Sam Zipper , Chris Wheeler, Delaney M. Peterson , Stephen C. Cook, Sarah E Godsey, Ken Aho

Non-perennial streams constitute over half the world’s stream miles but are not commonly included in national streamflow monitoring networks. Stream Temperature, Intermittency, and Conductivity (STIC) loggers are a widely used tool for studying non-perennial streams because they provide a relatively inexpensive and robust method for characterizing flow presence or absence. However, raw data downloaded from STIC loggers must be processed to generate hydrologically-meaningful data including temperature, conductivity, and interpreted classification of “wet” or “dry” readings at each timestep. To facilitate ‘FAIR’ (findable, accessible, interoperable, and reusable) stream intermittency science, we present an open-source package, STICr, written in the R language to provide a standardized framework for processing data from STIC loggers. STICr includes functions to tidy data, develop and apply sensor calibrations, classify data into wet/dry readings, and perform quality checks and validation on classified data. We also show a reproducible project-wide data workflow based on STICr for organizing and processing data from over 200 STIC loggers spanning multiple watersheds, years, and research groups, highlighting how interdisciplinary project considerations drive data processing considerations. Using South Fork Kings Creek (Konza Prairie, Kansas, USA) as a case study, we use STICr-processed data to identify spatial and temporal drivers of stream intermittency. For this watershed, stream intermittency is driven by the balance between monthly precipitation inputs and seasonal evapotranspiration fluxes, with spatial patterns of flow durations driven by underlying geology. This demonstrates how STICr can be used to create FAIR stream intermittency data and enable advances in hydrologic and ecosystem science.