@article {4389, title = {The effect of environmental conditions on Atlantic salmon smolts{\textquoteright} (Salmo salar) bioenergetic requirements and migration through an inland sea}, journal = {Environmental Biology of Fishes}, volume = {101}, year = {2018}, month = {Jan-10-2018}, pages = {1467 - 1482}, issn = {0378-1909}, doi = {10.1007/s10641-018-0792-5}, url = {http://link.springer.com/10.1007/s10641-018-0792-5http://link.springer.com/content/pdf/10.1007/s10641-018-0792-5.pdfhttp://link.springer.com/article/10.1007/s10641-018-0792-5/fulltext.htmlhttp://link.springer.com/content/pdf/10.1007/s10641-018-0792-5.pdf}, author = {Strople, Leah C. and Filgueira, Ram{\'o}n and Hatcher, Bruce G. and Denny, Shelley and Bordeleau, Xavier and Whoriskey, Frederick G. and Crossin, Glenn T.} } @article {4241, title = {Ecosystem modelling for ecosystem-based management of bivalve aquaculture sites in data-poor environment}, journal = {Aquacult Environ Interact}, volume = {4}, year = {2013}, pages = {117-133}, chapter = {117}, abstract = {Although models of carrying capacity have been around for some time, their use in aquaculture management has been limited. This is partially due to the cost involved in generating and testing the models. However, the use of more generic and flexible models could facilitate the implementation of modelling in management. We have built a generic core for coupling biogeochemical and hydrodynamic models using Simile (www.simulistics.com), a visual simulation environment software that is well-suited to accommodate fully spatial models. Specifically, Simile integrates PEST (model-independent parameter estimation, Watermark Numerical Computing, www.pesthomepage.org), an optimization tool that uses the Gauss-Marquardt-Levenberg algorithm and can be used to estimate the value of a parameter, or set of parameters, in order to minimize the discrepancies between the model results and a dataset chosen by the user. The other critical aspect of modelling exercises is the large amount of data necessary to set up, tune and groundtruth the ecosystem model. However, ecoinformatics and improvements in remote sensing procedures have facilitated acquisition of these datasets, even in data-poor environments. In this paper we describe the required datasets and stages of model development necessary to build a biogeochemical model that can be used as a decision-making tool for bivalve aquaculture management in data-poor environments.}, doi = {10.3354/aei00078}, url = {http://www.int-res.com/abstracts/aei/v4/n2/p117-133/}, author = {R. Filgueira and J. Grant and R. Stuart and M. S. Brown} } @article {4222, title = {Ein Ansatz zur r{\"a}umlich- dynamischen Modellierung am Beispiel der Tereskenernte im Ostpamir }, year = {2013}, url = {http://gispoint.de/fileadmin/user_upload/paper_gis_open/537533033.pdf}, author = {Georg HOHBERG} } @article {1534, title = {The effect of vegetation on pesticide dissipation from ponded treatment wetlands: Quantification using a simple model}, journal = {Chemosphere}, volume = {72}, year = {2008}, month = {07/2008}, pages = {999-1005}, chapter = {999}, abstract = {

Field data shows that plants accelerate pesticide dissipation from aquatic systems by increasing sedimentation, biofilm contact and photolysis. In this study, a graphical model was constructed and calibrated with site-specific and supplementary data to describe the loss of two pesticides, endosulfan and fluometuron, from a vegetated and a non-vegetated pond. In the model, the major processes responsible for endosulfan dissipation were alkaline hydrolysis and sedimentation, with the former process being reduced by vegetation and the latter enhanced. Fluometuron dissipation resulted primarily from biofilm reaction and photolysis, both of which were increased by vegetation. Here, greater photolysis under vegetation arose from faster sedimentation and increased light penetration, despite shading. Management options for employing constructed wetlands to polish pesticide-contaminated agricultural runoff are discussed. The lack of easily fulfilled sub-models and data describing the effect of aquatic vegetation on water chemistry and sedimentation is also highlighted.

