Evapotranspiration Data In A Humid Climate Biology Essay

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Statistically significant FAO-56 Penman-Monteith and adjusted Hargreaves reference evapotranspiration trends in monthly, seasonal and annual time basis were analysed by using linear regression, Mann-Kendall and Spearman's Rho tests at the 1 % and 5 % significance levels. For this purpose, meteorological data were used from 12 meteorological humid stations in Serbia over the period 1980-2010 and the software component for trend analysing was developed. All of the significant trends at the 1 % and 5 % significance levels were increasing. The FAO-56 PM ET0 trends were almost similar to the AHARG ET0 trends. On the seasonal time scale, the majority of stations with the significant increasing trends occurred in summer, while no significant positive or negative trends were detected by the trend tests in autumn for AHARG ET0 series. Moreover, 70 % of the stations were characterized by the significant increasing trends for both annual ET0 series.

Keywords

Trend analysis; reference evapotranspiration; linear regression; Mann-Kendall test; Spearman's Rho test

L`analyse des tendances des données sur l'évapotranspiration de référence dans un climat humide

Résumé

Les tendances FAO 56 Penman-Monteith (FAO 56 PM) mensuelles, annuelles et saisonnières statistiquement significatives et Hargreaves (AHARG) l`évapotranspiration de référence (ET0) corrigée ont été analysées par régression linéaire des tests de Mann Kendall et de Spearman Rho au niveau de signification de 1% et 5%. A cet effet, les données météorologiques ont été utilisées de 12 stations météorologiques en Serbie, pour la période 1980-2010 et le composant logiciel a été développé pour l'analyse des tendances. Toutes les tendances significatives au niveau de signification de 1% et 5% ont été à la hausse. Les FAO 56-PM ET0 tendances ont été presque semblables aux tendances AHARG ET0. Sur l'échelle de temps saisonnière, en été la plupart des stations avaient la tendance à la hausse mais, à l'automne, les tendances significatives positives et négatives n'ont pas été détectées par les tests de tendance des séries d'AHARG ET0. Par ailleurs, 70 % des stations ont été caractérisées par les tendances significatives à la hausse pour les deux ET0 séries annuelles.

Mots clefs

Analyse des tendances; Evapotranspiration de référence; Régression linéaire; Test de Mann-Kendall; Test de Spearman Rho 

1. Introduction

Analysis of trends in climate changes is one of the important environmental issues that have a significant impact on hydrological parameters such as soil moisture, ground water, evapotranspiration. Evapotranspiration (ET) is one of the major components in the hydrological cycle and its reliable estimation is essential to water resources planning and management. It is a physical process in which water passes from liquid to gaseous state while moving from the soil to the atmosphere, that refers both to evaporation from soil and vegetative surface and transpiration from plants. These two separate processes (evaporation and transpiration) occur simultaneously and there is no easy way of distinguishing one from the other.

A common procedure for estimating ET is to estimate reference evapotranspiration (ET0) and then apply an appropriate crop coefficient. ET0 is a complex nonlinear process which accurate estimation is needed for many studies such as hydrological water balance, irrigation system design, irrigation scheduling, and water resources planning and management.

In recent years, a plethora of scientists have compared and analysed the trends in ET0. Xu et al. (2006) calculated, compared and regionally mapped the Penman-Monteith ET0 and pan evaporation (Epan) at 150 meteorological stations during 1960-2000 in the Changjiang (China). They concluded that there is a significant decreasing trend in both the annual ET0 and Epan. Wang et al. (2007) found that Epan and ET0 decreased during the summer months in the upper and mid-lower Yangtze River basin of China from 1961 to 2000. Yin et al. (2010) analysed the trends in ET0 across China during the period 1961-2008. The results showed decreasing trends of ET0 in most regions and increasing trends in the cold temperate humid region and the tropical humid region. Li et al. (2012) examined the present (1961-2009) and future (2011-2099) spatiotemporal characteristics of ET0 on the Loess Plateau of China to understand the present and future changes in hydrology.

The results presented in Bandyopadhyay et al. (2009) showed a significant decreasing trend in ET0 estimated by the FAO 56 Penman-Monteith method over different agro-ecological regions of India during the period 1971-2002. In another study, Jhajharia et al. (2012) investigated the trends in ET0 estimated through the Penman-Monteith method over the humid region of northeast India by using the Mann-Kendall test. They found that ET0 decreased significantly at annual and seasonal time scales for six stations in northeast India.

