The Wavelets show it – the transit time of water varies in time
The ways how water from rain or melting snow flows over and beneath the Earth‘s surface affects the timing and intensity at which the same water leaves a catchment. Several mathematical techniques have been proposed to quantify the transit times of water by e.g. convolving the input-output tracer si...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Sciendo
2018-09-01
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| Series: | Journal of Hydrology and Hydromechanics |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/johh-2018-0001 |
| _version_ | 1811260822062628864 |
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| author | Onderka Milan Chudoba Vladimír |
| author_facet | Onderka Milan Chudoba Vladimír |
| author_sort | Onderka Milan |
| collection | DOAJ |
| description | The ways how water from rain or melting snow flows over and beneath the Earth‘s surface affects the timing and intensity at which the same water leaves a catchment. Several mathematical techniques have been proposed to quantify the transit times of water by e.g. convolving the input-output tracer signals, or constructing frequency response functions. The primary assumption of these techniques is that the transit time is regarded time-invariant, i.e. it does not vary with temporarily changing e.g. soil saturation, evaporation, storage volume, climate or land use. This raises questions about how the variability of water transit time can be detected, visualized and analyzed. In this paper we present a case study to show that the transit time is a temporarily dynamic variable. Using a real-world example from the Lower Hafren catchment, Wales, UK, and applying the Continuous Wavelet Transform we show that the transit time distributions are time-variant and change with streamflow. We define the Instantaneous Transit Time Distributions as a basis for the Master Transit Time Distribution. We show that during periods of elevated runoff the transit times are exponentially distributed. A bell-shaped distribution of travel times was observed during times of lower runoff. This finding is consistent with previous investigations based on mechanistic and conceptual modeling in the study area according to which the diversity of water flow-paths during wet periods is attributable to contributing areas that shrink and expand depending on the duration of rainfall. The presented approach makes no assumptions about the shape of the transit time distribution. The mean travel time estimated from the Master Transit Time Distribution was ~54.3 weeks. |
| first_indexed | 2024-04-12T18:53:23Z |
| format | Article |
| id | doaj.art-e02c705d260443ad98d27dcdf5c8b10e |
| institution | Directory Open Access Journal |
| issn | 0042-790X |
| language | English |
| last_indexed | 2024-04-12T18:53:23Z |
| publishDate | 2018-09-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Journal of Hydrology and Hydromechanics |
| spelling | doaj.art-e02c705d260443ad98d27dcdf5c8b10e2022-12-22T03:20:25ZengSciendoJournal of Hydrology and Hydromechanics0042-790X2018-09-0166329530210.2478/johh-2018-0001johh-2018-0001The Wavelets show it – the transit time of water varies in timeOnderka Milan0Chudoba Vladimír1Comenius University, Faculty of Mathematics, Physics and Informatics, Department of Astronomy, Physics of the Earth and Meteorology, Mlynská dolina,Bratislava, SlovakiaComenius University, Faculty of Mathematics, Physics and Informatics, Department of Astronomy, Physics of the Earth and Meteorology, Mlynská dolina,Bratislava, SlovakiaThe ways how water from rain or melting snow flows over and beneath the Earth‘s surface affects the timing and intensity at which the same water leaves a catchment. Several mathematical techniques have been proposed to quantify the transit times of water by e.g. convolving the input-output tracer signals, or constructing frequency response functions. The primary assumption of these techniques is that the transit time is regarded time-invariant, i.e. it does not vary with temporarily changing e.g. soil saturation, evaporation, storage volume, climate or land use. This raises questions about how the variability of water transit time can be detected, visualized and analyzed. In this paper we present a case study to show that the transit time is a temporarily dynamic variable. Using a real-world example from the Lower Hafren catchment, Wales, UK, and applying the Continuous Wavelet Transform we show that the transit time distributions are time-variant and change with streamflow. We define the Instantaneous Transit Time Distributions as a basis for the Master Transit Time Distribution. We show that during periods of elevated runoff the transit times are exponentially distributed. A bell-shaped distribution of travel times was observed during times of lower runoff. This finding is consistent with previous investigations based on mechanistic and conceptual modeling in the study area according to which the diversity of water flow-paths during wet periods is attributable to contributing areas that shrink and expand depending on the duration of rainfall. The presented approach makes no assumptions about the shape of the transit time distribution. The mean travel time estimated from the Master Transit Time Distribution was ~54.3 weeks.https://doi.org/10.2478/johh-2018-0001transit time distributiontracerchloridecontinuous wavelet transformnon-stationary |
| spellingShingle | Onderka Milan Chudoba Vladimír The Wavelets show it – the transit time of water varies in time Journal of Hydrology and Hydromechanics transit time distribution tracer chloride continuous wavelet transform non-stationary |
| title | The Wavelets show it – the transit time of water varies in time |
| title_full | The Wavelets show it – the transit time of water varies in time |
| title_fullStr | The Wavelets show it – the transit time of water varies in time |
| title_full_unstemmed | The Wavelets show it – the transit time of water varies in time |
| title_short | The Wavelets show it – the transit time of water varies in time |
| title_sort | wavelets show it the transit time of water varies in time |
| topic | transit time distribution tracer chloride continuous wavelet transform non-stationary |
| url | https://doi.org/10.2478/johh-2018-0001 |
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