Automated high frequency monitoring of Lake Maggiore through in situ sensors: system design, field test and data quality control

https://doi.org/10.4081/jlimnol.2021.2011

Authors

  • Rocco Tiberti National Research Council of Italy, Water Research Institute (CNR‐IRSA), Verbania, Italy. https://orcid.org/0000-0003-1617-8826
  • Rossana Caroni National Research Council of Italy, Water Research Institute (CNR‐IRSA), Verbania, Italy.
  • Massimiliano Cannata Institute of Earth Sciences, Department of Environment, Construction and Design, University of Applied Sciences of Southern Switzerland (SUPSI), Mendrisio, Eswatini. https://orcid.org/0000-0003-2527-1416
  • Andrea Lami National Research Council of Italy, Water Research Institute (CNR‐IRSA), Verbania, Italy.
  • Dario Manca National Research Council of Italy, Water Research Institute (CNR‐IRSA), Verbania, Italy.
  • Daniele Strigaro Institute of Earth Sciences, Department of Environment, Construction and Design, University of Applied Sciences of Southern Switzerland (SUPSI), Mendrisio, Switzerland.
  • Michela Rogora | michela.rogora@cnr.it National Research Council of Italy, Water Research Institute (CNR‐IRSA), Verbania, Italy. https://orcid.org/0000-0003-3515-0220

Abstract

A high frequency monitoring (HFM) system for the deep subalpine lakes Maggiore, Lugano and Como is under development within the EU INTERREG project SIMILE. The HFM system is designed to i) describe often neglected but potentially relevant processes occurring on short time scale; ii) become a cost-effective source of environmental data; and iii) strengthen the coordinated management of water resources in the subalpine lake district. In this project framework, a first HFM station (LM1) consisting of a monitoring buoy was placed in Lake Maggiore. LM1 represents a pilot experience within the project, aimed at providing the practical know-how needed for the development of the whole HFM system. To increase replicability and transferability, LM1 was developed in-house, and conceived as a low-cost modular system. LM1 is presently equipped with solar panels, a weather station, and sensors for water temperature, pH, dissolved oxygen, conductivity, and chlorophyll-a. In this study, we describe the main features of LM1 (hardware and software) and the adopted Quality Assurance/Quality Control (QA/QC) procedures. To this end, we provide examples from a test period, i.e., the first 9-months of functioning of LM1. A description of the software selected as data management software for the HFM system (IstSOS) is also provided. Data gathered during the study period provided clear evidence that coupling HFM and discrete sampling for QA/QC controls is necessary to produce accurate data and to detect and correct errors, mainly because of sensor fouling and calibration drift. These results also provide essential information to develop further the HFM system and shared protocols adapted to the local environmental (i.e., large subalpine lakes) and technical (expertise availability) context. Next challenge is making HFM not only a source of previously unaffordable information, but also a cost-effective tool for environmental monitoring.

Dimensions

Altmetric

PlumX Metrics

Downloads

Download data is not yet available.

References

Albaladejo C, Soto F, Torres R, Sánchez P, López JA, 2012. A low-cost sensor buoy system for monitoring shallow marine environments. Sensors 12:9613-9634. DOI: https://doi.org/10.3390/s120709613

Ambrosetti W, Barbanti L, 1999. Deep water warming in lakes: an indicator of climatic change. Journal of Limnology 58: 1-9. DOI: https://doi.org/10.4081/jlimnol.1999.1

APAT, IRSA-CNR, 2003. [Metodi analitici per le acque. Manuali e linee guida 29/2003].[Handbook in Italian]. APAT, IRSA-CNR.

APHA, AWWA, WEF, 2012. Standard Methods for the examination of water and wastewater. American Public Health Association, Washington DC.

Banas DP, Grillas I, Auby F, Lescuyer E, Coulet JC, Moreteau J, Millet B, 2005. Short time scale changes in underwater irradiance in a wind exposed lagoon (Vaccarès Lagoon, France): efficiency of infrequent field measurements of water turbidity of weather data to predict irradiance in the water column. Hydrobiologia 551:3-16.

Bertone E, Burford M, Hamilton D, 2018. Fluorescence probes for real-time remote cyanobacteria monitoring: A review of challenges and opportunities. Water Res. 141:152-162.

Bowling LC, Zamyadi A, Henderson RK, 2016. Assessment of in situ fluorometry to measure cyanobacterial presence in water bodies with diverse cyanobacterial populations. Water Res. 105:22-33.

Brovelli MA, Cannata M, Rogora M, 2020. SIMILE, a geospatial enabler of the monitoring of Sustainable Development Goal 6 (Ensure availability and sustainability of water for all). Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 42:4/W20.

