to significant changes in flora and fauna. The study investigates relationships between macroinvertebrate taxa and conductivity levels (µS cm−1) in Queensland stream systems. The analysed dataset contained occurrence patterns of frequently found macroinvertebrate taxa from edge (2580 s les) and riffle (1367 s les) habitats collected in spring and autumn over 8 years. Sensitivity analysis with predictive artificial neural network models and the taxon-specific mean conductivity values were used to assign a salinity sensitivity score (SSS) to each taxon (1—very tolerant, 5—tolerant, 10—sensitive). Salinity index (SI) based on the cumulative SSS was proposed as a measurement of change in macroinvertebrate communities caused by salinity increase. Changes in macroinvertebrate communities were observed at relatively low salinities, with SI rapidly decreasing to ~800–1000 µS cm−1 and decreasing further at a slower rate. Natural variability and water quality factors were ruled out as potential primary causes of the observed changes by using partial canonical correspondence analysis and subsets of the data with only good water quality.
Publisher: Elsevier BV
Date: 07-2023
Publisher: Springer Berlin Heidelberg
Date: 2006
No related grants have been discovered for Friedrich Recknagel.