Skip to main content
SpringerLink
Log in

Parameter Sensitivity of a Surface Water Quality Model of the Lower South Saskatchewan River—Comparison Between Ice-On and Ice-Off Periods

  • Published:
Environmental Modeling & Assessment Aims and scope Submit manuscript

Abstract

Little is known about seasonal differences (ice-on vs. ice-off periods) and the sensitivity of in-stream processes to surface water quality constituents in rivers that have a persistent ice cover in winter. The goal of this study is to investigate the sensitivity of nutrient transformation processes on surface water quality, especially rivers in cold regions where ice-covered conditions persist for a substantial part of the year. We established a sensitivity analysis framework for water quality modelling and monitoring of rivers in cold regions using the Water Quality Analysis Program WASP7. The lower South Saskatchewan River in the interior of western Canada, from the Gardiner Dam at Lake Diefenbaker to the confluence of the North and South Saskatchewan rivers, is used as a test case for this purpose. The study reveals that parameter sensitivities differ between ice-covered and ice-free periods and biological model parameters related to nutrient-phytoplankton dynamics can still be sensitive during the ice-covered season. For example, sediment oxygen demand is an important parameter during the ice-on period, whereas parameters related to nitrification are more sensitive in the ice-off period. These results provide insight into important water quality monitoring aspects in cold regions during different seasons.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. Withers, P. J. A., & Jarvie, H. P. (2008). Delivery and cycling of phosphorus in rivers: a review. Science of the Total Environment, 400(1), 379–395.

    Article  CAS  Google Scholar 

  2. Gober, P., & Wheater, H. (2013). Socio-hydrology and the science-policy interface: a case study of the Saskatchewan River Basin. Hydrology and Earth System Sciences – Discussion, 10(5), 6669–6693.

    Article  Google Scholar 

  3. Byrne, J., Kienzle, S., Johnson, S., Duke, D., Gannon, G., & Selinger, V. (2006). Current and future water issues in the Oldman River Basin of Alberta, Canada. Water Science and Technology, 53(10), 327–334.

    Article  CAS  Google Scholar 

  4. Koning, C. W., Saffran, K. A., Little, J. L., & Fent, L. (2006). Water quality monitoring: the basis for watershed management in the Oldman River Basin, Canada. Water Science and Technology, 53(10), 153–161.

    Article  CAS  Google Scholar 

  5. Ruszchi C. (2010) Water quality in the South Saskatchewan River Sub-Basin. SEAWA Web-based State of the Watershed Report. University of Alberta

  6. Xing, F., & Stefan, G. H. (1997). Simulated climate change effects on dissolved oxygen characteristics in ice-covered lakes. Ecological Modelling, 103(2), 209–229.

    Google Scholar 

  7. Shakibaeinia A, Dibike YB, Prowse TD. (2014) Numerical modelling of dissolved-oxygen in a cold-region river. In: International Environmental Modelling and Software Society (iEMSs) 7th Intl. Congress on Env. Modelling and Software. San Diego, CA, USA

  8. Prowse, T. D. (2001). River-ice ecology. I: Hydrologic, geomorphic, and water-quality aspects. Journal of Cold Regions Engineering, 15(1), 1–16.

    Article  CAS  Google Scholar 

  9. Weyhenmeyer, G. A., Westoo, A. K., & Willen, E. (2008). Increasingly ice-free winters and their effects on water. Hydrobiologia, 599(1), 111–118.

    Article  CAS  Google Scholar 

  10. Ambrose RB, Wool TA, Connolly JP, Schanz RW. (1988) WASP4, a hydrodynamic and water-quality model-model theory, user’s manual, and programmer’s guide. Environmental Protection Agency, Athens, GA (USA). Environmental Research Lab. 1988 No. PB-88-185095/XAB; EPA-600/3–87/039

  11. Wool, T. A., Davie, S. R., & Rodriguez, H. N. (2003). Development of three-dimensional hydrodynamic and water quality models to support total maximum daily load decision process for the Neuse River Estuary, North Carolina. Journal of Water Resources Planning and Management, 129(4), 295–306.

    Article  Google Scholar 

  12. Pastres, R., Franco, D., Pecenik, G., Solidoro, C., & Dejak, C. (1997). Local sensitivity analysis of a distributed parameters water quality model. Reliability Engineering and System Safety, 57, 21–30.

    Article  Google Scholar 

  13. Van Griensven, A., Meixner, T., Grunwald, S., Bishop, T., Diluzio, M., & Srinivasan, R. (2006). A global sensitivity analysis tool for the parameters of multi-variable catchment models. Journal of Hydrology, 324(1), 10–23.

