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  • 1
    Online Resource
    Online Resource
    Cham :Springer International Publishing AG,
    Keywords: Lake restoration. ; Electronic books.
    Type of Medium: Online Resource
    Pages: 1 online resource (603 pages)
    Edition: 1st ed.
    ISBN: 9783319930435
    Language: English
    Note: Intro -- In Memoriam: John Quinn, PhD, August 5, 1957-November 13, 2018 -- Foreword -- Preface -- Acknowledgements -- Contents -- Editors and Contributors -- Chapter 1: Context for Restoration -- 1.1 Introduction -- 1.2 History of Lake Research in New Zealand -- 1.3 Policy Framework for Lake Management and Restoration in New Zealand -- 1.4 International Context -- 1.5 Introduction to Chapters -- References -- Chapter 2: Modelling for Catchment Management -- 2.1 Introduction -- 2.2 The Purpose and Types of Catchment Models -- 2.2.1 The Purpose of Catchment Modelling -- 2.2.2 Types of Catchment Models -- 2.3 Detailed Descriptions of the Different Types of Catchment Models -- 2.3.1 Statistically Based Catchment Models -- 2.3.2 Process-Based Catchment Models -- 2.3.3 Hybrid Catchment Models -- 2.3.4 Regression-Based Versus Process-Based Catchment Models -- 2.4 Choosing and Evaluating the Right Model -- 2.4.1 Choosing the Right Model or Combination of Models -- 2.4.2 Evaluation of Catchment Models -- 2.4.3 The Need for Guidelines on How to Use the Model Selected -- 2.5 Catchment Modelling in New Zealand -- 2.5.1 Historical Context -- 2.5.2 Brief Summary of Catchment Models Commonly Used in New Zealand -- 2.5.2.1 GLEAMS -- 2.5.2.2 ROTAN -- 2.5.2.3 AquiferSim -- 2.5.2.4 OVERSEER -- 2.5.2.5 SPASMO -- 2.5.2.6 SPARROW -- 2.5.2.7 CLUES -- 2.5.2.8 SWAT -- 2.5.2.9 DHI MIKE -- 2.5.2.10 Delft3D -- 2.5.2.11 Hydroepidemiological Catchment Models -- 2.5.2.12 Nitrate and Phosphorus Leaching Maps -- 2.5.3 Availability of Model Formulation, Algorithms, and Source Code -- 2.5.4 Models for Compliance or Regulatory Purposes -- 2.5.5 A Small Catchment Context. A CLUES Example -- 2.5.6 A Large Catchment Context. A GLEAMS Example -- 2.6 Catchment Modelling in a Large Basin Outside of New Zealand: A SPARROW Example. , 2.7 Use of Catchment Model Output for Lake and Reservoir Modelling -- 2.8 Benefits of Integrated Monitoring and Modelling -- References -- Chapter 3: Modelling Water Quality to Support Lake Restoration -- 3.1 Introduction to Lake Models -- 3.2 Types of Models -- 3.2.1 Empirical Models -- 3.2.2 Neural Networks -- 3.2.3 Deterministic models -- 3.3 Selecting an Appropriate Model -- 3.4 Model Performance Criteria and Skill Assessment -- 3.5 Data Assimilation for Modelling -- 3.6 Synthesising Ecological Model Applications: The Importance of Calibration -- 3.7 Introduction to New Zealand Lake Model Applications -- 3.8 New Zealand Examples -- 3.8.1 Modelling to Inform Potential for Regime Shifts and Macrophyte Collapse in Waituna Lagoon, Southland -- 3.8.2 Modelling to Support an Engineered Inflow Diversion for Restoration of Water Quality in Lake Rotoiti -- 3.8.3 Modelling of Pest Fish and Catchment Effects on Lake Water Quality -- 3.8.4 Wastewater Dispersion Modelling in Lake Rotorua -- 3.9 Future Prospects -- References -- Chapter 4: Agricultural Catchment Restoration -- 4.1 Introduction -- 4.2 Sources of Contaminants -- 4.3 Pathways of Contaminant Loss -- 4.4 Mitigation Strategies -- 4.4.1 Implementing Mitigations to Decrease Catchment-Scale Losses -- 4.5 Conclusions and Future Prospects -- References -- Chapter 5: Nutrient Budgets in Lakes -- 5.1 Sources and Fates of Nutrients in Lakes -- 5.1.1 The Nutrient Mass Balance -- 5.1.2 Sources of Nutrients -- 5.1.2.1 Stream Inflows -- 5.1.2.2 Groundwater Inputs -- 5.1.2.3 Atmospheric Inputs -- 5.1.3 Internal Sources of Nutrients to the Water Column -- 5.1.3.1 Geochemistry Effects on Nutrient Release From the Sediment -- 5.1.3.2 Accumulation of Dissolved P in Hypolimnia -- 5.1.3.3 pH-Dependent P Release -- 5.1.3.4 Sediment N Release -- 5.1.3.5 Wind-Induced Nutrient Resuspension -- 5.1.3.6 N-Fixation. , 5.1.4 Fates of Nutrients in Lakes -- 5.1.4.1 Sequestration of N and P, and Conversion of Bioavailable Forms of N to N2 -- 5.1.4.2 Estimation of the Phosphorus Retention Coefficient -- 5.1.4.3 Estimation of the Nitrogen Retention Coefficient -- 5.1.4.4 Flushing and Seepage -- 5.2 Flipping Lakes -- 5.3 Nutrients Contained in Biota: Fish, Macrophytes, and Phytoplankton -- 5.4 Lake Restoration by Adjusting Components of the Nutrient Budget -- 5.5 Estimating Nutrient Concentrations and Internal Loading Using Empirical Models -- 5.5.1 Estimating Nutrient Concentrations Using Empirical Models -- 5.5.2 Net Versus Gross Internal Loading -- 5.5.3 Estimating the Net Internal Nutrient Load -- 5.5.4 Estimating Gross Internal Loading -- 5.5.5 Uncertainty in Mass-Balance Estimates -- 5.6 Future Prospects -- References -- Chapter 6: Physical Processes for In-Lake Restoration: Destratification and Mixing -- 6.1 Introduction -- 6.1.1 Thermal Stratification -- 6.1.2 Consequences of Thermal Stratification -- 6.1.2.1 Deoxygenation -- 6.1.2.2 Nutrient Release -- 6.1.2.3 Light Penetration -- 6.2 Lake Mixing -- 6.2.1 Natural Processes -- 6.2.2 Artificial Processes -- 6.2.3 Influence of Lake Size and Shape -- 6.2.4 Destratification and Aeration Mechanisms -- 6.2.4.1 Bubble-Plume Diffuser -- 6.2.4.2 Bubble-Plume Diffuser in a Non-stratified Water Body -- 6.2.4.3 Bubble-Plume Diffuser in a Stratified Water Body -- 6.2.5 Morphology and Timing -- 6.2.6 Aeration Protocols -- 6.3 Mechanical Mechanisms -- 6.3.1 Solar-Powered Water Mixers -- 6.3.2 Constrained Bubble Plumes -- 6.4 Selective Draw-Induced Stratification -- 6.4.1 Case Study: Upper Huia Reservoir -- 6.5 Oxygenation of the Hypolimnion Without Mixing -- 6.6 Down-Flow Mixing Devices -- 6.7 Computer Modelling -- 6.8 Aeration Case Studies -- 6.8.1 Opuha Dam -- 6.8.1.1 Background -- 6.8.1.2 Water Quality Problems. , 6.8.1.3 Design Issues -- 6.8.1.4 Mitigation Measures -- 6.8.2 Virginia Lake -- 6.8.2.1 Background -- 6.8.2.2 Water Quality Problems -- 6.8.2.3 Design Issues and Mitigation Measures -- 6.9 Future Developments -- References -- Chapter 7: Flocculants and Sediment Capping for Phosphorus Management -- 7.1 Introduction -- 7.1.1 Nutrient Limitation of Phytoplankton -- 7.1.2 Sources of Nutrients in Freshwater Lakes and Reservoirs -- 7.1.3 Internal Nutrient Cycling -- 7.1.4 Definitions -- 7.2 Flocculation -- 7.3 Sediment