Bubble plume oxygenation systems are commonly used to mitigate anoxia and its deleterious effects on water quality in thermally stratified lakes and reservoirs. Following installation, increases in sediment oxygen flux (JO2) are typically observed during oxygenation and are positively correlated with the bubble plume gas flow rate. Studies show that JO2 is controlled by the thickness of the diffusive boundary layer (DBL) at the sediment-water interface (SWI), which is in turn controlled by turbulence. As a result, JO2 can be quite spatially and temporally variable. Accurately predicting oxygenation-induced JO2 is vitally important for ensuring successful oxygenation system design and operation. Yet despite the current understanding of physical and chemical controls on JO2, methods for predicting oxygenation-induced JO2 are still based on empirical correlations and factors of safety. As hypolimnetic oxygenation becomes more widely used as a lake management tool for improving and maintaining water quality, there is a need to move from the current empirically based approach to a mechanistic approach and improve the ability to predict induced JO2. This work details field campaigns to investigate and identify appropriate models of oxygen supply to the SWI and oxygen demand exerted from the sediment, with the intent to use these models to predict oxygenation-induced JO2. Oxygen microprofiles across the SWI and near-sediment velocity measurements were collected in situ during three field campaigns on two oxygenated lakes, providing simultaneous measurements of JO2 and turbulence. Field observations show that oxygenation can increase JO2 by increasing bulk hypolimnetic oxygen concentrations, which increases the concentration gradient across the SWI. Oxygenation can also enhance turbulence, which decreases the DBL thickness and increases JO2. Existing models of interfacial flux were compared to field measurements to determine which model best predicted the observed JO2. Models based on the Batchelor scale, friction velocity, and film-renewal theory all agree reasonably well with field observations in both lakes. Additionally, the oxygen microprofiles were used to fit a transient model of oxygen kinetics in lake sediment and determine the appropriate kinetic model. Oxygen microprofiles in both lakes can be described using zero-order kinetics, rather than first-order kinetics. The interfacial flux and sediment kinetic models are incorporated into a coupled bubble plume and 3-D hydrodynamic lake model, allowing for spatial and temporal variation in simulated JO2. This comprehensive model was calibrated and validated to field data from two separate field campaigns on Carvin's Cove Reservoir, Virginia. Simulated temperature profiles agreed quite well with field observations, while simulated oxygen profiles differed from observed profiles, particularly in the bottom 1 m of the water column. The model overestimates oxygen concentrations near the sediment, which results in higher simulated JO2 than was observed during the field campaigns. These discrepancies are attributed to oxygen-consuming chemical processes, such as oxidation of soluble metals, which are not accounted for in the hydrodynamic model. Despite this, the model is still able to capture the impact of bubble plume operation on JO2, as simulated JO2 is higher when the diffusers are operating. With some additional improvements to the water quality modeling aspects of the model, as well as further calibration and validation, the model should be able to reproduce observed JO2 provided oxygen concentrations near the SWI are accurately reproduced as well. The current work is an attempt to push toward a comprehensive lake oxygenation model. A comprehensive model such as this should improve the ability to predict oxygenation-induced JO2 and lead to improvements in the design and operation of hypolimnetic oxygenation systems. / Ph. D.

