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.

中文翻译：

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不规则采样流温度时间序列分析：挑战与解决方案

水温是河流健康状况的关键物理指标，因此通常作为长期河流健康监测计划的一部分进行测量。然而，对长期溪流水温时间序列的分析可能具有挑战性，因为采样频率通常较低，加上溪流数据收集的共同特征，例如不同季节的观测间隔不均匀、常规采样频率的变化或改进随着时间的推移测量的准确性——称为采样伪影。虽然有许多模型经常用于估计长期河流温度数据集中的趋势和汇总统计数据，但人们对常见的采样伪影对其估计的准确性和不确定性的影响了解有限。本研究构建了蒙特卡罗模拟，以检查常见采样工件和分析模型的选择对覆盖热带、温带和寒冷气候的长期河流温度时间序列的趋势和平均估计的影响。我们发现，如果在分析过程中没有适当考虑，采样工件可能会掩盖真实趋势或汇总统计数据（例如站点平均值），并导致不准确或误导性的估计。然而，在存在长期河流温度数据集中常见的几乎所有采样伪影的情况下，在模型结构中包含用于解释季节变化的组件的模型可以以高置信度估计趋势和平均值。一天中采样时间的结构性偏差，例如总是在早上采样，或者在一天中的不同时间对记录的开始和结束进行采样，可能会使估计值高度不准确和不确定，因此应避免数据收集策略。这项工作的目的是通过识别在存在常见采样工件的情况下可靠执行的模型，促进对历史流温度数据集的自信分析。研究结果将有助于进一步全面了解河流温度，支持基于准确分析的管理决策，并协助使用具有成本效益的低频采样策略设计未来的河流采样计划。