A few studies have been conducted on the variability of ET0 and Epan in Iran. Tabari and Marofi (2011) investigated among other things temporal variations in Epan for 12 stations in Hamedan province in western Iran for the period 1982-2003. In another study, Tabari et al. (2011a) analysed the annual, seasonal and monthly trends of the ET0 series for 20 stations in the western half of Iran during 1966-2005. They concluded that the increasing trends in winter and summer ET0 were greater than those for the spring and autumn series. Furthermore, the results of the monthly ET0 analysis indicated that the highest numbers of stations with significant trends were found in February. In addition, Tabari et al. (2012) used Mann-Kendall test, Theil-Sen's estimator and Spearman test to identify trend in ET0 series with serial dependence in Iran. They found that the Mann-Kendall test was more sensitive than the Spearman test to the existence of the positive serial correlation in the ET0 series. Shadmani et al. (2012) analysed temporal trends of ET0 values in arid regions of Iran. Their results showed that increasing and decreasing trends were found for monthly ET0. On a seasonal scale, the highest numbers of significant trends were found in the summer and autumn series.

In trend analysis in Southern Spain, Espadafor et al. (2011) detected a statistically significant increase in Penman-Monteith ET0. Chaouche et al. (2010) focused on the western part of the French Mediterranean area and reported an increase trend in monthly potential evapotranspiration mainly in the spring.

The objectives of this study are: (1) to consider the trends on FAO-56 Penman-Monteith (FAO-56 PM) and adjusted Hargreaves (AHARG) ET0 time series in a humid climate, which were analysed by using the linear regression, Mann-Kendall and Spearman's Rho test methods and (2) to use the trend analysing component based on Web services to examine monthly, seasonal and annual ET0 trend analysis.

2. Materials and methods

2.1. Study areas and data collection

Serbia is located in the central part of the Balkan Peninsula with an area of 88.407 km2. Northern Serbia is mainly flat, while its central and southern areas consist of highlands and mountains. Its climate is temperate continental, with a gradual transition between the four seasons of the year.

Series of monthly meteorological data of maximum (Tmax) and minimum (Tmin) air temperatures, maximum (RHmax) and minimum (RHmin) relative humidities, actual vapor pressure (ea) and wind speed (U2) were collected from 12 humid stations from Serbia (Fig. 1) for the period 1980-2010 and were obtained from Republic Hydrometeorological Service of Serbia (http://www.hidmet.gov.rs/). These locations were chosen because: (1) they cover all the latitudes in Serbia (from 42°30'N to 46°10'N) and (2) they are situated at different elevations above the sea level. The description of the selected weather stations is given in Table 1.

Mean values with standard deviation of the variables used in this study for the 31-years period is summarized in Table 2. All selected weather stations had good quality datasets for estimating ET0 with both the FAO-56 PM and AHARG equations. Differences in the mean weather data for these locations are not very significant. The mean annual Tmax and Tmin for most locations varied between 12.3 and 17.9 °C and between 3.8 and 8.4 °C, respectively, while the mean RHmax and RHmin for these locations are ranged from 78.0 to 86.0 % and from 53.9 to 65.5 %, respectively. The mean annual ea is ranged from 0.9 to 1.4 kPa. The mean annual U2 was the lowest in Loznica (0.6 m s−1). It varied for all other locations between 0.9 and 1.9 m s−1.

The datasets were investigated for randomness, homogeneity and absence of trends. The Kendall autocorrelation test, the Mann-Kendall trend test and the homogeneity tests of Mann-Whitney for the mean and the variance, were used for this purpose.

2.2. Methods for estimating reference evapotranspiration

Numerous equations, classified as temperature-based, radiation-based, pan evaporation-based and combination-type, have been developed for estimating ET0 (Gocic and Trajkovic 2010, Trajkovic 2010, Tabari et al. 2011b). In this study, the FAO-56 PM and AHARG equations are used for estimating ET0 as a part of the model based on service-oriented paradigm (Gocic and Trajkovic 2011).

2.2.1. FAO-56 Penman-Monteith equation

The FAO-56 Penman-Monteith equation (FAO-56 PM) has been recommended by the Food and Agriculture Organization of the United Nations (FAO) as the standard equation for estimating ET0. It assumes the reference evapotranspiration as that from a hypothetical crop with an assumed crop height (0.12 m) and a fixed canopy resistance (70 s m-1) and albedo (0.23), closely resembling the evapotranspiration from an extensive surface of green grass cover of uniform height, actively growing, and not short of water, which is given by Allen et al. (1998):

(1)

where ET0 = reference evapotranspiration (mm day-1); Δ = slope of the saturation vapor pressure function (kPa oC -1); Rn = net radiation (MJ m-2 d-1); G = soil heat flux density (MJ m-2 d-1); γ = psychometric constant (k Pa oC -1); T = mean air temperature (oC); U2 = average 24-hour wind speed at 2 m height (m s-1); and VPD = vapor pressure deficit (kPa).

