Sensors in the Stream: The High-Frequency Wave of the Present.

New scientific understanding is catalyzed by novel technologies that enhance measurement precision, resolution or type, and that provide new tools to test and develop theory. Over the last 50 years, technology has transformed the hydrologic sciences by enabling direct measurements of watershed fluxes (evapotranspiration, streamflow) at time scales and spatial extents aligned with variation in physical drivers. High frequency water quality measurements, increasingly obtained by in situ water quality sensors, are extending that transformation. Widely available sensors for some physical (temperature) and chemical (conductivity, dissolved oxygen) attributes have become integral to aquatic science, and emerging sensors for nutrients, dissolved CO2, turbidity, algal pigments, and dissolved organic matter are now enabling observations of watersheds and streams at time scales commensurate with their fundamental hydrological, energetic, elemental, and biological drivers. Here we synthesize insights from emerging technologies across a suite of applications, and envision future advances, enabled by sensors, in our ability to understand, predict, and restore watershed and stream systems.

[1]  Rodney Anthony Stewart,et al.  Intelligent data mining of vertical profiler readings to predict manganese concentrations in water reservoirs , 2013 .

[2]  David K. Stevens,et al.  A sensor network for high frequency estimation of water quality constituent fluxes using surrogates , 2010, Environ. Model. Softw..

[3]  M. Rode,et al.  Disentangling the influence of hydroclimatic patterns and agricultural management on river nitrate dynamics from sub-hourly to decadal time scales. , 2016, The Science of the total environment.

[4]  Ophélie Fovet,et al.  Transit times—the link between hydrology and water quality at the catchment scale , 2016 .

[5]  Doerthe Tetzlaff,et al.  Generality of fractal 1/f scaling in catchment tracer time series, and its implications for catchment travel time distributions , 2010 .

[6]  D. Smart,et al.  Diurnal variability in riverine dissolved organic matter composition determined by in situ optical measurement in the San Joaquin River (California, USA) , 2007 .

[7]  M. Cohen,et al.  Direct and indirect coupling of primary production and diel nitrate dynamics in a subtropical spring‐fed river , 2010 .

[8]  Richard A. Skeffington,et al.  Hydrochemical processes in lowland rivers: insights from in situ, high-resolution monitoring , 2012 .

[9]  D. O’Donnell,et al.  Tributary Plunging in an Urban Lake (Onondaga Lake): Drivers, Signatures, and Implications 1 , 2009 .

[10]  C. Lorenzen,et al.  A method for the continuous measurement of in vivo chlorophyll concentration , 1966 .

[11]  Kenneth S. Johnson,et al.  In situ ultraviolet spectrophotometry for high resolution and long-term monitoring of nitrate, bromide and bisulfide in the ocean , 2002 .

[12]  A. Appling,et al.  Nutrient Limitation and Physiology Mediate the Fine-Scale (De)coupling of Biogeochemical Cycles , 2014, The American Naturalist.

[13]  David P. Hamilton,et al.  Predicting the resilience and recovery of aquatic systems: A framework for model evolution within environmental observatories , 2015 .

[14]  G. Minshall,et al.  The River Continuum Concept , 1980 .

[15]  P. Hanson,et al.  Seasonal dynamics, typhoons and the regulation of lake metabolism in a subtropical humic lake , 2008 .

[16]  Peter Arzberger,et al.  New Eyes on the World: Advanced Sensors for Ecology , 2009 .

[17]  J. Kirchner,et al.  Quantifying remediation effectiveness under variable external forcing using contaminant rating curves. , 2011, Environmental science & technology.

[18]  J. Newbold,et al.  Solute-specific scaling of inorganic nitrogen and phosphorus uptake in streams , 2013 .

[19]  Colin Neal,et al.  Universal fractal scaling in stream chemistry and its implications for solute transport and water quality trend detection , 2013, Proceedings of the National Academy of Sciences.

[20]  C. Wellen,et al.  Application of the SPARROW model in watersheds with limited information: a Bayesian assessment of the model uncertainty and the value of additional monitoring , 2014 .

[21]  Martin W. Doyle,et al.  Alternative Reference Frames in River System Science , 2009 .

