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Tytuł artykułu

Wavelet Characteristics of Hydrological and Dissolved Oxygen Time Series in a Lowland River

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this study, we investigated the temporal variability of dissolved oxygen and water temperature in conjunction with water level fluctuations and river discharge in the Narew lowland river reach. For this purpose, high resolution hydrologic and water quality time series have been used. Spectral analyses of time series using continuous wavelet transform scheme have been applied in order to identify characteristic scales, its duration, and localisation in time. The results of wavelet analysis have shown a great number of periodicities in time series at the inter-annual time scale when compared to the classical Fourier analysis. Additionally, wavelet coherence revealed the complex nature of the relationship between dissolved oxygen and hydrological variables dependent on the scale and localisation in time. Hence, the results presented in this paper may provide an alternative representation to a frequency analysis of time series.
Czasopismo
Rocznik
Strony
649--669
Opis fizyczny
Bibliogr. 35 poz.
Twórcy
  • Institute of Geophysics, Polish Academy of Sciences, Warszawa, Poland
autor
  • Institute of Geophysics, Polish Academy of Sciences, Warszawa, Poland
  • Institute of Biochemistry and Biophysics, Polish Academy of Sciences, Warszawa, Poland
  • Institute of Geophysics, Polish Academy of Sciences, Warszawa, Poland
Bibliografia
  • Carey, S.K., D. Tetzlaff, J. Buttle, H. Laudon, J. McDonnell, K. McGuire, J. Seibert, C. Soulsby, and J. Shanley (2013), Use of color maps and wavelet coherence to discern seasonal and interannual climate influences on streamflow variability in northern catchments, Water Resour. Res. 49, 10, 6194-6207, DOI: 10.1002/wrcr.20469.
  • Cazelles, B., M. Chavez, D. Bertaux, F. Ménard, J.O. Vik, S. Jenouvrier, and N.C. Stenseth (2008), Wavelet analysis of ecological time series, Oecologia 156, 2, 287-304, DOI: 10.1007/s00442-008-0993-2.
  • Chakraborty, A., and D. Okaya (1995), Frequency-time decomposition of seismic data using wavelet-based methods, Geophysics 60, 6, 1906-1916, DOI: 10.1190/1.1443922.
  • Cheng, B., T. Xu, B. Robbins, and Z. Shen (2015), Reef reservoir identification by wavelet decomposition and reconstruction: A case study from Yuanba gas field in China, Acta Geophys. 63, 4, 1025-1043, DOI: 10.1515/acgeo-2015- 0028.
  • Coulibaly, P., and D.H. Burn (2004), Wavelet analysis of variability in annual Canadian streamflows, Water Resour. Res. 40, 3, W03105, DOI: 10.1029/ 2003WR002667.
  • Daubechies, I. (1990), The wavelet transform, time-frequency localization and signal analysis, IEEE Trans. Inform. Theory 36, 5, 961-1005, DOI: 10.1109/ 18.57199.
  • Demars, B.O.L., J.R. Manson, J.S. Ólafsson, G.M. Gíslason, R. Gudmundsdóttir, G. Woodward, J. Reiss, D.E. Pichler, J.J. Rasmussen, and N. Friberg (2011), Temperature and metabolic balance of streams, Freshw. Biol. 56, 6 1106-1121, DOI: 10.1111/j.1365-2427.2010.02554.x.
  • Farge, M. (1992), Wavelet transforms and their applications to turbulence, Ann. Rev. Fluid Mech. 24, 395-458, DOI: 10.1146/annurev.fl.24.010192.002143.
  • Farge, M., K. Schneider, O. Pannekoucke, and R. Nguyen van yen (2013), Multiscale representations: fractals, self-similar random processes and wavelets, In: H.J.S. Fernando (ed.), Handbook of Environmental Fluid Dynamics, Vol. 2, CRC Press/Taylor & Francis Group, Boca Raton, 311-333.
  • Grinsted, A., J.C. Moore, and S. Jevrejeva (2004), Application of the cross wavelet transform and wavelet coherence to geophysical time series, Nonlin. Process. Geophys. 11, 5/6, 561-566, DOI: 10.5194/npg-11-561-2004.
  • Kalinowska, M.B., and P.M. Rowiński (2015), Thermal pollution in rivers – modelling of the spread of thermal plumes. In: P. Rowiński and A. RadeckiPawlik (eds.), Rivers – Physical, Fluvial and Environmental Processes, GeoPlanet: Earth and Planetary Sciences, Springer Int. Publ., 591-613, DOI: 10.1007/978-3-319-17719-9_24.
  • Kanani, A., and A.M. Ferreira da Silva (2015), Application of continuous wavelet transform to the study of large-scale coherent structures, Environ. Fluid Mech. 15, 6, 1293-1319, DOI: 10.1007/s10652-015-9428-x.
  • Kang, S., and H. Lin (2007), Wavelet analysis of hydrological and water quality signals in an agricultural watershed, J. Hydrol. 338, 1-2, 1-14, DOI: 10.1016/ j.jhydrol.2007.01.047.
  • Keinert, F. (2004), Wavelets and Multiwavelets, Chapman & Hall/CRC Press, Boca Raton, London.
  • Kirchner, J.W., and C. Neal (2013), Universal fractal scaling in stream chemistry and its implications for solute transport and water quality trend detection, Proc. Natl. Acad. Sci. USA 110, 30, 12213-12218, DOI: 10.1073/pnas. 1304328110.
