Flow monitoring with a camera: a case study on a flood event in the Tiber River

  • F. Tauro
  • G. Olivieri
  • A. Petroselli
  • M. Porfiri
  • S. Grimaldi


Monitoring surface water velocity during flood events is a challenging task. Techniques based on deploying instruments in the flow are often unfeasible due to high velocity and abundant sediment transport. A low-cost and versatile technology that provides continuous and automatic observations is still not available. Among remote methods, large-scale particle image velocimetry (LSPIV) is an optical method that computes surface water velocity maps from videos recorded with a camera. Here, we implement and critically analyze findings obtained from a recently introduced LSPIV experimental configuration during a flood event in the Tiber River at a cross section located in the center of Rome, Italy. We discuss the potential of LSPIV observations in challenging environmental conditions by presenting results from three tests performed during the hydrograph flood peak and recession limb of the event for different illumination and weather conditions. The obtained surface velocity maps are compared to the rating curve velocity and to benchmark velocity values. Experimental findings show that optical methods should be preferred in extreme conditions. However, their practical implementation may be associated with further hurdles and uncertainties.


Flow monitoring Surface flow Flow measurement Flow velocity Large-scale particle image velocimetry Camera observations 



This work was supported by the American Geophysical Union Horton (Hydrology) Research Grant for Ph.D. students, by the Ministero degli Affari Esteri project 2015 Italy-USA PGR00175, by the UNESCO Chair in Water Resources Management and Culture, and by the National Science Foundation under grant number BCS-1124795. The authors thank Roberto Rapiti and Giuliano Cipollari for help with the experiments and Francesco Mele, Domenico Spina, and Luigi D’Aquino from UIM for providing water level measurements and rating curves.


  1. Adrian, R. J. (1991). Particle-imaging techniques for experimental fluid-mechanics. Annual Review of Fluid Mechanics, 23, 261–304.CrossRefGoogle Scholar
  2. Alessandrini, V., Bernardi, G., & Todini, E. (2013). An operational approach to real-time dynamic measurement of discharge. Hydrology Research, 44, 953–964.CrossRefGoogle Scholar
  3. Bechle, A. J., & Wu, C. H. (2014). An entropy-based surface velocity method for estuarine discharge measurement. Water Resources Research, 50(7), 6106–6128.CrossRefGoogle Scholar
  4. Bechle, A., Wu, C., Liu, W., & Kimura, N. (2012). Development and application of an automated river-estuary discharge imaging system. Journal of Hydraulic Engineering, 138(4), 327–339.CrossRefGoogle Scholar
  5. Bradley, A. A., Kruger, A., Meselhe, E. A., & Muste, M. V. I. (2002). Flow measurement in streams using video imagery. Water Resources Research, 38(12), 1–8.CrossRefGoogle Scholar
  6. Buchanan, T. J., and W. P. Somers (1969), Discharge measurements at gaging stations: U.S. geological survey techniques of water-resources investigations, Tech. rep., U.S. Geological Survey.Google Scholar
  7. Chow, V. T. (1959). Open-Channel Hydraulics. New York: McGraw-Hill.Google Scholar
  8. Creutin, J. D., Muste, M., Bradley, A. A., Kim, S. C., & Kruger, A. (2003). River gauging using PIV techniques: a proof of concept experiment on the Iowa River. Journal of Hydrology, 277(3-4), 182–194.CrossRefGoogle Scholar