[22]  P. Hanson,et al.  Wireless Sensor Networks for Ecology , 2005 .

[23]  M. Bowes,et al.  Weekly flow cytometric analysis of riverine phytoplankton to determine seasonal bloom dynamics. , 2014, Environmental science. Processes & impacts.

[24]  Dawn Field,et al.  Catchment-scale biogeography of riverine bacterioplankton , 2014, The ISME Journal.

[25]  J. Kanwisher,et al.  Electrode System for Measuring Dissolved Oxygen , 1959 .

[26]  C. Neal,et al.  An analysis of long-term trends, seasonality and short-term dynamics in water quality data from Plynlimon, Wales. , 2012, The Science of the total environment.

[27]  M. R. Anis,et al.  Continuous In-Stream Assimilatory Nitrate Uptake from High-Frequency Sensor Measurements. , 2016, Environmental science & technology.

[28]  M. Cohen,et al.  Inferring nitrogen removal in large rivers from high‐resolution longitudinal profiling , 2014 .

[29]  C. Kendall,et al.  Assessing the sources and magnitude of diurnal nitrate variability in the San Joaquin River (California) with an in situ optical nitrate sensor and dual nitrate isotopes , 2009 .

[30]  A. Melland,et al.  Quantification of phosphorus transport from a karstic agricultural watershed to emerging spring water. , 2013, Environmental science & technology.

[31]  Gregory E Schwarz,et al.  Differences in phosphorus and nitrogen delivery to the Gulf of Mexico from the Mississippi River Basin. , 2008, Environmental science & technology.

[32]  J. Stanford,et al.  The serial discontinuity concept of lotic ecosystems , 1983 .

[33]  C. Wellen,et al.  Quantifying the uncertainty of nonpoint source attribution in distributed water quality models: A Bayesian assessment of SWAT’s sediment export predictions , 2014 .

[34]  S. Carpenter,et al.  Changes in ecosystem resilience detected in automated measures of ecosystem metabolism during a whole-lake manipulation , 2013, Proceedings of the National Academy of Sciences.

[35]  R. Gilliom,et al.  Mississippi River nitrate loads from high frequency sensor measurements and regression-based load estimation. , 2014, Environmental science & technology.

[36]  W. Graham,et al.  Hydrologic and biotic influences on nitrate removal in a subtropical spring‐fed river , 2010 .

[37]  K. Daly,et al.  Identifying contrasting influences and surface water signals for specific groundwater phosphorus vulnerability. , 2016, The Science of the total environment.

[38]  John L. Campbell,et al.  Quantity is Nothing without Quality: Automated QA/QC for Streaming Environmental Sensor Data , 2013 .

[39]  James W. Kirchner,et al.  The fine structure of water‐quality dynamics: the (high‐frequency) wave of the future , 2004 .

[40]  A. Michalak Study role of climate change in extreme threats to water quality , 2016, Nature.

[41]  B. Bergamaschi,et al.  Taking the pulse of snowmelt: in situ sensors reveal seasonal, event and diurnal patterns of nitrate and dissolved organic matter variability in an upland forest stream , 2012, Biogeochemistry.

[42]  Barbara Beckingham,et al.  Turbidity as a proxy for total suspended solids (TSS) and particle facilitated pollutant transport in catchments , 2013, Environmental Earth Sciences.

[43]  David K. Stevens,et al.  Surrogate Measures for Providing High Frequency Estimates of Total Suspended Solids and Total Phosphorus Concentrations 1 , 2011 .

[44]  S. W. Chung,et al.  Modelling the propagation of turbid density inflows into a stratified lake: Daecheong Reservoir, Korea , 2009, Environ. Model. Softw..

[45]  Michael Rode,et al.  Spatially distributed lateral nitrate transport at the catchment scale. , 2010, Journal of environmental quality.

[46]  M. Roederer,et al.  Flow cytometry strikes gold , 2015, Science.

[47]  Chantal Gascuel-Odoux,et al.  Fractal water quality fluctuations spanning the periodic table in an intensively farmed watershed. , 2014, Environmental science & technology.