  14. Razavi, S., & Gupta, H. V. (2015). What do we mean by sensitivity analysis? The need for comprehensive characterization of “global” sensitivity in Earth and Environmental systems models. Water Resources Research, 51(5), 3070--3092.

  15. Lindenschmidt, K. E., Pech, I., & Baborowski, M. (2009). Environmental risk of dissolved oxygen depletion of diverted flood waters in river polder systems—a quasi-2D flood modelling approach. Science of the Total Environment, 407(5), 1598–1612.

    Article  CAS  Google Scholar 

  16. Lindenschmidt, K. E., Huang, S., & Baborowski, M. (2008). A quasi-2D flood modelling approach to simulate substance transport in polder systems for environmental flood risk assessment. Science of the Total Environment, 397(1–3), 86–102.

    Article  CAS  Google Scholar 

  17. Mann, H. (1945). Nonparametric tests against trend. Econometrica, 13(3), 245–259.

    Article  Google Scholar 

  18. Kendall, M. (1975). Correlation methods (p. 196). Charles Griffin: London.

  19. Sen, P. (1968). Estimates of the regression coefficient based on Kendall’s tau. Journal of the 475 American Statistical Association, 63(324), 1379–1389.

    Article  Google Scholar 

  20. Zhang, X., Vincent, L. A., Hogg, W. D., & Niitsoo, A. (2000). Temperature and precipitation trends in Canada during the 20th century. Atmosphere-Ocean, 38(3), 395–429.

    Article  Google Scholar 

  21. Di Toro DM, Fitzpatrick JJ, Thomann RV. (1983) Water Quality Analysis Simulation Program (WASP) and Model Verification Program (MVP) documentation. User’s manual.

  22. Connolly JP, Winfield R. (1984) A user’s guide for WASTOX, a framework for modeling the fate of toxic chemicals in aquatic environments. Part 1: exposure concentration. EPA-600/3–84-077. Gulf Breeze: USEPA.

  23. Lindenschmidt, K. E. (2006). The effect of complexity on parameter sensitivity and model uncertainty in river water quality modelling. Ecological Modelling, 190(1), 72–86.

    Article  CAS  Google Scholar 

  24. Kaufman GB. (2003) Application of the Water Quality Analysis Simulation Program (WASP) to evaluate dissolved nitrogen concentrations in the Altamaha River Estuary, Georgia. Master Thesis. B.S., University of Florida.

  25. Franceschini, S., & Tsai, C. W. (2010). Assessment of uncertainty sources in water quality modeling in the Niagara River. Advances in Water Resources, 33, 493–503.

    Article  CAS  Google Scholar 

  26. Diduck, S. (1989). Water quality modelling South Saskatchewan River. Technical Report D.10. Regina, Saskatchewan: Saskatchewan Environment and Public Safety; Water Quality Branch Saskatchewan Environment and Public Safety.

    Google Scholar 

  27. Armengol, J., Caputo, L., Comerma, M., Eijoó, C., García, J. C., Marcé, R., Navarro, E., & Ordoñez, J. (2003). Sau reservoir’s light climate: relationships between Secchi depth. Limnetica, 22(1–2), 195–210.

    Google Scholar 

  28. Poole, H. H., & Atkins, W. R. (1929). Photo-electric measurements of submarine illumination throughout the year. Journal of the Marine Biological Association of the United Kingdom (New Series), 16(1), 297–324.

    Article  Google Scholar 

  29. Chapra, S. C. (1997). Surface water-quality modeling. New York: McGraw-Hill.

    Google Scholar 

  30. Tufford, D. L., & McKellar, H. N. (1999). Spatial and temporal hydrodynamic and water quality modeling analysis of a large reservoir on the South Carolina (USA) coastal plain. Ecological Modelling, 114, 137–173.

    Article  CAS  Google Scholar 

  31. Tang, Y., Reed, P., Wagener, T., & Werkhoven, K. V. (2007). Comparing sensitivity analysis methods to advance lumped. Hydrology and Earth System Sciences, 11, 793–817.

    Article  Google Scholar 

  32. Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27, 379–423.

    Article  Google Scholar 

  33. Gupta, H., Wagener, T., & Liu, Y. (2008). Reconciling theory with observations: elements of a diagnostic approach to model evaluation. Hydrological Processes, 22(18), 3802–3813.

    Article  Google Scholar 

  34. Weijs, S. V., Schoups, G., & Giesen, N. V. D. (2010). Why hydrological predictions should be evaluated using information theory. Hydrology and Earth System Sciences, 14(EPFL-ARTICLE-167375), 2545–2558.

