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Abstract. Streamflow, or discharge, is an essential measure in the study of rivers and streams. However, quantifying continuous discharge can be difficult, especially for nascent monitoring efforts, due to the challenges of establishing gauging locations, sensor protocols, and installations. Here, we investigate the potential for both simple and complex models to accurately estimate continuous discharge (at least daily estimates), using only discrete manual measurements of streamflow. We were inspired to do this work because some continuous discharge series generated by the National Ecological Observatory Network (NEON) during its pre- and early-operational phases (2015–present) are marked by anomalous data due to sensor drift, gauge movement, and incomplete rating curves. Using field-measured discharge as truth, we reconstructed continuous discharge for all 27 NEON stream gauges over this period via linear regression on nearby donor gauges and/or prediction from neural networks trained on a large corpus of established gauge data. Top reconstructions achieved median efficiencies of 0.83 (Nash-Sutcliffe, or NSE) and 0.81 (Kling-Gupta, or KGE) across all sites, and improved KGE at 11 sites versus published data. Estimates from this analysis inform ~199 site-months of missing data in the official record, and can be used jointly with NEON data to enhance the descriptive and predictive value of NEON’s stream data products. We provide 5-minute composite discharge series for each site that combine the best estimates across modeling approaches and NEON’s published data.
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Leveraging gauge networks and strategic discharge measurements to aid the development of continuous streamflow records
Abstract. Quantifying continuous discharge can be difficult, especially for nascent monitoring efforts, due to the challenges of establishing gauging locations, sensor protocols, and installations. Some continuous discharge series generated by the National Ecological Observatory Network (NEON) during its pre- and early-operational phases (2015–present) are marked by anomalies related to sensor drift, gauge movement, and incomplete rating curves. Here, we investigate the potential to estimate continuous discharge when discrete streamflow measurements are available at the site of interest. Using field-measured discharge as truth, we reconstructed continuous discharge for all 27 NEON stream gauges via linear regression on nearby donor gauges and/or prediction from neural networks trained on a large corpus of established gauge data. Reconstructions achieved median efficiencies of 0.83 (Nash–Sutcliffe, or NSE) and 0.81 (Kling–Gupta, or KGE) across all sites and improved KGE at 11 sites versus published data, with linear regression generally outperforming deep learning approaches due to the use of target site data for model fitting rather than evaluation only. Estimates from this analysis inform ∼199 site-months of missing data in the official record, and can be used jointly with NEON data to enhance the descriptive and predictive value of NEON's stream data products. We provide 5 min composite discharge series for each site that combine the best estimates across modeling approaches and NEON's published data. The success of this effort demonstrates the potential to establish “virtual gauges”, sites at which continuous streamflow can be accurately estimated from discrete measurements, by transferring information from nearby donor gauges and/or large collections of training data.
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- PAR ID:
- 10537221
- Publisher / Repository:
- European Geosciences Union
- Date Published:
- Journal Name:
- Hydrology and Earth System Sciences
- Volume:
- 28
- Issue:
- 3
- ISSN:
- 1607-7938
- Page Range / eLocation ID:
- 545 to 573
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Accurately estimating stream discharge is crucial for many ecological, biogeochemical, and hydrologic analyses. As of September 2022, The National Ecological Observatory Network (NEON) provided up to 5 years of continuous discharge estimates at 28 streams across the United States. NEON created rating curves at each site in a Bayesian framework, parameterized using hydraulic controls and manual measurements of discharge. Here we evaluate the reliability of these discharge estimates with three approaches. We (1) compared predicted to observed discharge, (2) compared predicted to observed stage, and (3) calculated the proportion of discharge estimates extrapolated beyond field measurements. We considered 1,523 site-months of continuous streamflow predictions published by NEON. Of these, 39% met our highest quality criteria, 11% fell into an intermediate classification, and 50% of site-months were classified as unreliable. We provided diagnostic metrics and categorical evaluations of continuous discharge and stage estimates by month for each site, enabling users to rapidly query for suitable NEON data.more » « less
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{"Abstract":["This dataset contains long-term precipitation measurements from\n graduated rain gauges (GRGs) at 21 sites in the Jornada Basin of\n southern New Mexico, USA. Gauges are located on the Jornada\n Experimental Range (JER) and the Chihuahuan Desert Rangeland\n Research Center (CDRRC), and this set of gauges includes all 15 net\n primary production (NPP) study sites monitored by the Jornada Basin\n LTER program. At each site a 4 inch diameter cylindrical graduated\n rain gauge (11" x 0.01" capacity) is mounted on a 4x4 inch\n diameter redwood post or a wooden exclosure post next to gate at or\n near each site. For NPP sites, the primary collection is made on the\n day that monthly hydroprobe soil water content measurements are\n made. This enables correlation of precipitation with belowground\n soil water content. Additional data collections during the month may\n be made in coordination with other studies. Observations at each\n site come primarily from GRGs. However, at some sites in the NPP\n study, GRGs were not installed until later, and the nearest\n available rain gauge in the area has been used to gapfill the\n precipitation record prior to installation (details in methods\n section). Rain gauge identity and field measurement date is recorded\n with each observation in the data file. Other gauge types that may\n be listed are the Standard Can Gauge (DSRG or dipstick rain gauge),\n Belfort Weigh Bucket Rain Gauge (WBRG), and Qualimetrics Tipping\n Bucket Rain Gauge (TBRG). Data collection is ongoing for all 21\n gauges in this dataset."]}more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.5194/hess-28-545-2024