Callieri C, Piscia R, 2002. Photosynthetic efficiency and seasonality of autotrophic picoplankton in Lago Maggiore after its recovery. Freshwater Biol. 47:941-956. DOI: https://doi.org/10.1046/j.1365-2427.2002.00821.x

Callieri C, Bertoni R, Contesini M, Bertoni F, 2014. Lake level fluctuations boost toxic cyanobacterial oligotrophic blooms. PLoS ONE 9:e109526. DOI: https://doi.org/10.1371/journal.pone.0109526

Campbell JL, Rustad LE, Porter JH, Taylor JR, Dereszynski EW, Shanley JB, Gries C, Henshaw DL, Martin ME, Sheldon WE, Boose ER, 2013. Quantity is nothing without quality: automated QA/QC for streaming environmental sensor data. BioScience 63:574-585. DOI: https://doi.org/10.1525/bio.2013.63.7.10

Cannata M, Antonovic M, Molinari M, Pozzoni M, 2015. IstSOS, a new sensor observation management system: software architecture and a real-case application for flood protection. Geomat. Nat. Haz. Risk 6:635-650. DOI: https://doi.org/10.1080/19475705.2013.862572

CNR-IRSA, 2019. [Ricerche sull'evoluzione del Lago Maggiore. Aspetti limnologici. Programma triennale 2016 – 2018. Campagna 2018 e rapporto triennale 2016-18].[Report in Italian]. Commissione Internazionale per la protezione delle acque italo-svizzere. 160 pp.

Dubelaar GBJ, Geerders PJF, Jonker RR, 2004. High frequency monitoring reveals phytoplankton dynamics. J. Environ. Monit. 6:946−952. DOI: https://doi.org/10.1039/b409350j

European Commission, 2000. Council Directive 2000/60/EC of 23 October 2000 establishing a framework for Community action in the field of water policy. Off. J. Eur. Commun. L327. p. 73.

Fastner J, Abella S, Litt A, Morabito G, Voros L, Palffy K, Straile D, Kummerlin R, Matthews D, Phillips G, Chorus I, 2016. Combating cyanobacterial proliferation by avoiding or treating inflows with high P load - experiences from eight case studies. Aquat. Ecol. 50:367-383. DOI: https://doi.org/10.1007/s10452-015-9558-8

Fenocchi A, Rogora M, Sibilla S, Dresti C, 2017. Relevance of inflows on the thermodynamic structure and on the modeling of a deep subalpine lake (Lake Maggiore, Northern Italy/Southern Switzerland). Limnologica 63:42-56. DOI: https://doi.org/10.1016/j.limno.2017.01.006

Fenocchi A, Rogora M, Sibilla S, Ciampittiello M, Dresti C, 2018. Forecasting the evolution in the mixing regime of a deep subalpine lake under climate change scenarios through numerical modelling (Lake Maggiore, Northern Italy/Southern Switzerland). Climate Dynam. 51:3521–3536. DOI: https://doi.org/10.1007/s00382-018-4094-6

Fiebrich CA, Morgan CR, McCombs AG, Hall PKJ, McPherson RA, 2010. Quality assurance procedures for mesoscale meteorological data. J. Atmos. Ocean. Technol. 27:1565-1582. DOI: https://doi.org/10.1175/2010JTECHA1433.1

Garel E, Nunes S, Neto J, Fernandes R, Neves R, Marques J, Ferreira, 2009. The autonomous Simpatico system for real-time continuous water-quality and current velocity monitoring: examples of application in three Portuguese estuaries. Geo-Mar. Lett. 29:331–341. DOI: https://doi.org/10.1007/s00367-009-0147-5

Gries C, Read JS, Winslow LA, Hanson PC, Weathers KC, 2014. Enabling innovative research by supporting the life cycle of high frequency streaming sensor data in the Global Lake Ecological Observatory Network (GLEON). In: AGU Fall Meeting Abstracts 2014.