  9. deLima, J. L. M. P., & Abrantes, J. R. C. B. (2014). Using a thermal tracer to estimate overland and rill flow velocities. Earth Surface Processes and Landforms, 39(10), 1293–1300.CrossRefGoogle Scholar
  10. Dramais, G., LeCoz, J., Camenen, B., & Hauet, A. (2011). Advantages of a mobile LSPIV method for measuring flood discharges and improving stage-discharge curves. Journal of Hydro-Environment Research, 5(4), 301–312.CrossRefGoogle Scholar
  11. Fujita, I., & Hino, T. (2003). Unseeded and seeded PIV measurements of river flows video from a helicopter. Journal of Visualization, 6(3), 245–252.CrossRefGoogle Scholar
  12. Fujita, I., & Kunita, Y. (2011). Application of aerial LSPIV to the 2002 flood of the Yodo River using a helicopter mounted high density video camera. Journal of Hydro-environment Research, 5(4), 323–331.CrossRefGoogle Scholar
  13. Fujita, I., Muste, M., & Kruger, A. (1997). Large-scale particle image velocimetry for flow analysis in hydraulic engineering applications. Journal of Hydraulic Research, 36(3), 397–414.CrossRefGoogle Scholar
  14. Fulton, J., & Ostrowski, J. (2008). Measuring real-time streamflow using emerging technologies: radar, hydroacoustics, and the probability concept. Journal of Hydrology, 357(1-2), 1–10.CrossRefGoogle Scholar
  15. Grimaldi, S., Petroselli, A., Alonso, G., & Nardi, F. (2010). Flow time estimation with variable hillslope velocity in ungauged basins. Advances in Water Resources, 33(10), 1216–1223.CrossRefGoogle Scholar
  16. Gui, L. (2014), EDPIV—evaluation software for digital particle image velocimetry,
  17. Gunawan, B., Sun, X., Sterling, M., Shiono, K., Tsubaki, R., Rameshwaran, P., Knight, D., Chandler, J., Tang, X., & Fujita, I. (2012). The application of LS-PIV to a small irregular river for inbank and overbank flows. Flow Measurement and Instrumentation, 24, 1–12.CrossRefGoogle Scholar
  18. Hauet, A., Kruger, A., Krajewski, W. F., Bradley, A., Muste, M., Creutin, J.-D., & Wilson, M. (2008a). Experimental system for real-time discharge estimation using an image-based method. Journal of Hydrologic Engineering, 13(2), 105–110.Google Scholar
  19. Hauet, A., Creutin, J. D., & Belleudy, P. (2008b). Sensitivity study of large-scale particle image velocimetry measurement of river discharge using numerical simulation. Journal of Hydrology, 349(1-2), 178–190.Google Scholar
  20. Hauet, A., Muste, M., & Ho, H.-C. (2009). Digital mapping of riverine waterway hydrodynamic and geomorphic features. Earth Surface Processes and Landforms, 34(2), 242–252.CrossRefGoogle Scholar
  21. Hilgersom, K. P., & Luxemburg, W. M. J. (2012). Technical note: how image processing facilitates the rising bubble technique for discharge measurement. Hydrology and Earth System Sciences, 16, 345–356.CrossRefGoogle Scholar
  22. Hrachowitz, M., Savenije, H. H. G., Bogaard, T., Tetzlaff, D., & Soulsby, C. (2013a). What can flux tracking teach us about water age distribution patterns and their temporal dynamics? Hydrology and Earth System Sciences, 17, 533–564.Google Scholar
  23. Hrachowitz, M., Savenije, H. H. G., Blöschl, G., McDonnell, J. J., Sivapalan, M., Pomeroy, J. W., Arheimer, B., Blume, T., Clark, M. P., Ehret, U., Fenicia, F., Freer, J. E., Gelfan, A., Gupta, H. V., Hughes, D. A., Hut, R. W., Montanari, A., Pande, S., Tetzlaff, D., Troch, P. A., Uhlenbrook, S., Wagener, T., Winsemius, H. C., Woods, R. A., Zehe, E., & Cudennec, C. (2013b). A decade of predictions in ungauged basins (PUB)—a review. Hydrological Sciences Journal, 58(6), 1198–1255.Google Scholar
