Articles | Volume 24, issue 11
https://doi.org/10.5194/hess-24-5173-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-24-5173-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Nov 2020
Research article |
| 09 Nov 2020
| 09 Nov 2020
Identifying the optimal spatial distribution of tracers for optical sensing of stream surface flow
Department of European and Mediterranean Cultures, University of
Basilicata, Matera, 75100, Italy
Silvano F. Dal Sasso
Department of European and Mediterranean Cultures, University of
Basilicata, Matera, 75100, Italy
Matthew T. Perks
School of Geography, Politics and Sociology, Newcastle University,
Newcastle-upon-Tyne, NE1 7RU, UK
Salvatore Manfreda
Department of Civil, Architectural and Environmental Engineering,
University of Naples Federico II, Naples, 80125, Italy
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Subject: Engineering Hydrology | Techniques and Approaches: Instruments and observation techniques
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Ravi Kumar Meena, Sumit Sen, Aliva Nanda, Bhargabnanda Dass, and Anurag Mishra
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Cited articles
Adrian, R.: Particle-Imaging Techniques For Experimental Fluid-Mechanics, Annu. Rev. Fluid Mech., 23, 261–304, https://doi.org/10.1146/annurev.fluid.23.1.261, 1991.
Adrian, R. J.: Twenty years of particle image velocimetry, Exp. Fluids, 39, 159–169, 2005.
Anderson, K. E., Paul, A. J., McCauley, E., Jackson, L. J., Post, J. R., and
Nisbet, R. M.: Instream flow needs in streams and rivers: The importance of
understanding ecological dynamics, Front. Ecol. Environ., 4, 309–318,
https://doi.org/10.1890/1540-9295(2006)4[309:IFNISA]2.0.CO;2, 2006.
Batalla, R. J. and Vericat, D.: Hydrological and sediment transport dynamics
of flushing flows: Implications for management in large Mediterranean rivers, River Res. Appl., 25, 297–314, https://doi.org/10.1002/rra.1160, 2009.
Bechle, A., Wu Chin, H., Liu, W.-C., and Kimura, N.: Development and Application of an Automated River-Estuary Discharge Imaging System, J.
Hydraul. Eng., 138, 327–339, https://doi.org/10.1061/(ASCE)HY.1943-7900.0000521, 2012.
Brevis, W., Niño, Y., and Jirka, G. H.: Integrating cross-correlation and
relaxation algorithms for particle tracking velocimetry, Exp. Fluids, 50,
135–147, 2011.
Buckingham, E.: On physically similar systems; Illustrations of the use of
dimensional equations, Phys. Rev., 4, 345–376, https://doi.org/10.1103/PhysRev.4.345, 1914.
Cardwell, N. D., Vlachos, P. P., and Thole, K. A.: A multi-parametric
particle-pairing algorithm for particle tracking in single and multiphase
flows, Meas. Sci. Technol., 22, 105406, https://doi.org/10.1088/0957-0233/22/10/105406, 2011.
Dal Sasso, S. F., Pizarro, A., Samela, C., Mita, L., and Manfreda, S.:
Exploring the optimal experimental setup for surface flow velocity
measurements using PTV, Environ. Monit. Assess., 190, 460, https://doi.org/10.1007/s10661-018-6848-3, 2018.
Dal Sasso, S. F., Pizarro, A., and Manfreda, S.: Metrics for the quantification of seeding characteristics to enhance image velocimetry
performance in rivers, Remote Sens., 12, 1789, https://doi.org/10.3390/rs12111789, 2020.
Detert, M., Johnson, E. D., and Weitbrecht, V.: Proof-of-concept for low-cost
and non-contact synoptic airborne river flow measurements, Int. J. Remote
Sens., 38, 2780–2807, https://doi.org/10.1080/01431161.2017.1294782, 2017.
Efron, B.: Double exponential families and their use in generalized linear
regression, J. Am. Stat. Assoc., 81, 709–721, https://doi.org/10.1080/01621459.1986.10478327, 1986.