This study addresses the regulation of large river phytoplankton by climate-related drivers with the help of three different approaches, i.e. analyses of long-term data and spatial dynamics (longitudinal samplings) as well as mathematical modeling. The central hypothesis is that discharge has a dominant role among climate-related variables which strongly alters phytoplankton biomass development. A multi-factorial statistical analysis on the basis of long-term data (1990 – 2009; 1994 – 2009) from two measuring stations of the rivers Rhine and Elbe revealed that discharge conditions and light availability were the main driving forces regulating phytoplankton spring bloom dynamics. For the Rhine, a trend towards an earlier occurrence of the spring bloom event and a decrease in seasonal mean phytoplankton biomass could be detected, whereas for the Elbe no shift in the timing of the spring bloom and a tendency towards increasing seasonal mean phytoplankton biomass was found. Longitudinal sampling campaigns served to analyze the spatial plankton development on a short-term scale. River-internal growth and loss processes, as well as import mediated by tributaries were examined. Four longitudinal profiles were realized at different seasons in recent years (2009 – 2011) and it was revealed that tributaries mainly had a diluting impact on plankton densities in the Elbe and provided an additional import of phyto- and zooplankton densities in the Rhine. In the present study, high bivalve abundances were detected in the Rhine, probably leading to river-internal losses of phytoplankton which could compensate phytoplankton production resulting in low phytoplankton concentrations. In the Elbe, low abundances of bivalves and a low benthic grazing pressure prevailed. On the other hand, an unusually low discharge event in spring 2011 in the Rhine demonstrated that loss processes can at times be superimposed by strong phytoplankton production leading to extremely high phytoplankton biomasses and chlorophyll a values. Hence, despite the observed long-term trend of decreasing chlorophyll values in the last two decades, extreme environmental conditions can provoke regime shifts with exceptional phytoplankton mass developments. To assess the potential impacts of future climate change on water quality, the water quality simulation model QSim was used to establish a model for the free-flowing part of the Rhine. The modeling approach was implemented by changing the hydrological and climatologic input data according to different climate projections for the near (2021 – 2050) and the far future (2071 – 2100). The model results indicated a weak response of phytoplankton biomass in the Rhine towards altered climatic conditions, including discharge reductions and water temperature increases. The study suggests that changes in discharge rather than water temperature mediate climate change effects on large river phytoplankton. However, the effects are river specific as a consequence of system specific differences in main control mechanisms (e.g. ‘bottom-up’ versus ‘top-down’). / Anhand von drei verschiedenen Ansätzen analysiert die vorliegende Arbeit die Regulierung von Phytoplankton in großen Flüssen durch klimabedingte Faktoren: Auswertung von Langzeitdaten, räumliche Dynamik (fließzeitkonforme, longitudinale Beprobungen) und mathematische Modellierung. Die zentrale Hypothese ist, dass Abfluss eine dominante Rolle unter den klimabedingten Faktoren spielt und die Phytoplanktonbiomasse stark beeinflusst. Eine multifaktorielle statistische Analyse basierend auf Langzeitdaten (1990 – 2009; 1994 –2009) von zwei Stationen der Flüsse Rhein und Elbe zeigten, dass hauptsächlich Abflussbedingungen und Lichtverfügbarkeit die Antriebskräfte bei der Regulierung der Phytoplanktonfrühjahrsblüte darstellten. Während sich am Rhein ein Trend hin zu einem früheren Auftreten der Frühjahrsblüte und einer Abnahme der mittleren Phytoplankton-biomasse während der Vegetationsperiode zeigte, konnte für die Elbe keine zeitliche Verschiebung der Frühjahrsblüte