Integrated Environmental Modelling Framework for Cumulative Effects Assessment

Global warming and population growth have resulted in an increase in the intensity of natural and anthropogenic stressors. Investigating the complex nature of environmental problems requires the integration of different environmental processes across major components of the environment, including water, climate, ecology, air, and land. Cumulative effects assessment (CEA) not only includes analyzing and modeling environmental changes, but also supports planning alternatives that promote environmental monitoring and management. Disjointed and narrowly focused environmental management approaches have proved dissatisfactory. The adoption of integrated modelling approaches has sparked interests in the development of frameworks which may be used to investigate the processes of individual environmental component and the ways they interact with each other. Integrated modelling systems and frameworks are often the only way to take into account the important environmental processes and interactions, relevant spatial and temporal scales, and feedback mechanisms of complex systems for CEA. This book examines the ways in which interactions and relationships between environmental components are understood, paying special attention to climate, land, water quantity and quality, and both anthropogenic and natural stressors. It reviews modelling approaches for each component and reviews existing integrated modelling systems for CEA. Finally, it proposes an integrated modelling framework and provides perspectives on future research avenues for cumulative effects assessment

APPLICATION OF HYDRODYNAMIC MODELS IN SIMULATING THE THERMAL REGIME OF LAKE SUPERIOR

In large systems, such as the Great Lakes and coastal oceans, physical processes have a significant influence on chemical and biological phenomena. Hydrodynamic modeling assists in describing these physical characteristics and in recent years, these models have been extensively applied in the Great Lakes basin to study the response of the lake ecosystem to long-term meteorological forcing conditions. Due to its role in mediating physical, biological and chemical processes in lake environments, water temperature (and the attendant thermal regime) has been the parameter of interest in many of these mathematical modeling studies and was adopted as the primary metric for this research. Owing to its pristine waters and relatively undisturbed (lowest-urban-impact) watershed, Lake Superior, the largest, deepest and northernmost of the Great Lakes, was selected as the study site for this doctoral work. This study first describes the calibration and confirmation procedure for a three-dimensional (3D) hydrodynamic model developed for the western basin of Lake Superior, with an emphasis on evaluating model performance using a multi-criteria approach, and the introduction of a new goodness-of-fit criterion that finds applicability in an ecological context. The following segment introduces a one-dimensional (1D) hydrodynamic framework, adapted to explore spatio-temporal patterns in thermal stratification in Lake Superior (large lakes), supporting the development of coupled 1D frameworks to provide a computationally efficient and accurate approach to parameterize and test complex 3D ecosystem models. This 1D hydrodynamic model was further applied, in conjunction with field measurements of water temperature, to identify differences in the response of the thermal regime of Lake Superior in the nearshore and offshore regions to the divergent forcing conditions in the unusually warm year (2012) and the extreme cold year (2014)

Geo-Spatial Analysis in Hydrology

Geo-spatial analysis has become an essential component of hydrological studies to process and examine geo-spatial data such as hydrological variables (e.g., precipitation and discharge) and basin characteristics (e.g., DEM and land use land cover). The advancement of the data acquisition technique helps accumulate geo-spatial data with more extensive spatial coverage than traditional in-situ observations. The development of geo-spatial analytic methods is beneficial for the processing and analysis of multi-source data in a more efficient and reliable way for a variety of research and practical issues in hydrology. This book is a collection of the articles of a published Special Issue Geo-Spatial Analysis in Hydrology in the journal ISPRS International Journal of Geo-Information. The topics of the articles range from the improvement of geo-spatial analytic methods to the applications of geo-spatial analysis in emerging hydrological issues. The results of these articles show that traditional hydrological/hydraulic models coupled with geo-spatial techniques are a way to make streamflow simulations more efficient and reliable for flood-related decision making. Geo-spatial analysis based on more advanced methods and data is a reliable resolution to obtain high-resolution information for hydrological studies at fine spatial scale

One- and Two-Dimensional Hydrological Modelling and Their Uncertainties

Earth processes, which occur in land, air and ocean in different environment and at different scales, are very complex. Flooding is also a part of the complex processes, which need to be assessed accurately to know the accurate spatial and temporal changes of flooding and their causes. Hydrological modelling has been used by several researchers in river and floodplain modelling for flood analysis. In this chapter, factors affecting flash flood, possible options of basic input parameters in one- and two-dimensional hydrological models in data sparse environment, some case studies and uncertainty in hydrological modelling were discussed. This discussion will help the readers to understand the flooding factors, selection of input parameters in data sparse environment, a brief insight of one- and two-dimensional hydrological models and uncertainties in their input and model parameters and model structures

Modeling reservoir surface temperatures for regional and global climate models: A multi-model study on the inflow and level variation effects

UIDB/04292/2020 KI-853/13 KI-853- 16 UIDB/04292/2020The complexity of the state-of-the-art climate models requires high computational resources and imposes rather simplified parameterization of inland waters. The effect of lakes and reservoirs on the local and regional climate is commonly parameterized in regional or global climate modeling as a function of surface water temperature estimated by atmosphere-coupled one-dimensional lake models. The latter typically neglect one of the major transport mechanisms specific to artificial reservoirs: heat and mass advection due to inflows and outflows. Incorporation of these essentially two-dimensional processes into lake parameterizations requires a trade-off between computational efficiency and physical soundness, which is addressed in this study. We evaluated the performance of the two most used lake parameterization schemes and a machine-learning approach on high-resolution historical water temperature records from 24 reservoirs. Simulations were also performed at both variable and constant water level to explore the thermal structure differences between lakes and reservoirs. Our results highlight the need to include anthropogenic inflow and outflow controls in regional and global climate models. Our findings also highlight the efficiency of the machine-learning approach, which may overperform process-based physical models in both accuracy and computational requirements if applied to reservoirs with long-term observations available. Overall, results suggest that the combined use of process-based physical models and machine-learning models will considerably improve the modeling of air-lake heat and moisture fluxes. A relationship between mean water retention times and the importance of inflows and outflows is established: reservoirs with a retention time shorter than ĝ1/4g100gd, if simulated without inflow and outflow effects, tend to exhibit a statistically significant deviation in the computed surface temperatures regardless of their morphological characteristics.publishersversionpublishe

Water Quality Modeling of Rivers and Lakes

Oxygen depletion, eutrophication, acidification, toxic pollution, poor hygienic state, salinity, and excess suspended matter are among the issues usually included in the concept of water quality problems. This report focuses on inland surface waters, namely on rivers, lakes, and reservoirs, to each of which the problem categories listed above are relevant. The variety of problems, the multidisciplinarity among water quality experts, the wide spectrum of societal water needs, together with the high diversity of computational approaches presently available contribute to the present situation in which applicable methodology is developing from a variety of fronts and directions, among which dynamical, partial differential equation models were taken here in focus. Transport, flow and reaction equations for shallow water bodies are reviewed for 1 and 2-dimensional cases, including an introduction to and illustration of a selection of numerical techniques. Four case studies on eutrophication modeling, and a review and discussion on the application of decision support systems on water quality management are presented thereafter

The Illinois River decision support system (ILRDSS)

"Prepared for the Illinois Department of Natural Resources.

Assimilation of current measurements into a circulation model of Lake Michigan

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95190/1/wrcr11238.pd