Eltner, A., Sardemann, H., and Grundmann, J.: Technical Note: Flow velocity
and discharge measurement in rivers using terrestrial and unmanned-aerial-vehicle imagery, Hydrol. Earth Syst. Sci., 24, 1429–1445, https://doi.org/10.5194/hess-24-1429-2020, 2020.
Evans, J. H.: Dimensional Analysis and the Buckingham Pi Theorem, Am. J.
Phys., 40, 1815–1822, https://doi.org/10.1119/1.1987069, 1972.
Fujita, I., Muste, M., and Kruger, A.: Large-scale particle image velocimetry
for flow analysis in hydraulic engineering applications, J. Hydraul. Res., 36, 397–414, 1998.
Good, S. P., Rodriguez-Iturbe, I., and Caylor, K. K.: Analytical expressions
of variability in ecosystem structure and function obtained from threedimensional stochastic vegetation modelling, P. Roy. Soc. A, 469, 20130003, https://doi.org/10.1098/rspa.2013.0003, 2013.
Huang, W. C., Young, C. C., and Liu, W. C.: Application of an automated
discharge imaging system and LSPIV during typhoon events in Taiwan, Water, 10, 280, https://doi.org/10.3390/w10030280, 2018.
ISO 748: Measurement of Liquid Flow in Open Channel – Velocity-Area Methods, 1997.
Kinzel, P. J. and Legleiter, C. J.: sUAS-based remote sensing of river
discharge using thermal particle image velocimetry and bathymetric lidar,
Remote Sens., 11, 2317, https://doi.org/10.3390/rs11192317, 2019.
Le Coz, J., Hauet, A., Pierrefeu, G., Dramais, G., and Camenen, B.: Performance of image-based velocimetry (LSPIV) applied to flash-flood discharge measurements in Mediterranean rivers, J. Hydrol., 394, 42–52, 2010.
Leitão, J. P., Peña-Haro, S., Lüthi, B., Scheidegger, A., and Moy de Vitry, M.: Urban overland runoff velocity measurement with consumer-grade surveillance cameras and surface structure image velocimetry, J. Hydrol., 565, 791–804, https://doi.org/10.1016/j.jhydrol.2018.09.001, 2018.
Lloyd, P. M., Stansby, P. K., and Ball, D. J.: Unsteady surface-velocity field measurement using particle tracking velocimetry, J. Hydraul. Res., 33, 519–534, 1995.
Manfreda, S.: On the derivation of flow rating curves in data-scarce environments, J. Hydrol., 562, 151–154, https://doi.org/10.1016/j.jhydrol.2018.04.058, 2018.
Manfreda, S., Caylor, K. K., and Good, S. P.: An ecohydrological framework to
explain shifts in vegetation organization across climatological gradients,
Ecohydrology, 10, e1809, https://doi.org/10.1002/eco.1809, 2017.
Manfreda, S., Link, O., and Pizarro, A.: A theoretically derived probability
distribution of scour, Water, 10, 1520, https://doi.org/10.3390/w10111520, 2018a.
Manfreda, S., McCabe, M. F., Miller, P. E., Lucas, R., Madrigal, V. P.,
Mallinis, G., Dor, E. B., Helman, D., Estes, L., Ciraolo, G., Müllerová, J., Tauro, F., de Lima, M. I., de Lima, J. L. M. P.,
Maltese, A., Frances, F., Caylor, K., Kohv, M., Perks, M., Ruiz-Pérez, G., Su, Z., Vico, G., and Toth, B.: On the use of unmanned aerial systems for
environmental monitoring, Remote Sens., 10, 641, https://doi.org/10.3390/rs10040641, 2018b.
Manfreda, S., Pizarro, A., Moramarco, T., Cimorelli, L., Pianese, D., and
Barbetta, S.: Potential advantages of flow-area rating curves compared to
classic stage-discharge-relations, J. Hydrol., 585, 124752, https://doi.org/10.1016/j.jhydrol.2020.124752, 2020.