festgestellt werden und mittlere Phytoplanktonbiomassen zeigten hier eine steigende Tendenz. Longitudinale Fließzeitbeprobungen dienten zur Analyse der kurzfristigen räumlichen Entwicklung des Planktons. Interne Produktions- und Verlustprozesse, sowie der Eintrag durch Zuflüsse wurden untersucht. Vier longitudinale Profile wurden zu unterschiedlichen Jahreszeiten der letzten Jahre (2009 – 2011) realisiert und es wurde gezeigt, dass die Nebenflüsse der Elbe vorwiegend einen Verdünnungseffekt auf die Planktondichten des Hauptstromes hatten, während sie für den Rhein einen wichtigen zusätzlichen Eintrag von Phyto- und Zooplankton darstellten. In der vorliegenden Arbeit wurden hohe Muscheldichten im Rhein gefunden, die möglicherweise zu hohen internen Planktonverlusten durch Fraß beigetragen haben, und somit zu niedrigen Phytoplankton-konzentrationen geführt haben könnten. In der Elbe waren die Muscheldichten dagegen gering und somit war auch der benthische Fraßdruck niedriger. Auf der anderen Seite zeigte ein ungewöhnlich niedriges Abflussereignis im Frühjahr 2011 am Rhein, dass diese Verlustprozesse zeitweise von starker Phytoplanktonproduktion überlagert werden können und dadurch extrem hohe Phytoplanktonbiomassen und Chlorophyllwerte entstehen können. Demzufolge können trotz der Beobachtung eines langfristigen abnehmenden Trends in den Chlorophyllgehalten während der letzen zwei Jahrzehnte extreme Umweltbedingungen einen Regime-shift mit außergewöhnlichen Massenentwicklungen des Phytoplanktons hervorrufen. Um den möglichen Einfluss des zukünftigen Klimawandels auf die Gewässergüte abzuschätzen, wurde mithilfe des Gewässergütesimulationsmodells QSim ein Modell für den frei fließenden Abschnitt des Rheins erstellt. Für den Modellierungsansatz wurden die hydrologischen und klimatologischen Eingangsdaten entsprechend der verschiedenen Klimaprojektionen für die nahe (2021 – 2050) und ferne Zukunft (2071 – 2100) verändert. Die Modellergebnisse zeigten, dass sich Änderungen in den klimatischen Bedingungen, einschließlich Abflussreduktion und Wassertemperaturanstieg, nur geringfügig auf die Phytoplankton¬biomasse des Rheins auswirkten. Die vorliegende Arbeit deutet darauf hin, dass Klimawandeleffekte eher durch Änderungen der Abflussverhältnisse auf das Phytoplankton in großen Flüssen wirken als durch Änderungen der Wassertemperatur. Der Effekt ist jedoch flusssystemspezifisch, da die Auswirkungen von systemspezifischen Unterschieden in den Hauptkontrollmechanismen gesteuert werden (z.B. ‚Bottom-up’ versus ‚Top-down’).

Human impacts from land and water development have degraded water quality and altered the physical, chemical, and biological integrity of Nevada's Walker River. Reduced instream flows and increased nutrient concentrations affect native fish populations through warm daily stream temperatures and low nightly dissolved oxygen concentrations. Environmental water purchases are being considered to maintain instream flows, improve water quality, and enhance habitat for native fish species, such as Lahontan cutthroat trout. This study uses the River Modeling System (RMSv4), an hourly, physically-based hydrodynamic and water quality model, to estimate streamflows, temperatures, and dissolved oxygen concentrations in the Walker River. Stream temperature and dissolved oxygen changes were simulated from potential environmental water purchases to prioritize the time periods and locations that water purchases most enhance stream temperatures and dissolved oxygen concentrations for aquatic habitat. Environmental water purchases ranged from 0.03 cms to 1.41 cms average daily increases. Modeling results indicate that increased water purchases generally affect dissolved oxygen in two ways. First, environmental water purchases increase the thermal mass of the river, cooling daily stream temperatures and warming nightly temperatures. This prevents conditions that cause the lowest nightly dissolved oxygen concentrations (moderate production impairment thresholds are

No description available.

No description available.

