Using In‐Situ Optical Sensors to Understand the Biogeochemistry of Dissolved Organic Matter Across a Stream Network

Wymore, Adam S.; Potter, Jody D.; Rodriguez-Cardona, B.; McDowell, William H.

doi:10.1002/2017wr022168

Cited by 27 publications

(19 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Model predictions were also sensitive to inclusion of FDOM and pH. FDOM is an effective proxy for dissolved DOC (e.g., Snyder et al, 2018; Wymore et al, 2018), which our analysis also suggested. However, K + was similarly sensitive to FDOM, which is not intuitive.…”

Section: Discussionsupporting

confidence: 64%

Predicting high‐frequency variation in stream solute concentrations with water quality sensors and machine learning

Green

Pardo

Bailey

et al. 2020

Hydrological Processes

Self Cite

View full text Add to dashboard Cite

Stream solute monitoring has produced many insights into ecosystem and Earth system functions. Although new sensors have provided novel information about the fine-scale temporal variation of some stream water solutes, we lack adequate sensor technology to gain the same insights for many other solutes. We used two machine learning algorithms-Support Vector Machine and Random Forestto predict concentrations at 15-min resolution for 10 solutes, of which eight lack specific sensors. The algorithms were trained with data from intensive stream sensing and manual stream sampling (weekly) for four full years in a hydrologic reference stream within the Hubbard Brook Experimental Forest in New Hampshire, USA. The Random Forest algorithm was slightly better at predicting solute concentrations than the Support Vector Machine algorithm (Nash-Sutcliffe efficiencies ranged from 0.35 to 0.78 for Random Forest compared to 0.29 to 0.79 for Support Vector Machine). Solute predictions were most sensitive to the removal of fluorescent dissolved organic matter, pH and specific conductance as independent variables for both algorithms, and least sensitive to dissolved oxygen and turbidity. The predicted concentrations of calcium and monomeric aluminium were used to estimate catchment solute yield, which changed most dramatically for aluminium because it concentrates with stream discharge. These results show great promise for using a combined approach of stream sensing and intensive stream discrete sampling to build information about the highfrequency variation of solutes for which an appropriate sensor or proxy is not available.

show abstract

Section: Discussionsupporting

confidence: 64%

Predicting high‐frequency variation in stream solute concentrations with water quality sensors and machine learning

Green

Pardo

Bailey

et al. 2020

Hydrological Processes

Self Cite

View full text Add to dashboard Cite

show abstract

“…FDOM H export fluxes were 90–187% higher than that of the 2014 and 2015 pre‐dry seasons after El Niño reached its strongest phase. Similar to other river and estuarine systems (Bauer & Bianchi, 2011; Saraceno et al, 2009; Wymore et al, 2018), there was a significant correlation between FDOM H and DOC in the Jiulong River Estuary (Figure S8; Guo et al, 2011). Thus, it is expected that the export pattern of DOC from Jiulong River resembles that of FDOM H .…”

Section: Discussionsupporting

confidence: 76%

“…The excitation‐emission wavelength of current commercial FDOM sensors represents humic‐like FDOM (FDOM H ) components, which are considered a good proxy for the terrestrial DOM fraction (Coble, 1996; Guo et al, 2014; Yang et al, 2019). In many aquatic systems, FDOM is highly correlated with DOC (Guo et al, 2011; Webb et al, 2018), suggesting that FDOM sensors provide an effective tool for monitoring watershed DOM dynamics and export (Shultz et al, 2018; Wymore et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…Long‐term perturbation of El Niño events on watersheds makes it crucial to deploy effective monitoring tools that provide reliable, continuous, and high‐frequency DOM measurements, such as in situ optical fluorescent DOM (FDOM) sensors (Saraceno et al, 2009; Wymore et al, 2018). The excitation‐emission wavelength of current commercial FDOM sensors represents humic‐like FDOM (FDOM H ) components, which are considered a good proxy for the terrestrial DOM fraction (Coble, 1996; Guo et al, 2014; Yang et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

El Niño‐Driven Dry Season Flushing Enhances Dissolved Organic Matter Export From a Subtropical Watershed

