Search for: All records

Abstract. Water quality in lakes is an emergent property of complex biotic and abiotic processes that differ across spatial and temporal scales. Water quality is also a determinant of ecosystem services that lakes provide and is thus of great interest to ecologists. Machine learning and other computer science techniques are increasingly being used to predict water quality dynamics as well as to gain a greater understanding of water quality patterns and controls. To benefit the sciences of both ecology and computer science, we have created a benchmark dataset of lake water quality time series and vertical profiles. LakeBeD-US contains over 500 million unique observations of lake water quality collected by multiple long-term monitoring programs across 17 water quality variables from 21 lakes in the United States. There are two published versions of LakeBeD-US: the “Ecology Edition” published in the Environmental Data Initiative repository (https://doi.org/10.6073/pasta/c56a204a65483790f6277de4896d7140, McAfee et al., 2024) and the “Computer Science Edition” published in the Hugging Face repository (https://doi.org/10.57967/hf/3771, Pradhan et al., 2024). Each edition is formatted in a manner conducive to inquiries and analyses specific to each domain. For ecologists, LakeBeD-US: Ecology Edition provides an opportunity to study the spatial and temporal dynamics of several lakes with varying water quality, ecosystem, and landscape characteristics. For computer scientists, LakeBeD-US: Computer Science Edition acts as a benchmark dataset that enables the advancement of machine learning for water quality prediction. 

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. 

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). 

LakeBeD-US: Ecology Edition is a harmonized lake water quality dataset containing time series and vertical profiles of 21 lakes in the United States monitored by long-term monitoring institutions. These institutions include the North Temperate Lakes Long-Term Ecological Research program (NTL-LTER), Niwot Ridge Long-Term Ecological Research program (NWT-LTER), National Ecological Observatory Network (NEON), and the Carey Lab at Virginia Tech as part of the Virginia Reservoirs Long-Term Research in Environmental Biology (LTREB) site in collaboration with the Western Virginia Water Authority. The data include depth-discrete observations of 17 water quality variables including temperature, dissolved oxygen, chemical properties, Secchi depth, and more. Observations are divided into data collected by automated sensors at a relatively high temporal frequency and manually sampled data at a relatively low temporal frequency. All data were collected in situ. The data are available as Apache Parquet files, and the included R scripts give guidance on how to utilize and query the dataset in R. LakeBeD-US: Ecology Edition is an ecological science-oriented companion to LakeBeD-US: Computer Science Edition. The Computer Science Edition is available on the Hugging Face Hub.