To propose a methodology for the uncertainty estimation in water quality modeling as related to TMDL development, four Monte Carlo (MC) based techniques—single-phase MC, two-phase MC, Generalized Likelihood Uncertainty Estimation (GLUE), and Markov Chain Monte Carlo (MCMC) —were applied to a Hydrological Simulation Program–FORTRAN (HSPF) model developed for the Mossy Creek bacterial TMDL in Virginia. Predictive uncertainty in percent violations of instantaneous fecal coliform concentration criteria for the prediction period under two TMDL pollutant allocation scenarios was estimated. The average percent violations of the applicable water quality criteria were less than 2% for all the evaluated techniques. Single-phase MC reported greater uncertainty in percent violations than the two-phase MC for one of the allocation scenarios. With the two-phase MC, it is computationally expensive to sample the complete parameter space, and with increased simulations, the estimates of single and two-phase MC may be similar. Two-phase MC reported significantly greater effect of knowledge uncertainty than stochastic variability on uncertainty estimates. Single and two-phase MC require manual model calibration as opposed to GLUE and MCMC that provide a framework to obtain posterior or calibrated parameter distributions based on a comparison between observed and simulated data and prior parameter distributions. Uncertainty estimates using GLUE and MCMC were similar when GLUE was applied following the log-transformation of observed and simulated FC concentrations. GLUE provides flexibility in selecting any model goodness of fit criteria for calculating the likelihood function and does not make any assumption about the distribution of residuals, but this flexibility is also a controversial aspect of GLUE. MCMC has a robust formulation that utilizes a statistical likelihood function, and requires normal distribution of model errors. However, MCMC is computationally expensive to apply in a watershed modeling application compared to GLUE. Overall, GLUE is the preferred approach among all the evaluated uncertainty estimation techniques, for the application of watershed modeling as related to bacterial TMDL development. However, the application of GLUE in watershed-scale water quality modeling requires further research to evaluate the effect of different likelihood functions, and different parameter set acceptance/rejection criteria. / Ph. D.

<p>Ecohydrological models are extensively used to evaluate land use, land management and climate change impacts on hydrology and in-stream water quality conditions. The scale at which these models operate influences the complexity of processes incorporated within the models. For instance, a large scale hydrological model such as Soil and Water Assessment Tool (SWAT) that runs on a daily scale may ignore the sub-daily scale in-stream processes. The key processes affecting in-stream solute transport such as advection, dispersion and transient storage (dead zone) exchange can have considerable effect on the predicted stream solute concentrations, especially for localized studies. To represent realistic field conditions, it is therefore required to modify the in-stream water quality algorithms of SWAT by including these additional processes. Existing reach-scale solute transport models like OTIS (One-dimensional Transport with Inflow and Storage) considers these processes but excludes the actual biochemical reactions occurring in the stream and models nutrient uptake using an empirical first-order decay equation. Alternatively, comprehensive stream water quality models like QUAL2E (The Enhanced Stream Water Quality Model) incorporates actual biochemical reactions but neglects the transient storage exchange component which is crucial is predicting the peak and timing of solute concentrations. In this study, these two popular models (OTIS and QUAL2E) are merged to integrate all essential solute transport processes into a single in-stream water quality model known as ‘Enhanced OTIS model’. A generalized model with an improved graphical user interface was developed on MATLAB platform that performed reasonably well for both experimental data and previously published data (R²=0.76). To incorporate this model into large-scale hydrological models, it was necessary to find an alternative to estimate transient storage parameters, which are otherwise derived through calibration using experimental tracer tests. Through a meta-analysis approach, simple regression models were therefore developed for dispersion coefficient (D), storage zone area (A_s) and storage exchange