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Relation Between Stream Temperature and Landscape Characteristics Using Distance Weighted Metrics

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  • Eric Craig Watson

    (Portland State University)

  • Heejun Chang

    (Portland State University)

Abstract

Stream ecosystems have experienced significant negative impacts from land use, resource exploitation, and urban development. Statistical models allow researchers to explore the relations between these landscape variables and stream conditions. Weighting the relevant landscape variables based on hydrologically defined distances offers a potential method of increasing the predictive capacity of statistical models. Using observations from three grouped watersheds in the Portland-Vancouver Metro Area (n = 66), we explored the use of three different weighting schemes against the standard method of weighted areal average. These four different model groups were applied to four stream temperature metrics: mean seven-day moving average maximum daily temperature (Mean7dTmax), number of days exceeding 17.8 °C (Tmax7d >17.8), mean daily range in stream temperature (Mean_DTR), and the coefficient of variation in maximum daily temperature (CV_Tmax) for each month in the 2011 dry season. The results demonstrate mixed effectiveness of the weighting schemes, dependent on both the stream temperature metric being predicted as well as the time scale under investigation. Models for Mean7dTmax showed no benefit from the inclusion of distance weighted metrics, while models for Mean_DTR consistently improved using distance weighted explanatory variables. Trends in the models for Tmax7d > 17.8 and CV_Tmax varied based on temporal scale. Additionally, all model groups demonstrated greater explanatory power in early summer months than in late summer months.

Suggested Citation

  • Eric Craig Watson & Heejun Chang, 2018. "Relation Between Stream Temperature and Landscape Characteristics Using Distance Weighted Metrics," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(3), pages 1167-1192, February.
    • Handle: RePEc:spr:waterr:v:32:y:2018:i:3:d:10.1007_s11269-017-1861-9
    DOI: 10.1007/s11269-017-1861-9
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    References listed on IDEAS

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    1. Ver Hoef, Jay & Peterson, Erin & Clifford, David & Shah, Rohan, 2014. "SSN: An R Package for Spatial Statistical Modeling on Stream Networks," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 56(i03).
    2. Robert W. Hoyer & Heejun Chang, 2014. "Development of Future Land Cover Change Scenarios in the Metropolitan Fringe, Oregon, U.S., with Stakeholder Involvement," Land, MDPI, vol. 3(1), pages 1-20, March.
    3. Brian Knaeble & Seth Dutter, 2017. "Reversals of Least-Square Estimates and Model-Invariant Estimation for Directions of Unique Effects," The American Statistician, Taylor & Francis Journals, vol. 71(2), pages 97-105, April.
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