112 citations as recorded by crossref.

1. Synoptic sampling to identify the major anions and cations of coal ash transport river by the application of isometric log-ratio transformation V. Kumar et al. https://doi.org/10.1007/s10653-025-02521-w
2. Nonlinear empirical modeling to estimate phosphorus exports using continuous records of turbidity and discharge C. Minaudo et al. https://doi.org/10.1002/2017WR020590
3. Detecting dominant changes in irregularly sampled multivariate water quality data sets C. Lehr et al. https://doi.org/10.5194/hess-22-4401-2018
4. Sampling frequency for water quality variables in streams: Systems analysis to quantify minimum monitoring rates N. Chappell et al. https://doi.org/10.1016/j.watres.2017.06.047
5. A Lab-on-Chip Analyzer for in Situ Measurement of Soluble Reactive Phosphate: Improved Phosphate Blue Assay and Application to Fluvial Monitoring G. Clinton-Bailey et al. https://doi.org/10.1021/acs.est.7b01581
6. QUAL-NET, a high temporal-resolution eutrophication model for large hydrographic networks C. Minaudo et al. https://doi.org/10.5194/bg-15-2251-2018
7. Understanding the impact of the changes in weather conditions on surface water quality P. Bhurtun et al. https://doi.org/10.1016/j.scitotenv.2018.10.246
8. High-resolution monitoring of nutrients in groundwater and surface waters: process understanding, quantification of loads and concentrations, and management applications F. van Geer et al. https://doi.org/10.5194/hess-20-3619-2016
9. Land Use Change to Reduce Freshwater Nitrogen and Phosphorus will Be Effective Even with Projected Climate Change A. Wade et al. https://doi.org/10.3390/w14050829
10. Uncertainty in nitrate load calculations evaluated using Monte Carlo simulations based on in-situ nitrate sensors in two Danish headwater streams S. van’t Veen et al. https://doi.org/10.1016/j.jhydrol.2025.133816
11. Estimating nitrate-nitrogen retention in a large constructed wetland using high-frequency, continuous monitoring and hydrologic modeling C. Drake et al. https://doi.org/10.1016/j.ecoleng.2018.03.014
12. Rain Pattern Deeply Reshaped Total Phosphorus Load Pattern in Watershed: A Case Study from Northern China H. Ding et al. https://doi.org/10.3390/w15162910
13. Citizen-led sampling to monitor phosphate levels in freshwater environments using a simple paper microfluidic device S. Richardson et al. https://doi.org/10.1371/journal.pone.0260102
14. Characterising phosphorus and nitrate inputs to a rural river using high-frequency concentration–flow relationships M. Bowes et al. https://doi.org/10.1016/j.scitotenv.2014.12.086
15. Climate change and water in the UK – past changes and future prospects G. Watts et al. https://doi.org/10.1177/0309133314542957
16. In situ Determination of Nitrate and Hydrogen Sulfide in the Baltic Sea Using an Ultraviolet Spectrophotometer D. Meyer et al. https://doi.org/10.3389/fmars.2018.00431
17. Transit times—the link between hydrology and water quality at the catchment scale M. Hrachowitz et al. https://doi.org/10.1002/wat2.1155
18. Intense summer floods may induce prolonged increases in benthic respiration rates of more than one year leading to low river dissolved oxygen M. Hutchins et al. https://doi.org/10.1016/j.hydroa.2020.100056
19. Combining multi-scale geophysical techniques for robust hydro-structural characterisation in catchments underlain by hard rock in post-glacial regions R. Cassidy et al. https://doi.org/10.1016/j.jhydrol.2014.06.004
20. Predicting nitrate discharge dynamics in mesoscale catchments using the lumped StreamGEM model and Bayesian parameter inference S. Woodward et al. https://doi.org/10.1016/j.jhydrol.2017.07.021
21. Deep learning based soft-sensor for continuous chlorophyll estimation on decentralized data J. Sáinz-Pardo Díaz et al. https://doi.org/10.1016/j.watres.2023.120726
22. Best practice in high-frequency water quality monitoring for improved management and assessment; a novel decision workflow J. Rozemeijer et al. https://doi.org/10.1007/s10661-025-13795-z
