Long‐term hydrological, biogeochemical, and climatological data from Walker Branch Watershed, East Tennessee, <scp>USA</scp>

Griffiths, Natalie A.; Mulholland, Patrick J.

doi:10.1002/hyp.14110

Cited by 3 publications

(4 citation statements)

References 45 publications

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“…Streams and rivers included in our data set range in size from small first-order headwaters to some of the major waterways on the Earth's surface including the Mississippi, Congo, and the Yukon and Ob Rivers. Data sets were obtained from multiple sources including the NSF-sponsored Long-Term Ecological Research (LTER) program and the Andrews, Bonanza Creek, Hubbard Brook (Campbell et al, 2021), Luquillo (McDowell et al, 2021, Niwot Ridge, and Plum-Island LTERs; the NSF-sponsored Arctic GRO and PARTNERS projects; the United Kingdom NERC DOMAINE program (Yates et al, 2019) and the N speciation data set compiled for the European N Assessment as reported in Durand et al (2011); the Finnish Environment Institute (SYKE; partly described in Kortelainen et al, 2006;Mattsson et al, 2005); the Oak Ridge National Laboratory (Walker Branch; Griffiths & Mulholland, 2021); the NSF-sponsored Lotic Intersite Nitrogen eXperiment (LINX II; [Mulholland et al, 2008]); the Lamprey River Hydrological Observatory (Wymore et al, 2021); the US Geological Survey (USGS) as presented in Helton et al (2015); with additional data synthesized from Brookshire et al (2017), Lewis (2003, 2005) and Spencer et al (2016). See Supplemental Table S3 for the full citation list.…”

Section: Data Synthesismentioning

confidence: 99%

Gradients of Anthropogenic Nutrient Enrichment Alter N Composition and DOM Stoichiometry in Freshwater Ecosystems

Wymore

Johnes

Bernal

et al. 2021

Global Biogeochemical Cycles

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A comprehensive cross-biome assessment of major nitrogen (N) species that includes dissolved organic N (DON) is central to understanding interactions between inorganic nutrients and organic matter in running waters. Here, we synthesize stream water N chemistry across biomes and find that the composition of the dissolved N pool shifts from highly heterogeneous to primarily comprised of inorganic N, in tandem with dissolved organic matter (DOM) becoming more N-rich, in response to nutrient enrichment from human disturbances. We identify two critical thresholds of total dissolved N (TDN) concentrations where the proportions of organic and inorganic N shift. With low TDN concentrations (0-1.3 mg/L N), the dominant form of N is highly variable, and DON ranges from 0% to 100% of TDN. At TDN concentrations above 2.8 mg/L, inorganic N dominates the N pool and DON rarely exceeds 25% of TDN. This transition to inorganic N dominance coincides with a shift in the stoichiometry of the DOM pool, where DOM becomes progressively enriched in N and DON concentrations are less tightly associated with concentrations of dissolved organic carbon (DOC). This shift in DOM stoichiometry (defined as DOC:DON ratios) suggests that fundamental changes in the biogeochemical cycles of C and N in freshwater ecosystems are occurring across the globe as human activity alters inorganic N and DOM sources and availability. Alterations to DOM stoichiometry are likely to have important implications for both the fate of DOM and its role as a source of N as it is transported downstream to the coastal ocean.Plain Language Summary Ammonium and nitrate in freshwaters have received considerable attention due to their clear ecological and health effects. A comprehensive assessment of N in freshwaters that includes DON is lacking. Including DON in studies of surface water chemistry is important because it can cause eutrophication and certain forms can be rapidly removed by microbial communities. Here, we document how elevated levels of TDN impact the concentrations and relative proportions of all three forms of dissolved N and the stoichiometry of DOM. Our results suggest that human activities fundamentally alter the composition of the dissolved nitrogen pool and the WYMORE ET AL.