coefficient (α) by relating them to easily obtainable hydraulic characteristics such as discharge, velocity, flow width and flow depth. For experimental data from two study sites, breakthrough curves and storage potential of conservative tracers were predicted with good accuracy (R²>0.5) by using the new regression equations. These equations were hence recommended as a tool for obtaining preliminary and approximate estimates of D, A_s and α when reach-specific calibration is unfeasible. </p> <p> </p> <p>The existing water quality module in SWAT was replaced with the newly developed ‘Enhanced OTIS model’ along with the regression equations for storage parameters. Water quality predictions using the modified SWAT model (Mir-SWAT) for a study catchment in Germany showed that the improvements in process representation yields better results for dissolved oxygen (DO), phosphate and Chlorophyll-a. While the existing model simulated extreme low values of DO, Mir-SWAT improved these values with a 0.11 increase in R² value between modeled and measured values. No major improvement was observed for nitrate loads but modeled phosphate peak loads were reduced to be much closer to measured values with Mir-SWAT model. A qualitative analysis on Chl-<i>a</i> concentrations also indicated that average and maximum monthly Chl-<i>a</i> values were better predicted with Mir-SWAT when compared to SWAT model, especially for winter months. The newly developed in-stream water quality model is expected to act as a stand alone model or coupled with larger models to improve the representation of solute transport processes and nutrient uptake in these models. The improvements made to SWAT model will increase the model confidence and widen its extent of applicability to short-term and localized studies that require understanding of fine-scale solute transport dynamics. </p>

Nutrients are the number one water pollution problem for U.S. lakes, reservoirs, and ponds. Excessive nutrients, such as nitrogen and phosphorus, lead to eutrophication, a condition that can include low oxygen levels, noxious algal blooms, and fish kills. Since eutrophication is a condition that manifests itself differently in different systems, there is not a criterion variable with a clear threshold that can be used to set the criterion level. This dissertation presents an approach to address the question: How should States and Tribes choose nutrient criteria to determine eutrophication-related impairments of the designated use? To address this question I used a combination of water quality modeling and decision analysis to determine the optimal nutrient criterion variables and levels. To choose criterion variables that are predictive of the designated use, I utilized statistical models (structural equation models, multiple regression, and binomial regression model) to link the measured water quality variables to expert elicited categories of eutrophication and the designated uses. These models were applied successfully to single waterbodies, the Kissimmee Chain-of-Lakes region, and the State of North Carolina to assess which candidate criterion variables were the most predictive. Additionally, the models indicated that the variables that were most predictive of eutrophication were also the most predictive of the designated use. Using the predictive nutrient criteria variables, I applied a decision-analytic approach to nutrient criteria setting in North Carolina. I developed a nutrient criteria value model that included two submodels, a water quality model and a multiattribute value model. The submodels were parameterized using a combination of water quality data, expert elicitation data, and utility assessments. The outcome of the nutrient criteria value model is the overall expected value for a criterion level choice; the optimal criterion level would be the choice that maximized the expected value. Using the preferences of North Carolina environmental decision-makers and a total phosphorus criterion variable, the optimal criterion level was between 0.03 mg/L and 0.07 mg/L. Ultimately, I hope this research will establish methodology used to set appropriate water quality criteria. / Dissertation