Capping -- 7.3.1 Passive Sediment Capping Agents -- 7.3.2 Active Sediment Capping Agents -- 7.3.3 Specific P-Inactivation Agents -- 7.3.3.1 Allophane -- 7.3.3.2 Iron -- 7.3.3.3 Alum -- 7.3.4 Surface Application Issue -- 7.3.5 Does Brief Exceedance of the Permitted Maximum Have an Effect? -- 7.3.6 Aqual-P -- 7.3.7 Phoslock -- 7.3.8 Calcite -- 7.4 pH Effects and Speciation -- 7.4.1 Causes of High pH -- 7.4.2 Timing -- 7.5 Flocking and Locking -- 7.6 P-Binding Capacity -- 7.7 Calculating Dose Rates -- 7.8 Other Issues -- 7.9 Future Prospects -- 7.9.1 Sediment Capping Targeting N -- 7.10 Conclusions -- References -- Chapter 8: Control of Invasive Aquatic Plants -- 8.1 Introduction -- 8.1.1 Native Flora -- 8.1.2 Invasive Aquatic Plants and Their Impacts -- 8.1.3 Recognising a Weed -- 8.2 How Weeds Can Be Managed to Restore Biodiversity in Lakes -- 8.2.1 Control Tools -- 8.2.1.1 Habitat Manipulation -- 8.2.1.2 Mechanical and Manual Control -- 8.2.1.3 Biological Control -- 8.2.1.4 Chemical Control -- 8.2.1.5 Integrated Control -- 8.3 Management Targets: Restoring Function and Indigenous Values -- 8.3.1 Nuisance Control -- 8.3.2 Restoration -- 8.3.3 Eradication -- 8.3.4 Monitoring and Surveillance of Aquatic Weeds for Lake Restoration -- 8.4 Examples of Controlling Invasive Aquatic Weeds and Restoration Outcomes for Lakes. , 8.4.1 Management Strategies for Large Lakes or Regions -- 8.4.1.1 Lake Wanaka -- 8.4.1.2 Northland Region: Lakes Prioritisation, Surveillance, Incursion Response, and Eradication Progress -- 8.4.2 Selective Target Weed Control -- 8.4.2.1 Lake Okataina -- 8.4.2.2 Yellow Flag Iris in Lake Rotoroa -- 8.4.3 Eradication of Invasive Aquatic Plants to Enable Native Plant Regeneration -- 8.4.3.1 Endothall to Eradicate Hornwort and Lagarosiphon -- 8.4.3.2 Grass Carp to Eradicate Egeria and Hydrilla -- 8.5 Future Prospects -- 8.5.1 New Species Detections and `Sleeping Giants´ -- 8.5.2 Climate Change -- 8.5.3 The Tool Box -- References -- Chapter 9: Management of Non-indigenous Lacustrine Animals -- 9.1 Introduction -- 9.2 Inventories and Distributions -- 9.2.1 Invertebrates -- 9.2.2 Fish -- 9.2.3 Other Vertebrates -- 9.3 Review of Control Techniques -- 9.3.1 Pre-border Controls -- 9.3.2 Border Control -- 9.3.3 Post-border, Pre-establishment Management -- 9.3.4 Post-establishment Control Measures: Invertebrates -- 9.3.5 Control Measures: Vertebrates -- 9.4 Conclusions and Future Prospects -- References -- Chapter 10: Restoration of Native Fish in New Zealand Lakes and Reservoirs -- 10.1 Introduction -- 10.2 Littoral Habitat Restoration -- 10.2.1 Smelt -- 10.2.2 Dwarf Inanga -- 10.2.3 Common Bully -- 10.3 Restoring Connectivity -- 10.3.1 Longfin and Shortfin Eels -- 10.4 Piscivore Control -- 10.4.1 Perch Predation on Common Bully -- 10.4.2 Trout Predation on Galaxias in Dune Lakes -- 10.4.3 Trout Predation on Banded Kokopu -- 10.4.4 Trout Predation on Koaro -- 10.5 Translocation -- 10.5.1 Common Bully -- 10.5.2 Smelt -- 10.5.3 Dwarf Inanga -- 10.6 Future Prospects -- References -- Chapter 11: Indicators of Ecological Integrity -- 11.1 Ecological Integrity -- 11.1.1 New Zealand Definitions of Ecological Integrity. , 11.2 Key Attributes of Lake Ecological Integrity.
