Abstract

Only a few scientific research studies, especially dealing with extremely low flow conditions, have been compiled so far, in Greece. The present study, aiming to contribute in this specific area of hydrologic investigation, generates synthetic low stream flow time series of an entire calendar year considering the stream flow data recorded during a center interval period of the year 2015. We examined the goodness of fit tests of eleven theoretical probability distributions to daily low stream flow data acquired at a certain location of the absolutely channelized urban stream which crosses the roads junction formed by Iokastis road an Chrisostomou Smirnis road, Agios Loukas residential area, Kavala city, NE Greece, using a 3-inches conventional portable Parshall flume and calculated the corresponding probability distributions parameters. The Kolmogorov-Smirnov, Anderson-Darling and Chi-Squared, GOF tests were employed to show how well the probability distributions fitted the recorded data and the results were demonstrated through interactive tables providing us the ability to effectively decide which model best fits the observed data. Finally, the observed against the calculated low flow data are plotted, compiling a log-log scale chart and calculate statistics featuring the comparison between the recorded and the forecasted low flow data.

Highlights

  • Artificial stream flow time series generation is a means of paramount importance in hydrology and water resources management in order to handle efficiently precarious or doubtful situations, pertinent to a natural watercourse’s flow regime, associated to a short period of stream flow rate data acquisition

  • A total number of 49 individual daily low stream flow rate measurements were performed within 49 consecutive days, between 25 July 2015 and 11 September 2015, at the Iokastis

  • The daily lowest flows were undergone a probability distribution analysis and eleven candidate probability distribution functions were fitted to the low stream flow rate time series data proving that the Dagum (4P) probability distribution function best fitted the data based on three different goodness of fit tests

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Summary

Introduction

Artificial stream flow time series generation is a means of paramount importance in hydrology and water resources management in order to handle efficiently precarious or doubtful situations, pertinent to a natural watercourse’s flow regime, associated to a short period of stream flow rate data acquisition. The necessity to minimize dubiety and ambivalence in estimating the flow regime of a natural watercourse constitutes a challenging task in the sector of hydrology and water resources management. This difficulty can only be adequately worked out employing artificial stream flow time series generation procedures and techniques. Logistic (2P), (8) Burr (4P), (9) Burr (3P), (10) Wakeby (5P) and (11) Dagum (4P) distributions

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