[48]  D. Robertson,et al.  Control of nitrogen and phosphorus transport by reservoirs in agricultural landscapes , 2015, Biogeochemistry.

[49]  David K. Stevens,et al.  Surrogate Measures for Providing High-frequency Estimates of Total Suspended Solids and Phosphorus Concentrations , 2007 .

[50]  J. Kirchner,et al.  Upland streamwater nitrate dynamics across decadal to sub-daily timescales: a case study of Plynlimon, Wales , 2013 .

[51]  Chad R. Foster,et al.  Controls on diel metal cycles in a biologically productive carbonate-dominated river , 2013 .

[52]  Matthew C. Mowlem,et al.  Lab-on-chip measurement of nitrate and nitrite for in situ analysis of natural waters. , 2012, Environmental science & technology.

[53]  M Loewenthal,et al.  Identifying multiple stressor controls on phytoplankton dynamics in the River Thames (UK) using high-frequency water quality data. , 2016, The Science of the total environment.

[54]  Kenneth S Johnson,et al.  Chemical sensor networks for the aquatic environment. , 2007, Chemical reviews.

[55]  M. Twiss,et al.  Phytoplankton community assessment in eight Lake Ontario tributaries made using fluorimetric methods , 2008 .

[56]  T. Gisiger Scale invariance in biology: coincidence or footprint of a universal mechanism? , 2001, Biological reviews of the Cambridge Philosophical Society.

[57]  Heather Wickham,et al.  High‐frequency precipitation and stream water quality time series from Plynlimon, Wales: an openly accessible data resource spanning the periodic table , 2013 .

[58]  J. Newman,et al.  The Water Quality of the River Enborne, UK: Observations from High-Frequency Monitoring in a Rural, Lowland River System , 2014 .

[59]  T. Laurila,et al.  Real-time determination of metal concentrations in liquid flows using microplasma emission spectroscopy , 2012, 2012 Photonics Global Conference (PGC).

[60]  Matthew C. Mowlem,et al.  Trends in microfluidic systems for in situ chemical analysis of natural waters , 2015 .

[61]  Serghei A. Bocaniov,et al.  Reservoirs as sentinels of catchments: the Rappbode Reservoir Observatory (Harz Mountains, Germany) , 2013, Environmental Earth Sciences.

[62]  A. Wade,et al.  Riparian shading controls instream spring phytoplankton and benthic algal growth. , 2016, Environmental science. Processes & impacts.

[63]  Jeffrey G. Arnold,et al.  CUMULATIVE UNCERTAINTY IN MEASURED STREAMFLOW AND WATER QUALITY DATA FOR SMALL WATERSHEDS , 2006 .

[64]  S. Carpenter,et al.  Early-warning signals for critical transitions , 2009, Nature.

[65]  S. Carpenter,et al.  Early Warnings of Regime Shifts: A Whole-Ecosystem Experiment , 2011, Science.

[66]  Carole M. Sakamoto,et al.  Submersible, Osmotically Pumped Analyzer for Continuous Determination of Nitrate in situ , 1994 .

[67]  V. Acuña,et al.  Regulation causes nitrogen cycling discontinuities in Mediterranean rivers. , 2016, The Science of the total environment.

[68]  Andrew J. Wade,et al.  Using high-frequency water quality data to assess sampling strategies for the EU Water Framework Directive , 2015 .

[69]  Chad R. Foster,et al.  Diel phosphorus variation and the stoichiometry of ecosystem metabolism in a large spring-fed river , 2013 .

[70]  Hans Peter Broers,et al.  Improving load estimates for NO3 and P in surface waters by characterizing the concentration response to rainfall events. , 2010, Environmental science & technology.

[71]  M. Doyle,et al.  Nutrient spiraling in streams and river networks , 2006 .

[72]  Rachel Cassidy,et al.  Limitations of instantaneous water quality sampling in surface-water catchments: Comparison with near-continuous phosphorus time-series data , 2011 .

[73]  A. Wade,et al.  Characterising phosphorus and nitrate inputs to a rural river using high-frequency concentration-flow relationships. , 2015, The Science of the total environment.

[74]  Irena Hajnsek,et al.  A Network of Terrestrial Environmental Observatories in Germany , 2011 .