Streamflow simulation methods for ungauged and poorly gauged watersheds

Rainfall–runoff modelling procedures for ungauged and poorly gauged watersheds are developed in this study. A well-established hydrological model, the University of British Columbia (UBC) watershed model, is selected and applied in five different river basins located in Canada, Cyprus, and Pakistan....

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Main Authors: A. Loukas, L. Vasiliades
Format: Article
Language:English
Published: Copernicus Publications 2014-07-01
Series:Natural Hazards and Earth System Sciences
Online Access:http://www.nat-hazards-earth-syst-sci.net/14/1641/2014/nhess-14-1641-2014.pdf
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spelling doaj-9645ded702b1439ab38c51270d34a4502020-11-24T23:08:41ZengCopernicus PublicationsNatural Hazards and Earth System Sciences1561-86331684-99812014-07-011471641166110.5194/nhess-14-1641-2014Streamflow simulation methods for ungauged and poorly gauged watershedsA. Loukas0L. Vasiliades1Department of Civil Engineering, University of Thessaly, Pedion Areos, 38334 Volos, GreeceDepartment of Civil Engineering, University of Thessaly, Pedion Areos, 38334 Volos, GreeceRainfall–runoff modelling procedures for ungauged and poorly gauged watersheds are developed in this study. A well-established hydrological model, the University of British Columbia (UBC) watershed model, is selected and applied in five different river basins located in Canada, Cyprus, and Pakistan. Catchments from cold, temperate, continental, and semiarid climate zones are included to demonstrate the procedures developed. Two methodologies for streamflow modelling are proposed and analysed. The first method uses the UBC watershed model with a universal set of parameters for water allocation and flow routing, and precipitation gradients estimated from the available annual precipitation data as well as from regional information on the distribution of orographic precipitation. This method is proposed for watersheds without streamflow gauge data and limited meteorological station data. The second hybrid method proposes the coupling of UBC watershed model with artificial neural networks (ANNs) and is intended for use in poorly gauged watersheds which have limited streamflow measurements. The two proposed methods have been applied to five mountainous watersheds with largely varying climatic, physiographic, and hydrological characteristics. The evaluation of the applied methods is based on the combination of graphical results, statistical evaluation metrics, and normalized goodness-of-fit statistics. The results show that the first method satisfactorily simulates the observed hydrograph assuming that the basins are ungauged. When limited streamflow measurements are available, the coupling of ANNs with the regional, non-calibrated UBC flow model components is considered a successful alternative method to the conventional calibration of a hydrological model based on the evaluation criteria employed for streamflow modelling and flood frequency estimation.http://www.nat-hazards-earth-syst-sci.net/14/1641/2014/nhess-14-1641-2014.pdf
collection DOAJ
language English
format Article
sources DOAJ
author A. Loukas
L. Vasiliades
spellingShingle A. Loukas
L. Vasiliades
Streamflow simulation methods for ungauged and poorly gauged watersheds
Natural Hazards and Earth System Sciences
author_facet A. Loukas
L. Vasiliades
author_sort A. Loukas
title Streamflow simulation methods for ungauged and poorly gauged watersheds
title_short Streamflow simulation methods for ungauged and poorly gauged watersheds
title_full Streamflow simulation methods for ungauged and poorly gauged watersheds
title_fullStr Streamflow simulation methods for ungauged and poorly gauged watersheds
title_full_unstemmed Streamflow simulation methods for ungauged and poorly gauged watersheds
title_sort streamflow simulation methods for ungauged and poorly gauged watersheds
publisher Copernicus Publications
series Natural Hazards and Earth System Sciences
issn 1561-8633
1684-9981
publishDate 2014-07-01
description Rainfall–runoff modelling procedures for ungauged and poorly gauged watersheds are developed in this study. A well-established hydrological model, the University of British Columbia (UBC) watershed model, is selected and applied in five different river basins located in Canada, Cyprus, and Pakistan. Catchments from cold, temperate, continental, and semiarid climate zones are included to demonstrate the procedures developed. Two methodologies for streamflow modelling are proposed and analysed. The first method uses the UBC watershed model with a universal set of parameters for water allocation and flow routing, and precipitation gradients estimated from the available annual precipitation data as well as from regional information on the distribution of orographic precipitation. This method is proposed for watersheds without streamflow gauge data and limited meteorological station data. The second hybrid method proposes the coupling of UBC watershed model with artificial neural networks (ANNs) and is intended for use in poorly gauged watersheds which have limited streamflow measurements. The two proposed methods have been applied to five mountainous watersheds with largely varying climatic, physiographic, and hydrological characteristics. The evaluation of the applied methods is based on the combination of graphical results, statistical evaluation metrics, and normalized goodness-of-fit statistics. The results show that the first method satisfactorily simulates the observed hydrograph assuming that the basins are ungauged. When limited streamflow measurements are available, the coupling of ANNs with the regional, non-calibrated UBC flow model components is considered a successful alternative method to the conventional calibration of a hydrological model based on the evaluation criteria employed for streamflow modelling and flood frequency estimation.
url http://www.nat-hazards-earth-syst-sci.net/14/1641/2014/nhess-14-1641-2014.pdf
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