Investigation of Monthly Pan Evaporation in Turkey with Geostatistical Technique

The aim of this study is to evaluate the spatial variations of monthly average pan evaporation amounts throughout Turkey by applying Geostatistical methods. Monthly averages of Class A pan evaporation data are reported by the General Directorate of State Meteorological Works using series of record lengths between 20 and 45 years at about 200 stations scattered over an 814.578 km2 surface area of Turkey. The data belonging to the summer months of June, July, and August are used in this study because the evaporation in this three-month period is greater than the sum of those of the other nine months. Monthly averages of the observed pan evaporation data are considered and the spatial variation of evaporation is analyzed. Kriging estimate maps are drawn and interpreted for the summer months. The study indicates that the spatial variation of monthly average pan evaporation values can be reasonably estimated by the geostatistical method based on observed pan evaporation data. It is suggested that this map may be used by decision-makers for accurate estimation of monthly pan evaporation in any reservoir management or irrigation projects where data availability is limited

Prediction of groundwater levels from lake levels and climate data using ann approach

There are many environmental concerns relating to the quality and quantity of surface and groundwater. It is very important to estimate the quantity of water by using readily available climate data for managing water resources of the natural environment. As a case study an artificial neural network (ANN) methodology is developed for estimating the groundwater levels (upper Floridan aquifer levels) as a function of monthly averaged precipitation, evaporation, and measured levels of Magnolia and Brooklyn Lakes in north-central Florida. Groundwater and surface water are highly interactive in the region due to the characteristics of the geological structure, which consists of a sandy surficial aquifer, and a highly transmissive limestoneconfined aquifer known as the Floridan aquifer system (FAS), which are separated by a leaky clayey confining unit. In a lake groundwater system that is typical of many karst lakes in Florida, a large part of the groundwater outflow occurs by means of vertical leakage through the underlying confining unit to a deeper highly transmissive upper Floridan aquifer. This providesa direct hydraulic connection between the lakes and the aquifer, which creates fast and dynamic surface water/groundwater interaction. Relationships among lake levels, groundwater levels, rainfall, and evapotranspiration were determined using ANN-based models and multiple-linear regression (MLR) and multiple-nonlinear regression (MNLR) models. All the models were fitted to the monthly data series and their performances were compared. ANN-based models performed better than MLR and MNLR models in predicting groundwater levels.Keywords: groundwater, surface water, interaction, artificial neural networ

Modifying Ritchie equation for estimation of reference evapotranspiration at coastal regions of Anatolia

Evapotranspiration (ET) is of great importance in many disciplines, including irrigation system design, irrigation scheduling and hydrologic and drainage studies. A large number of more or less empirical methods have been developed to estimate the evapotranspiration from different climatic variables. The Food and Agriculture Organization (FAO) rates the Penman- Monteith equation as the major model for estimation of reference (grass) evapotranspiration (ET0) because of the fact that it gives more accurate and consistent results as compared to the other empirical models. However, the main disadvantage of this method is that it cannot be used when the sufficient data are not available. The FAO-56 PM equation requires quite a few independent variables such as solar radiation, air temperature, wind speed, and relative humidity in predicting ET0. Worldwide, the weather stations measuring all these variables are few as the majority measure air temperature only. Therefore, for regions which may not be measuring all these meteorological variables, the temperature based models like Ritchie, Hargreaves-Samani and Thornthwaite equations is necessarily used instead of the FAO-56 PM equation. In this study, the Ritchie equation is applied on the measured data recorded at 158 stations at the Coastal are of Turkey (Mediterranean, Aegean, Marmara and Black Sea regions of Anatolia), and the monthly ET0 values computed by it are observed to be smaller than those given by the Penman-Monteith equation. Next, average values for the coefficients of the Ritchie equation, which are constants originally developed in [6], are recomputed using the ET0 values given by the FAO-56 PM equation at all weather stations in coastal regions of Anatolia (Turkey). The Ritchie equation modified in such manner is observed to yield greater determination coefficients (R2), smaller root mean square errors (MSE), and smaller mean absolute relative errors (MARE) as compared to the original versions of Ritchie equation suggested by [6]. It is concluded that for estimation of reference evapotranspiration at coastal regions of Anatolia where the meteorological measurements are scarce, the modified Ritchie equation can be easily used for estimating the ET0 values