Hamilton DP, Carey CC, Arvola L, Arzberger P, Brewer C, Cole JJ, Gaiser E, Hanson PC, Ibelings BW, Jennings E, Kratz TK, Lin FP, McBride CG, de Motta MD, Muraoka K, Nishri A, Qin B, Read JS, Rose KC, Ryder E, Weathers KC, Zhu G, Trolle D, Brookes JD, 2014. A global lake ecological observatory network (GLEON) for synthesising high-frequency sensor data for validation of deterministic ecological models. Inland Waters 5:49-56. DOI: https://doi.org/10.5268/IW-5.1.566

Hill DJ, Minsker BS, Amir E, 2009. Real-time Bayesian anomaly detection in streaming environmental data. Water Resour. Res. 45:10.1029/2008WR006956. DOI: https://doi.org/10.1029/2008WR006956

Horsburgh JS, Reeder SL, Spackman Jones A, Meline J, 2015. Open source software for visualization and quality control of continuous hydrologic and water quality sensor data. Environ. Modell. Softw. 70:32-44. DOI: https://doi.org/10.1016/j.envsoft.2015.04.002

Hunter PD, Tyler AN, Gilvear DJ, Willby NJ, 2009. Using remote sensing to aid the assessment of human health risks from blooms of potentially- toxic cyanobacteria. Environ. Sci. Technol. 43:2627-2633. DOI: https://doi.org/10.1021/es802977u

ISO, 1992. Water quality - Measurement of biochemical parameters - Spectrometric determination of the chlorophyll-a concentration. Norm ISO 10260:1992. International Organization for Standardization Publ., Geneva.

Itvánovics V, Honti M, Osztoics A, Shafik HM, Padisák J, Yacobi Y, Eckert W, 2005. Continuous monitoring of phytoplankton dynamics in Lake Balaton (Hungary) using on-line delayed fluorescence excitation spectroscopy. Freshwater Biol. 50:1950-1970.

Jennings E, Jones S, Arvola L, Staehr PA, Gaiser E, Jones ID, Weathers KC, Weyhenmeyer GA, Chiu CY, de Eyto E, 2012. Effects of weather-related episodic events in lakes: an analysis based on high-frequency data. Freshwater Biol. 57:589-601.

Johnson KS, Needoba JA, Riser SC, Showers WJ, 2007. Chemical sensor networks for the aquatic environment. Chem. Rev. 107:623-640.

Khan H, Laas A, Marcé R, Obrador B, 2020. Major effects of alkalinity on the relationship between metabolism and dissolved inorganic carbon dynamics in lakes. Ecosystems 23: 1566–1580.

Klug JL, Richardson DC, Ewing HA, Hargreaves BR, Samal NR, Vachon D, Pierson DC, Lindsey AM, O’Donnell DM, Effler SW, Weathers KC, 2012. Ecosystem effects of a tropical cyclone on a network of lakes in Northeastern North America. Environ. Sci. Technol. 46:11693-11701. DOI: https://doi.org/10.1021/es302063v

Laas A, de Eyto E, Pierson D, Jennings E, 2016. NETLAKE Guidelines for automatic monitoring station development. Technical report. NETLAKE COST Action ES1201. 58 pp.

Laborde S, Antenucci J P, Copetti D, Imberger J, 2010. Inflow intrusions at multiple scales in a large temperate lake. Limnol. Oceanogr. 55:1301-1312.

Lerner B, Boose E, Osterweil L, Ellison A, Clarke L, 2011. Provenance and quality control in sensor networks, p. 98-103. In: M. Jones and C. Gries (eds.), Proceedings of the Environmental Information Management Conference, Santa Barbara, University of California.

Le Vu B, Vincon-Leite B, Lemaire BJ, Bensoussan N, Calzas M, Drezen C, Deroubaix JF, Escoffier N, Degres Y, Freissinet C, Groleau A, Humbert JF, Paolini G, Prévot F, Quiblier C, Rioust E, Tassin B, 2011. High-frequency monitoring of phytoplankton dynamics within the European water framework directive: application to metalimnetic cyanobacteria. Biogeochemistry 106:229-242.

Lorenzen CJ, 1967. Determination of chlorophyll and phaeophytin Spectrophotometric equations. Limnol. Oceanogr. 12:343-346.

Marra J, 1997. Analysis of diel variability in chlorophyll fluorescence. J. Mar. Res. 55:767-784.

Marcé R, George G, Buscarinu P, Deidda M, Dunalska J, de Eyto E, Flaim G, Grossart H, Istvanovics V, Lenhardt M, Moreno-Ostos E, Obrador B, Ostrovsky I, Pierson DC, Potužák J, Poikane S, Rinke K, Rodríguez-Mozaz S, Staehr PA, Šumberová K, Waajen G, Weyhenmeyer GA, Weathers KC, Zion M, Ibelings BW, Jennings E, 2016. Automatic high frequency monitoring for improved lake and reservoir management. Environ. Sci. Technol. 50:10780-10794.

McBride C, Rose KC, 2018. Automated high-frequency monitoring and research, p. 419-461. In: D.P. Hamilton, K.J. Collier, J.M. Quinn and C. Howard-Williams (eds.), Lake restoration handbook: A New Zealand perspective. Cham: Springer.