  24. Jodeau, M., Hauet, A., Paquier, A., Le Coz, J., & Dramais, G. (2008). Application and evaluation of LS-PIV technique for the monitoring of river surface velocities in high flow conditions. Flow Measurement and Instrumentation, 19(2), 117–127.CrossRefGoogle Scholar
  25. Kantoush, S. A., Schleiss, A. J., Sumi, T., & Murasaki, M. (2011). LSPIV implementation for environmental flow in various laboratory and field cases. Journal of Hydro-environment Research, 5(4), 263–276.CrossRefGoogle Scholar
  26. Kim, Y. (2006), Uncertainty analysis for non-intrusive measurement of river discharge using image velocimetry, Ph.D. thesis, Graduate College of the University of Iowa.Google Scholar
  27. Kreibich, H., Piroth, K., Seifert, I., Maiwald, H., Kunert, U., Schwarz, J., Merz, B., & Thieken, A. H. (2009). Is flow velocity a significant parameter in flood damage modelling? Natural Hazards and Earth System Sciences, 9(5), 1679–1692.CrossRefGoogle Scholar
  28. LeCoz, J., Hauet, A., Pierrefeu, G., Dramais, G., & Camenen, B. (2010). Performance of image-based velocimetry LSPIV applied to flash-flood discharge measurements in mediterranean rivers. Journal of Hydrology, 394(1–2), 42–52.CrossRefGoogle Scholar
  29. Leibundgut, C., Maloszewski, P., & Külls, C. (2009). Tracers in Hydrology. Oxford: Wiley-Blackwell.CrossRefGoogle Scholar
  30. Manoj, K. C., & Fang, X. (2015). Estimating time parameters of overland flow on impervious surfaces by the particle tracking method. Hydrological Sciences Journal, 60(2), 294–310.CrossRefGoogle Scholar
  31. Manzo, M., Ioppolo, T., Ayaz, U. K., LaPenna, V., & Ötügen, M. V. (2012). A photonic wall pressure sensor for fluid mechanics applications. Review of Scientific Instruments, 83, 105003.CrossRefGoogle Scholar
  32. McMillan, H., Freer, J., Pappenberger, F., Krueger, T., & Clark, M. (2010). Impacts of uncertain river flow data on rainfall-runoff model calibration and discharge predictions. Hydrological Processes, 24(10), 1270–1284.Google Scholar
  33. Montanari, A., Young, G., Savenije, H. H. G., Hughes, D., Wagener, T., Ren, L. L., Koutsoyiannis, D., Cudennec, C., Toth, E., Grimaldi, S., Blöschl, G., Sivapalan, M., Beven, K., Gupta, H., Hipsey, M., Schaefli, B., Arheimer, B., Boegh, E., Schymanski, S. J., Di Baldassarre, G., Yu, B., Hubert, P., Huang, Y., Schumann, A., Post, D. A., Srinivasan, V., Harman, C., Thompson, S., Rogger, M., Viglione, A., McMillan, H., Characklis, G., Pang, Z., & Belyaev, V. (2013). Panta Rhei—everything flows: change in hydrology and society—The IAHS scientific decade 2013-2022. Hydrological Sciences Journal, 58(6), 1256–1275.CrossRefGoogle Scholar
  34. Muste, M., Fujita, I., & Hauet, A. (2008). Large-scale particle image velocimetry for measurements in riverine environments. Water Resources Research, 44(4), W00D19.CrossRefGoogle Scholar
  35. Planchon, O., Silvera, N., Gimenez, R., Favis-Mortlock, D., Wainwright, J., Le Bissonnais, Y., & Govers, G. (2005). An automated salt-tracing gauge for flow-velocity measurement. Earth Surface Processes and Landforms, 30(7), 833–844.CrossRefGoogle Scholar
  36. Quénot, G. M., Pakleza, J., & Kowalewski, T. A. (1998). Particle image velocimetry with optical flow. Experiments in Fluids, 25, 177–189.CrossRefGoogle Scholar
  37. Raffel, M., Willert, C. E., Wereley, S. T., & Kompenhans, J. (2007). Particle image velocimetry. A practical guide. New York: Springer.Google Scholar
