Diurnal Patterns in Solute Concentrations Measured with In Situ UV-Vis Sensors: Natural Fluctuations or Artefacts?

In situ spectrophotometers measuring in the UV-visible spectrum are increasingly used to collect high-resolution data on stream water quality. This provides the opportunity to investigate short-term solute dynamics, including diurnal cycling. This study reports unusual changes in diurnal patterns observed when such sensors were deployed in four tropical headwater streams in Kenya. The analysis of a 5-year dataset revealed sensor-specific diurnal patterns in nitrate and dissolved organic carbon concentrations and different patterns measured by different sensors when installed at the same site. To verify these patterns, a second mobile sensor was installed at three sites for more than 3 weeks. Agreement between the measurements performed by these sensors was higher for dissolved organic carbon (r > 0.98) than for nitrate (r = 0.43–0.81) at all sites. Higher concentrations and larger amplitudes generally led to higher agreement between patterns measured by the two sensors. However, changing the position or level of shading of the mobile sensor resulted in inconsistent changes in the patterns. The results of this study show that diurnal patterns measured with UV-Vis spectrophotometers should be interpreted with caution. Further work is required to understand how these measurements are influenced by environmental conditions and sensor-specific properties.

[1]  Stefan Krause,et al.  Frontiers in real‐time ecohydrology – a paradigm shift in understanding complex environmental systems , 2015 .

[2]  Lars Kutzbach,et al.  Application of high-resolution spectral absorbance measurements to determine dissolved organic carbon concentration in remote areas , 2014 .

[3]  D. Pelster,et al.  Land use affects total dissolved nitrogen and nitrate concentrations in tropical montane streams in Kenya. , 2017, The Science of the total environment.

[4]  J. Osborne,et al.  Using in situ ultraviolet‐visual spectroscopy to measure nitrogen, carbon, phosphorus, and suspended solids concentrations at a high frequency in a brackish tidal marsh , 2014 .

[5]  Jerald L. Schnoor,et al.  High-frequency monitoring for the identification of hydrological and bio-geochemical processes in a Mediterranean river basin , 2010 .

[6]  Matthew P. Miller,et al.  Patterns of diel variation in nitrate concentrations in the Potomac River , 2015, Freshwater Science.

[7]  Francesco Ciocca,et al.  High‐frequency monitoring of catchment nutrient exports reveals highly variable storm event responses and dynamic source zone activation , 2017 .

[8]  W. McDowell.  NEON and STREON: opportunities and challenges for the aquatic sciences , 2014, Freshwater Science.

[9]  Paul J Worsfold,et al.  Nitrogen cycling in natural waters using in situ, reagentless UV spectrophotometry with simultaneous determination of nitrate and nitrite. , 2007, Environmental science & technology.

[10]  David Scholefield,et al.  Concerted diurnal patterns in riverine nutrient concentrations and physical conditions. , 2005, The Science of the total environment.

[11]  L. Breuer,et al.  New Seasonal Shift in In-Stream Diurnal Nitrate Cycles Identified by Mining High-Frequency Data , 2016, PloS one.

[12]  K. Schilling,et al.  Nitrate uptake in an agricultural stream estimated from high-frequency, in-situ sensors , 2018, Environmental Monitoring and Assessment.

[13]  J. Holden,et al.  Improved automation of dissolved organic carbon sampling for organic-rich surface waters. , 2016, The Science of the total environment.

[14]  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 .

[15]  J. Freer,et al.  Using hysteresis analysis of high-resolution water quality monitoring data, including uncertainty, to infer controls on nutrient and sediment transfer in catchments. , 2016, The Science of the total environment.

[16]  M. Rufino,et al.  Using High‐Resolution Data to Assess Land Use Impact on Nitrate Dynamics in East African Tropical Montane Catchments , 2018 .

[17]  J. Zarnetske,et al.  Using in-situ optical sensors to study dissolved organic carbon dynamics of streams and watersheds: A review. , 2017, The Science of the total environment.

[18]  Eun Ju Lee,et al.  Comparison of UV-VIS and FDOM sensors for in situ monitoring of stream DOC concentrations , 2014 .

[19]  Ulrike Bende-Michl,et al.  High-frequency nutrient monitoring to infer seasonal patterns in catchment source availability, mobilisation and delivery , 2013, Environmental Monitoring and Assessment.

[20]  Javier Tomasella,et al.  Export of organic carbon in run‐off from an Amazonian rainforest blackwater catchment , 2006 .