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Section: Data Synthesismentioning

confidence: 99%

Gradients of Anthropogenic Nutrient Enrichment Alter N Composition and DOM Stoichiometry in Freshwater Ecosystems

Wymore

Johnes

Bernal

et al. 2021

Global Biogeochemical Cycles

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“…One dataset focuses on the highly impacted East Fork Poplar Creek: high‐frequency observations of stream discharge and water quality data beginning in 2012 (Brooks et al, 2021). The second dataset provides long‐term observations of climatology, hydrology, and biogeochemistry from the less‐impacted Walker Branch watershed, with some records dating back to 1969 (Griffiths & Mulholland, 2021).…”

Section: Site Description and Methodsmentioning

confidence: 99%

Multidecadal biological monitoring and abatement program\ assessing human impacts on aquatic ecosystems within the Oak Ridge Reservation in eastern Tennessee, USA

Matson

Stevenson

Griffiths

et al. 2021

Hydrological Processes

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Human activities can be powerful drivers of ecosystem change within catchments. While most long-term catchment studies have been conducted at pristine sites, such studies are less common from sites more impacted by human activity. The Oak Ridge National Laboratory's Biological Monitoring and Abatement Program (BMAP) was developed in the mid-1980s to (1) assess compliance with environmental regulations, (2) identify causes of adverse ecological impacts, (3) provide data for human and ecological risk assessments, and (4) evaluate the effectiveness of remedial actions taken to mitigate the impacts of contaminants in soils, groundwater, and surface water by documenting ecological recovery on the Oak Ridge Reservation (ORR), a federally owned 33 476-acre site in eastern Tennessee, managed by the US Department of Energy. The ORR is composed of multiple watersheds containing many small to mid-size streams. BMAP uses an integrated approach for determining stream health; its databases include long-term seasonal records of contaminant concentrations in water and biota, data from aquatic toxicity testing, and surveys of macroinvertebrate and fish assemblages from impacted and reference streams. These longterm data provide valuable records of degradation and recovery in catchment ecosystems.Our objective here is to describe our study system and data series in order to increase awareness of the availability of these long-term data to the catchment science community.

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“…WBW is located in East Tennessee, US, and is part of the Clinch River which ultimately drains into the Mississippi River (Curlin and Nelson, 1968;Griffiths and Mulholland, 2021). WBW includes the West Fork and East Fork catchments, which are 0.384 km 2 and 0.591 km 2 in size, respectively.…”

Section: Rain-driven Walker Branch Watershed (Wbw)mentioning

confidence: 99%

“…WBW has a complex geomorphological structure and interconnected hydrological processes (Griffiths and Mulholland, 2021). Many topographical, geological, soil, and ecological factors affect streamflow dynamics.…”

Section: Streamflow Prediction In Rain-driven Wbwmentioning

confidence: 99%

Uncertainty quantification of machine learning models to improve streamflow prediction under changing climate and environmental conditions

Liu

Painter

et al. 2023

Front. Water

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Machine learning (ML) models, and Long Short-Term Memory (LSTM) networks in particular, have demonstrated remarkable performance in streamflow prediction and are increasingly being used by the hydrological research community. However, most of these applications do not include uncertainty quantification (UQ). ML models are data driven and can suffer from large extrapolation errors when applied to changing climate/environmental conditions. UQ is required to quantify the influence of data noises on model predictions and avoid overconfident projections in extrapolation. In this work, we integrate a novel UQ method, called PI3NN, with LSTM networks for streamflow prediction. PI3NN calculates Prediction Intervals by training 3 Neural Networks. It can precisely quantify the predictive uncertainty caused by the data noise and identify out-of-distribution (OOD) data in a non-stationary condition to avoid overconfident predictions. We apply the PI3NN-LSTM method in the snow-dominant East River Watershed in the western US and in the rain-driven Walker Branch Watershed in the southeastern US. Results indicate that for the prediction data which have similar features as the training data, PI3NN precisely quantifies the predictive uncertainty with the desired confidence level; and for the OOD data where the LSTM network fails to make accurate predictions, PI3NN produces a reasonably large uncertainty indicating that the results are not trustworthy and should avoid overconfidence. PI3NN is computationally efficient, robust in performance, and generalizable to various network structures and data with no distributional assumptions. It can be broadly applied in ML-based hydrological simulations for credible prediction.

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Long‐term hydrological, biogeochemical, and climatological data from Walker Branch Watershed, East Tennessee, USA

Cited by 3 publications

References 45 publications

Gradients of Anthropogenic Nutrient Enrichment Alter N Composition and DOM Stoichiometry in Freshwater Ecosystems

Gradients of Anthropogenic Nutrient Enrichment Alter N Composition and DOM Stoichiometry in Freshwater Ecosystems

Multidecadal biological monitoring and abatement program\ assessing human impacts on aquatic ecosystems within the Oak Ridge Reservation in eastern Tennessee, USA

Uncertainty quantification of machine learning models to improve streamflow prediction under changing climate and environmental conditions

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