A Review of Harmful Algal Bloom Prediction Models for Lakes and Reservoirs

Anthropogenic activity has led to eutrophication in water bodies across the world. This eutrophication promotes blooms, cyanobacteria being among the most notorious bloom organisms. Cyanobacterial blooms (more commonly referred to as harmful algal blooms (HABs)) can devastate an ecosystem. Cyanobacteria are resilient microorganisms that have adapted to survive under a variety of conditions, often outcompeting other phytoplankton. Some species of cyanobacteria produce toxins that ward off predators. These toxins can negatively affect the health of the aquatic life, but also can impact animals and humans that drink or come in contact with these noxious waters. Although cyanotoxin’s effects on humans are not as well researched as the growth, behavior, and ecological niche of cyanobacteria, their health impacts are of large concern. It is important that research to mitigate and understand cyanobacterial blooms and cyanotoxin production continues. This project supports continued research by addressing an approach to collect and summarize published articles that focus on techniques and models to predict cyanobacterial blooms with the goal of understanding what research has been done to promote future work. The following report summarizes 34 articles from 2003 to 2020 that each describe a mechanistic or data driven model developed to predict the occurrence of cyanobacterial blooms or the presence of cyanotoxins in lakes or reservoirs with similar climates to Utah. These articles showed a shift from more mechanistic approaches to more data driven approaches with time. This resulted in a more individualistic approach to modeling, meaning that models are often produced for a single lake or reservoir and are not easily comparable to other models for different systems

The major characteristics of environmental policies and agro-ecological technologies to be studied in Test case 2

Agricultural and Food Policy, Environmental Economics and Policy, Farm Management, Land Economics/Use,

Long-term nutrient load management and lake restoration: Case of Säkylän Pyhäjärvi (SW Finland)

