Slavisa Trajkovic

Comparative analysis of 31 reference evapotranspiration methods under humid conditions

Evaluation of simple reference evapotranspiration (ETo) methods has received considerable attention in developing countries where the weather data needed to estimate ETo by the Penman–Monteith FAO 56 (PMF-56) model are often incomplete and/or not available. In this study, eight pan evaporation-based, seven temperature-based, four radiation-based and ten mass transfer-based methods were evaluated against the PMF-56 model in the humid climate of Iran, and the best and worst methods were selected from each group. In addition, two radiation-based methods for estimating ETo were derived using air temperature and solar radiation data based on the PMF-56 model as a reference. Among pan evaporation-based and temperature-based methods, the Snyder and Blaney–Criddle methods yielded the best ETo estimates. The ETo values obtained from the radiation-based equations developed here were better than those estimated by existing radiation-based methods. The Romanenko equation was the best model in estimating ETo among the mass transfer-based methods. Cross-comparison of the 31 tested methods showed that the five best methods as compared with the PMF-56 model were: the two radiation-based equations developed here, the temperature-based Blaney–Criddle and Hargreves-M4 equations and the Snyder pan evaporation-based equation.

Estimating Reference Evapotranspiration Using Limited Weather Data

The FAO-56 Penman-Monteith combination equation (FAO-56 PM) has been recommended by the Food and Agriculture Organization of the United Nations (FAO) as the standard equation for estimating reference evapotranspiration (ET 0 ). The FAO-56 PM equation requires the numerous weather data that are not available in the most of the stations. This paper examines the potential of FAO-56 PM equation in estimating the ET 0 under humid conditions from limited weather data. For this study, full weather data sets were collected from six humid weather stations from Serbia, South East Europe. FAO-56 reduced-set PM ET 0 estimates were in closest agreement with FAO-56 full set PM ET 0 estimates at the most of locations. The difference between FAO-56 full set PM ET 0 estimates and FAO-56 PM reduced-set ET 0 estimates generally increases by increasing the number of estimated weather parameters. Overall results indicate that FAO-56 reduced-set PM approaches mostly provided better results compared to Turc equation, adjusted Hargreaves equation and temperature-based RBF network. This fact strongly supports using the FAO-56 PM equation even in the absence of the complete weather data set. The minimum and maximum air temperature data and local default wind speed value are the minimum data requirements necessary to successfully use the FAO-56 PM equation under humid conditions.

Determination of the most influential weather parameters on reference evapotranspiration by adaptive neuro-fuzzy methodology

The monthly ET0 data were obtained by the Penman-Monteith method.ANFIS was applied for selection of the most influential ET0 parameters.Tmin, ea and sunshine hours are the most influential for ET0 estimation.Variables selection with ANFIS improves ET0 predictive accuracies.The ANFIS model can be used for ET0 estimation with high reliability. The adaptive neuro-fuzzy inference system (ANFIS) is applied for selection of the most influential reference evapotranspiration (ET0) parameters. This procedure is typically called variable selection. It is identical to finding a subset of the full set of recorded variables that illustrates good predictive abilities. The full weather datasets for seven meteorological parameters were obtained from twelve weather stations in Serbia during the period 1980-2010. The monthly ET0 data are obtained by the Penman-Monteith method, which is proposed by Food and Agriculture Organization of the United Nations as the standard method for the estimation of ET0. As the performance evaluation criteria of the ANFIS models the following statistical indicators were used: the root mean squared error (RMSE), Pearson correlation coefficient (r) and coefficient of determination (R2). Sunshine hours are the most influential single parameter for ET0 estimation (RMSE=0.4398mm/day). The obtained results indicate that among the input variables sunshine hours, actual vapor pressure and minimum air temperature, are the most influential for ET0 estimation. The maximum relative humidity and maximum air temperature are the most influential optimal combination of two parameters (RMSE=0.2583mm/day).

Spatio-temporal patterns of precipitation in Serbia

The monthly precipitation data from 29 synoptic stations for the period 1946–2012 were analyzed using a number of different multivariate statistical analysis methods to investigate the spatial variability and temporal patterns of precipitation across Serbia. R-mode principal component analysis was used to study the spatial variability of the precipitation. Three distinct sub-regions were identified by applying the agglomerative hierarchical cluster analysis to the two component scores: C1 includes the north and the northeast part of Serbia, while C2 includes the western part of Central Serbia and southwestern part of Serbia and C3 includes central, east, south and southeast part of Serbia. The analysis of the identified sub-regions indicated that the monthly and seasonal precipitation in sub-region C2 had the values above average, while C1 and C3 had the precipitation values under average. The analysis of the linear trend of the mean annual precipitation showed an increasing trend for the stations located in Serbia and three sub-regions. From the result of this analysis, one can plan land use, water resources and agricultural production in the region.

Comparison of simplified pan-based equations for estimating reference evapotranspiration.

Accurate estimation of reference evapotranspiration ( ET0 ) is essential for irrigation practice. Conversion from pan evaporation data to reference evapotranspiration is commonly practiced. The objective of this study was to evaluate the reliability of simplified pan-based approaches for estimating ET0 directly that do not require the data of relative humidity and wind speed. In this study, three pan-based (FAO-24 pan, Snyder ET0 , and Ghare ET0 ) equations were compared against lysimeter measurements of grass evapotranspiration using daily data from Policoro, Italy. Based on summary statistics, the Snyder ET0 equation ranked first with the lowest RMSE value ( 0.449 mm  day−1 ) . The pan-based equations were additional tested using mean daily data collected in Novi Sad, Serbia. The Snyder ET0 equation best matched ET0 estimates by Penman-Monteith equation at Novi Sad with lowest root mean square error value of 0.288 mm  day−1 . The obtained results demonstrate that simplified pan-based equations can be su...