Melville, B. W. and Sutherland, A. J.: Design method for local scour at bridge piers, J. Hydraul. Eng., 114, 1210–1226, 1988.
Muste, M., Fujita, I., and Hauet, A.: Large-scale particle image velocimetry
for measurements in riverine environments, Water Resour. Res., 44, W00D19, https://doi.org/10.1029/2008WR006950, 2008.
Nobach, H., Damaschke, N., and Tropea, C.: High-precision sub-pixel
interpolation in particle image velocimetry image processing, Exp. Fluids,
39, 299–304, 2005.
Ohmi, K. and Li, H.-Y.: Particle-tracking velocimetry with new algorithms,
Meas. Sci. Technol., 11, 603–616, https://doi.org/10.1088/0957-0233/11/6/303, 2000.
Owe, M.: Long-term streamflow observations in relation to basin development,
J. Hydrol., 78, 243–260, 1985.
Pearce, S., Ljubičić, R., Peña-Haro, S., Perks, M., Tauro, F.,
Pizarro, A., Dal Sasso, S., Strelnikova, D., Grimaldi, S., Maddock, I., Paulus, G., Plavšić, J., Prodanović, D., and Manfreda, S.: An
Evaluation of Image Velocimetry Techniques under Low Flow Conditions and
High Seeding Densities Using Unmanned Aerial Systems, Remote Sens., 12, 232, https://doi.org/10.3390/rs12020232, 2020.
Perks, M., Sasso, S. F. D., Hauet, A., Le Coz, J., Pearce, S., Peña-Haro, S., Tauro, F., Grimaldi, S., Hortobágyi, B., Jodeau, M., Maddock, I., Pénard, L., and Manfreda, S.: Towards harmonization of image velocimetry techniques for river surface velocity observations, Earth Syst. Sci. Data, 12, 1545–1559, https://doi.org/10.5194/essd-12-1545-2020, 2020.
Perks, M. T., Russell, A. J., and Large, A. R. G.: Technical Note: Advances in flash flood monitoring using unmanned aerial vehicles (UAVs), Hydrol. Earth Syst. Sci., 20, 4005–4015, https://doi.org/10.5194/hess-20-4005-2016, 2016.
Peterson, S. D., Chuang, H. S., and Wereley, S. T.: Three-dimensional particle tracking using micro-particle image velocimetry hardware, Meas. Sci. Technol., 19, 115406, https://doi.org/10.1088/0957-0233/19/11/115406, 2008.
Pizarro, A., Samela, C., Fiorentino, M., Link, O., and Manfreda, S.: BRISENT:
An Entropy-Based Model for Bridge-Pier Scour Estimation under Complex Hydraulic Scenarios, Water, 9, 889, https://doi.org/10.3390/w9110889, 2017a.
Pizarro, A., Ettmer, B., Manfreda, S., Rojas, A., and Link, O.: Dimensionless
effective flow work for estimation of pier scour caused by flood waves, J.
Hydraul. Eng., 143, 06017006, https://doi.org/10.1061/(ASCE)HY.1943-7900.0001295, 2017b.
Pizarro, A., Dal Sasso, S. F., Perks, M. T., and Manfreda, S.: Data on spatial distribution of tracers for optical sensing of stream surface flow
(Version 0.1), [Dataset], Zenodo, https://doi.org/10.5281/zenodo.3761859, 2020a.
Pizarro, A., Dal Sasso, S. F., Perks, M. T., and Manfreda, S.: Identifying
the optimal spatial distribution of tracers for optical sensing of stream
surface flow (Version 0.1), [codes], OSF, https://doi.org/10.17605/OSF.IO/8EGQW, 2020b.
