<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Lagtimes and times of concentration are frequently determined parameters in hydrological design and greatly aid in understanding natural watershed dynamics. In unmonitored catchments, they are usually calculated using empirical or semiempirical equations developed in other studies, without critically considering where those equations were obtained and what basic assumptions they entailed. In this study, we determined the lagtimes (LT) between the middle point of rainfall events and the discharge peaks in a watershed characterized by volcanic soils and swamp forests in southern Chile. Our results were compared with calculations from 24 equations found in the literature. The mean LT for 100 episodes was 20 hours (ranging between 0.6–58.5 hours). Most formulae that only included physiographic predictors severely underestimated the mean LT, while those including the rainfall intensity or stream velocity showed better agreement with the average value. The duration of the rainfall events related significantly and positively with LTs. Thus, we accounted for varying LTs within the same watershed by including the rainfall duration in the equations that showed the best results, consequently improving our predictions. Izzard and velocity methods are recommended, and we suggest that lagtimes and times of concentration must be locally determined with hyetograph-hydrograph analyses, in addition to explicitly considering precipitation patterns.</p></abstract>

Lagtimes and times of concentration are frequently determined parameters in hydrological design and greatly aid in understanding natural watershed dynamics. In unmonitored catchments, they are usually calculated using empirical or semiempirical equations developed in other studies, without critically considering where those equations were obtained and what basic assumptions they entailed. In this study, we determined the lagtimes (LT) between the middle point of rainfall events and the discharge peaks in a watershed characterized by volcanic soils and swamp forests in southern Chile. Our results were compared with calculations from 24 equations found in the literature. The mean LT for 100 episodes was 20 hours (ranging between 0.6–58.5 hours). Most formulae that only included physiographic predictors severely underestimated the mean LT, while those including the rainfall intensity or stream velocity showed better agreement with the average value. The duration of the rainfall events related significantly and positively with LTs. Thus, we accounted for varying LTs within the same watershed by including the rainfall duration in the equations that showed the best results, consequently improving our predictions. Izzard and velocity methods are recommended, and we suggest that lagtimes and times of concentration must be locally determined with hyetograph-hydrograph analyses, in addition to explicitly considering precipitation patterns.

Assessing methods for the estimation of response times of stream discharge: the role of rainfall duration

Departamento de Recursos Hídricos, Centro CRHIAM

Journal of Hydrology and Hydromechanics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Lagtimes and times of concentration are frequently determined parameters in hydrological design and greatly aid in understanding natural watershed dynamics. In...

Assessing methods for the estimation of response times of stream discharge: the role of rainfall duration

Pubblicato online: 28 mar 2019

Pagine: 143 - 153

Ricevuto: 15 nov 2017

Accettato: 23 mag 2018

DOI: https://doi.org/10.2478/johh-2018-0043

Parole chiave
Empirical equations, Lagtimes, Semiempirical equations, Streamflow, Times of concentration

© 2019 Jaime G. Cuevas et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Assessing methods for the estimation of response times of stream discharge: the role of rainfall duration

Pubblicato online: 28 mar 2019

Pagine: 143 - 153

Ricevuto: 15 nov 2017

Accettato: 23 mag 2018

DOI: https://doi.org/10.2478/johh-2018-0043

Parole chiaveEmpirical equations, Lagtimes, Semiempirical equations, Streamflow, Times of concentration

© 2019 Jaime G. Cuevas et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Parole chiave
Empirical equations, Lagtimes, Semiempirical equations, Streamflow, Times of concentration