23. Recent trends in water quality in an agricultural catchment in Eastern Scotland: elucidating the roles of hydrology and land use S. Dunn et al. https://doi.org/10.1039/c3em00698k
24. Weekly water quality monitoring data for the River Thames (UK) and its major tributaries (2009–2013): the Thames Initiative research platform M. Bowes et al. https://doi.org/10.5194/essd-10-1637-2018
25. A Virtual Sensing Concept for Nitrogen and Phosphorus Monitoring Using Machine Learning Techniques T. Paepae et al. https://doi.org/10.3390/s22197338
26. Seasonal variability of stream water quality response to storm events captured using high-frequency and multi-parameter data O. Fovet et al. https://doi.org/10.1016/j.jhydrol.2018.02.040
27. Hydrogeophysical investigations for assessment of groundwater aquifer at Wadi El Assuity, Egypt S. Araffa et al. https://doi.org/10.1080/20909977.2024.2402157
28. Estimation of the water quality of a large urbanized river as defined by the European WFD: what is the optimal sampling frequency? L. Vilmin et al. https://doi.org/10.1007/s11356-016-7109-z
29. Advanced Continuous Monitoring System—Tools for Water Resource Management and Decision Support System in Salt Affected Delta M. Reljić et al. https://doi.org/10.3390/agriculture13020369
30. Identifying multiple stressor controls on phytoplankton dynamics in the River Thames (UK) using high-frequency water quality data M. Bowes et al. https://doi.org/10.1016/j.scitotenv.2016.06.239
31. Identifying seasonal patterns of phosphorus storm dynamics with dynamic time warping R. Dupas et al. https://doi.org/10.1002/2015WR017338
32. Technical note: High-frequency, multi-elemental stream water monitoring – experiences, feedbacks and suggestions from 7 years of running three French field laboratories (Riverlabs) N. Brekenfeld et al. https://doi.org/10.5194/hess-29-2615-2025
33. Determination of phosphorus in natural waters: A historical review P. Worsfold et al. https://doi.org/10.1016/j.aca.2016.02.047
34. Impact of legacy soil phosphorus on losses in drainage and overland flow from grazed grassland soils R. Cassidy et al. https://doi.org/10.1016/j.scitotenv.2016.07.063
35. Reconceptualizing water environmental capacity as a network-driven property: A spatially cascading framework for basin-scale phosphorus management J. Cheng et al. https://doi.org/10.1016/j.jclepro.2026.149370
36. High-frequency monitoring reveals nutrient sources and transport processes in an agriculture-dominated lowland water system B. van der Grift et al. https://doi.org/10.5194/hess-20-1851-2016
37. Water quality change of rivers during rainy events in a watershed with different land uses in Southern Brazil R. Girardi et al. https://doi.org/10.1590/2318-0331.011615179
38. The concentration-discharge slope as a tool for water quality management M. Bieroza et al. https://doi.org/10.1016/j.scitotenv.2018.02.256
39. A Community Dataset for Large-Scale River Nitrogen Modeling in the United States S. Chang et al. https://doi.org/10.1038/s41597-026-06873-5
40. Evaluation of Continuous Monitoring as a Tool for Municipal Stormwater Management Programs W. McDonald et al. https://doi.org/10.1061/JSWBAY.0000842
41. The impacts of biofouling on automated phosphorus analysers during long-term deployment Y. Chen & J. Crossman https://doi.org/10.1016/j.scitotenv.2021.147188
42. Using high-resolution phosphorus data to investigate mitigation measures in headwater river catchments J. Campbell et al. https://doi.org/10.5194/hess-19-453-2015
43. Meroplanktic phytoplankton play a crucial role in responding to peak discharge events in the middle lowland section of the Loire River (France) A. Pannard et al. https://doi.org/10.1007/s10750-023-05420-2
44. Understanding nutrient biogeochemistry in agricultural catchments: the challenge of appropriate monitoring frequencies M. Bieroza et al. https://doi.org/10.1039/C4EM00100A
45. Development of transparent high-frequency soft sensor of total nitrogen and total phosphorus concentrations in rivers using stacked convolutional auto-encoder and explainable AI A. Ba-Alawi et al. https://doi.org/10.1016/j.jwpe.2023.103661