    Article  Google Scholar 

  35. Pechlivanidis, I. G., Jackson, B., McMillan, H., Gupta, H., Pechlivandis, I. G., et al. (2012). Using an informational entropy-based metric as a diagnostic of flow duration to drive model parameter identification. In the special issue of the Global NEST. Journal on Hydrology and Water Resources, 14(3), 325–333.

    Google Scholar 

  36. Chun, K. P., Wheater, H., & Onof, C. (2012). Prediction of the impact of climate change on drought: an evaluation of six UK catchments using two stochastic approaches. Hydrological Processes, 27, 1600–1614. doi:10.1002/hyp.9259.

    Article  Google Scholar 

  37. Boyd S, Vandenberghe L. (2004) Convex optimization. Cambridge University Press.

  38. MacKay. (2003) Information theory, inference, and learning algorithms. Cambridge University Press.

  39. Hudson, J. (2015). Spatial and temporal patterns in physical properties and dissolved oxygen in Lake Diefenbaker, a large reservoir on the Canadian prairies. Journal of Great Lakes Research, 41, 22–33.

    Article  CAS  Google Scholar 

  40. Shrestha, R. R., Osenbrück, K., & Rode, M. (2013). Assessment of catchment response and calibration of a hydrological model using high-frequency discharge–nitrate concentration data. Hydrology Research, 44(6), 995–1012.

    Article  Google Scholar 

  41. Shakibaeinia, A., Kashyap, S., Dibike, Y. B., & Prowse, T. D. (2016). An integrated numerical framework for water quality modelling in cold-region rivers: a case of the lower Athabasca River. Science of the Total Environment, 569, 634–646.

    Article  Google Scholar 

  42. Bongartz, K., Steele, T. D., Baborowski, M., & Lindenschmidt, K. E. (2007). Monitoring, assessment and modelling using water quality data in the Saale River Basin, Germany. Environmental Monitoring and Assessment, 135(1–3), 227–240.

    Article  CAS  Google Scholar 

  43. Ji, Z. G. (2008). Hydrodynamics and water quality: modeling rivers, lakes, and estuaries. New Jersey: Wiley.

    Book  Google Scholar 

  44. Carr, G. M., & Chambers, P. A. (1998). Macrophyte growth and sediment phosphorus and nitrogen in a Canadian prairie river. Freshwater Biology, 39(3), 525–536.

    Article  CAS  Google Scholar 

  45. Tones, P., Waite, D., & Fast, D. (1980). Report on nutrient Imo Acton South Saskatchewan River. Saskatoon: Water Pollution Control Branch, Saskatchewan Environment WPC-25A and WPC-25B.

    Google Scholar 

  46. Chambers, P. (1993). Nutrient dynamics and aquatic plant growth: the case of the South Saskatchewan River. Saskatoon: Environment Canada; National Hydrology Research Institute, Environment Canada NHRI Contribution No. 93001.

    Google Scholar 

  47. Constable, M. (2001). Ecological survey of the South Saskatchewan River downstream of the City of Saskatoon wastewater treatment plant. EPS 5/AT/2. Edmonton: Environmental Protection Branch, Environment Canada; Environmental Protection Branch Prairie and Northern Region, Environment Canada EPS 5/AT/2.

    Google Scholar 

Download references

Acknowledgements

The authors acknowledge the Water Security Agency and the Saskatchewan Ministry of Environment for providing data used in this study. They also thank the Global Institute for Water Security and the University of Saskatchewan for funding this project.

Author information

Authors and Affiliations

  1. Global Institute for Water Security, University of Saskatchewan, 11 Innovation Boulevard, Saskatoon, SK, S7N 3H5, Canada

    Nasim Hosseini, Howard Wheater & Karl-Erich Lindenschmidt

  2. Department of Geography, Hong Kong Baptist University, Kowloon Tong, Hong Kong

    Kwok Pan Chun

Authors
  1. Nasim Hosseini
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kwok Pan Chun
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Howard Wheater
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Karl-Erich Lindenschmidt
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nasim Hosseini.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hosseini, N., Chun, K.P., Wheater, H. et al. Parameter Sensitivity of a Surface Water Quality Model of the Lower South Saskatchewan River—Comparison Between Ice-On and Ice-Off Periods. Environ Model Assess 22, 291–307 (2017). https://doi.org/10.1007/s10666-016-9541-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10666-016-9541-3

Keywords

Search

Navigation