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

[22]  Ulrike Bende-Michl,et al.  A systematic approach to choosing an automated nutrient analyser for river monitoring. , 2010, Journal of environmental monitoring : JEM.

[23]  B. Manny,et al.  Diurnal changes in dissolved organic and inorganic carbon and nitrogen in a hardwater stream , 1973 .

[24]  Laurent Pfister,et al.  Diel fluctuations of viscosity-driven riparian inflow affect streamflow DOC concentration , 2017 .

[25]  C. Dahm,et al.  Nutrient dynamics in an alpine headwater stream: use of continuous water quality sensors to examine responses to wildfire and precipitation events , 2015 .

[26]  Stephen R. Parker,et al.  Diel biogeochemical processes and their effect on the aqueous chemistry of streams: A review , 2011 .

[27]  F. Sabater,et al.  Green light: gross primary production influences seasonal stream N export by controlling fine-scale N dynamics. , 2016, Ecology.

[28]  J. Koskiaho,et al.  High-frequency monitoring reveals seasonal and event-scale water quality variation in a temporally frozen river , 2018, Journal of Hydrology.

[29]  M. Zimmer,et al.  Temporal Variability in Nitrate‐Discharge Relationships in Large Rivers as Revealed by High‐Frequency Data , 2019, Water Resources Research.

[30]  M. Cohen,et al.  Inference of riverine nitrogen processing from longitudinal and diel variation in dual nitrate isotopes , 2012 .

[31]  S. Schiff,et al.  Diffusive and ebullitive transport of methane and nitrous oxide from streams: Are bubble‐mediated fluxes important? , 2011 .

[32]  William H. McDowell,et al.  The metabolic regimes of flowing waters , 2018 .

[33]  S. Schiff,et al.  Night and day: short‐term variation in nitrogen chemistry and nitrous oxide emissions from streams , 2012 .

[34]  P. Blum,et al.  Technical Note: Field experiences using UV/VIS sensors for high-resolution monitoring of nitrate in groundwater , 2014 .

[35]  J. Molson,et al.  Does river restoration affect diurnal and seasonal changes to surface water quality? A study along the Thur River, Switzerland. , 2015, The Science of the total environment.

[36]  Mark S. Johnson,et al.  Submersible UV-Vis Spectroscopy for Quantifying Streamwater Organic Carbon Dynamics: Implementation and Challenges before and after Forest Harvest in a Headwater Stream , 2012, Sensors.

[37]  P. Mulholland,et al.  In‐stream biotic control on nutrient biogeochemistry in a forested stream, West Fork of Walker Branch , 2007 .

[38]  M. Cohen,et al.  On the emergence of diel solute signals in flowing waters , 2016 .

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

[40]  A. Heathwaite,et al.  Unravelling organic matter and nutrient biogeochemistry in groundwater-fed rivers under baseflow conditions: Uncertainty in in situ high-frequency analysis. , 2016, The Science of the total environment.

[41]  R. T. Powell,et al.  High frequency measurement of nitrate concentration in the Lower Mississippi River, USA , 2014 .

[42]  W. Tych,et al.  First dynamic model of dissolved organic carbon derived directly from high-frequency observations through contiguous storms. , 2014, Environmental science & technology.

[43]  Christophe Ley,et al.  Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median , 2013 .

[44]  François Birgand,et al.  Addressing the fouling of in situ ultraviolet-visual spectrometers used to continuously monitor water quality in brackish tidal marsh waters. , 2013, Journal of environmental quality.

[45]  J. Tenhunen,et al.  Differential storm responses of dissolved and particulate organic carbon in a mountainous headwater stream, investigated by high‐frequency, in situ optical measurements , 2012 .

[46]  W. Bowden,et al.  Using in situ UV‐Visible spectrophotometer sensors to quantify riverine phosphorus partitioning and concentration at a high frequency , 2018, Limnology and Oceanography: Methods.

[47]  Michael Rode,et al.  Sensors in the Stream: The High-Frequency Wave of the Present. , 2016, Environmental science & technology.

[48]  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.

[49]  P. Mulholland,et al.  Effects of light on NO3− uptake in small forested streams: diurnal and day-to-day variations , 2006, Journal of the North American Benthological Society.

[50]  R. Dahlgren,et al.  Diel patterns of algae and water quality constituents in the San Joaquin River, California, USA , 2011 .

[51]  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 .

[52]  Gerard Kiely,et al.  Seasonal variation of DOC concentration and annual loss of DOC from an Atlantic blanket bog in South Western Ireland , 2009 .