IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v277y2014icp87-96.html
   My bibliography  Save this article

Spatial and temporal variability of in-stream water quality parameter influence on dissolved oxygen and nitrate within a regional stream network

Author

Listed:
  • Bailey, Ryan T.
  • Ahmadi, Mehdi

Abstract

Maintaining elevated aqueous concentrations of dissolved oxygen (DO) and decreased concentrations of nitrate (NO3) within stream environments is critical to sustaining aquatic life and the overall environmental health of a river system. Identifying system processes and system inputs that govern in-stream concentrations of DO and NO3 is paramount to achieving satisfactory concentrations or implementing efficient remediation methods. As these processes and inputs often depend on a multitude of climatic, environmental, and anthropogenic factors, it is essential to determine the spatio-temporal variability in their control of DO and NO3. In this study, a sensitivity analysis is applied to a regional-scale stream system of the Lower Arkansas River Basin in southeastern Colorado using a coupled QUAL2E-OTIS model to investigate the factors that govern DO and NO3 in space and time. Using the revised Morris scheme, a total of 34 model input factors (boundary conditions, flow and mass inputs, model parameters) are included in the analysis. Besides identifying the model input factors that govern DO and NO3 concentrations globally, the methodology also ascertains the influence of these factors according to location within the regional stream network and to season of the year. Results show that upstream solute concentrations, algal processes, channel roughness, groundwater discharge and solute mass loadings to the stream, and oxygen reaeration are the most influential processes and parameters in determining DO and NO3 concentrations. Many processes (algal growth and respiration, chemical kinetic reactions) have a time-varying influence due to seasonal changes in water temperature and solar radiation. Other processes (groundwater discharge and solute mass loading) are of moderate influence in the Arkansas River but of very strong influence in the tributaries. These results not only identify parameters and processes that should be targeted during field data collection and model calibration, but also highlight the possibility of implementing efficient remediation strategies that target processes at different locations and at different times of the year.