Wu²,

et al. 2020

Geophysical Research Letters

View full text Add to dashboard Cite

The 2015/2016 super El Niño event resulted in a positive precipitation anomaly during dry seasons in the Jiulong River watershed, southeast China. Four years (2014-2017) of high frequency, in situ humic-like fluorescent DOM (FDOM H) data in the Jiulong Estuary were coupled with extrapolation to a freshwater end-member FDOM H concentration and river discharge data to estimate riverine FDOM H export. The wetter El Niño dry season was followed by lower FDOM H export during the subsequent wet season. Furthermore, in the dry season after El Niño reached its strongest phase, a 90-187% increase in FDOM H export occurred. If widespread, this pattern suggests El Niño events may enhance export of FDOM H from south China rivers in dry seasons resulting in seasonal and annual shunting of the terrestrial DOM export, modulating coastal carbon cycling. This study highlights the need to incorporate climate-driven regulation patterns on DOM transport across the land-ocean interface.

show abstract

“…DOM consists of dissolved organic carbon (DOC), N, P, and other nutrients in molecular forms ranging from complex molecules to simple compounds. The molecular composition of the DOM influences its biodegradability and photoreactivity, affecting its persistence in the ecosystem and influence on nutrient cycles (Wymore et al, 2018;Harjung et al, 2019). In addition to biodegradability incubations (details below), we calculated two optical proxies of DOM composition: specific ultra-violet absorbance at 254 nm (SUVA 254 ) and the spectral ratio (S R ) of slopes within the 275-290 and 350-400 nm range (Weishaar et al, 2003;Helms et al, 2008;Vonk et al, 2015).…”

Section: Proxies Of Biogeochemical Transformationmentioning

confidence: 99%

Predicting Nutrient Incontinence in the Anthropocene at Watershed Scales

Frei

Abbott

Dupas

et al. 2020

Front. Environ. Sci.

View full text Add to dashboard Cite

Quantifying nutrient attenuation at watershed scales requires long-term water chemistry data, water discharge, and detailed nutrient input chronicles. Consequently, nutrient attenuation estimates are largely limited to long-term research areas or modeling studies, constraining understanding of the ecological characteristics controlling nutrient attenuation and complicating efforts to protect or restore water quality in developed and developing regions. Here, we combined long-term data and a broad suite of biogeochemical parameters from 49 watersheds in northwestern France to test how well instantaneous measurements can predict nitrogen (N) and phosphorus (P) attenuation at watershed scales. We evaluated 13 biogeochemical and 12 hydrological proxies of hydrological flowpaths, residence time, and biogeochemical transformation. Across the 49 watersheds, nutrient attenuation ranged from 88 to −2% for N and 99-96% for P. The strongest biogeochemical proxies of N attenuation were NO − 3 isotopes, rare earth elements (REEs), radon, and turbidity, together explaining 75% of observed variation. For P attenuation, REEs, NO − 3 isotopes, molecular weight of dissolved organic matter, and radon were the strongest proxies, but only explained 27% of observed variation. However, a single hydrological parameter-annual runoff-explained 91% of N attenuation and the relative abundance of schist bedrock explained 56% of P attenuation. We discuss how runoff both controls and reflects watershed hydrology, biogeochemistry, and nutrient attenuation. For example, runoff was correlated with long-term decreases in nutrient concentration, demonstrating how leakier watersheds recover more quickly from nutrient saturation. Given the immense fertilization capacity of modern society, we propose that eutrophication can only be solved by reducing nutrient inputs, though hydrochemical proxies can provide valuable information on where to carry out essential food production activities.

show abstract

Using In‐Situ Optical Sensors to Understand the Biogeochemistry of Dissolved Organic Matter Across a Stream Network

Cited by 27 publications

References 60 publications

Predicting high‐frequency variation in stream solute concentrations with water quality sensors and machine learning

Predicting high‐frequency variation in stream solute concentrations with water quality sensors and machine learning

El Niño‐Driven Dry Season Flushing Enhances Dissolved Organic Matter Export From a Subtropical Watershed

Predicting Nutrient Incontinence in the Anthropocene at Watershed Scales

Contact Info

Product

Resources

About