A Note on the Collection and Cleaning of Water Temperature Data

Abstract

Inexpensive remote temperature data loggers have allowed for a dramatic increase of data describing water temperature regimes. This data is used in understanding the ecological functioning of natural riverine systems and in quantifying changes in these systems. However, an increase in the quantity of yearly temperature data necessitates complex data management, efficient summarization, and an effective data-cleaning regimen. This note focuses on identifying events where data loggers failed to record correct temperatures using data from the Sauk River in Northwest Washington State as an example. By augmenting automated checks with visual comparisons against air temperature, related sites, multiple years, and available flow data, dewatering events can be more accurately and efficiently identified.