An official website of the United States government
Abstract. Air–water gas exchange is essential to understanding and quantifying many biogeochemical processes in streams and rivers, including greenhouse gas emissions and metabolism. Gas exchange depends on two factors, which are often quantified separately: (1) the air–water concentration gradient of the gas and (2) the gas exchange velocity. There are fewer measurements of gas exchange velocity compared to concentrations in streams and rivers, which limits accurate characterization of air–water gas exchange (i.e., flux rates). The National Ecological Observatory Network (NEON) conducts SF6 gas-loss experiments in 22 of their 24 wadeable streams using standardized methods across all experiments and sites, and publishes raw concentration data from these experiments on the NEON data portal. NEON also conducts NaCl injections that can be used to characterize hydraulic geometry at all 24 wadeable streams. These NaCl injections are conducted both as part of the gas-loss experiments and separately. Here, we use these data to estimate gas exchange and water velocity using the reaRate R package. The dataset presented includes estimates of hydraulic parameters, cleaned raw concentration SF6 tracer-gas data (including removing outliers and failed experiments), estimated SF6 gas-loss rates, normalized gas exchange velocities (k600; m d−1) and normalized depth-dependent gas exchange rates (K600; d−1). This dataset provides one of the largest compilations of gas-loss experiments (n=339) in streams to date. This dataset is unique in that it contains gas exchange estimates from repeated experiments in geographically diverse streams across a range of discharges. In addition, this dataset contains information on the hydraulic geometry of all 24 NEON wadeable streams, which will support future research using NEON aquatic data. This dataset is a valuable resource that can be used to explore both within- and across-reach variability in the hydraulic geometry and gas exchange velocity in streams. The data are available at https://doi.org/10.6073/pasta/18dcc1871ee71cf0b69f2ee4082839d0 (Aho et al., 2024), and the reaRate R package code is available at https://doi.org/10.5281/zenodo.12786089 (Cawley et al., 2024).
more »
« less
TIERRAS Tracer Injection Experiments in RiveRs And Streams
The TIERRAS project is an open-access platform that compiles a database of more than 400 tracer injection experiments in rivers and streams, sourced from previously published studies and reports. It also includes interactive features that allow users to explore, download, and contribute new data. The goal is to provide a centralized and accessible repository for researchers, environmental managers, and anyone interested in water quality, hydrological modeling, and stream solute dynamics. These experiments were collected from various sources, including published studies, unpublished data, and technical reports from different authors. The original data were in diverse formats and units; all data were curated and standardized to a consistent format and to the Imperial (U.S. customary) units. Visit TIERRAS at https://www.tierras.org/ Cite: Rodríguez, L., Tunby, P., Abusang, A., Tartakovsky, A., Carroll, K., Ginn, T., & González-Pinzón, R. (2025). TIERRAS Tracer Injection Experiments in RiveRs And Streams (2.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15794259
more »
« less
- Award ID(s):
- 2142691
- PAR ID:
- 10620843
- Publisher / Repository:
- Zenodo
- Date Published:
- Subject(s) / Keyword(s):
- Hydrology Water Quality
- Format(s):
- Medium: X
- Right(s):
- Creative Commons Attribution Non Commercial 4.0 International; Copyright (C) 2025 Lina Rodriguez. Licensed under CC BY-NC 4.0.