Meinson P, Idrizaj A, Nõges P, Nõges T, Laas A, 2016. Continuous and high-frequency measurements in limnology: history, applications, and future challenges. Environ. Rev. 24:1-11. DOI: https://doi.org/10.1139/er-2015-0030

Morabito G, Oggioni A, Austoni M, 2012. Resource ratio and human impact: How diatom assemblages in Lake Maggiore responded to oligotrophication and climatic variability. Hydrobiologia 698:47-60. DOI: https://doi.org/10.1007/s10750-012-1094-0

Morabito G, Rogora M, Austoni M, Ciampittiello M, 2018. Could the extreme meteorological events in Lake Maggiore watershed determine a climate-driven eutrophication process? Hydrobiologia 824:163–175. DOI: https://doi.org/10.1007/s10750-018-3549-4

Mourad M, Bertrand-Krajewski JL, 2002. A method for automatic validation of long time series of data in urban hydrology. Water Sci. Technol. 45:263-270.

Moatar F, Miquel J, Poirel A, 2001. A quality-control method for physical and chemical monitoring data. Application to dissolved oxygen levels in the river Loire (France). J. Hydrol. 252:25-36.

Nõges T, Anneville O, Guillard J, Haberman J, Järvalt A, Manca M, Morabito G, Rogora M, Thackeray SJ, Volta P, Winfield IJ, Nõges P, 2017. Fisheries impacts on lake ecosystem structure in the context of a changing climate and trophic state. J. Limnol. 77:1640. DOI: https://doi.org/10.4081/jlimnol.2017.1640

Pilotti M, Valerio G, Leoni B, 2013. Data set for hydrodynamic lake model calibration: A deep pre-alpine case. Water Resour. Res. 49:1–5. DOI: https://doi.org/10.1002/wrcr.20506

Pires MD, 2010. Evaluation fluorometers for the in situ monitoring of chlorophyll and/or cyanobacteria. Deltares Report. Project 1203593-000. 17 pp.

Pozzoni M, Salvetti A, Cannata M, 2020. Retrospective and prospective of hydro-met monitoring system in the Canton Ticino, Switzerland. Hydrol. Sci. J. Ahead-of-print 1-15. DOI: https://doi.org/10.1080/02626667.2020.1760280

R Development Core Team, 2019. R: A Language and Environment for Statistical Computing. Version 3.5.2. R Foundation for Statistical Computing, Vienna, Austria.

Read JS, Garner B, Pellerin B, Loken L, 2015. sensorQC. USGS-R.

Read JS, Hamilton DP, Jones ID, Muraoka K, Winslow LA, Kroiss R, Wu CH, Gaiser E, 2011. Derivation of lake mixing and stratification indices from high-resolution lake buoy data. Environ. Modell. Softw. 26:1325-1336. DOI: https://doi.org/10.1016/j.envsoft.2011.05.006

Richardson TL, Lawrenz E, Pinckney JL, Guajardo RC, Walker EA, Paerl HW, MacIntyre HL, 2010. Spectral fluorometric characterization of phytoplankton community composition using the Algae Online Analyser®. Water Res. 44:2461-2472. DOI: https://doi.org/10.1016/j.watres.2010.01.012

Rogora M, Buzzi F, Dresti C, Leoni B, Lepori F, Mosello R, Patelli M, Salmaso N, 2018. Climatic effects on vertical mixing and deep-water oxygen content in the subalpine lakes in Italy. Hydrobiologia 824:33-50. DOI: https://doi.org/10.1007/s10750-018-3623-y

Salmaso N, Mosello R, 2010. Limnological research in the deep southern subalpine lakes: synthesis, directions and perspectives. Adv. Oceanogr. Limnol. 1:29-66.

Salmaso N, Buzzi F, Capelli C, Cerasino L, Leoni B, Lepori F, Rogora M, 2020. Responses to local and global stressors in the large southern perialpine lakes: Present status and challenges for research and management. J. Great Lakes Res. 46:752-766.

Sheldon WMJ, 2008. Dynamic, rule-based quality control framework for real-time sensor data, p. 145-150. In: C. Gries and M.B. Jones (eds.), Proceedings of the Environmental Information Management Conference: Sensor Networks.

Skeffington RA, Halliday SJ, Wade AJ, Bowes MJ, Loewenthal M, 2015. Using high-frequency water quality data to assess sampling strategies for the EU Water Framework Directive. Hydrol. Earth Syst. Sci. 19:2491-2504.

