Empirical determination of the average annual runoff coefficient in the Mediterranean area

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Abstract

Runoff estimation in ungauged basin is a challenge for the hydrological engineers and planners. For any hydrological study on an ungauged basin, a methodology has to be appropriately selected for the determination of runoff at its outlet. Several methods have been used to estimate the basin runoff production. In this study the empirical Kennessey method to determine average annual runoff coefficient, RC, is tested on 61 Sicilian basins characterized by different climate conditions, surface permeability, mean slope and vegetation cover. A comparison between observed and calculated RC showed that a calibration of the Kennessey model could be necessary. The slight and not satisfying improvement of the calibrated model suggested that the main factors accounted for the Kennessey method could not be enough to describe mean runoff production. So the analysis has been focused on researching empirical relations between RC and other variables which could play a significant role on RC estimation. Finally, the best result on RC estimate was obtained by a simple linear regression for two Sicilian sub-zones, by considering only two main climatic parameters, average annual rainfall depth and average annual temperature.
Lingua originaleEnglish
pagine (da-a)89-95
Numero di pagine7
RivistaAmerican Journal of Applied Sciences
Volume11
Stato di pubblicazionePublished - 2014

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runoff
basin
climate conditions
vegetation cover
permeability
calibration
rainfall
methodology
Mediterranean Area
method
temperature
analysis
parameter
comparison

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title = "Empirical determination of the average annual runoff coefficient in the Mediterranean area",
abstract = "Runoff estimation in ungauged basin is a challenge for the hydrological engineers and planners. For any hydrological study on an ungauged basin, a methodology has to be appropriately selected for the determination of runoff at its outlet. Several methods have been used to estimate the basin runoff production. In this study the empirical Kennessey method to determine average annual runoff coefficient, RC, is tested on 61 Sicilian basins characterized by different climate conditions, surface permeability, mean slope and vegetation cover. A comparison between observed and calculated RC showed that a calibration of the Kennessey model could be necessary. The slight and not satisfying improvement of the calibrated model suggested that the main factors accounted for the Kennessey method could not be enough to describe mean runoff production. So the analysis has been focused on researching empirical relations between RC and other variables which could play a significant role on RC estimation. Finally, the best result on RC estimate was obtained by a simple linear regression for two Sicilian sub-zones, by considering only two main climatic parameters, average annual rainfall depth and average annual temperature.",
keywords = "Runoff Coefficient; Kennessey Model; Empirical Models",
author = "Giorgio Baiamonte and Francesco D'Asaro and Giovanni Grillone",
year = "2014",
language = "English",
volume = "11",
pages = "89--95",
journal = "American Journal of Applied Sciences",
issn = "1546-9239",
publisher = "Science Publications",

}

TY - JOUR

T1 - Empirical determination of the average annual runoff coefficient in the Mediterranean area

AU - Baiamonte, Giorgio

AU - D'Asaro, Francesco

AU - Grillone, Giovanni

PY - 2014

Y1 - 2014

N2 - Runoff estimation in ungauged basin is a challenge for the hydrological engineers and planners. For any hydrological study on an ungauged basin, a methodology has to be appropriately selected for the determination of runoff at its outlet. Several methods have been used to estimate the basin runoff production. In this study the empirical Kennessey method to determine average annual runoff coefficient, RC, is tested on 61 Sicilian basins characterized by different climate conditions, surface permeability, mean slope and vegetation cover. A comparison between observed and calculated RC showed that a calibration of the Kennessey model could be necessary. The slight and not satisfying improvement of the calibrated model suggested that the main factors accounted for the Kennessey method could not be enough to describe mean runoff production. So the analysis has been focused on researching empirical relations between RC and other variables which could play a significant role on RC estimation. Finally, the best result on RC estimate was obtained by a simple linear regression for two Sicilian sub-zones, by considering only two main climatic parameters, average annual rainfall depth and average annual temperature.

AB - Runoff estimation in ungauged basin is a challenge for the hydrological engineers and planners. For any hydrological study on an ungauged basin, a methodology has to be appropriately selected for the determination of runoff at its outlet. Several methods have been used to estimate the basin runoff production. In this study the empirical Kennessey method to determine average annual runoff coefficient, RC, is tested on 61 Sicilian basins characterized by different climate conditions, surface permeability, mean slope and vegetation cover. A comparison between observed and calculated RC showed that a calibration of the Kennessey model could be necessary. The slight and not satisfying improvement of the calibrated model suggested that the main factors accounted for the Kennessey method could not be enough to describe mean runoff production. So the analysis has been focused on researching empirical relations between RC and other variables which could play a significant role on RC estimation. Finally, the best result on RC estimate was obtained by a simple linear regression for two Sicilian sub-zones, by considering only two main climatic parameters, average annual rainfall depth and average annual temperature.

KW - Runoff Coefficient; Kennessey Model; Empirical Models

UR - http://hdl.handle.net/10447/97415

M3 - Article

VL - 11

SP - 89

EP - 95

JO - American Journal of Applied Sciences

JF - American Journal of Applied Sciences

SN - 1546-9239

ER -