}, keywords = {Cotton, Herbicide, Insecticide, Macrophyte, Phytoremediation, Runoff water}, doi = {10.1016/j.chemosphere.2008.04.059}, url = {http://dx.doi.org/10.1016/j.chemosphere.2008.04.059}, author = {Michael T. Rose and Angus N. Crossan and Ivan R. Kennedy} } @article {1538, title = {The EROI of U.S. offshore energy extraction: A net energy analysis of the Gulf of Mexico}, journal = {Ecological Economics}, volume = {63}, year = {2007}, month = {08/2007}, pages = {355-364}, abstract = {

In 2004, the U.S. Department of the Interior{\textquoteright}s Minerals Management Service estimated that 49\% of the oil and 57\% of the natural gas yet to be discovered offshore in the United States are located in the Gulf of Mexico Outer Continental Shelf region. While the existence of these energy resources is critical to the nation{\textquoteright}s future economic well being, of equal importance is the amount of already extracted energy that will be required to deliver the new fuel to society in a useful form. The difference between the two energy quantities is the net supply. In many respects, net energy is the most relevant measure of fuel supply because it represents the energy available to produce final-demand economic goods and services. Unfortunately, there currently exists no standard procedure for determining net energy, and so the data are extremely limited and inconsistent. In this paper, we present an \“energy return on investment\”, or \“EROI\”-based approach. EROI is defined as the ratio of gross energy produced by an energy supply process to the total, direct plus indirect, energy cost of its production. If the EROI of an energy supply process is known, then it{\textquoteright}s net energy output can be derived easily given gross production data. Below, we specify an empirical computer model programmed to simulate the productivity dynamics of offshore energy extraction in the Gulf of Mexico and estimate the EROI of the \"offshore process\" over a twenty-year period (1985\–2004). At the conclusion of the simulation, the model calculates the EROI of the process to range from 10 to 25, depending on how energy costs have been defined. In comparison, it has been estimated that the EROI of U.S. domestic petroleum extraction in the 1930s was approximately 100.

}, keywords = {EROI, Net energy, Offshore energy extraction}, doi = {doi:10.1016/j.ecolecon.2007.02.015}, author = {Mark Gately} } @article {1537, title = {Evaluation and comparison of models and modelling tools simulating nitrogen processes in treatment wetlands }, journal = {Simulation Modelling Practice and Theory}, volume = {16}, year = {2007}, note = {

From Bachelor thesis, http://liu.diva-portal.org/smash/record.jsf?pid=diva2:20221

Modelica was compared with Simile, Stella and PowerSim

It would be interesting to evaluate the comments on Simile.

}, pages = {26-49}, publisher = {Elsevier}, abstract = {

In this paper, two ecological models of nitrogen processes in treatment wetlands have been evaluated and compared. These models were implemented, simulated, and visualized using the Modelica modelling and simulation language [P. Fritzson, Principles of Object-Oriented Modelling and Simulation with Modelica 2.1 (Wiley-IEEE Press, USA, 2004).] and an associated tool. The differences and similarities between the MathModelica Model Editor and three other ecological modelling tools have also been evaluated. The results show that the models can well be modelled and simulated in the MathModelica Model Editor, and that nitrogen decrease in a constructed treatment wetland should be described and simulated using the Nitrification/Denitrification model as this model has the highest overall quality score and provides a more variable environment.

}, keywords = {Denitrification, Ecological modelling, Evaluation, Modelica, Nitrification, Nitrogen, Treatment wetlands}, doi = {doi:10.1016/j.simpat.2007.08.010 }, author = {Stina Edelfeldt and Peter Fritzson} } @book {1594, title = {Environmental Modelling: Finding Simplicity in Complexity}, year = {2003}, pages = {430}, publisher = {Wiley}, organization = {Wiley}, keywords = {environmental modelling, GIS}, isbn = {978-0471496182}, url = {http://eu.wiley.com/WileyCDA/WileyTitle/productCd-0471496189.html}, author = {John Wainwright and Mark Mulligan} }