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Here we observe and predict the location of beaded stream catchments throughout the pan-Arctic domain by combining the location of known beaded streams with recent advances in computer vision and high-resolution (3 meter (m)) satellite imagery. Specifically, we use the location of known existing beaded streams to classify potential river catchments as beaded or non-beaded, then download high resolution imagery across those regions, and use the latest You-Only-Look-Once (YOLO) object detection algorithm to identify beaded streams throughout the pan-Arctic, estimating 138,500 ± 43,700 beaded catchments globally, occurring in an estimated one third of all pan-Arctic catchments. In the largest dataset of beaded streams to date (Arp et al., 2015), only 375 catchments that contain beaded streams were identified, thus our estimate significantly expands our current understanding of the location and prevalence of Arctic beaded streams. Data is accessible through the alternate identifier link on Zenodo: https://doi.org/10.5281/zenodo.7223256more » « less
-
ABSTRACT Anthropogenic increases in nitrogen (N) and phosphorus (P) concentrations can strongly influence the structure and function of ecosystems. Even though lotic ecosystems receive cumulative inputs of nutrients applied to and deposited on land, no comprehensive assessment has quantified nutrient‐enrichment effects within streams and rivers. We conducted a meta‐analysis of published studies that experimentally increased concentrations of N and/or P in streams and rivers to examine how enrichment alters ecosystem structure (state: primary producer and consumer biomass and abundance) and function (rate: primary production, leaf breakdown rates, metabolism) at multiple trophic levels (primary producer, microbial heterotroph, primary and secondary consumers, and integrated ecosystem). Our synthesis included 184 studies, 885 experiments, and 3497 biotic responses to nutrient enrichment. We documented widespread increases in organismal biomass and abundance (mean response = +48%) and rates of ecosystem processes (+54%) to enrichment across multiple trophic levels, with no large differences in responses among trophic levels or between autotrophic or heterotrophic food‐web pathways. Responses to nutrient enrichment varied with the nutrient added (N, P, or both) depending on rateversusstate variable and experiment type, and were greater in flume and whole‐stream experiments than in experiments using nutrient‐diffusing substrata. Generally, nutrient‐enrichment effects also increased with water temperature and light, and decreased under elevated ambient concentrations of inorganic N and/or P. Overall, increased concentrations of N and/or P altered multiple food‐web pathways and trophic levels in lotic ecosystems. Our results indicate that preservation or restoration of biodiversity and ecosystem functions of streams and rivers requires management of nutrient inputs and consideration of multiple trophic pathways.more » « less
-
This EDI data package contains instructional materials necessary to teach Macrosystems EDDIE Module 6: Understanding Uncertainty in Ecological Forecasts, a ~3-hour educational module for undergraduates. Ecological forecasting is an emerging approach that provides an estimate of the future state of an ecological system with uncertainty, allowing society to prepare for changes in important ecosystem services. Forecast uncertainty is derived from multiple sources, including model parameters and driver data, among others. Knowing the uncertainty associated with a forecast enables forecast users to evaluate the forecast and make more informed decisions. This module will guide students through an exploration of the sources of uncertainty within an ecological forecast, how uncertainty can be quantified, and steps that can be taken to reduce the uncertainty in a forecast that students develop for a lake ecosystem, using data from the National Ecological Observatory Network (NEON). Students will visualize data, build a model, generate a forecast with uncertainty, and then compare the contributions of various sources of forecast uncertainty to total forecast uncertainty. The flexible, three-part (A-B-C) structure of this module makes it adaptable to a range of student levels and course structures. There are two versions of the module: an R Shiny application which does not require students to code, and an RMarkdown version which requires students to read and alter R code to complete module activities. The R Shiny application is published to shinyapps.io and is available at the following link: https://macrosystemseddie.shinyapps.io/module6/. GitHub repositories are available for both the R Shiny (https://github.com/MacrosystemsEDDIE/module6) and RMarkdown versions (https://github.com/MacrosystemsEDDIE/module6_R) of the module, and both code repositories have been published with DOIs to Zenodo (R Shiny version at https://zenodo.org/doi/10.5281/zenodo.10380759 and RMarkdown version at https://zenodo.org/doi/10.5281/zenodo.10380339). Readers are referred to the module landing page for additional information (https://serc.carleton.edu/eddie/teaching_materials/modules/module6.html).more » « less
-
This dataset contains estimates of gas exchange velocity, gas exchange rate, and hydraulic parameters for streams calculated from tracer-gas experiments and conservative tracer injections collected by the National Ecological Observatory Network (NEON). All input data were collected by NEON and is available on the NEON data portal at https://data.neonscience.org. Specifically, the NEON Reaeration field and lab collection data product (DP1.20190.001) was used to calculate these estimates. Gas exchange was estimated in two ways: first, following an unpooled frequentist approach and second, following a partially pooled Bayesian approach. In addition, a salt-correction was applied to gas exchange estimates for sites where it was possible and necessary. All estimates of gas exchange are included in the file gasExchange_ds.csv. A recommended selection of these estimates is included in the dataset (best_k600_mPerDay and best_K600_mPerDay). The stanfit objects used for the partially pooled Bayesian approach are also included as site-specific model objects for gas exchange velocities and rates. In addition, water velocity was calculated from conservative tracer injections, and mean water depth was calculated from these water velocity estimates and measurements of wetted width and water discharge. All hydraulic parameters are included in the file hydraulics_ds.csv. All processing code is available in the reaRates R package. NEON is sponsored by the National Science Foundation (NSF) and operated under cooperative agreement by Battelle. This material is based in part upon work supported by NSF through the NEON Program.more » « less
