WHONDRS River Corridor Dissolved Oxygen, Temperature, Sediment Aerobic Respiration, Grain Size, and Water Chemistry from Machine-Learning-Informed Sites across the Contiguous United States

Forbes, Brieanne; Barnes, Morgan; Boehnke, Brandon T.; Cornwell, Kali; Delgado, Dillman; G., Fulton, Stephanie; Garayburu‐Caruso, Vanessa; Gary, Stefan F.; Goldman, Amy; Gonzalez, Brianna I.; Samantha, Grieger,; Hammond, Glenn Edward; Jiang, Peishi; Kaufman, Matthew H.; Laan, Maggi; Li, Bing; Li, Zhi; A., McKever, Sophia; Mudunuru, Maruti K.; Muller, Katherine A.; Myers‐Pigg, Allison; Opal, Otenburg,; Pelly, Aaron; Peta, Kelsey; Regier, Peter; Renteria, Lupita; Roebuck, Alan; Scheibe, Timothy D.; Son, Kyongho; Torgeson, Joshua M.; Stegen, James C.; Consortium, The WHONDRS

doi:10.15485/1923689

Cited by 2 publications

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“…Visual checks of calibration curves, samples, blanks, and check standard peaks were performed before exporting data. Concentrations below the limit of detection (LOD) of the instrument, or below the standard curve, were flagged 71 . If the concentration of an analyte was below the LOD, the flag for that sample was replaced with a value corresponding to half of the lowest concentration measured for that analyte.…”

Section: Surface Water Samplingmentioning

confidence: 99%

Sediment-associated processes drive spatial variation in ecosystem respiration in the Yakima River basin

Kaufman,

Garayburu-Caruso,

Forbes

et al. 2024

Preprint

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Areas where groundwater and surface water mix (i.e., hyporheic zones, HZ) contribute substantially to stream ecosystem respiration (ERtot). We rely on reactive transport models to understand HZ respiration at large scales; however, model outputs have not been evaluated with field estimates of ERtot. Here we evaluate the degree to which spatial variation in model-predicted HZ respiration can explain spatial variation in field-estimated ERtot across 32 sites in the Yakima River basin (YRB). We find that predicted HZ respiration did not explain spatial variation in ERtot. We hypothesize that ERtot is influenced by processes that integrate contributions from sediments, such as benthic algae, submerged macrophytes, and shallow HZ. Our results indicate that sediment-associated processes hydrologically connected to the active channel are primary drivers of spatial variation in ERtot in the YRB. We encourage conceptual and physical models of stream ERtot to integrate shallow hyporheic exchange with sediment associated primary production.

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Section: Surface Water Samplingmentioning

confidence: 99%

Sediment-associated processes drive spatial variation in ecosystem respiration in the Yakima River basin

Kaufman,

Garayburu-Caruso,

Forbes

et al. 2024

Preprint

Self Cite

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Prediction of Distributed River Sediment Respiration Rates Using Community‐Generated Data and Machine Learning

Gary,

Scheibe,

Rexer

et al. 2024

Journal of Geophysical Research: Machine Learning and Computati

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River sediment microbial respiration is a key indicator of ecosystem functioning and the biogeochemical fluxes across this critical zone link surface and subsurface waters. As such, there is tremendous interest in measuring and mapping these respiration rates. Respiration observations are expensive and labor intensive; there is limited data available to the community. An open science, collaborative initiative is collecting samples for respiration rate analysis and multi‐scale metadata; this evolving data set is being used for making machine learning (ML) predictions at unsampled sites to help inform continued community engagement. However, it is a challenge to find an optimum configuration for ML models to work with this feature‐rich (i.e., 100+ possible input variables) data set. Here, we present results from a two‐tiered approach to managing the analysis of this complex data set: (a) a stacked ensemble of models that automatically optimizes hyperparameters and manages the training of many models and (b) feature permutation importance to detect the most important features in the models. The major elements of this workflow are modular, portable, open, and cloud‐based thus making this implementation a potential template for other applications. The models developed here predict that sediment organic matter chemistry is one of the most important features for predicting sediment respiration rate. Other larger‐scale, important features fall into the categories of climatic, ecological, geological, and fluvial settings. Leveraging these larger‐scale features to generate data‐driven estimates of river sediment respiration rates reveals spatially consistent but heterogeneous patterns across the river network of the Columbia River Basin.

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WHONDRS River Corridor Dissolved Oxygen, Temperature, Sediment Aerobic Respiration, Grain Size, and Water Chemistry from Machine-Learning-Informed Sites across the Contiguous United States

Cited by 2 publications

References 0 publications

Sediment-associated processes drive spatial variation in ecosystem respiration in the Yakima River basin

Sediment-associated processes drive spatial variation in ecosystem respiration in the Yakima River basin

Prediction of Distributed River Sediment Respiration Rates Using Community‐Generated Data and Machine Learning

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