Non-perennial streams, which lack year-round flow, constitute more than half the global stream network length. Identifying the sources of water that sustain flow in non-perennial streams is necessary to understand their potential impacts on downstream water resources and inform current policy and management. Here, we used water isotopes (δ18O and δ2H) to partition the evolution of streamwater age compositions and inferred sources through the 2021 summer dry-down period of a non-perennial stream network at the Konza Prairie (KS). During dry-down, the isotopic composition of non-perennial streams was progressively enriched in δ18O and δ2H. Integrating two different isotope-based models of water age, we found a substantial amount of summer streamflow (median 54.4%) is young water that had been stored in the subsurface for less than 3 months. Streamwater shifted to older sources and variability in age increased as summer progressed. The shift in water age suggests a shift away from rapid fracture flow towards slower matrix flow that creates a sustained but localized surface water presence during the driest parts of the summer. Further, our analysis suggests that unmixing-based approaches are well-suited for estimating water age in non-perennial systems that lack year-round flow necessary for fitting time series-based models. The substantial proportion of young water highlights the vulnerability of non-perennial streams to short-term hydroclimatic change, while the late-summer shift to older water reveals a sensitivity to longer-term changes in groundwater dynamics. Combined, this suggests that local changes may propagate through non-perennial stream networks to influence downstream water availability and quality.
Non-perennial streams constitute over half the world’s stream miles, and require hydrologic characterization to understand their flow regimes and impacts on ecosystems and society. 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 is not immediately suitable for analysis or integration with other datasets and must be processed to generate a usable dataset including temperature, conductivity, and interpreted classification of “wet” or “dry” readings at each timestep. To facilitate rapid, reproducible, and methodologically consistent analyses with STIC data, we present an open-source package written in the R language (STICr) and associated workflow to provide a standardized framework for tidying and processing data from STIC loggers. STICr features include functions to tidy data, develop and apply calibration curves to convert logger output to specific conductivity, classify data into wet/dry readings, and perform quality checks on resulting output data. Using STICr, we demonstrate a reproducible workflow that serves as a project-wide data pipeline for organizing and processing data from over 200 STIC loggers spanning multiple watersheds, years, and research groups. Given the importance of methodologically consistent inter-site and inter-regional comparison in hydrology, as well as a need for increased computational reproducibility in the discipline, we believe that STICr and the associated reproducible workflow represents an important advance for stream intermittency science.
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