Raffel, M., Willert, C. E., Scarano, F., Kähler, C. J., Wereley, S. T.,
and Kompenhans, J.: Particle image velocimetry: a practical guide, Springer, Cham, Switzerland, 2018.
Strelnikova, D., Paulus, G., Käfer, S., Anders, K.-H., Mayr, P., Mader,
H., Scherling, U., and Schneeberger, R.: Drone-Based Optical Measurements of
Heterogeneous Surface Velocity Fields around Fish Passages at Hydropower Dams, Remote Sens., 12, 384, https://doi.org/10.3390/rs12030384, 2020.
Tauro, F. and Grimaldi, S.: Ice dices for monitoring stream surface velocity, J. Hydro-environ. Res., 14, 143–149, 2017.
Tauro, F. and Salvatori, S.: Surface flows from images: ten days of observations from the Tiber River gauge-cam station, Hydrol. Res., 48, 646–655, 2017.
Tauro, F., Porfiri, M., and Grimaldi, S.: Orienting the camera and firing
lasers to enhance large scale particle image velocimetry for streamflow
monitoring, Water Resour. Res., 50, 7470–7483, 2014.
Tauro, F., Pagano, C., Phamduy, P., Grimaldi, S., and Porfiri, M.: Large-scale particle image velocimetry from an unmanned aerial vehicle,
IEEE/ASME T. Mechatron., 20, 3269–3275, 2015.
Tauro, F., Petroselli, A., Porfiri, M., Giandomenico, L., Bernardi, G.,
Mele, F., Spina, D., and Grimaldi, S.: A novel permanent gauge-cam station
for surface-flow observations on the Tiber River, Geosci. Instrum. Meth. Data Syst., 5, 241–251, 2016.
Tauro, F., Piscopia, R., and Grimaldi, S.: Streamflow Observations From Cameras: Large-Scale Particle Image Velocimetry or Particle Tracking Velocimetry?, Water Resour. Res., 53, 10374–10394, 2017.
Tauro, F., Selker, J., Van De Giesen, N., Abrate, T., Uijlenhoet, R., Porfiri, M., Manfreda, S., Caylor, K., Moramarco, T., Benveniste, J., Ciraolo, G., Estes, L., Domeneghetti, A., Perks, M. T., Corbari, C., Rabiei,
E., Ravazzani, G., Bogena, H., Harfouche, A., Broccai, L., Maltese, A.,
Wickert, A., Tarpanelli, A., Good, S., Lopez Alcala, J. M., Petroselli, A.,
Cudennec, C., Blume, T., Hut, R., and Grimaldia, S.: Measurements and
observations in the XXI century (MOXXI): Innovation and multi-disciplinarity
to sense the hydrological cycle, Hydrolog. Sci. J., 63, 169–196, https://doi.org/10.1080/02626667.2017.1420191, 2018.
Tauro, F., Piscopia, R., and Grimaldi, S.: PTV-Stream: A simplified particle
tracking velocimetry framework for stream surface flow monitoring, Catena,
172, 378–386, https://doi.org/10.1016/j.catena.2018.09.009, 2019.
Thielicke, W. and Stamhuis, E. J.: PIVlab – Towards User-friendly, Affordable and Accurate Digital Particle Image Velocimetry in MATLAB, J. Open Res. Softw., 2, e30, https://doi.org/10.5334/jors.bl, 2014.
Wu, Q. X. and Pairman, D.: A relaxation labeling technique for computing sea
surface velocities from sea surface temperature, IEEE T. Geosci. Remote, 33, 216–220, 1995.
Short summary
An innovative approach is presented to optimise image-velocimetry performances for surface flow velocity estimates (and thus remotely sensed river discharges). Synthetic images were generated under different tracer characteristics using a numerical approach. Based on the results, the Seeding Distribution Index was introduced as a descriptor of the optimal portion of the video to analyse. A field case study was considered as a proof of concept of the proposed framework showing error reductions.
An innovative approach is presented to optimise image-velocimetry performances for surface flow...
