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

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

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

The need for operational reasoning in data-driven rating curve prediction of suspended sediment

The use of data-driven modelling techniques to deliver improved suspended sediment rating curves has received considerable interest in recent years. Studies indicate an increased level of performance over traditional approaches when such techniques are adopted. However, closer scrutiny reveals that, unlike their traditional counterparts, data-driven solutions commonly include lagged sediment data as model inputs and this seriously limits their operational application. In this paper we argue the need for a greater degree of operational reasoning underpinning data-driven rating curve solutions and demonstrate how incorrect conclusions about the performance of a data-driven modelling technique can be reached when the model solution is based upon operationally-invalid input combinations. We exemplify the problem through the re-analysis and augmentation of a recent and typical published study which uses gene expression programming to model the rating curve. We compare and contrast the previously-published, solutions, whose inputs negate their operational application, with a range of newly developed and directly comparable traditional and data-driven solutions which do have operational value. Results clearly demonstrate that the performance benefits of the published gene expression programming solutions are dependent on the inclusion of operationally-limiting, lagged data inputs. Indeed, when operationally inapplicable input combinations are discounted from the models, and the analysis is repeated, gene expression programming fails to perform as well as many simpler, more standard multiple linear regression, piecewise linear regression and neural network counterparts. The potential for overstatement of the benefits of the data-driven paradigm in rating curve studies is thus highlighted

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

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

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