Estimation of river and stream temperature trends under haphazard sampling

Statistical Methods & Applications
By: , and 

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Abstract

Long-term temporal trends in water temperature in rivers and streams are typically estimated under the assumption of evenly-spaced space-time measurements. However, sampling times and dates associated with historical water temperature datasets and some sampling designs may be haphazard. As a result, trends in temperature may be confounded with trends in time or space of sampling which, in turn, may yield biased trend estimators and thus unreliable conclusions. We address this concern using multilevel (hierarchical) linear models, where time effects are allowed to vary randomly by day and date effects by year. We evaluate the proposed approach by Monte Carlo simulations with imbalance, sparse data and confounding by trend in time and date of sampling. Simulation results indicate unbiased trend estimators while results from a case study of temperature data from the Illinois River, USA conform to river thermal assumptions. We also propose a new nonparametric bootstrap inference on multilevel models that allows for a relatively flexible and distribution-free quantification of uncertainties. The proposed multilevel modeling approach may be elaborated to accommodate nonlinearities within days and years when sampling times or dates typically span temperature extremes.

Additional publication details

Publication type Article
Publication Subtype Journal Article
Title Estimation of river and stream temperature trends under haphazard sampling
Series title Statistical Methods & Applications
DOI 10.1007/s10260-015-0334-7
Volume 25
Issue 1
Year Published 2015
Language English
Publisher Springer-Verlag
Publisher location Heidelberg, Germany
Contributing office(s) Upper Midwest Environmental Sciences Center
Description 27 p.
First page 89
Last page 105
Country United States
Other Geospatial Illinois River
Online Only (Y/N) N
Additional Online Files (Y/N) N