Spencer Rhea
Duke University | Environmental Data Scientist
Subject Areas: | Hydrology, Biogeochemistry, Watershed Science, Water quality, Ecology |
Recent Activity
ABSTRACT:
Accurately estimating stream discharge is crucial for many ecological, biogeochemical, and hydrologic analyses. As of 2023, The National Ecological Observatory Network (NEON) provides up to 5 years of continuous discharge and uncertainty estimates at 28 stream and river sites across the United States. NEON generated annual estimates using Bayesian rating curves that were parameterized based on hydraulic controls and point estimates of discharge collected via acoustic doppler current profilers, salt tracer releases, and flow meter measurements. Inputs to the models were sensor-measured continuous surface water elevations. Here we evaluate the reliability of these discharge estimates, with four approaches. We (1) compared predicted to observed discharge values, (2) compared predicted to observed surface water elevation values, (3) compiled data availability, and (4) calculated the proportion of discharge estimates extrapolated beyond field measurement. We provided diagnostic metrics and evaluations of continuous discharge estimates and continuous stage estimates by month for each site in which continuous discharge data was available for NEON's 2023 data release, enabling users to rapidly query for suitable NEON data.
See publication for details on methods:
Rhea, S., Gubbins, N., DelVecchia, A.G. et al. User-focused evaluation of National Ecological Observatory Network streamflow estimates. Sci Data 10, 89 (2023). https://doi.org/10.1038/s41597-023-01983-w
For more information on NEON sites, see their depictions on the NEON page: https://www.neonscience.org/field-sites/explore-field-sites
For detailed information of each NEON watershed, shapefiles and associated information can be found here: https://www.neonscience.org/data-samples/data/spatial-data-maps
This update to the original evaluation dataset includes NEON's Continuous Discharge RELEASE-2023 dataset. See our code repository to update this analysis for future NEON data releases and for a function to set user specified Tier classification.
ABSTRACT:
Accurately estimating stream discharge is crucial for many ecological, biogeochemical, and hydrologic analyses. As of 2022, The National Ecological Observatory Network (NEON) provides up to 5 years of continuous discharge and uncertainty estimates at 28 stream and river sites across the United States. NEON generated annual estimates using Bayesian rating curves that were parameterized based on hydraulic controls and point estimates of discharge collected via acoustic doppler current profilers, salt tracer releases, and flow meter measurements. Inputs to the models were sensor-measured continuous surface water elevations. Here we evaluate the reliability of these discharge estimates, with four approaches. We (1) compared predicted to observed discharge values, (2) compared predicted to observed surface water elevation values, (3) compiled data availability, and (4) calculated the proportion of discharge estimates extrapolated beyond field measurement. We provided diagnostic metrics and evaluations of continuous discharge estimates and continuous stage estimates by month for each site in which continuous discharge data was available as of December 2021, enabling users to rapidly query for suitable NEON data.
Please note, this evaluation is was performed on NEON Discharge REALSE-2022 data and provisional data after 09/30/2019. See new versions of this dataset for future NEON data releases or our code repository to update this analysis locally: https://github.com/spencerrhea/neon_discharge_eval
See publication for details on methods:
Rhea, S, et al. User-focused evaluation of National Ecological Observatory Network streamflow estimates, Sci. Data, 2023.
For more information on NEON sites, see their depictions on the NEON page: https://www.neonscience.org/field-sites/explore-field-sites
For detailed information of each NEON watershed, shapefiles and associated information can be found here: https://www.neonscience.org/data-samples/data/spatial-data-maps
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ABSTRACT:
Accurately estimating stream discharge is crucial for many ecological, biogeochemical, and hydrologic analyses. As of 2022, The National Ecological Observatory Network (NEON) provides up to 5 years of continuous discharge and uncertainty estimates at 28 stream and river sites across the United States. NEON generated annual estimates using Bayesian rating curves that were parameterized based on hydraulic controls and point estimates of discharge collected via acoustic doppler current profilers, salt tracer releases, and flow meter measurements. Inputs to the models were sensor-measured continuous surface water elevations. Here we evaluate the reliability of these discharge estimates, with four approaches. We (1) compared predicted to observed discharge values, (2) compared predicted to observed surface water elevation values, (3) compiled data availability, and (4) calculated the proportion of discharge estimates extrapolated beyond field measurement. We provided diagnostic metrics and evaluations of continuous discharge estimates and continuous stage estimates by month for each site in which continuous discharge data was available as of December 2021, enabling users to rapidly query for suitable NEON data.
Please note, this evaluation is was performed on NEON Discharge REALSE-2022 data and provisional data after 09/30/2019. See new versions of this dataset for future NEON data releases or our code repository to update this analysis locally: https://github.com/spencerrhea/neon_discharge_eval
See publication for details on methods:
Rhea, S, et al. User-focused evaluation of National Ecological Observatory Network streamflow estimates, Sci. Data, 2023.
For more information on NEON sites, see their depictions on the NEON page: https://www.neonscience.org/field-sites/explore-field-sites
For detailed information of each NEON watershed, shapefiles and associated information can be found here: https://www.neonscience.org/data-samples/data/spatial-data-maps
ABSTRACT:
Accurately estimating stream discharge is crucial for many ecological, biogeochemical, and hydrologic analyses. As of 2023, The National Ecological Observatory Network (NEON) provides up to 5 years of continuous discharge and uncertainty estimates at 28 stream and river sites across the United States. NEON generated annual estimates using Bayesian rating curves that were parameterized based on hydraulic controls and point estimates of discharge collected via acoustic doppler current profilers, salt tracer releases, and flow meter measurements. Inputs to the models were sensor-measured continuous surface water elevations. Here we evaluate the reliability of these discharge estimates, with four approaches. We (1) compared predicted to observed discharge values, (2) compared predicted to observed surface water elevation values, (3) compiled data availability, and (4) calculated the proportion of discharge estimates extrapolated beyond field measurement. We provided diagnostic metrics and evaluations of continuous discharge estimates and continuous stage estimates by month for each site in which continuous discharge data was available for NEON's 2023 data release, enabling users to rapidly query for suitable NEON data.
See publication for details on methods:
Rhea, S., Gubbins, N., DelVecchia, A.G. et al. User-focused evaluation of National Ecological Observatory Network streamflow estimates. Sci Data 10, 89 (2023). https://doi.org/10.1038/s41597-023-01983-w
For more information on NEON sites, see their depictions on the NEON page: https://www.neonscience.org/field-sites/explore-field-sites
For detailed information of each NEON watershed, shapefiles and associated information can be found here: https://www.neonscience.org/data-samples/data/spatial-data-maps
This update to the original evaluation dataset includes NEON's Continuous Discharge RELEASE-2023 dataset. See our code repository to update this analysis for future NEON data releases and for a function to set user specified Tier classification.