46. Advances in Catchment Science, Hydrochemistry, and Aquatic Ecology Enabled by High-Frequency Water Quality Measurements M. Bieroza et al. https://doi.org/10.1021/acs.est.2c07798
47. Toward an In Situ Phosphate Sensor in Natural Waters Using a Microfluidic Flow Loop Analyzer Y. Chen et al. https://doi.org/10.1149/2.0891814jes
48. Effects of sampling frequency on estimation accuracies of annual loadings for water quality parameters in different sized watersheds L. Gao et al. https://doi.org/10.2166/wqrj.2020.012
49. Understanding the relationship between nutrient availability and freshwater cyanobacterial growth and abundance M. Burford et al. https://doi.org/10.1080/20442041.2023.2204050
50. Nutrient inputs from subarctic rivers into Hudson Bay J. Lee et al. https://doi.org/10.1525/elementa.2021.00085
51. Characteristics of very-large-scale motions in natural rivers based on large eddy simulation Z. Wang et al. https://doi.org/10.1063/5.0202522
52. Winter runoff events pose an unquantified continental-scale risk of high wintertime nutrient export E. Seybold et al. https://doi.org/10.1088/1748-9326/ac8be5
53. Coupling High-Frequency Stream Metabolism and Nutrient Monitoring to Explore Biogeochemical Controls on Downstream Nitrate Delivery H. Jarvie et al. https://doi.org/10.1021/acs.est.8b03074
54. Seasonal variation in phosphorus concentration–discharge hysteresis inferred from high-frequency in situ monitoring M. Bieroza & A. Heathwaite https://doi.org/10.1016/j.jhydrol.2015.02.036
55. Effects of stream nitrate data frequency on watershed model performance and prediction uncertainty S. Jiang et al. https://doi.org/10.1016/j.jhydrol.2018.11.049
56. Determining the Effect of Drying Time on Phosphorus Solubilization from Three Agricultural Soils under Climate Change Scenarios K. Forber et al. https://doi.org/10.2134/jeq2017.04.0144
57. Perspectives on Water Quality Monitoring Approaches for Behavioral Change Research P. Jordan & R. Cassidy https://doi.org/10.3389/frwa.2022.917595
58. Dominant mechanisms for the delivery of fine sediment and phosphorus to fluvial networks draining grassland dominated headwater catchments M. Perks et al. https://doi.org/10.1016/j.scitotenv.2015.03.008
59. Hydrodynamics and phosphorus loading in an urbanized river channel influences response to future managed change: Insights from advection-dispersion modelling M. Borota et al. https://doi.org/10.1016/j.scitotenv.2024.171958
60. Water quality status and trends in agriculture-dominated headwaters; a national monitoring network for assessing the effectiveness of national and European manure legislation in The Netherlands J. Rozemeijer et al. https://doi.org/10.1007/s10661-014-4059-0
61. Identifying Flow Pathways for Phosphorus Transport Using Observed Event Forensics and the CRAFT (Catchment Runoff Attenuation Flux Tool) R. Adams et al. https://doi.org/10.3390/w12041081
62. Riparian shading controls instream spring phytoplankton and benthic algal growth S. Halliday et al. https://doi.org/10.1039/C6EM00179C
63. Combining traditional nutrient load analysis with storm hydrograph separation reveals concealed patterns in event-driven nutrient export in a rural headwater catchment L. Ditzel et al. https://doi.org/10.5194/hess-30-4209-2026
64. Evolution of physicochemical species concentration in streams based on heavy rainfall event data obtained for high-frequency monitoring R. Girardi et al. https://doi.org/10.1590/2318-0331.011616055
65. Estimation of high frequency nutrient concentrations from water quality surrogates using machine learning methods M. Castrillo & Á. García https://doi.org/10.1016/j.watres.2020.115490
66. Diurnal Patterns in Solute Concentrations Measured with In Situ UV-Vis Sensors: Natural Fluctuations or Artefacts? S. Jacobs et al. https://doi.org/10.3390/s20030859
67. Impact of Wastewater Treatment Plant Discharge on Water Quality of a Heavily Urbanized River in Milan Metropolitan Area: Traditional and Emerging Contaminant Analysis S. Tasselli et al. https://doi.org/10.3390/w17223276
