Comparative application of two mathematical models to predict sedimentation in Yermasoyia Reservoir, Cyprus

Hrissanthou, Vlassios

doi:10.1002/hyp.6167

Cited by 18 publications

(12 citation statements)

References 11 publications

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“…The watershed area is 157 km 2 and its elevation ranges from 70 m up to 1400 m. The length of the main stream of the basin is about 25 km [12] and the average basin wide annual precipitation is 640 mm, ranging from 450 mm at the low elevations up to 850 mm at the upper parts of the watershed. The 65% of the mean annual runoff is generated by rainfall during winter months.…”

Section: Datamentioning

confidence: 99%

Bivariate Flood Frequency Analysis Using Copulas

2018

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Flood frequency estimation for the design of hydraulic structures is usually performed as a univariate analysis of flood event magnitudes. However, recent studies show that for accurate return period estimation of the flood events, the dependence and the correlation pattern among flood attribute characteristics, such as peak discharge, volume and duration should be taken into account in a multivariate framework. The primary goal of this study is to compare univariate and joint bivariate return periods of floods that all rely on different probability concepts in Yermasoyia watershed, Cyprus. Pairs of peak discharge with corresponding flood volumes are estimated and compared using annual maximum series (AMS) and peaks over threshold (POT) approaches. The Lyne-Hollick recursive digital filter is applied to separate baseflow from quick flow and to subsequently estimate flood volumes from the quick flow timeseries. Marginal distributions of flood peaks and volumes are examined and used for the estimation of typical design periods. The dependence between peak discharges and volumes is then assessed by an exploratory data analysis using K-plots and Chi-plots, and the consistency of their relationship is quantified by Kendall's correlation coefficient. Copulas from Archimedean, Elliptical and Extreme Value families are fitted using a pseudo-likelihood estimation method, verified using both graphical approaches and a goodness-of-fit test based on the Cramér-von Mises statistic and evaluated according to the corrected Akaike Information Criterion. The selected copula functions and the corresponding joint return periods are calculated and the results are compared with the marginal univariate estimations of each variable. Results indicate the importance of the bivariate analysis in the estimation of design return period of the hydraulic structures.

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Section: Datamentioning

confidence: 99%

Bivariate Flood Frequency Analysis Using Copulas

2018

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“…Most of the area is covered by typical Mediterranean-type forest and sparse vegetation. A reservoir with storage capacity of 13.6 million m 3 was constructed downstream of the mouth of the watershed in 1969 for irrigation and municipal water supply purposes (Hrissanthou, 2006). The climate of the area is of Mediterranean maritime climate, with mild winters and hot and dry summers.…”

Section: Study Basins and Databasementioning

confidence: 99%

Streamflow simulation methods for ungauged and poorly gauged watersheds

Λουκάς

Vasiliades

2014

Nat. Hazards Earth Syst. Sci.

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Abstract. 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.

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“…3), was divided into four natural sub-basins for more precise calculations (Fig. 4, Hrissanthou, 2006). The sub-basin areas vary between 14 and 44 km 2 .…”

Section: Application To the Basin Of Yermasoyia Reservoirmentioning

confidence: 99%

Computation of Lake or Reservoir Sedimentation in Terms of Soil Erosion

Hrissanthou¹

2011

Sediment Transport in Aquatic Environments

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Comparative application of two mathematical models to predict sedimentation in Yermasoyia Reservoir, Cyprus

Cited by 18 publications

References 11 publications

Bivariate Flood Frequency Analysis Using Copulas

Bivariate Flood Frequency Analysis Using Copulas

Streamflow simulation methods for ungauged and poorly gauged watersheds

Computation of Lake or Reservoir Sedimentation in Terms of Soil Erosion

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