2.2.2. Adjusted Hargreaves equation

The lack of weather data motivated Hargreaves et al. (1985) to develop a simpler approach where only minimum and maximum air temperature values were required. The Hargreaves equation (HARG) can be written as

(2)

where ET0,harg = ET0 estimated by the Hargreaves equation (mm day-1); Ra = extraterrestrial radiation (mm day-1); Tmax = daily maximum air temperature (oC); Tmin = daily minimum air temperature (oC); HC = empirical Hargreaves coefficient, HE = empirical Hargreaves exponent, and HT = empirical temperature coefficient [HC = 0.0023, HE = 0.5, and HT = 17.8 (Hargreaves 1994)].

Allen et al. (1998) have proposed that when sufficient data to solve the FAO-56 PM equation are not available then the Hargreaves equation can be used. However, this equation generally overestimates ET0 at humid locations (Jensen et al. 1990). These results motivated Trajkovic (2007) to develop the adjusted Hargreaves equation that provides the close agreement with FAO-56 PM estimates at Serbian humid locations.

The adjusted Hargreaves (AHARG) equation can be written as (Trajkovic 2007):

(3)

where ET0,aharg = ET0 estimated by the adjusted Hargreaves equation (mm day-1), Tmax and Tmin = maximum and minimum air temperature (oC), respectively, and Ra = extraterrestrial radiation (MJ m-2 d-1). The AHARG equation requires temperature and latitude data for estimating ET0.

2.3. Trend analysis methods

Many statistical techniques have been developed to detect trends within time series such as Bayesian procedure, Spearman's Rho test, Mann-Kendall test, Sen's slope estimator. In this study, one parametric method (linear regression) and two non-parametric methods (Mann-Kendall and Spearman's Rho) were used to detect the ET0 trends.

2.3.1. Linear regression method

A linear regression method attempts to explain the relationship between two or more variables using a straight line. Regression refers to the fact that although observed data are variable, they tend to regress towards their mean, while linear refers to the type of equation we use in our models.

A linear regression line has an equation of the form

(4)

where x = the explanatory variable, y = the dependent variable, b = the slope of the line and a = the intercept.

The slope indicates the mean temporal change of the studied variable. Positive values of the slope show increasing trends, while negative values of the slope indicate decreasing trends.

Linear regression analysis is used for detecting and analysing trends in time series.

2.3.2. Mann-Kendall trend test

The Mann-Kendall statistical test (Mann 1945, Kendall 1975) has been frequently used to quantify the significance of trends in hydro-meteorological time series (Douglas et al. 2000, Yue et al. 2002a, Partal and Kahya 2006, Modarres and Silva 2007, Hamed 2008, Tabari and Marofi 2011, Tabari et al. 2011a).

The Mann-Kendall test statistic S is calculated by using

(5)

where n is the number of data points, xi and xj are the data values in time series i and j (), respectively and is the sign function determined as:

(6)

The variance is computed as

(7)

where n is the number of data points, m is the number of tied groups and ti denotes the number of ties of extent i. A tied group is a set of sample data having the same value.

In the absence of ties between the observations, the variance is computed as:

(8)

In cases where the sample size, the standard normal test statistic ZS is computed as:

(9)

Positive values of ZS indicate increasing trends while the negative ZS show decreasing trends.

Testing of trends is done at a specific α significance level. In this study, significance levels of α = 0.01 and α = 0.05 were used. At the 5 % significance level, the null hypothesis of no trend is rejected if |ZS| > 1.96 and rejected if |ZS| > 2.576 at the 1 % significance level.

The p-value (local significance level or probability value, p) for Mann-Kendall trend test can be obtained from (Yue et al. 2002b)

(10)

where

(11)

denotes the cumulative distribution function of a standard normal variable.

Given the significance level (α), if the value p < α, then a trend is considered to be statistically significant. For example, at the significance level of 0.05, if p ≤ 0.05, then the existing trend is assessed to be statistically significant.