  38. Centro Funzionale Regionale – Regione Lazio (2015),
  39. Sassi, M. G., Hoitink, A. J. F., Vermeulen, B., and Hidayat (2011), Discharge estimation from H-ADCP measurements in a tidal river subject to sidewall effects and a mobile bed, Water Resources Research, 47(6), W06504.Google Scholar
  40. Tang, H.-W., Chen, C., Chen, H., & Huang, J.-T. (2008). An improved PTV system for large-scale physical river model, Journal of Hydrodynamics, Ser. Journal of Hydrodynamics, 20(6), 669–678.CrossRefGoogle Scholar
  41. Tarpanelli, A., Barbetta, S., Brocca, L., & Moramarco, T. (2013). River discharge estimation by using altimetry data and simplified flood routing modeling. Remote Sensing, 5(9), 4145–4162.CrossRefGoogle Scholar
  42. Tauro, F., Grimaldi, S., Petroselli, A., & Porfiri, M. (2012). Fluorescent particle tracers in surface hydrology: a proof of concept in a natural stream. Water Resources Research, 48(6), W06528.CrossRefGoogle Scholar
  43. Tauro, F., Porfiri, M., & Grimaldi, S. (2013a). Fluorescent eco-particles for surface flow physics analysis. AIP Advances, 3(3), 032108.Google Scholar
  44. Tauro, F., Rapiti, E., Al-Sharab, J. F., Ubertini, L., Grimaldi, P., & Porfiri, M. (2013b). Characterization of eco-friendly fluorescent nanoparticle doped-tracers for environmental sensing. Journal of Nanoparticle Research, 15(9), 1884.Google Scholar
  45. Tauro, F., Porfiri, M., & Grimaldi, S. (2014a). Orienting the camera and firing lasers to enhance large scale particle image velocimetry for stream flow monitoring. Water Resources Research, 50(9), 7470–7483.Google Scholar
  46. Tauro, F., Porfiri, M., & Grimaldi, S. (2014b). Unraveling flow patterns through nonlinear manifold learning. PLoS One, 9(3), e91131.Google Scholar
  47. Tauro, F., A. Petroselli, and E. Arcangeletti (2015a), Assessment of drone-based surface flow observations, Hydrological Processes,  10.1002/hyp.10698
  48. Tauro, F., Pagano, C., Phamduy, P., Grimaldi, S., & Porfiri, M. (2015b). Large-scale particle image velocimetry from an unmanned aerial vehicle. IEEE/ASME Transactions on Mechatronics, 20(6), 3269–3275.Google Scholar
  49. Tazioli, A. (2011). Experimental methods for river discharge measurements: comparison among tracers and current meter [Méthodes expérimentales pour mesurer le débit des cours d’eau: Comparaison entre les traceurs artificiels et le courantomètre]. Hydrol Sci J, 56(7), 1314–1324.CrossRefGoogle Scholar
  50. Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4), 600–612.CrossRefGoogle Scholar
  51. Yorke, T. H., & Oberg, K. A. (2002). Measuring river velocity and discharge with acoustic Doppler profilers. Flow Measurement and Instrumentation, 13(5–6), 191–195.CrossRefGoogle Scholar
  52. Zeng, J., Constantinescu, G., Blanckaert, K., & Weber, L. (2008). Flow and bathymetry in sharp open-channel bends: experiments and predictions. Water Resources Research, 44(9), W09401.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • F. Tauro
    • 1
  • G. Olivieri
    • 1
  • A. Petroselli
    • 2
  • M. Porfiri
    • 3
  • S. Grimaldi
    • 1
    • 3
  1. 1.Dipartimento per l’Innovazione nei Sistemi Biologici, Agroalimentari e ForestaliUniversity of TusciaViterboItaly
  2. 2.Dipartimento di Scienze Agrarie e ForestaliUniversity of TusciaViterboItaly
  3. 3.Department of Mechanical and Aerospace EngineeringNew York University Tandon School of EngineeringBrooklynUSA

Personalised recommendations