Cette thèse a pour objectif d'implémenter un schéma d'assimilation de données dans le modèle hydro-biogéochimique ProSe, afin d’assimiler les mesures en continu d’oxygène dissous de la colonne d’eau et de déterminer l’évolution temporelle des propriétés physiologiques des communautés vivantes. Dans un premier temps, une nouvelle version parallélisée de ProSe, ProSe-P, est développée en couplant les librairies hydraulique, de transport et biogéochimique (C-RIVE). Dans un deuxième temps, une analyse de sensibilité du module C-RIVE permet d'identifier un nombre restreint de paramètres influençant fortement les concentrations en oxygène dissous. Basé sur cette sélection, un algorithme de filtrage particulaire est implémenté afin d'assimiler séquentiellement les données haute fréquence d'oxygène dissous. Le couple ProSe-P-filtre particulaire, ProSe-PA, est ensuite appliqué sur un cas synthétique afin d'identifier les paramètres numériques pertinents et de valider l'efficacité du filtre particulaire pour les modèles de qualité de l'eau en rivière. Enfin, les mesures en continu d'O2 dissous de l'année 2011 en Seine sont assimilées par ProSe-PA. Les résultats montrent que ProSe-PA améliore significativement la simulation des concentrations en oxygène dissous, notamment les dynamiques alguales et les chutes d'oxygène pendant les périodes de crise. L'application aux données réelles révèle cependant les limites de l'approche développée, notamment la sensibilité aux conditions aux limites. Plusieurs pistes sont proposées afin d'améliorer les performances de ProSe-PA. / The aim of the thesis is to implement a data assimilation scheme in the hydro-biogeochemical model ProSe, in order to assimilate continuous measurements of dissolved oxygen in the water column and to determine the temporal evolution of the physiological properties of the communities of living species. First, a new parallel version of ProSe, ProSe-P, is developed coupling the three packages: hydrodynamic, transport and biogeochemical (C-RIVE). Second, a sensitivity analysis of the C-RIVE model allows the identification of a limited number of influentiel parameters controlling the dissolved oxygen concentrations. Based on the selection, a particle filtering algorithm is implemented in order to assimilate sequentially the high frequency oxygen data. The coupling ProSe-P-particle filtre, ProSe-PA is then applied on a synthetic case to tune the numerical settings for the data assimilation and to test the efficiency of the particle filter in river water quality models. Finally, the continuous measurements of dissolved oxygen of the year 2011 in the Seine River are assimilated by ProSe-PA. The results show that ProSe-PA improves significantly the simulation of the dissolved oxygen concentrations, especially the dynamics of algal blooms periods and the fast chute of O2 for the critical periods. This application to the real oxygen data reveals however some limits of the developed approach, especially the sensitivity to the boundary conditions. Some ideas are proposed to improve the performances of ProSe-PA.

This study addresses the regulation of large river phytoplankton by climate-related drivers with the help of three different approaches, i.e. analyses of long-term data and spatial dynamics (longitudinal samplings) as well as mathematical modeling. The central hypothesis is that discharge has a dominant role among climate-related variables which strongly alters phytoplankton biomass development. A multi-factorial statistical analysis on the basis of long-term data (1990 – 2009; 1994 – 2009) from two measuring stations of the rivers Rhine and Elbe revealed that discharge conditions and light availability were the main driving forces regulating phytoplankton spring bloom dynamics. For the Rhine, a trend towards an earlier occurrence of the spring bloom event and a decrease in seasonal mean phytoplankton biomass could be detected, whereas for the Elbe no shift in the timing of the spring bloom and a tendency towards increasing seasonal mean phytoplankton biomass was found. Longitudinal sampling campaigns served to analyze the spatial plankton development on a short-term scale. River-internal growth and loss processes, as well as import mediated by tributaries were examined. Four longitudinal profiles were realized at different seasons in recent years (2009 – 2011) and it was revealed that tributaries mainly had a diluting impact on plankton densities in the Elbe and provided an additional import of phyto- and zooplankton densities in the Rhine. In the present study, high bivalve abundances were detected in the Rhine, probably leading to river-internal losses of phytoplankton which could compensate phytoplankton production resulting in low phytoplankton concentrations. In the Elbe, low abundances of bivalves and a low benthic grazing pressure prevailed. On the other hand, an unusually low discharge event in spring 2011 in the Rhine demonstrated that loss processes can at times be superimposed by strong phytoplankton production leading to extremely