scholarly journals Do mixing models with different input requirement yield similar streamflow source contributions? Case study: a tropical montane catchment

2021 ◽  
Author(s):  
Jorge Ramón ◽  
Alicia Correa ◽  
Edison Timbe ◽  
Giovanny M. Mosquera ◽  
Enma Mora ◽  
...  
Keyword(s):  
Mixing Models ◽  
Source Contributions

Isotope mixing models require individual isotopic tracer content for correct quantification of sediment source contributions

2018 ◽  
Vol 32 (7) ◽  
pp. 981-989 ◽  
Author(s):  
Hari Ram Upadhayay ◽  
Samuel Bodé ◽  
Marco Griepentrog ◽  
Roshan Man Bajracharya ◽  
William Blake ◽  
...  
Keyword(s):  
Mixing Models ◽  
Sediment Source ◽  
Isotopic Tracer ◽  
Source Contributions ◽  
Isotope Mixing Models

Using the chemical mass balance model to estimate VOC source contributions in newly built timber frame houses: a case study

2017 ◽  
Vol 24 (31) ◽  
pp. 24156-24166 ◽  
Author(s):  
Herve Plaisance ◽  
Pierre Mocho ◽  
Nicolas Sauvat ◽  
Jane Vignau-Laulhere ◽  
Katarzyna Raulin ◽  
...  
Keyword(s):  
Mass Balance ◽  
Balance Model ◽  
Chemical Mass Balance ◽  
Mass Balance Model ◽  
Timber Frame ◽  
Source Contributions ◽  
Chemical Mass Balance Model

scholarly journals Best practices for use of stable isotope mixing models in food-web studies

2014 ◽  
Vol 92 (10) ◽  
pp. 823-835 ◽  
Author(s):  
Donald L. Phillips ◽  
Richard Inger ◽  
Stuart Bearhop ◽  
Andrew L. Jackson ◽  
Jonathan W. Moore ◽  
...  
Keyword(s):  
Stable Isotope ◽  
Concentration Dependence ◽  
Probability Distributions ◽  
Mixing Models ◽  
Web Studies ◽  
Source Contributions ◽  
Spatio Temporal ◽  
Discrimination Factors ◽  
Isotope Mixing Models ◽  
User Friendly

Stable isotope mixing models are increasingly used to quantify consumer diets, but may be misused and misinterpreted. We address major challenges to their effective application. Mixing models have increased rapidly in sophistication. Current models estimate probability distributions of source contributions, have user-friendly interfaces, and incorporate complexities such as variability in isotope signatures, discrimination factors, hierarchical variance structure, covariates, and concentration dependence. For proper implementation of mixing models, we offer the following suggestions. First, mixing models can only be as good as the study and data. Studies should have clear questions, be informed by knowledge of the system, and have strong sampling designs to effectively characterize isotope variability of consumers and resources on proper spatio-temporal scales. Second, studies should use models appropriate for the question and recognize their assumptions and limitations. Decisions about source grouping or incorporation of concentration dependence can influence results. Third, studies should be careful about interpretation of model outputs. Mixing models generally estimate proportions of assimilated resources with substantial uncertainty distributions. Last, common sense, such as graphing data before analyzing, is essential to maximize usefulness of these tools. We hope these suggestions for effective implementation of stable isotope mixing models will aid continued development and application of this field.

scholarly journals Assumptions on mixing heights influence the quantification of emission sources: A case study for Cyprus

2016 ◽  
Author(s):  
Imke Hüser ◽  
Hartwig Harder ◽  
Angelika Heil ◽  
Johannes W. Kaiser
Keyword(s):  
Forest Fires ◽  
Mixing Layer ◽  
Particle Dispersion ◽  
Emission Sources ◽  
Source Contributions ◽  
Mixing Layer Height ◽  
Spatio Temporal ◽  
The Impact ◽  
Flexpart Model

Abstract. Lagrangian particle dispersion models (LPDMs) in backward mode are widely-used to quantify the impact of transboundary pollution on downwind sites. Most LPDM applications assume mixing of surface emissions in a boundary layer that is constant in height. The height of this mixing layer (ML), however, is subject to strong spatio-temporal variability. Neglecting this variability may introduce substantial errors in the quantification of source contributions. Here, we perform backward trajectory simulations with the FLEXPART model starting at Cyprus to quantify these errors. The simulations calculate the sensitivity to emissions of upwind pollution sources within the ML height. The emission sensitivity is used to quantify source contributions at the receptor and support the interpretation of ground measurements carried out during the CYPHEX campaign in July 2014. It is determined by two interacting factors: the dilution of pollutants within the ML and the number of trajectories impacted by the emissions. In this study, we calculate the emission sensitivity for a constant ML height of 300 m and a dynamical ML height to compare the resulting differences. The results show that the impact of emission sources is predominantly overestimated by the neglected dilution in expanded daytime ML heights. There is, however, substantial variability in the simulated differences. For shallow marine or nocturnal ML heights, for example, a ML assumed to high may lead to an underestimation of the intensive concentrations. This variability is predominantly caused by the spatio-temporal changes in ML heights and the meteorological conditions that drive the dispersion of the trajectories. In an application example, the impact of CO emissions from hypothetical forest fires is simulated and source contributions are compared for different ML heights. The resulting difference shows that the 300 m overestimates the total CO contributions from upwind sources by 16 %. Thus, it is recommended to generally implement a dynamic mixing layer height parametrization in LPDMs to prevent these errors.

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