Song KS, Li L, Li S, Tedesco L, Li LH, Hall B, 2012. Hyperspectral remote sensing of total phosphorus (TP) in three central Indiana water supply reservoirs. Water Air Soil Poll. 223:1481-1502. DOI: https://doi.org/10.1007/s11270-011-0959-6

Stirbet A, Lazár D, Papageorgiou GC, 2019. Chlorophyll a fluorescence in cyanobacteria: relation to photosynthesis, p. 79-130. In: A.K. Mishra, D.N. Tiwari and A.N. Rai (eds.) Cyanobacteria. Academic Press.

Stockwell JD, Doubek JP, Adrian R, et al., 2020. Storm impacts on phytoplankton community dynamics in lakes. Glob. Change Biol. 26:2756-2784.

Strigaro D, Cannata M, Antonovic M, 2019. Boosting a weather monitoring system in low income economies using open and non-conventional systems: Data quality analysis. Sensors 19:1185.

Stumpf RP, Wynne TT, Baker DB, Fahnenstiel GL, 2012. Interannual variability of cyanobacterial blooms in Lake Erie. PLoS One 7:e42444.

Tanentzap AJ, Morabito G, Volta P, Rogora M, Yan ND, Manca M, 2020. Climate warming restructures an aquatic food web over 28 years. Glob. Change Biol. 26:6852-6866. DOI: https://doi.org/10.1111/gcb.15347

Tapolczai K, Anneville O, Padisák J, Salmaso N, Morabito G, Zohary T, Tadonléké RD, Rimet F, 2015. Occurrence and mass development of Mougeotia spp. (Zygnemataceae) in large, deep lakes. Hydrobiologia 745:17-29. DOI: https://doi.org/10.1007/s10750-014-2086-z

Taylor JR, Loescher HL, 2013. Automated quality control methods for sensor data: a novel observatory approach. Biogeosciences 10:4957-4971. DOI: https://doi.org/10.5194/bg-10-4957-2013

Tran Khac V, Hong Y, Plec D, Lemaire BJ, Dubois P, Saad M, Vinçon-Leite B, 2018. An automatic monitoring system for high-frequency measuring and real-time management of cyanobacterial blooms in urban water bodies. Processes 6:11. DOI: https://doi.org/10.3390/pr6020011

Vitale AJ, Perillo GE, Genchi SA, Arias AH, Piccolo M, 2018. Low-cost monitoring buoys network tracking biogeochemical changes in lakes and marine environments – a regional case study. Pure Appl. Chem. 90:1631-1646. DOI: https://doi.org/10.1515/pac-2018-0508

von Lehmden DJ, Nelson C, 1977. Quality assurance handbook for air pollution measurement systems. Volume II. Ambient air specific methods (No. PB-273518; EPA-600/4/77/027a). Environmental Protection Agency, Research Triangle Park, NC: 346 pp.

Wagner C, Adrian R, 2009. Cyanobacteria dominance: Quantifying the effects of climate change. Limnol. Oceanogr. 54:2460-2468. DOI: https://doi.org/10.4319/lo.2009.54.6_part_2.2460

Wagner RJ, Boulger RWJ, Oblinger CJ, Smith BA, 2006. Guidelines and standard procedures for continuous water-quality monitors: station operation, record computation, and data reporting. U.S. Geological Survey Techniques and Methods 1–D3, Reston: 51 pp. DOI: https://doi.org/10.3133/tm1D3

Weathers KC, Hanson PC,Arzberger P, Brentrup J, Brookes J, Carey CC, Gaiser E, Gaiser E, Hamilton DP, Hong GS, Ibelings B, Istvánovics V, Jennings E, Kim B, Kratz T, Lin F‐P, Muraoka K, O'Reilly C, Rose KC, Ryder E, Zhu G, 2013. The Global Lake Ecological Observatory Network (GLEON): the evolution of grassroots network science. Limnol. Oceanogr. Bull. 22:71-73. DOI: https://doi.org/10.1002/lob.201322371

Downloads

Published
2021-06-21
Info
Issue
Section
Original Articles
Edited by
Diego Fontaneto, CNR-IRSA Water Research Institute, Verbania, Italy
Keywords:
Water quality, chlorophyll, data management, sensors, SIMILE project
Statistics
  • Abstract views: 313

  • PDF: 78
How to Cite
1.
Tiberti R, Caroni R, Cannata M, Lami A, Manca D, Strigaro D, Rogora M. Automated high frequency monitoring of Lake Maggiore through <em>in situ</em> sensors: system design, field test and data quality control. J Limnol [Internet]. 2021 Jun. 21 [cited 2021 Sep. 24];80(2). Available from: https://jlimnol.it/index.php/jlimnol/article/view/2011

Most read articles by the same author(s)

1 2 3 4 > >>