high phytoplankton biomasses and chlorophyll a values. Hence, despite the observed long-term trend of decreasing chlorophyll values in the last two decades, extreme environmental conditions can provoke regime shifts with exceptional phytoplankton mass developments. To assess the potential impacts of future climate change on water quality, the water quality simulation model QSim was used to establish a model for the free-flowing part of the Rhine. The modeling approach was implemented by changing the hydrological and climatologic input data according to different climate projections for the near (2021 – 2050) and the far future (2071 – 2100). The model results indicated a weak response of phytoplankton biomass in the Rhine towards altered climatic conditions, including discharge reductions and water temperature increases. The study suggests that changes in discharge rather than water temperature mediate climate change effects on large river phytoplankton. However, the effects are river specific as a consequence of system specific differences in main control mechanisms (e.g. ‘bottom-up’ versus ‘top-down’). / Anhand von drei verschiedenen Ansätzen analysiert die vorliegende Arbeit die Regulierung von Phytoplankton in großen Flüssen durch klimabedingte Faktoren: Auswertung von Langzeitdaten, räumliche Dynamik (fließzeitkonforme, longitudinale Beprobungen) und mathematische Modellierung. Die zentrale Hypothese ist, dass Abfluss eine dominante Rolle unter den klimabedingten Faktoren spielt und die Phytoplanktonbiomasse stark beeinflusst. Eine multifaktorielle statistische Analyse basierend auf Langzeitdaten (1990 – 2009; 1994 –2009) von zwei Stationen der Flüsse Rhein und Elbe zeigten, dass hauptsächlich Abflussbedingungen und Lichtverfügbarkeit die Antriebskräfte bei der Regulierung der Phytoplanktonfrühjahrsblüte darstellten. Während sich am Rhein ein Trend hin zu einem früheren Auftreten der Frühjahrsblüte und einer Abnahme der mittleren Phytoplankton-biomasse während der Vegetationsperiode zeigte, konnte für die Elbe keine zeitliche Verschiebung der Frühjahrsblüte festgestellt werden und mittlere Phytoplanktonbiomassen zeigten hier eine steigende Tendenz. Longitudinale Fließzeitbeprobungen dienten zur Analyse der kurzfristigen räumlichen Entwicklung des Planktons. Interne Produktions- und Verlustprozesse, sowie der Eintrag durch Zuflüsse wurden untersucht. Vier longitudinale Profile wurden zu unterschiedlichen Jahreszeiten der letzten Jahre (2009 – 2011) realisiert und es wurde gezeigt, dass die Nebenflüsse der Elbe vorwiegend einen Verdünnungseffekt auf die Planktondichten des Hauptstromes hatten, während sie für den Rhein einen wichtigen zusätzlichen Eintrag von Phyto- und Zooplankton darstellten. In der vorliegenden Arbeit wurden hohe Muscheldichten im Rhein gefunden, die möglicherweise zu hohen internen Planktonverlusten durch Fraß beigetragen haben, und somit zu niedrigen Phytoplankton-konzentrationen geführt haben könnten. In der Elbe waren die Muscheldichten dagegen gering und somit war auch der benthische Fraßdruck niedriger. Auf der anderen Seite zeigte ein ungewöhnlich niedriges Abflussereignis im Frühjahr 2011 am Rhein, dass diese Verlustprozesse zeitweise von starker Phytoplanktonproduktion überlagert werden können und dadurch extrem hohe Phytoplanktonbiomassen und Chlorophyllwerte entstehen können. Demzufolge können trotz der Beobachtung eines langfristigen abnehmenden Trends in den Chlorophyllgehalten während der letzen zwei Jahrzehnte extreme Umweltbedingungen einen Regime-shift mit außergewöhnlichen Massenentwicklungen des Phytoplanktons hervorrufen. Um den möglichen Einfluss des zukünftigen Klimawandels auf die Gewässergüte abzuschätzen, wurde mithilfe des Gewässergütesimulationsmodells QSim ein Modell für den frei fließenden Abschnitt des Rheins erstellt. Für den Modellierungsansatz wurden die hydrologischen und klimatologischen Eingangsdaten entsprechend der verschiedenen Klimaprojektionen für die nahe (2021 – 2050) und ferne Zukunft (2071 – 2100) verändert. Die Modellergebnisse zeigten, dass sich Änderungen in den klimatischen Bedingungen, einschließlich Abflussreduktion und Wassertemperaturanstieg, nur geringfügig auf die Phytoplankton¬biomasse des Rheins auswirkten. Die vorliegende Arbeit deutet darauf hin, dass Klimawandeleffekte eher durch Änderungen der Abflussverhältnisse auf das Phytoplankton in großen Flüssen wirken als durch Änderungen der Wassertemperatur. Der Effekt ist jedoch flusssystemspezifisch, da die Auswirkungen von systemspezifischen Unterschieden in den Hauptkontrollmechanismen gesteuert werden (z.B. ‚Bottom-up’ versus ‚Top-down’).