  • Kirchner, J.W., X. Feng, C. Neal, and A.J. Robson (2004), The fine structure of water-quality dynamics: the (high-frequency) wave of the future, Hydrol. Process. 18, 7, 1353-1359, DOI: 10.1002/hyp.5537.
  • Kumar, P., and E. Foufoula-Georgiou (1997), Wavelet analysis for geophysical applications, Rev. Geophys. 35, 4, 385-412, DOI: 10.1029/97RG00427. Labat, D. (2008), Wavelet analysis of the annual discharge records of the world’s largest rivers, Adv. Water Resour. 31, 1, 109-117, DOI: 10.1016/ j.advwatres.2007.07.004.
  • Lafrenière, M., and M. Sharp (2003), Wavelet analysis of inter-annual variability in the runoff regimes of glacial and nival stream catchments, Bow Lake, Alberta, Hydrol. Process. 17, 6, 1093-1118, DOI: 10.1002/hyp.1187.
  • Lau, K.M., and H. Weng (1995), Climate signal detection using wavelet transform: How to make a time series sing, Bull. Am. Meteorol. Soc. 76, 12, 2391- 2402, DOI: 10.1175/1520-0477(1995)076 2.0.CO;2.
  • Maraun, D., and J. Kurths (2004), Cross wavelet analysis: significance testing and pitfalls, Nonlin. Process. Geophys. 11, 4, 505-514, DOI: 10.5194/npg-11- 505-2004.
  • Marion, A., V. Nikora, S. Puijalon, T. Bouma, K. Koll, F. Ballio, S. Tait, M. Zaramella, A. Sukhodolov, M. O’Hare, G. Wharton, J. Aberle, M. Tregnaghi, P. Davies, H. Nepf, G. Parker, and B. Statzner (2014), Aquatic interfaces: a hydrodynamic and ecological perspective, J. Hydraul. Res. 52, 6, 744-758, DOI: 10.1080/00221686.2014.968887.
  • Percival, D.B., and A.T. Walden (2000), Wavelet Methods for Time Series Analysis, Cambridge Series in Statistical and Probabilistic Mathematics, Cambridge University Press, Cambridge, DOI: 10.1017/CBO9780511841040.
  • Rajwa-Kuligiewicz, A., R.J. Bialik, and P.M. Rowiński (2015), Dissolved oxygen and water temperaturę dynamics in lowland rivers over various timescales, J. Hydrol. Hydromech. 63, 4, 353-363, DOI: 10.1515/johh-2015-0041.
  • Rajwa-Kuligiewicz, A., R.J. Bialik, and P.M. Rowiński (2016), Spatio-temporal variability of water temperature in an anastomosing section of the Narew river. In: Proc. 11th Int. Symp. Ecohydraulics, 7-12 February 2016, Melbourne, Australia, Paper 26204.
  • Saco, P., and P. Kumar (2000), Coherent modes in multiscale variability of streamflow over the United States, Water Resour. Res. 36, 4, 1049-1067, DOI: 10.1029/1999WR900345.
  • Scanlon, T.M., and J.D. Albertson (2001), Turbulent transport of carbon dioxide and water vapor within a vegetation canopy during unstable conditions: Identification of episodes using wavelet analysis, J. Geophys. Res. 106, D7, 7251-7262, DOI: 10.1029/2000JD900662.
  • Schaefli, B., D. Maraun, and M. Holschneider (2007), What drives high flow events in the Swiss Alps? Recent developments in wavelet spectral analysis and their application to hydrology, Adv. Water Resour. 30, 12, 2511-2525, DOI: 10.1016/j.advwatres.2007.06.004.
  • Smith, L.C., D.L. Turcotte, and B.L. Isacks (1998), Stream flow characterization and feature detection using a discrete wavelet transform, Hydrol. Process. 12, 2, 233-249, DOI: 10.1002/(SICI)1099-1085(199802)12:23.0.CO;2-3.
  • Szolgayová, E., J. Arlt, G. Blöschl, and J. Szolgay (2014), Wavelet based deseasonalization for modelling and forecasting of daily discharge series considering long range dependence, J. Hydrol. Hydromech. 62, 1, 24-32, DOI: 10.2478/ johh-2014-0011.
  • Torrence, C., and G.P. Compo (1998), A practical guide to wavelet analysis, Bull. Am. Meteorol. Soc. 79, 1, 61-78, DOI: 10.1175/1520-0477(1998)079 2.0.CO;2.
  • Torrence, C., and P.J. Webster (1999), Interdecadal changes in the ENSO-monsoon system, J. Climatol. 12, 8, 2679-2690, DOI: 10.1175/1520-0442(1990) 0122.0.CO;2.
  • Venugopal, V., S.G. Roux, E. Foufoula-Georgiou, and A. Arneodo (2006), Revisiting multifractality of high-resolution temporal rainfall using a waveletbased formalism, Water Resour. Res. 42, 6, W06D14, DOI: 10.1029/ 2005WR004489.
  • Zamani, A., A.P. Kolahi Azar, and A.A. Safavi (2014), Wavelet-based multifractal analysis of earthquakes temporal distribution in Mammoth Mountain volcano, Mono County, Eastern California, Acta Geophys. 62, 3, 585-607, DOI: 10.2478/s11600-013-0184-3.
  • Zolezzi, G., A. Bellin, M.C. Bruno, B. Maiolini, and A. Siviglia (2009), Assessing hydrological alternations at multiple temporal scales: Adige River, Italy, Water Resour. Res. 45, 12, W12421, DOI: 10.1029/2008WR007266.
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-60e8cd57-2025-417c-87c8-41fd7d95edda
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