Evapotranspiration estimation by two different neuro-fuzzy inference systems

The potential of two different adaptive network-based fuzzy inference systems (ANFIS) based neuro-fuzzy systems in modeling of reference evapotranspiration (ET0) are investigated in this paper. The two neuro-fuzzy systems are: (1) grid partition based fuzzy inference system, named G-ANFIS, and (2) subtractive clustering based fuzzy inference system, named S-ANFIS. In the first part of the study, the performance of resultant FIS was compared and the effect of parameters was investigated. Various daily climatic data, that is, solar radiation, air temperature, relative humidity and wind speed from Santa Monica, in Los Angeles, USA, are used as inputs to the FIS models so as to estimate ET0 obtained using the FAO-56 Penman-Monteith equation. In the second part of the study, the estimates of the FIS models are compared with those of artificial neural network (ANN) approach, namely, multi-layer perceptron (MLP), and three empirical models, namely, CIMIS Penman, Hargreaves and Ritchie methods. Root mean square error, mean absolute error and determination coefficient statistics are used as comparing criteria for the evaluation of the models' performances. Based on the comparisons, it is found that the S-ANFIS model yields plausible accuracy with fewer amounts of computations as compared to the G-ANFIS and MLP models in modeling the ET0 process. (C) 2010 Elsevier B.V. All rights reserved

Reference evapotranspiration based on Class A pan evaporation via wavelet regression technique

Accurate estimation of reference evapotranspiration (ET0) is important for water resources engineering. Therefore, a large number of empirical or semi-empirical equations have been developed for assessing ET0 from numerous meteorological data. However, records of such weather variables are often incomplete or not always available for many locations, which is a shortcoming of these complex models. Therefore, practical and simpler methods are required for estimating the ET0. In this study, the efficiency of a wavelet regression (WR) model in estimating reference evapotranspiration based on only Class A pan evaporation is examined. The results of the WR model are compared with those of three pan-based equations, namely the FAO-24 pan, Snyder ET0 and Ghare ET0 equations and their calibrated versions. Daily Class A pan evaporation data from the Fresno and Bakersfield stations of the United States Environmental Protection Agency in California, USA, are used in the study. The WR model estimates are compared against those of the FAO-56 Penman-Monteith equation. Results showed that the WR model is capable of accurately predicting the ET0 values as a product of pan evaporation data

Feasibility of hydropower plant installation to existing irrigation dams

Because the cost of energy has risen considerably in recent decades, the addition of a suitable capacity hydropower plant (HPP) to the end of the pressure conduit of an existing irrigation dam may be economically feasible. First, a computer program capable of realistically calculating total losses from the inlet to outlet throughout any pressure conduit for many discharges (Qs)from the minimum to the maximum at small increments is coded, the outcome of which enables the determination of the C coefficient of the Total Head Loss = C.Q2. Next, a computer program is used to determine the hydroelectric energy produced at monthly periods, the present worth (PW) of their monetary gains. The average annual energy produced by a HPP is then coded. Inflows series, irrigation water requirements, evaporation rates, turbine running time ratios, and the C coefficient are the input data of this program. Running the program with a synthetically generated M number of m-year-long inflows series, histograms of both the average annual energies and the PWs of energy incomes are determined to which suitable probability distributions are then fitted. This model has been applied to ten randomly chosen irrigation dams in Turkey, and a regression equation is obtained to estimate the average annual energy as a function of gross head available and the annual volume of irrigation water, which should be useful for reconnaissance studies

Modeling River Stage-Discharge Relationships Using Different Neural Network Computing Techniques

One of the most important problems in hydrology is the establishment of rating curves. The statistical tools that are commonly used for river stage-discharge relationships are regression and curve fitting. However, these techniques are not adequate in view of the complexity of the problems involved. Three different neural network techniques, i.e., multi-layer perceptron neural network with Levenberg-Marquardt and quasi-Newton algorithms and radial basis neural networks, are used for the development of river stage-discharge relationships by constructing nonlinear relationships between stage and discharge. Daily stage and flow data from three stations, Yamula, Tuzkoy and Sogutluhan, on the Kizilirmak River in Turkey were used. Regression techniques are also applied to the same data. Different input combinations including the previous stages and discharges are used. The models' results are compared using three criteria, i.e., root mean square errors, mean absolute error and the determination coefficient. The results of the comparison reveal that the neural network techniques are much more suitable for setting up stage-discharge relationships than the regression techniques. Among the neural network methods, the radial basis neural network is found to be slightly better than the others