Suggested Citation

  • Bailey, Ryan T. & Ahmadi, Mehdi, 2014. "Spatial and temporal variability of in-stream water quality parameter influence on dissolved oxygen and nitrate within a regional stream network," Ecological Modelling, Elsevier, vol. 277(C), pages 87-96.
  • Handle: RePEc:eee:ecomod:v:277:y:2014:i:c:p:87-96
    DOI: 10.1016/j.ecolmodel.2014.01.015
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380014000581
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ecolmodel.2014.01.015?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Valenti, D. & Tranchina, L. & Brai, M. & Caruso, A. & Cosentino, C. & Spagnolo, B., 2008. "Environmental metal pollution considered as noise: Effects on the spatial distribution of benthic foraminifera in two coastal marine areas of Sicily (Southern Italy)," Ecological Modelling, Elsevier, vol. 213(3), pages 449-462.
    2. Saltelli, Andrea & Ratto, Marco & Tarantola, Stefano & Campolongo, Francesca, 2006. "Sensitivity analysis practices: Strategies for model-based inference," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1109-1125.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Haas, Marcelo B. & Guse, Björn & Pfannerstill, Matthias & Fohrer, Nicola, 2015. "Detection of dominant nitrate processes in ecohydrological modeling with temporal parameter sensitivity analysis," Ecological Modelling, Elsevier, vol. 314(C), pages 62-72.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ping, Zhu, 2023. "Analytical equivalent transformation method for nonlinear stochastic dynamics with multiple noises in high dimensions," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    2. Imron, Muhammad Ali & Gergs, Andre & Berger, Uta, 2012. "Structure and sensitivity analysis of individual-based predator–prey models," Reliability Engineering and System Safety, Elsevier, vol. 107(C), pages 71-81.
    3. Cao, Jiaokun & Du, Farong & Ding, Shuiting, 2013. "Global sensitivity analysis for dynamic systems with stochastic input processes," Reliability Engineering and System Safety, Elsevier, vol. 118(C), pages 106-117.
    4. Drignei, Dorin, 2011. "A general statistical model for computer experiments with time series output," Reliability Engineering and System Safety, Elsevier, vol. 96(4), pages 460-467.
    5. Rui Zhang & Taotao Chen & Daocai Chi, 2020. "Global Sensitivity Analysis of the Standardized Precipitation Evapotranspiration Index at Different Time Scales in Jilin Province, China," Sustainability, MDPI, vol. 12(5), pages 1-19, February.
    6. Mondal, Chirodeep & Kesh, Dipak & Mukherjee, Debasis, 2023. "Global stability and bifurcation analysis of an infochemical induced three species discrete-time phytoplankton–zooplankton model," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    7. Raja Sekhara Rao, P. & Naresh Kumar, M., 2015. "A dynamic model for infectious diseases: The role of vaccination and treatment," Chaos, Solitons & Fractals, Elsevier, vol. 75(C), pages 34-49.
    8. Reder, Klara & Alcamo, Joseph & Flörke, Martina, 2017. "A sensitivity and uncertainty analysis of a continental-scale water quality model of pathogen pollution in African rivers," Ecological Modelling, Elsevier, vol. 351(C), pages 129-139.
    9. Masciantonio, Sergio, 2013. "Identifying, ranking and tracking systemically important financial institutions (SIFIs), from a global, EU and Eurozone perspective," MPRA Paper 46788, University Library of Munich, Germany.
    10. Thalles Vitelli Garcez & Helder Tenório Cavalcanti & Adiel Teixeira de Almeida, 2021. "A hybrid decision support model using Grey Relational Analysis and the Additive-Veto Model for solving multicriteria decision-making problems: an approach to supplier selection," Annals of Operations Research, Springer, vol. 304(1), pages 199-231, September.
    11. Melito, Gian Marco & Müller, Thomas Stephan & Badeli, Vahid & Ellermann, Katrin & Brenn, Günter & Reinbacher-Köstinger, Alice, 2021. "Sensitivity analysis study on the effect of the fluid mechanics assumptions for the computation of electrical conductivity of flowing human blood," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    12. Mara, Thierry A. & Tarantola, Stefano, 2012. "Variance-based sensitivity indices for models with dependent inputs," Reliability Engineering and System Safety, Elsevier, vol. 107(C), pages 115-121.
    13. Deman, G. & Kerrou, J. & Benabderrahmane, H. & Perrochet, P., 2015. "Sensitivity analysis of groundwater lifetime expectancy to hydro-dispersive parameters: The case of ANDRA Meuse/Haute-Marne site," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 276-286.
    14. Soha Saad & Florence Ossart & Jean Bigeon & Etienne Sourdille & Harold Gance, 2021. "Global Sensitivity Analysis Applied to Train Traffic Rescheduling: A Comparative Study," Energies, MDPI, vol. 14(19), pages 1-29, October.
    15. Xiang Peng & Xiaoqing Xu & Jiquan Li & Shaofei Jiang, 2021. "A Sampling-Based Sensitivity Analysis Method Considering the Uncertainties of Input Variables and Their Distribution Parameters," Mathematics, MDPI, vol. 9(10), pages 1-18, May.
    16. Narkuniene, Asta & Poskas, Povilas & Kilda, Raimondas & Bartkus, Gytis, 2015. "Uncertainty and sensitivity analysis of radionuclide migration through the engineered barriers of deep geological repository: Case of RBMK-1500 SNF," Reliability Engineering and System Safety, Elsevier, vol. 136(C), pages 8-16.
    17. Wu, Jian-Li & Duan, Wei-Long & Luo, Yuhui & Yang, Fengzao, 2020. "Time delay and non-Gaussian noise-enhanced stability of foraging colony system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 553(C).
    18. Ahmadi, Mehdi & Ascough, James C. & DeJonge, Kendall C. & Arabi, Mazdak, 2014. "Multisite-multivariable sensitivity analysis of distributed watershed models: Enhancing the perceptions from computationally frugal methods," Ecological Modelling, Elsevier, vol. 279(C), pages 54-67.
    19. Ma, Tianchi & Shen, Junxian & Song, Di & Xu, Feiyun, 2022. "Unsaturated piecewise bistable stochastic resonance with three kinds of asymmetries driven by multiplicative and additive noise," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    20. Chiachío, Juan & Chiachío, Manuel & Sankararaman, Shankar & Saxena, Abhinav & Goebel, Kai, 2015. "Condition-based prediction of time-dependent reliability in composites," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 134-147.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecomod:v:277:y:2014:i:c:p:87-96. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/ecological-modelling .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.