68. Daily, seasonal, and flood-dependent dynamics of water quality in a lowland urban stream in a Mediterranean climate T. Godinger et al. https://doi.org/10.1016/j.watres.2025.124075
69. Projections of future deterioration in UK river quality are hampered by climatic uncertainty under extreme conditions M. Hutchins et al. https://doi.org/10.1080/02626667.2016.1177186
70. Using high-frequency phosphorus monitoring for water quality management: a case study of the upper River Itchen, UK G. Fones et al. https://doi.org/10.1007/s10661-020-8138-0
71. Data Augmentation for a Virtual-Sensor-Based Nitrogen and Phosphorus Monitoring T. Paepae et al. https://doi.org/10.3390/s23031061
72. Nitrate uptake in an agricultural stream estimated from high-frequency, in-situ sensors C. Jones et al. https://doi.org/10.1007/s10661-018-6599-1
73. Real-time monitoring of nutrients and dissolved organic matter in rivers: Capturing event dynamics, technological opportunities and future directions P. Blaen et al. https://doi.org/10.1016/j.scitotenv.2016.06.116
74. Unravelling organic matter and nutrient biogeochemistry in groundwater-fed rivers under baseflow conditions: Uncertainty in in situ high-frequency analysis M. Bieroza & A. Heathwaite https://doi.org/10.1016/j.scitotenv.2016.02.046
75. The impact of data quality and outlier detection in high-frequency water quality data on water management and process understanding N. Weber & M. Bieroza https://doi.org/10.1016/j.jhydrol.2026.135171
76. Chlorophyll-a, dissolved organic carbon, turbidity and other variables of ecological importance in river basins in southern Ontario and British Columbia, Canada K. Zolfaghari et al. https://doi.org/10.1007/s10661-019-7800-x
77. Aquatic ecosystem metabolism as a tool in environmental management K. Jankowski et al. https://doi.org/10.1002/wat2.1521
78. Analysis of Hydrochemical and Environmental Conditions of the Right-bank Tributaries of the Mezen River N. Klimovskii et al. https://doi.org/10.3103/S1068373922110097
79. Real-time monitoring of water quality to identify pollution pathways in small and middle scale rivers A. Meyer et al. https://doi.org/10.1016/j.scitotenv.2018.10.069
80. Iowa Statewide Stream Nitrate Load Calculated Using In Situ Sensor Network C. Jones et al. https://doi.org/10.1111/1752-1688.12618
81. Field and Laboratory Tests of Flow-Proportional Passive Samplers for Determining Average Phosphorus and Nitrogen Concentration in Rivers P. Jordan et al. https://doi.org/10.1021/es304108e
82. The application of high temporal resolution data in river catchment modelling and management strategies L. Crockford et al. https://doi.org/10.1007/s10661-017-6174-1
83. Monitoring the riverine pulse: Applying high‐frequency nitrate data to advance integrative understanding of biogeochemical and hydrological processes D. Burns et al. https://doi.org/10.1002/wat2.1348
84. Improving nitrate load estimates in an agricultural catchment using Event Response Reconstruction S. Jomaa et al. https://doi.org/10.1007/s10661-018-6700-9
85. Disentangling the influence of hydroclimatic patterns and agricultural management on river nitrate dynamics from sub-hourly to decadal time scales R. Dupas et al. https://doi.org/10.1016/j.scitotenv.2016.07.053
86. A Droplet Microfluidic-Based Sensor for Simultaneous in Situ Monitoring of Nitrate and Nitrite in Natural Waters A. Nightingale et al. https://doi.org/10.1021/acs.est.9b01032
87. High‐frequency monitoring of catchment nutrient exports reveals highly variable storm event responses and dynamic source zone activation P. Blaen et al. https://doi.org/10.1002/2017JG003904
88. Technical Note: Field experiences using UV/VIS sensors for high-resolution monitoring of nitrate in groundwater M. Huebsch et al. https://doi.org/10.5194/hess-19-1589-2015
89. Using high-frequency water quality data to assess sampling strategies for the EU Water Framework Directive R. Skeffington et al. https://doi.org/10.5194/hess-19-2491-2015