Analysis of trends in reference evapotranspiration data in a humid climate

Abstract Statistically significant FAO-56 Penman-Monteith (FAO-56 PM) and adjusted Hargreaves (AHARG) reference evapotranspiration (ET0) trends at monthly, seasonal and annual time scales were analysed by using linear regression, Mann-Kendall and Spearman’s Rho tests at the 1 and 5% significance levels. Meteorological data were used from 12 meteorological stations in Serbia, which has a humid climate, for the period 1980–2010. Web-based software for conducting the trend analyses was developed. All of the trends significant at the 1 and 5% significance levels were increasing. The FAO-56 PM ET0 trends were almost similar to the AHARG trends. On the seasonal time scale, for the majority of stations significant increasing trends occurred in summer, while no significant positive or negative trends were detected by the trend tests in autumn for the AHARG series. Moreover, 70% of the stations were characterized by significant increasing trends for both annual ET0 series. Editor Z.W. Kundzewicz; Associate editor S. Grimaldi Citation Gocic, M. and Trajkovic, S., 2013. Analysis of trends in reference evapotranspiration data in a humid climate. Hydrological Sciences Journal, 59 (1), 165–180.

Long-Term Precipitation Analysis and Estimation of Precipitation Concentration Index Using Three Support Vector Machine Methods

The monthly precipitation data from 29 stations in Serbia during the period of 1946–2012 were considered. Precipitation trends were calculated using linear regression method. Three CLINO periods (1961–1990, 1971–2000, and 1981–2010) in three subregions were analysed. The CLINO 1981–2010 period had a significant increasing trend. Spatial pattern of the precipitation concentration index (PCI) was presented. For the purpose of PCI prediction, three Support Vector Machine (SVM) models, namely, SVM coupled with the discrete wavelet transform (SVM-Wavelet), the firefly algorithm (SVM-FFA), and using the radial basis function (SVM-RBF), were developed and used. The estimation and prediction results of these models were compared with each other using three statistical indicators, that is, root mean square error, coefficient of determination, and coefficient of efficiency. The experimental results showed that an improvement in predictive accuracy and capability of generalization can be achieved by the SVM-Wavelet approach. Moreover, the results indicated the proposed SVM-Wavelet model can adequately predict the PCI.

Drought Characterisation Based on Water Surplus Variability Index

Drought assessment, characterisation and monitoring increasingly requires considering not only precipitation but also the other meteorological parameters such as an evapotranspiration. Thus, some new drought indices based on precipitation and evapotranspiration have been developed. This study introduces a new drought index named the water surplus variability index (WSVI). The procedure to estimate the index involves accumulation water surplus at different time scales. To approve the proposed procedure, the WSVI is compared with the standardized precipitation index (SPI), the reconnaissance drought index (RDI) and the standardized precipitation evapotranspiration index (SPEI) based on 1-, 3-, 6- and 12-month timescales using data from several weather stations located in regions with different aridity index. Near perfect agreement (d ~ 1) between WSVI and SPI, RDI and SPEI was indicated in humid and sub-humid locations. The results also showed that the correlation coefficients between WSVI and SPI, RDI and SPEI were higher for semi-arid stations than for arid ones.

Water Surplus Variability Index as an Indicator of Drought

AbstractDrought assessment requires considering not only precipitation but also the other meteorological parameters such as an evapotranspiration. This paper presents the water surplus variability index (WSVI) as an indicator of drought. The calculation procedure incorporates both precipitation and reference evapotranspiration through the estimation of water surplus at different timescales. The WSVI is compared with the standardized precipitation index (SPI) and the reconnaissance drought index (RDI) based on 1-month, 3-month, 6-month, and 12-month timescales using data from four weather stations located in humid and subhumid climate. To approve the proposed procedure, the graphical and statistical tools such as the scatter plots, the linear trend, the coefficient of determination R2, the index of agreement d, and the Pearson correlation coefficient r were applied. The WSVI was highly correlated with both the RDI and the SPI whereas r>0.9. Moreover, d is greater than 0.93 which indicates near perfect agre...

Precipitation concentration index management by adaptive neuro-fuzzy methodology

This paper reconsiders the precipitation concentration index (PCI) in Serbia using precipitation measurements such as the mean winter precipitation amount, annual total precipitation, mean summer precipitation amount, mean spring precipitation amount, mean autumn precipitation amount and the mean of precipitation for the vegetation period (April–September). Potentials for further improvement of PCI prediction lie in the improvement of current prediction strategies. One of the options is the introduction of model predictive control. To manage the PCI, it is good to select factors or parameters that are the most important for PCI estimation and prediction, i.e. to conduct variable selection procedure. In the present study, a regression based on the adaptive neuro-fuzzy inference system (ANFIS) is applied for selection of the most influential PCI inputs based on the precipitation measurements. The effectiveness of the proposed strategy is verified according to the simulation results. The results show that the mean autumn precipitation amount is the most influential for PCI prediction and estimation and could be used for the simplification of predictive methods to avoid multiple input variables.