Prediction of hydropower energy using ANN for the feasibility of hydropower plant installation to an existing irrigation dam

Recently, artificial neural networks (ANNs) have been used successfully for many engineering problems. This paper presents a practical way of predicting the hydropower energy potential using ANNs for the feasibility of adding a hydropower plant unit to an existing irrigation dam. Because the cost of energy has risen considerably in recent decades, addition of a suitable capacity hydropower plant (HPP) to the end of the pressure conduit of an existing irrigation dam may become economically feasible. First, a computer program to realistically calculate all local, frictional, and total head losses (THL) throughout any pressure conduit in detail is coded, whose end-product enables determination of the C coefficient of the highly significant model for total losses as: THL = C.Q(2). Next, a computer program to determine the hydroelectric energies produced at monthly periods, the present worth (PW) of their monetary gains, and the annual average energy by a HPP is coded, which utilizes this simple but precise model for quantification of total energy losses from the inlet to the turbine. Inflows series, irrigation water requirements, evaporation rates, turbine running time ratios, and the C coefficient are the input data of this program. This model is applied to randomly chosen 10 irrigation dams in Turkey, and the selected input variables are gross head and reservoir capacity of the dams, recorded monthly inflows and irrigation releases for the prediction of hydropower energy. A single hidden-layered feed forward neural network using Levenberg-Marquardt algorithm is developed with a detailed analysis of model design of those factors affecting successful implementation of the model, which provides for a realistic prediction of the annual average hydroelectric energy from an irrigation dam in a quick-cut manner without the excessive operation studies needed conventionally. Estimation of the average annual energy with the help of this model should be useful for reconnaissance studies

Frequency analysis of annual maximum earthquakes within a geographical region

The risk formula, expressing the probability of at least one occurrence of earthquakes of greater-than-design-value magnitudes over the economic life of a structure, is modified taking into consideration the probability of no-earthquake years. The annual maximum earthquake magnitudes of three scales: Richter magnitude, also known as local magnitude (M-L), body-wave magnitude (M-b), and moment magnitude (M-M) in a geographical area encompassing the Bingiil Province in Turkey are taken from two sources: (1) report by Kalafat et al. (2007) 1141 and (2) the web site reporting data by Kandilli Observatory which has been recording earthquakes occurring in and around Turkey since 1900. Statistical frequency analyses are applied on the three sample series using various probability distribution models, and magnitude versus average return period relationships are determined. The values of the M-L, M-b, and M-M series for 10% and 2% risk are computed to be around 7.2 and 8.3. The tectonic structure and seismic properties of the Bingol region are also given briefly. (C) 2012 Elsevier Ltd. All rights reserved

Frequency analyses of annual extreme rainfall series from 5 min to 24 h

The parameter estimation methods of (1) moments, (2) maximum-likelihood, (3) probability-weighted moments (PWM) and (4) self-determined PWM are applied to the probability distributions of Gumbel, general extreme values, three-parameter log-normal (LN3), Pearson-3 and log-Pearson-3. The special method of computing parameters so as to make the sample skewness coefficient zero is also applied to LN3, and hence, altogether 21 candidate distributions resulted. The parameters of these distributions are computed first by original sample series of 14 successive-duration annual extreme rainfalls recorded at a rain-gauging station. Next, the parameters are scaled by first-degree semi-log or log-log polynomial regressions versus rainfall durations from 5 to 1440 min (24 h). Those distributions satisfying the divergence criterion for frequency curves are selected as potential distributions, whose better-fit ones are determined by a conjunctive evaluation of three goodness-of-fit tests. Frequency tables, frequency curves and intensity-duration-frequency curves are the outcome. Copyright (C) 2010 John Wiley & Sons, Ltd