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Amr Gharib
Department of Civil and Environmental Engineering, University of Alberta, Edmonton, Alberta, Canada

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Articles
Published: 10 September 2018 in Hydrological Sciences Journal
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This paper describes a new approach to fill missing data in hydrologic series. Based on a multiple-order autoregressive model, our algorithm represents the random term with an empirical distribution function that includes different parameters for the low, medium and high ranges of the modelled hydrologic variable. The algorithm involves a corrective mechanism that preserves the original statistical distribution of the series that are filled, while also eliminating the possibility of obtaining negative values for low flows. The algorithm requires multiple correlated hydrologic time series with sufficient data to permit accurate calculation of their statistical properties. It ensures that both the original statistical dependence among the data series and the statistical distribution functions will be preserved after the missing data had been filled. The model has been tested using 15 streamflow series in the Upper Bow River watershed in Alberta, Canada.

ACS Style

Nesa Ilich; Amr Gharib; Evan G. R. Davies. Kernel distributed residual function in a revised multiple order autoregressive model and its applications in hydrology. Hydrological Sciences Journal 2018, 63, 1745 -1758.

AMA Style

Nesa Ilich, Amr Gharib, Evan G. R. Davies. Kernel distributed residual function in a revised multiple order autoregressive model and its applications in hydrology. Hydrological Sciences Journal. 2018; 63 (12):1745-1758.

Chicago/Turabian Style

Nesa Ilich; Amr Gharib; Evan G. R. Davies. 2018. "Kernel distributed residual function in a revised multiple order autoregressive model and its applications in hydrology." Hydrological Sciences Journal 63, no. 12: 1745-1758.

Journal article
Published: 10 September 2017 in Water
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Floods are costly natural disasters that are projected to increase in severity and frequency into the future. Exceedances over a high threshold and analysis of their distributions, as determined through the Peak Over Threshold (POT) method and approximated by a Generalized Pareto Distribution (GPD), respectively, are widely used for flood frequency analysis. This study investigates the combined effects of threshold selection and GPD parameter estimation on the accuracy of flood quantile estimates, and develops a new, widely-applicable framework that significantly improves the accuracy of flood quantile estimations. First, the performance of several parameter estimators (i.e., Maximum Likelihood; Probability Weighted Moments; Maximum Goodness of Fit; Likelihood Moment; Modified Likelihood Moment; and Nonlinear Weighted Least Square Error) for the GPD was compared through Monte Carlo simulation. Then, a calibrated Soil and Water Assessment Tool (SWAT) model for the province of Alberta, Canada, was used to reproduce daily streamflow series for 47 watersheds distributed across the province, and the POT was applied to each. The Goodness of Fit for the resulting flood frequency models was measured by the upper tail Anderson-Darling (AD) test and the root-mean-square error (RMSE) and demonstrated improvements for more than one-third of stations by averages of 65% (AD) and 47% (RMSE), respectively.

ACS Style

Amr Gharib; Evan G. R. Davies; Greg G. Goss; Monireh Faramarzi. Assessment of the Combined Effects of Threshold Selection and Parameter Estimation of Generalized Pareto Distribution with Applications to Flood Frequency Analysis. Water 2017, 9, 692 .

AMA Style

Amr Gharib, Evan G. R. Davies, Greg G. Goss, Monireh Faramarzi. Assessment of the Combined Effects of Threshold Selection and Parameter Estimation of Generalized Pareto Distribution with Applications to Flood Frequency Analysis. Water. 2017; 9 (9):692.

Chicago/Turabian Style

Amr Gharib; Evan G. R. Davies; Greg G. Goss; Monireh Faramarzi. 2017. "Assessment of the Combined Effects of Threshold Selection and Parameter Estimation of Generalized Pareto Distribution with Applications to Flood Frequency Analysis." Water 9, no. 9: 692.