90. High-frequency monitoring of nitrogen and phosphorus response in three rural catchments to the end of the 2011–2012 drought in England F. Outram et al. https://doi.org/10.5194/hess-18-3429-2014
91. Changing climate and nutrient transfers: Evidence from high temporal resolution concentration-flow dynamics in headwater catchments M. Ockenden et al. https://doi.org/10.1016/j.scitotenv.2015.12.086
92. The Water Quality of the River Enborne, UK: Observations from High-Frequency Monitoring in a Rural, Lowland River System S. Halliday et al. https://doi.org/10.3390/w6010150
93. The implications of climate change for the water environment in England N. Arnell et al. https://doi.org/10.1177/0309133314560369
94. Understanding links between water-quality variables and nitrate concentration in freshwater streams using high frequency sensor data C. Kermorvant et al. https://doi.org/10.1371/journal.pone.0287640
95. High frequency variability of environmental drivers determining benthic community dynamics in headwater streams M. Snell et al. https://doi.org/10.1039/C3EM00680H
96. The Catchment Runoff Attenuation Flux Tool, a minimum information requirement nutrient pollution model R. Adams et al. https://doi.org/10.5194/hess-19-1641-2015
97. Upland streamwater nitrate dynamics across decadal to sub-daily timescales: a case study of Plynlimon, Wales S. Halliday et al. https://doi.org/10.5194/bg-10-8013-2013
98. Decomposing the Bulk Electrical Conductivity of Streamflow To Recover Individual Solute Concentrations at High Frequency P. Benettin & B. van Breukelen https://doi.org/10.1021/acs.estlett.7b00472
99. Sensors in the Stream: The High-Frequency Wave of the Present M. Rode et al. https://doi.org/10.1021/acs.est.6b02155
100. Small villages and their sanitary infrastructure—an unnoticed influence on water quantity and a threat to water quality in headwater catchments C. Spill et al. https://doi.org/10.1007/s10661-023-12051-6
101. Interpretation of river water quality data is strongly controlled by measurement time and frequency I. Elfferich et al. https://doi.org/10.1016/j.scitotenv.2024.176626
102. Downstream evolution of wastewater treatment plant nutrient signals using high‐temporal monitoring S. Ledford & L. Toran https://doi.org/10.1002/hyp.13640
103. End-user perspective of low-cost sensors for urban stormwater monitoring: a review Q. Zhu et al. https://doi.org/10.2166/wst.2023.142
104. Advancing SDG 6.3.2 with machine learning-based virtual sensors for high-frequency nutrient monitoring B. Ngwenya et al. https://doi.org/10.1016/j.jwpe.2025.108831
105. High‐frequency water quality monitoring in an urban catchment: hydrochemical dynamics, primary production and implications for the Water Framework Directive S. Halliday et al. https://doi.org/10.1002/hyp.10453
106. Continuous field estimation of dissolved organic carbon concentration and biochemical oxygen demand using dual‐wavelength fluorescence, turbidity and temperature K. Khamis et al. https://doi.org/10.1002/hyp.11040
107. Dissolved oxygen and water temperature dynamics in lowland rivers over various timescales A. Rajwa-Kuligiewicz et al. https://doi.org/10.1515/johh-2015-0041
108. A post-event stratified random sampling scheme for monitoring event-based water quality using an automatic sampler J. Lessels & T. Bishop https://doi.org/10.1016/j.jhydrol.2018.12.063
109. A novel assessment considering spatial and temporal variations of water quality to identify pollution sources in urban rivers S. Yang et al. https://doi.org/10.1038/s41598-021-87671-4
110. Design and development of a low-cost automatic runoff sampler for time distributed sampling G. Kumar et al. https://doi.org/10.1016/j.jhydrol.2020.125845
111. Groundwater impacts on surface water quality and nutrient loads in lowland polder catchments: monitoring the greater Amsterdam area L. Yu et al. https://doi.org/10.5194/hess-22-487-2018
112. Nitrate transport and supply limitations quantified using high-frequency stream monitoring and turning point analysis C. Jones et al. https://doi.org/10.1016/j.jhydrol.2017.04.041