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  • 2
    Keywords: Ecology ; Marine Sciences ; Nature Conservation ; Environmental management ; Marine Sciences ; Nature Conservation ; Environmental management ; Ecology ; Environmental monitoring. ; Freshwater. ; Geoecology. ; Environmental geology.
    Description / Table of Contents: Lakes across the globe require help. The Lake Restoration Handbook: A New Zealand Perspective addresses this need through a series of chapters that draw on recent advances in modelling and monitoring tools, citizen science and First Peoples’ roles, catchment and lake-focused restoration techniques, and policy implementation. New Zealand lakes, like lakes across the globe, are subject to multiple pressures that have increased in severity and scale as land use has intensified, invasive species have spread and global climate change becomes manifest. This books builds on the popular Lake Managers Handbook (1987), which provided guidance on undertaking investigations into, and understanding lake ecosystems in New Zealand. The Lake Restoration Handbook: A New Zealand Perspective synthesises contemporary issues related to lake restoration and rehabilitation, integrated with social science and cultural viewpoints, and complemented by authoritative topic-area summaries by renowned scientists and practitioners from across the globe. The book examines the progress of lake restoration and the new and emerging tools available to managers for predicting and effecting change. The book will be a valuable resource for natural and social scientists, policy writers, lake managers, and anyone interested in the health of lake ecosystems
    Type of Medium: Online Resource
    Pages: Online-Ressource (XIX, 599 p. 133 illus., 123 illus. in color, online resource)
    Edition: Springer eBook Collection. Biomedical and Life Sciences
    ISBN: 9783319930435
    Series Statement: SpringerLink
    Language: English
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  • 3
    ISSN: 1420-9055
    Keywords: Key words: Simulation, buoyancy regulation, Microcystis aeruginosa, diurnal stratification.
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology
    Notes: Abstract: Vertical distributions of the cyanobacterium Microcystis aeruginosa are examined in a shallow lake in relation to mixing and thermal stratification over three days. A model of buoyancy regulation by Microcystis aeruginosa, applicable for turbulent environments, is coupled with a one-dimensional hydrodynamic model. The coupled model is applied to Thomsons Lake in Western Australia to examine the relationship between buoyancy regulation and the daily stratification/destratification cycle. The vertical distribution of Microcystis aeruginosa in Thomsons Lake depends on the carbohydrate ballast dynamics and the colony size. When thermal stratification occurs, all the simulations show a similar general pattern of diurnal vertical migration of the Microcystis aeruginosa colonies. The colonies accumulate at the surface during the night and in the morning the colonies lose buoyancy, which leads to a reduction by $ \sim $ 50% in colony concentration in the top 0.2—0.3 m of the water column. Afternoon winds redistribute the population over the entire water column. When the lake is fully mixed, the vertical migration pattern of the Microcystis aeruginosa colonies may be affected, depending on the colony size and the intensity of the mixing.
    Type of Medium: Electronic Resource
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  • 4
    Electronic Resource
    Electronic Resource
    Springer
    Hydrobiologia 317 (1996), S. 209-220 
    ISSN: 1573-5117
    Keywords: suspended solids ; wave action ; shear stress ; macrophytes
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology
    Notes: Abstract Suspended solids concentrations were measured at routine 2–3 week intervals and on additional windy days for at least one year in each of seven shallow (mean depth 〈 2 m) south Island, New Zealand lakes. Surface wave characteristics were estimated from water depths and local meteorological data using a shallow-water wave forecasting model for fetch-limited waves. Bottom shear stresses were computed from surface wave characteristics for the sampling stations and for a hypothetical lake-average station. The calculated shear stresses were, on average, much better predictors of suspended solids concentrations than alternative models using two different functions of wind speed, wave height2/depth or wavelength/depth. A combination of the sample station and lake average shear stresses provided slightly better predictions than the sample station values alone, suggesting that currents also contribute significantly to the concentration at a given point. Regressions of suspended solids on the combined functions had r 2 values ranging from 0.74–0.73 in the seven lakes. The slopes of these regressions were negatively related to the settling velocity of the lowest quartile of the sediment, and to macrophyte biomass, in multiple regression (r 2 = 0.94, p 〈 0.01).