Eutrophication caused by anthropogenic nutrient pollution has become one of the most severe threats to water bodies. Nutrients enter water bodies from atmospheric precipitation, industrial and domestic wastewaters and surface runoff from agricultural and forest areas. As point pollution has been significantly reduced in developed countries in recent decades, agricultural non-point sources have been increasingly identified as the largest source of nutrient loading in water bodies. In this study, Lake Säkylän Pyhäjärvi and its catchment are studied as an example of a long-term, voluntary-based, co-operative model of lake and catchment management. Lake Pyhäjärvi is located in the centre of an intensive agricultural area in southwestern Finland. More than 20 professional fishermen operate in the lake area, and the lake is used as a drinking water source and for various recreational activities. Lake Pyhäjärvi is a good example of a large and shallow lake that suffers from eutrophication and is subject to measures to improve this undesired state under changing conditions. Climate change is one of the most important challenges faced by Lake Pyhäjärvi and other water bodies. The results show that climatic variation affects the amounts of runoff and nutrient loading and their timing during the year. The findings from the study area concerning warm winters and their influences on nutrient loading are in accordance with the IPCC scenarios of future climate change. In addition to nutrient reduction measures, the restoration of food chains (biomanipulation) is a key method in water quality management. The food-web structure in Lake Pyhäjärvi has, however, become disturbed due to mild winters, short ice cover and low fish catch. Ice cover that enables winter seining is extremely important to the water quality and ecosystem of Lake Pyhäjärvi, as the vendace stock is one of the key factors affecting the food web and the state of the lake. New methods for the reduction of nutrient loading and the treatment of runoff waters from agriculture, such as sand filters, were tested in field conditions. The results confirm that the filter technique is an applicable method for nutrient reduction, but further development is needed. The ability of sand filters to absorb nutrients can be improved with nutrient binding compounds, such as lime. Long-term hydrological, chemical and biological research and monitoring data on Lake Pyhäjärvi and its catchment provide a basis for water protection measures and improve our understanding of the complicated physical, chemical and biological interactions between the terrestrial and aquatic realms. In addition to measurements carried out in field conditions, Lake Pyhäjärvi and its catchment were studied using various modelling methods. In the calibration and validation of models, long-term and wide-ranging time series data proved to be valuable. Collaboration between researchers, modellers and local water managers further improves the reliability and usefulness of models. Lake Pyhäjärvi and its catchment can also be regarded as a good research laboratory from the point of view of the Baltic Sea. The main problem in both of them is eutrophication caused by excess nutrients, and nutrient loading has to be reduced – especially from agriculture. Mitigation measures are also similar in both cases.Ihmisen aiheuttamasta ravinnekuormituksesta johtuva rehevöityminen on yksi pahimmista vesistöjä uhkaavista ilmiöistä. Ravinteet kulkeutuvat vesiin ilmalaskeumana, teollisuuden ja yhdyskuntien jätevesissä sekä maatalous- ja metsäalueilta tulevissa valumavesissä. Kehittyneissä maissa pistekuormitus on merkittävästi vähentynyt viime vuosikymmeninä, ja hajakuormituksen, erityisesti maatalouden, on todettu olevan merkittävin vesistöjen ravinnekuormittaja. Tässä tutkimuksessa Säkylän Pyhäjärveä ja sen valuma-aluetta käytetään esimerkkinä pitkäjänteisestä, vapaaehtoisuuteen perustuvasta yhteistyömallista järven ja valuma-alueen vesien tilan parantamiseksi. Pyhäjärvi sijaitsee Lounais-Suomen intensiivisesti viljellyllä alueella. Järvellä toimii yli 20 ammattikalastajaa, sen vettä käytetään raakavetenä ja myös virkistyskäyttö on monipuolista ja intensiivistä. Pyhäjärvi on erinomainen esimerkki isosta, matalasta rehevöitymisen oireista kärsivästä järvestä, jonka tilaa pyritään määrätietoisesti parantamaan muuttuvissa olosuhteissa. Ilmastonmuutos on yksi suurimmista vesiensuojelun haasteista niin Pyhäjärvellä kuin muissakin vesistöissä. Tulokset osoittavat, että ilmastollinen vaihtelu vaikuttaa valunnan ja ravinnekuormituksen määriin sekä niiden vuodenaikaisuuksiin. Havainnot tutkimusalueelta koskien lämpimien talvien vaikutusta ravinnekuormitukseen ovat linjassa IPCC:n ilmastonmuutosskenaarioiden kanssa. Paitsi ravinnekuormituksen vähentäminen, myös ravintoketjukunnostus (biomanipulaatio) on keskeinen keino veden laadun hallinnassa. Ravintoketjun rakenne on kuitenkin häiriintynyt leutojen talvien, lyhyen jääpeiteajan ja vähäisen kalansaaliin vuoksi. Talvinuottauksen mahdollistavalla jääpeitteellä ja sen pituudella on suuri merkitys Pyhäjärven veden laadun ja ekosysteemin kannalta, sillä muikkukanta on yksi ravintoketjua ja järven tilaa säätelevistä tekijöistä. Ravinnekuormituksen vähentämiseksi ja maatalouden valumavesien käsittelemiseksi kehitettyjä uusia menetelmiä, esimerkiksi hiekkasuodattimia, on testattu kenttäolosuhteissa. Suodatintekniikka osoittautui käyttökelpoiseksi menetelmäksi ravinteiden vähentämiseksi, mutta kehitystyötä on edelleen jatkettava. Hiekkasuodattimien ravinteiden poistoa voidaan tehostaa erilaisilla ravinteita sitovilla yhdisteillä, esimerkiksi kalkkipohjaisilla materiaaleilla. Pyhäjärven ja sen valuma-alueen pitkäkestoiset hydrologiset, kemialliset ja biologiset seuranta- ja tutkimusaikasarjat ovat vesiensuojelun perusta ja niiden avulla lisätään ymmärrystä monimutkaisista järven ja valuma-alueen fysikaalisista, kemiallisista ja ekologisista vuorovaikutussuhteista. Kenttäolosuhteissa tehtyjen mittausten lisäksi Pyhäjärveä ja sen valuma-aluetta on tutkittu erilaisilla mallinnusmenetelmillä. Mallien kalibroinnissa ja validoinnissa pitkät ja monipuoliset aikasarjat osoittautuivat arvokkaiksi. Mallintajien, tutkijoiden ja käytännön vesiensuojelun toteuttajien yhteistyöllä voidaan edelleen parantaa mallien luotettavuutta ja hyödynnettävyyttä. Pyhäjärveä valuma-alueineen voidaan tarkastella myös Itämeren kaltaisena luonnonlaboratoriona. Ylimääräisten ravinteiden aiheuttama rehevöityminen on molempien ongelma, ja ravinnekuormitusta on molemmissa tapauksissa vähennettävä – erityisesti maataloudesta. Vähentämismenetelmät ovat niin ikään samoja.Siirretty Doriast

Exploring, exploiting and evolving diversity of aquatic ecosystem models: A community perspective

