<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></abstract>

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.

The Wavelets show it – the transit time of water varies in time

Journal of Hydrology and Hydromechanics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"The Wavelets show it \u2013 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...

The Wavelets show it – the transit time of water varies in time

Published Online: Aug 14, 2018

Page range: 295 - 302

Received: May 19, 2017

Accepted: Sep 26, 2017

DOI: https://doi.org/10.2478/johh-2018-0001

Keywords
Transit time distribution, Tracer, Chloride, Continuous wavelet transform, Non-stationary

© 2018 Milan Onderka, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

The Wavelets show it – the transit time of water varies in time

Published Online: Aug 14, 2018

Page range: 295 - 302

Received: May 19, 2017

Accepted: Sep 26, 2017

DOI: https://doi.org/10.2478/johh-2018-0001

KeywordsTransit time distribution, Tracer, Chloride, Continuous wavelet transform, Non-stationary

© 2018 Milan Onderka, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Keywords
Transit time distribution, Tracer, Chloride, Continuous wavelet transform, Non-stationary