    Type of Medium: Electronic Resource
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  • 5
    Publication Date: 2017-09-20
    Description: A Bayesian network model was developed to assess the combined influence of nutrient conditions and climate on the occurrence of cyanobacterial blooms within lakes of diverse hydrology and nutrient supply. Physicochemical, biological, and meteorological observations were collated from 20 lakes located at different latitudes and characterized by a range of sizes and trophic states. Using these data, we built a Bayesian network to (1) analyze the sensitivity of cyanobacterial bloom development to different environmental factors and (2) determine the probability that cyanobacterial blooms would occur. Blooms were classified in three categories of hazard (low, moderate, and high) based on cell abundances. The most important factors determining cyanobacterial bloom occurrence were water temperature, nutrient availability, and the ratio of mixing depth to euphotic depth. The probability of cyanobacterial blooms was evaluated under different combinations of total phosphorus and water temperature. The Bayesian network was then applied to quantify the probability of blooms under a future climate warming scenario. The probability of the “high hazardous” category of cyanobacterial blooms increased 5% in response to either an increase in water temperature of 0.8°C (initial water temperature above 24°C) or an increase in total phosphorus from 0.01 mg/L to 0.02 mg/L. Mesotrophic lakes were particularly vulnerable to warming. Reducing nutrient concentrations counteracts the increased cyanobacterial risk associated with higher temperatures.
    Type: Article , PeerReviewed
    Format: text
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  • 6
    Publication Date: 2021-01-08
    Description: In recent years, there has been a widespread deployment of submersible fluorescence sensors by water utilities. They are used to measure diagnostic pigments and estimate algae and cyanobacteria abundance in near real-time. Despite being useful and promising tools, operators and decision-makers often rely on the data provided by these probes without a full understanding of their limitations. As a result, this may lead to wrong and misleading estimations which, in turn, means that researchers and technicians distrust these sensors. In this review paper, we list and discuss the main limitations of such probes, as well as identifying the effect of environmental factors on pigment production, and in turn, the conversion to cyanobacteria abundance estimation. We argue that a comprehensive calibration approach to obtain reliable readings goes well beyond manufacturers’ recommendations, and should involve several context-specific experiments. We also believe that if such a comprehensive set of experiments is conducted, the data collected from fluorescence sensors could be used in artificial intelligence modelling approaches to reliably predict, in near real-time, the presence and abundance of different cyanobacteria species. This would have significant benefits for both drinking and recreational water management, given that cyanobacterial toxicity, and taste and odour compounds production, are species-dependent.
    Type: Article , PeerReviewed
    Format: text
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  • 7
    Publication Date: 2015-01-01
    Description: A Bayesian network model was developed to assess the combined influence of nutrient conditions and climate on the occurrence of cyanobacterial blooms within lakes of diverse hydrology and nutrient supply. Physicochemical, biological, and meteorological observations were collated from 20 lakes located at different latitudes and characterized by a range of sizes and trophic states. Using these data, we built a Bayesian network to (1) analyze the sensitivity of cyanobacterial bloom development to different environmental factors and (2) determine the probability that cyanobacterial blooms would occur. Blooms were classified in three categories of hazard (low, moderate, and high) based on cell abundances. The most important factors determining cyanobacterial bloom occurrence were water temperature, nutrient availability, and the ratio of mixing depth to euphotic depth. The probability of cyanobacterial blooms was evaluated under different combinations of total phosphorus and water temperature. The Bayesian network was then applied to quantify the probability of blooms under a future climate warming scenario. The probability of the ?high hazardous? category of cyanobacterial blooms increased 5% in response to either an increase in water temperature of 0.8°C (initial water temperature above 24°C) or an increase in total phosphorus from 0.01 mg/L to 0.02 mg/L. Mesotrophic lakes were particularly vulnerable to warming. Reducing nutrient concentrations counteracts the increased cyanobacterial risk associated with higher temperatures. # doi:10.1890/13-1677.1
    Print ISSN: 1051-0761
    Electronic ISSN: 1939-5582
    Topics: Biology
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