Here, we present a community perspective on how to explore, exploit and evolve the diversity in aquatic ecosystem models. These models play an important role in understanding the functioning of aquatic ecosystems, filling in observation gaps and developing effective strategies for water quality management. In this spirit, numerous models have been developed since the 1970s. We set off to explore model diversity by making an inventory among 42 aquatic ecosystem modellers, by categorizing the resulting set of models and by analysing them for diversity. We then focus on how to exploit model diversity by comparing and combining different aspects of existing models. Finally, we discuss how model diversity came about in the past and could evolve in the future. Throughout our study, we use analogies from biodiversity research to analyse and interpret model diversity. We recommend to make models publicly available through open-source policies, to standardize documentation and technical implementation of models, and to compare models through ensemble modelling and interdisciplinary approaches. We end with our perspective on how the field of aquatic ecosystem modelling might develop in the next 5–10 years. To strive for clarity and to improve readability for non-modellers, we include a glossary

Artificial neural network analysis of factors controling ecosystem metabolism in coastal systems

Knowing the metabolic balance of an ecosystem is of utmost importance in determining whether the system is a net source or net sink of carbon dioxide to the atmosphere. However, obtaining these estimates often demands significant amounts of time and manpower. Here we present a simplified way to obtain an estimation of ecosystem metabolism. We used artificial neural networks (ANNs) to develop a mathematical model of the gross primary production to community respiration ratio (GPP:CR) based on input variables derived from three widely contrasting European coastal ecosystems (Scheldt Estuary, Randers Fjord, and Bay of Palma). Although very large gradients of nutrient concentration, light penetration, and organic-matter concentration exist across the sites, the factors that best predict the GPP:CR ratio are sampling depth, dissolved organic carbon (DOC) concentration, and temperature. We propose that, at least in coastal ecosystems, metabolic balance can be predicted relatively easily from these three predictive factors. An important conclusion of this work is that ANNs can provide a robust tool for the determination of ecosystem metabolism in coastal ecosystems

A Parametric Modeling Study of the Climate Change Impact on River Eutrophication and Water Quality

The potential impact of climate change on river eutrophication and ecosystems are emerging problems that are of great concern to international and domestic societies. Scientific research and developing methods to address these problems are challenging. This study aims to analyze the impact of climate change on algal bloom problems in large river systems by utilizing a parametric river eutrophication model that is established involving indicators of climate changes, hydrological regimes, water quality and nutrient loads. Specifically, the developed parametric modeling method is based on statistical and simulation methods including: Multiple Linear Regressions (MLR), Multiple Non-linear Regressions (MNR), Artificial Neural Network (ANN) based on Back-propagation (BP) algorithms, as well as an integrated river eutrophication model. The developed modeling method has been applied to the Wuhan section of Han River, which is one of major freshwater sources in China. The predicted probability of algal bloom occurrence for the next 40 years by the method is used to identify the impacts of climate change and human activities on the formation mechanisms of river algal blooms under three scenarios. The principles of possible adaptation options are discussed in this thesis. The modeling results indicate the temperature is one of the direct factors contributing to river eutrophication and the change of river water quality. It has also been recognized that the climate change, which can alter water temperature and hydrological regimes, in conjunction with human activities can significantly influence water quality and the river ecosystem. The present study is expected to give theoretical supports and directions for further relevant research

Numerical modeling of thermal bar and stratification pattern in Lake Ontario using the EFDC model

Thermal bar is an important phenomenon in large, temperate lakes like Lake Ontario. Spring thermal bar formation reduces horizontal mixing, which in turn, inhibits the exchange of nutrients. Evolution of the spring thermal bar through Lake Ontario is simulated using the 3D hydrodynamic model Environmental Fluid Dynamics Code (EFDC). The model is forced with the hourly meteorological data from weather stations around the lake, flow data for Niagara and St. Lawrence rivers, and lake bathymetry. The simulation is performed from April to July, 2011; on a 2-km grid. The numerical model has been calibrated by specifying: appropriate initial temperature and solar radiation attenuation coefficients. The existing evaporation algorithm in EFDC is updated to modified mass transfer approach to ensure correct simulation of evaporation rate and latent heatflux. Reasonable values for mixing coefficients are specified based on sensitivity analyses. The model simulates overall surface temperature profiles well (RMSEs between 1-2°C). The vertical temperature profiles during the lake mixed phase are captured well (RMSEs < 0.5°C), indicating that the model sufficiently replicates the thermal bar evolution process. An update of vertical mixing coefficients is under investigation to improve the summer thermal stratification pattern. Keywords: Hydrodynamics, Thermal BAR, Lake Ontario, GIS