2.3.3. Spearman's Rho test

Spearman's Rho test is non-parametric method commonly used to verify the absence of trends. The null hypothesis (H0) is that all the data in the time series are independent and identically distributed, while the alternative hypothesis (H1) is that increasing or decreasing trends exist (Yue et al. 2002b).

The Spearman's Rho test statistic D and the standardized test statistic ZD are expressed as follows

(Lehmen 1975, Sneyers 1990):

(12)

(13)

where is the rank of ith observation Xj in the time series and n is the length of the time series. The sample size in this study is n = 31.

Positive values of ZD indicate increasing trends while negative ZD show decreasing trends. At the 5 % significance level, the null hypothesis of no trend is rejected if |ZD| > 2.08 and rejected if |ZD| > 2.831 at the 1 % significance level.

2.3.4. Serial autocorrelation test

To remove serial correlation from the series, von Storch and Navarra (1995) suggested to pre-whiten the series before applying the Mann-Kendall test. This study incorporates this suggestion in both Mann-Kendall and Spearman's Rho test and computes the lag-1 serial correlation coefficient (designated by r1) as

(14)

where is the mean of the first n -1 observations and is the mean of the last n - 1 observations.

2.4. Trend analysing component based on Web services

The trend analysing component based on Web services was developed to investigate trends in FAO-56 PM and AHARG ET0 time series. Software component architecture for ET0 trend analysing is shown in Fig. 2. This architecture is a follow-up study of Gocic and Trajkovic (2011). The first step is data entering by using Input Data Provider. The data from the measuring stations are parsed and stored in SQL database (hydrological database) using storage Web service.

The main input data are: date format dd/mm/yy, daily maximum temperature (oC, Tmax), daily minimum temperature (oC, Tmin), wind speed, latitude (o), elevation (m), daily minimum and maximum relative air humidities (RHmin, RHmax), daily dew-point temperature (oC, Tdew) and vapour pressure (VP). Information on latitude and elevation of measuring station and date are required for the estimation of extra-terrestrial solar radiation (Ra) and the maximum sunshine hours (N).

ET0 model consists of two components: Model Equation and Numerical Estimator. Model Equation can contain the following ET0 equations: temperature-based, radiation-based, pan evaporation-based and combination-type. This study is based on FAO-56 PM and AHARG equations.

A part used for numerical estimation (Numerical Estimator) calculates the output data. It contains the logic for the selection of appropriate ET0 equation depending on the choice of input parameters.

Trend Analyzer component contains a logic for selecting parametric or non-parametric methods for monthly, seasonal and annual trend analysing. This study is based on using linear regression, Mann-Kendall and Spearman's Rho methods. Each of trend methods is implemented as a Web service, which is written in C#. End user can select the appropriate studying period, weather station and statistical method. After selecting, the results are published in table. This component can be used to facilitate the trend analysing process.

The trend analysing Web services and accompanying WSDL and SOAP 1.2 documentation are available for free download from the website http://www.gaf.ni.ac.rs/mgocic/TrendWebServices.htm. More information about Web services can be found in Staab et al. (2003), Alonso et al. (2004), Papazoglou et al. (2007), Papazoglou and Heuvel (2007).

The output data from this component can be obtained by Output Data Provider. Output data are: ET0, Ra, N, daily net radiation (Rn), estimated missing weather data and monthly, seasonal and annual trend analysing of data.

3. Results

Statistic characteristics of the estimated FAO-56 PM and AHARG ET0 at 12 weather stations during the period 1980-2010 are summarized in Table 3. The mean daily estimates by the FAO-56 PM and AHARG methods are ranged from 1.975 to 2.552 and 1.820 to 2.405 mm d−1, respectively. The highest coefficient of variation (CV) of the FAO-56 PM ET0 values was observed at the Palic station located in the north Serbia at the rate of 8.99 %, while the highest CV of 6.96 % was observed at Zlatibor for the AHARG ET0 values. The lowest CV of 6.68 % was found at Dimitrovgrad for the FAO-56 PM ET0, while the lowest CV of 4.59 % was observed at Vranje for the AHARG ET0 values.

Autocorrelation plots for the FAO-56 PM and AHARG ET0 at the 12 weather stations are presented in Fig. 3. Both FAO-56 PM and AHARG ET0 series had a positive lag-1 serial correlation coefficient at all of the stations. The highest serial correlations of 0.59 and 0.62 were obtained at Negotin (FAO-56 PM) and Zlatibor (AHARG) stations, respectively. The lowest serial correlations of 0.01 and 0.03 were detected at Loznica (AHARG) and Dimitrovgrad (FAO-56 PM) stations, respectively.

