Seasonal variability of stream water quality response to storm events captured using high-frequency and multi-parameter data

Abstract

of stream water quality response to storm events captured using high-frequency and multi-parameter data,

Monitoring the riverine pulse: Applying high‐frequency nitrate data to advance integrative understanding of biogeochemical and hydrological processes

“…Bende‐Michl et al () provide an excellent example of a conceptual model that describes seasonal stages of river nutrient concentrations (NO 3 − , NH 4 + , TP, and reactive P) as affected by the availability of sources and hydrologic mobilization processes. Such a model is more readily developed where detailed year‐round high‐frequency data are available, and similar system conceptualizations were recently developed by others (Blaen et al, ; Davis et al, 2014; Dupas et al, ; Fovet et al, ). High‐frequency NO 3 − data have also aided development of quantitative flow path or runoff source models based on hydrograph separation (Kronholm & Capel, ; Miller et al, ) and served as soft data in multiobjective calibration of a hydrologic model (Shrestha et al, ).…”

“…These high‐frequency data have allowed examination of hysteresis for every storm in a watershed, leading to improved understanding of seasonal and threshold patterns of NO 3 − sources and land use and climate variation effects on storm response (Baker & Showers, ; Bowes et al, ; Duncan, Welty, Kemper, Groffman, & Band, ; Dupas et al, ; Feinson, Gibs, Imbrigiotta, & Garrett, ). While clockwise hysteresis in NO 3 − concentrations is reported more frequently than anticlockwise hysteresis (Bowes et al, ; Duncan, Band, et al, ; Lloyd, Freer, Johnes, & Collins, ; Thomas, Abbott, Troccaz, Baudry, & Pinay, ; Vaughan et al, ), patterns can vary within the same watershed as governed by factors such as season, antecedent climatic conditions, and storm magnitude (Baker & Showers, ; Dupas et al, ; Eludoyin et al, ; Fovet et al, ). Rotational patterns also may vary among nearby sites, driven by differences in the presence of artificial drainage and point sources, land use, geology, and soil drainage properties (Bowes et al, ; Koenig, Shattuck, Snyder, Potter, & McDowell, ; Outram et al, ; Vaughan et al, ).…”

“…This approach improves on limitations with past hysteresis indexing and classification (Evans & Davies, ; Langlois, Johnson, & Mehuys, ; Lawler, Petts, Foster, & Harper, ; Williams, ), and is similar to another recently proposed HI (Zuecco, Penna, Borga, & Meerveld, ). Recent investigations suggest that the HI is being rapidly adopted by those who study nutrient runoff patterns during high flow (Baker & Showers, ; Blaen et al, ; Dupas et al, ; Fovet et al, ; Vaughan et al, ). Limitations of the HI have been discussed and a pattern recognition method using machine learning was recently pioneered to delineate suspended sediment hysteresis typology during events (Hamshaw, Dewoolkar, Schroth, Wemple, & Rizzo, ).…”

Stability of spatial patterns in water chemistry across temperate ecoregions

“…These monitoring efforts are often designed to identify pollution sources and assess compliance with environmental legislation (Heathwaite 2010, Hering et al 2010, Skeffington et al 2015. Because water chemistry varies widely on event, seasonal, and interannual timescales (Kirchner and Neal 2013, Isaak et al 2014, Dupas et al 2018, Abbott et al 2018b, most monitoring frameworks sample locations repeatedly, in some cases nearly continuously (Jordan et al 2007, Skeffington et al 2015, Rode et al 2016, Bieroza et al 2018, Fovet et al 2018. While these high-frequency datasets can reveal important ecological dynamics (e.g.…”

River Water‐Quality Concentration and Flux Estimation Can be Improved by Accounting for Serial Correlation Through an Autoregressive Model

“…However, scientific advancement is needed to directly estimate the model coefficients for these models for the irregularly sampled water-quality data and to develop the analytical approach to fill the gaps between adjacent samples. Finally, as high-frequency monitoring sites and records become more available (Fovet et al, 2018;Halliday et al, 2015;Outram et al, 2014;Pellerin et al, 2012;Pellerin et al, 2016), WRTDS-K has the potential to estimate missing days when the sensors become inoperable, but this would require further research to evaluate the usefulness of WRTDS-K and its optimal settings for varying gap lengths within continuous records. Moving forward, scientists and researchers need to advance the understanding on these aspects and to translate the understanding to software tools that can be broadly applied.…”