Analysis of irregularly sampled stream temperature time series: challenges and solutions

Abstract

Water temperature is a key physical indicator of stream health, and as such, is commonly measured as part of long-term river health monitoring programs. However, analysis of long-term stream water temperature time series can be challenging, due to typically low frequencies of sampling combined with common characteristics of data collection from streams – such as observations unevenly spaced across seasons, changes to routine sampling frequencies, or improvements in the accuracy of measurements over time – known as sampling artifacts. While there are many models regularly used to estimate trends and summary statistics in long-term stream temperature datasets, there is limited understanding of the impact that commonly encountered sampling artifacts have on the accuracy and uncertainty of their estimates. This study constructed Monte-Carlo simulations to examine the influence that common sampling artifacts and the choice of analysis model can have on trend and mean estimates from long-term stream temperature time series covering tropical, temperate and cold climates. We found that, if not appropriately accounted for during analysis, sampling artifacts may obscure true trends or summary statistics, such as site means, and lead to inaccurate or misleading estimates. However, models that included components to account for seasonal variation within the model structure could estimate trends and means with high confidence, in the presence of almost all of the sampling artifacts commonly found in long-term stream temperature datasets. Structural biases in the time-of-day of sampling, such as always sampling in the morning, or where the start and end of the record are sampled at different times of day, could make estimates highly inaccurate and uncertain, and should be avoided in data collection strategies. This work aims to facilitate the analysis of historical stream temperature datasets with confidence, through identifying models that perform reliably in the presence of common sampling artifacts. The findings will enable further global insights into stream temperature, support management decisions based on accurate analysis and assist the design of future stream sampling programs using cost-effective, low-frequency sampling strategies.