Rainfall is one of the most important sources of water on earth supporting the existence of the majority of living organisms. Time series analysis, modeling and forecasting constitutes a tool of paramount importance with reference to a wide range of scientific purposes in meteorology (e.g. precipitation, humidity, temperature, solar radiation, floods and draughts). The present research applies the Box-Jenkins approach, employing SARIMA (Seasonal Autorregressive Integrated Moving Average) model to perform short term forecasts of monthly rainfall in Kavala city, Kavala Prefecture, Region of Eastern Macedonia-Thrace, North-Eastern Greece, North-Eastern Mediterranean Basin, modeling past rainfall time series components structure and predicting future quantities in accordance to the past. The model which is mostly fit to both describe the past rainfall data and thus generate the most reliable future forecasts is selected rated by means of both the AIC- and BIC- (SBC-) model evaluation criteria. The conclusions of this research will provide local authorities (e.g. General Secretariat for Civil Protection, European Center for Forest Fires, Deputy Governor of Agricultural Economy, daily fire risk maps designers, hydraulic, irrigation and environmental engineers, city inhabitants, farmers etc.) to develop strategic plans, policies and appropriate use of available water resources in Kavala city district

The present paper deals with the applicability of the Meyer–Peter and Müller (MPM) bed load transport formula. The performance of the formula is examined on data collected in a particular location of Nestos River in Thrace, Greece, in comparison to a proposed Εnhanced MPM (EMPM) formula and to two typical machine learning methods, namely Random Forests (RF) and Gaussian Processes Regression (GPR). The EMPM contains new adjustment parameters allowing calibration. The EMPM clearly outperforms MPM and, also, it turns out to be quite competitive in comparison to the machine learning schemes. Calibrations are repeated with suitably smoothed measurement data and, in this case, EMPM outperforms MPM, RF and GPR. Data smoothing for the present problem is discussed in view of a special nearest neighbor smoothing process, which is introduced in combination with nonlinear regression.

In the framework of diploma theses elaborated in the Division of Hydraulic Engineering, Department of Civil Engineering, Democritus University of Thrace, measurements of stream discharge and bed load transport rate were carried out at the outlet of the mountain part of Nestos River basin (northeastern Greece). In concrete terms, 68 measurements of stream discharge and bed load transport rate were carried out at a location, near the basin outlet. Apart from the measurements, bed load transport rate was computed by means of Meyer-Peter and Müller formula, after calibrating the above formula in terms of roughness coefficient. The comparison results between computations and measurements of bed load transport rate, on the basis of well-known statistical criteria, were fairly satisfactory

Within the framework of diploma theses elaborated at the Division of Hydraulic Engineering, Department of Civil Engineering, Democritus University of Thrace, measurements of stream discharge and bed load transport rate (experiments in enviromental flow) were carried out at the outlet of the mountainous part of the Nestos River basin (northeastern Greece). In concrete terms, 68 measurements of stream discharge and bed load transport rate were carried out at a location near the basin outlet. Apart from the measurements, the bed load transport rate was computed using the Meyer-Peter-Muller formula, after calibrating the formula in terms of the roughness coefficient. Comparison of the results obtained from the computations and measurements of the bed load transport rate, based on well-known statistical criteria, were fairly satisfactory. Finally, we build an artificial neural network, a data-driven technique, and investigate how well this simulates the site-measured bed load transport rate values.

In the Second EWaS International Conference (June 2016, Chania, Crete, Greece), the bed load transport formula of Meyer-Peter and Müller (1948) was calibrated with respect to the bed roughness coefficient for Nestos River. The calibration was manual and incremental, taking five measured values of bed load transport rate at a time. In contrast, the present study carries out a nonlinear optimization of two suitable parameters, while utilizing the average value of the roughness coefficient k_st found by the manual calibration. Thus, a uniform calibration is attained, by taking at once the totality of the available 68 measurement points. The results did not show any marked fitting improvement in comparison to the previous study. However, considering moving averages of the measured bed load transport values yields a better adjustment of the model to the measured results.

The importance of precipitation data acquisition and analysis in order to forecast several parts of climate patterns and profoundly intemperate events related to hydrological processes, (e.g. floods and droughts), has been significantly acknowledged by the involved scientific as well as governmental organizations, societies and authorities (e.g. meteorological, hydrological, agricultural, civil protection) worldwide. Focusing on the Mediterranean Basin, and especially Greece, there have been a lot of extreme rainfall events so far, which in association with the considerably steep orography of the Mediterranean Basin landscape unavoidably resulted to flash flooding phenomena and in addition in some cases with sequential debris-flows. The study area of the present research is the Kavala city in Kavala prefecture, North-Eastern Greece, North-Eastern Mediterranean Basin, where, a single, private meteorological station has been operating in Kavala city (Dexameni area), since the beginning of December 2005. The two most important aims which this present specific research concentrates are concluded as following: (i) to create a stochastic rainfall model dealing with the wet day episode existence and daily rainfall depth, and (ii) to produce total accumulated daily rainfall bulk depths (thus consequently daily, monthly and yearly artificial rainfall time series) by means of the two-parameters (α,β) Gamma distribution (the model parameters values were calculated based on past rainfall measurements). For this present specific research purposes, Gamma rainfall distribution function is used, aiming to constitute the powerful tool which most perfectly fits the so far recorded daily rainfall data distribution in Kavala city area