3.1. Trends of reference evapotranspiration

Trends of ET0 are considered statistically at the 1 % and 5 % significance levels. When a significant trend is identified by three statistical methods, the trend is presented in bold character in the table.

3.1.1. Monthly Analysis

The results of the three statistical tests for the monthly FAO-56 PM ET0 over the period 1980-2010 are summarised in Table 4. As shown, the applied Mann-Kendall and Spearman's Rho tests for trend analysing of monthly ET0 are similar. All stations exhibited no significant trends in the months of January, February, March, September, October and December. The results also suggest that there was only the significant increasing trend, while at the Nis and Vranje weather stations exhibited no significant trends. The magnitudes of the significant increasing trends in FAO-56 PM ET0 series varied from 0.114 mm/month at the Loznica station in November to 0.990 mm/month at the Belgrade station in July.

The results of the three statistical tests for the monthly AHARG ET0 over the period 1980-2010 are summarised in Table 5. All stations had no significant trends in the months of January, February, March, September, October and December, which is similar to FAO-56 PM ET0 series. The slope of the significant increasing trends in AHARG ET0 series was ranged from 0.148 mm/month at the Vranje station in November to 0.872 mm/month at the Zlatibor station in May.

Fig. 4 shows the percentage of stations with significant positive trends for the monthly FAO-56 PM and AHARG ET0 during the period 1980-2010. The highest numbers of stations with significant trends were found in the AHARG ET0 series at the 5 % significance level in August and November (66.67 %), while the lowest numbers of stations with significant trends were found in the FAO-56 PM ET0 series at the 1 % and 5 % significance levels in June and July (8.33 %), respectively.

The spatial distribution of the Mann-Kendall trend at the 1 % and 5 % significance levels for the monthly FAO-56 PM ET0 over the period 1980-2010 is presented in Fig. 5. FAO-56 PM ET0 significant increased in the North and central of Serbia, with one exception in the South in November.

3.1.2. Seasonal Analysis

Results of the statistical tests for seasonal FAO-56 PM and AHARG ET0 during the period 1980-2010 are presented in Table 6 and 7. According to these results, it is clear that the significant increasing trend was observed for both FAO-56 PM and AHARG ET0 series.

The analysis of the ET0 series revealed that there were significant increasing trends in spring at Negotin, Palic and Sombor for FAO-56 PM, and at Nis and Zlatibor for AHARG series. In summer, the significant increasing trends were at the 1 % significance level for FAO-56 PM ET0 series except Dimitrovgrad, Nis and Vranje were no significant trends. Furthermore, it can be concluded that all stations except Loznica had the significant increasing trends for summer AHARG ET0 series.

The results also suggest that there were no visible trends indicating an increase or decrease in autumn AHARG ET0 series, while there was significant increasing trend at Sombor at the 5 % significance level in autumn FAO-56 PM ET0 series. Besides, significant increasing trends were obtained at Loznica and Sombor (FAO-56 PM ET0) and at Nis and Zlatibor (AHARG ET0) at the 5 % significance level in winter.

Fig. 6 shows the spatial distribution of seasonal FAO-56 PM ET0 trends at the 1 % and 5 % significance levels by the Mann-Kendall test at 12 weather stations during the period of 1980-2010. The stations with significant positive trends are mainly distributed at the southern and central Serbia in summer. The significant positive trends are located in the North in spring, autumn and winter, with two exceptions: first, in the East in spring and second, in the West in winter.

3.1.3. Annual Analysis

Results of applied the Mann-Kendall test, the Spearman's Rho test and the linear regression for the annual FAO-56 PM and AHARG ET0 series during the period 1980-2010 are shown in Table 8. All of the significant trends at the 1 % and 5 % significance levels were increasing. The significant increasing trends in annual FAO-56 PM ET0 varied from 3.772 mm/year at the Negotin station to 5.163 mm/year at the Sombor station. In annual AHARG ET0 series, the significant increasing trends were ranged from 1.810 mm/year at the Sombor station to 3.623 mm/year at the Zlatibor station. The results also indicated that 41.67 % and 25 % of the stations had no significant trends for FAO-56 PM and AHARG ET0 series, respectively.

The spatial distribution of annual FAO-56 PM ET0 trends at the 1 % and 5 % significance levels by the Mann-Kendall test in observed weather stations during the period 1980-2010 is presented in Fig. 6. The significant increasing trends were identified in the North and central of Serbia, while there were no significant trends in southern Serbia.

Time series, linear trends and coefficient of determination (R2) of annual FAO-56 PM and AHARG ET0 at the stations with significant trends at α = 0.01 are presented in Fig. 7. According to these results, the significant increasing trends in annual FAO-56 PM ET0 varied from 3.772 mm/year at the Negotin station to 5.163 mm/year at the Sombor station, while in annual AHARG ET0 varied from 2.211 mm/year at the Vranje station to 3.623 mm/year at the Zlatibor station.

4. Discussion

ET0 depends on changes in air temperatures (minimum and maximum), solar radiation, relative humidity (RH) and wind speed (Gocic and Trajkovic 2010, Tabari et al. 2011a, Liu and McVicar 2012). Furthemore, we investigated the relationship between these meteorological variables and ET0 trends.

According to Türkes and Sümer (2004) and Dhorde et al. (2009), the local physical geographic and atmospheric circulation features can impact on the nature and magnitude of the maximum and minimum temperature trends. These factors can also influence the changes of the ET0, which can be seen on seasonal scale between the flatlands in northern Serbia (Novi Sad, Palic, Sombor) and highlands in central and southern Serbia (Kragujevac, Zlatibor, Nis, Vranje).

The most significant increasing trends of the Tmin and Tmax series and the significant decreasing trend of RH series were found in summer (Djordjevic 2008, Gocic and Trajkovic 2013). This can cause the existence of the significant increasing ET0 in summer (Table 6 and Fig. 6).

Moreover, Ducic et al. (2008) found that the air temperature increases in northern Serbia approximately 1.5 times higher in the near-surface layer, compared to lower and middle layers of troposphere. According to these results, there were the most significant increasing trends of ET0 in spring and summer. As addressed in Table 6, the significant increasing trend in summer season was observed for both FAO-56 PM and AHARG ET0 series in about 75 % and 91.7 %, respectively.

The Mann-Kendall test detected that approximately 70 % of the stations showed a significant decreasing trend in wind speed both in seasonal and in annual scale (Gocic and Trajkovic 2013). Similar results are detected by Jiang et al. (2010), which concluded that the fundamental reason for the decreasing trend in wind speed is the change of atmospheric circulation. Only at Palic station, significant increased trend was indicated. The decreasing trend in wind speed can increase ET0 (Table 8).

5. Conclusions

The linear regression, the Mann-Kendall and the Spearman's Rho tests were applied to analyse monthly, seasonal and annual trends in the FAO-56 PM and AHARG ET0 series. Monthly weather data for this study were used from 12 weather stations from Serbia for the period 1980-2010.

The statistical methods were developed as Web services and presented as the part of the trend analyser component. In general, this study showed that there is the great similarity between the statistical results from three statistical methods. The similar conclusion has been confirmed by Yue et al. (2002b), Tabari et al. (2011a) and Shadmani et al. (2012).

In general, all of the significant trends at the 1 % and 5 % significant levels were increasing. Furthermore, all stations exhibited no significant trends in the months of January, February, March, September, October and December for both FAO-56 PM and AHARG ET0 series. According to Mann-Kendall test, the highest numbers of stations with significant trends were found in the monthly FAO-56 PM ET0 series in July and August, while the lowest numbers of stations with significant trends were found in April.

The positive ET0 trends were significant at the 1 % and 5 % significance levels according to the statistical tests in the spring, summer, autumn and winter seasons in about 25 %, 75 %, 8.33 % and 16.67 % of the stations (FAO-56 PM) and about 16.67 %, 91.67 %, 0 % and 16.67 % of the stations (AHARG), respectively. Moreover, the highest significant increasing trend was detected in summer season at the Palic station.

On the annual time scale, the significant increasing trends varied from 3.772 mm/year at the Negotin station to 5.163 mm/year at Sombor station for FAO-56 PM ET0, and from 1.810 mm/year at the Sombor station to 3.623 mm/year at the Zlatibor station for AHARG ET0 series. The increasing trends were significant in 70 % of the stations at the 1 % and 5 % significance levels.

The analysed results can be helpful for planning the efficient use of water resources to improve agricultural production. Further research in analysing relationship between meteorological variables and ET0 trends is recommended.

Acknowledgements

The paper is a part of the research done within the projects TR 37003 and COST ES1004. We would like to thank anonymous referees for their valuable comments and their constructive suggestions that helped us improve the final version of the article.

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