Hossein Tabari

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.

Estimation of daily pan evaporation using artificial neural network and multivariate non-linear regression

Measurement of evaporation (E) rate from various natural surfaces is known as the key element in any hydrological cycle and hydrometeorological studies. Due to the shortage of pan evaporation (EP) data, the estimation of EP for such studies seems necessary. The main aim of this paper was to estimate daily EP using artificial neural network (ANN) and multivariate non-linear regression (MNLR) methods in semi-arid region of Iran. Five different ANN and MNLR models comprising various combinations of daily meteorological variables, that is, relative humidity (RH), air temperature (T), solar radiation (SR), wind speed (U) and precipitation (P) were developed to evaluate degree of effect of each of these variables on EP. The comparison of models estimates showed that the ANN 5 model characterized by Delta-Bar-Delta learning algorithm and Sigmoid activation function which uses all input parameters (T, U, SR, RH, P) performed best in prediction of daily EP. The sensitivity analysis revealed that the estimated EP data are more sensitive to T and U, respectively. A comparison of the model performance between ANN and MNLR models indicated that ANN method presents the best estimates of daily EP.

Local Calibration of the Hargreaves and Priestley-Taylor Equations for Estimating Reference Evapotranspiration in Arid and Cold Climates of Iran Based on the Penman-Monteith Model

The Food and Agricultural Organization of the United Nations (FAO)-56 version of Penman-Monteith (PMF-56) model has been established as a standard for calculating reference evapotranspiration (ETo). An important constraint of application of the PMF-56 model is the requirement of solar radiation, wind speed, air temperature, and humidity data, which may not be available for a given location, especially in developing countries. The Hargreaves (HG) and Priestley-Taylor (P-T) equations are simple equations that require few weather data inputs, although regional calibration of the equations is needed for acceptable performance before applying them for ETo estimation. In this study, the HG and P-T equations were calibrated on the basis of the PMF-56 method in arid and cold climates of Iran using data from 12 stations during 1994–2005. After calibration of the HG equation, the average value of the adjusted HG coefficient for arid climate was 0.0031, which is about 34% higher than the original value (0.0023). Sim...

Long-term variations of water quality parameters in the Maroon River, Iran

Sixteen water quality parameters have been monitored at four stations located along the Maroon River during 1989–2008. The trend analysis was performed on seasonal and annual time-scales using the Mann–Kendall test, the Sen’s slope estimator and the linear regression. The relationships of the water quality parameters to river discharge were also investigated. The statistical methods showed both positive and negative trends in annual water quality data. However, significant trends were detected by the statistical methods only in calcium, magnesium, sodium absorption ratio, pH, and turbidity series. The results indicated that the concentrations of the water quality parameters increased in spring and winter seasons, while the concentrations were diluted in summer and autumn seasons in the last two decades. Moreover, the highest numbers of significant trends were found in the spring and summer series, respectively. According to the regression analysis, most of the water quality parameters were negatively correlated with river discharge.

Regional Estimation of Reference Evapotranspiration in Arid and Semiarid Regions

Evapotranspiration is critical to many applications including water resource management, irrigation scheduling, and environmental studies. Many models based on meteorological data have already been developed to estimate reference evapotranspiration ( ET0 ) in various climatic and geographical conditions. The main purpose of this study was to evaluate the performances of the Makkink, Priestley-Taylor, and Hargreaves models versus the Penman-Monteith FAO-56 (PMF-56) method in arid and semiarid regions of Iran during 1993–2005 and to identify the alternative ET0 model that presents results closest to the PMF-56 method. Additionally, a regional estimation of monthly ET0 with the best-performed model is presented by using the spatially distributed physical parameters and geographical information system. The results indicated that the Hargreaves model was the best model to estimate ET0 in eastern arid and semiarid regions of Iran. The spatial distribution maps of ET0 showed that ET0 values increased from north ...

Investigation of groundwater level fluctuations in the north of Iran

Groundwater is the main source of water supply for drinking and agriculture uses in Mazandaran province. In recent years, the rapid growth of population and the increased need for water and food has put its land and water resources under severe stress. The main objective of this study was to investigate the temporal trends in annual, seasonal and monthly groundwater level using the Mann–Kendall test and the Sen’s slope estimator in the area during 1985–2007. The results indicated a mix of negative and positive trends in the groundwater level series. However, the positive trends were much more than negative ones. The statistical tests detected a significant increasing trend in more than 28% of the wells. The stronger increasing trends were identified in the series in summer and spring compared with those in autumn and winter. Moreover, the highest numbers of wells with significant positive trends occurred in August and July, respectively. The results of spatial analysis showed that the significant positive trends were concentrated in the central parts of Mazandaran province where paddy fields are the major water demanders. Analysis of climatic parameters revealed that decreasing trend of relative humidity and increasing trends of minimum and maximum air temperature can be attributed to groundwater level fluctuations in the study region. The research will be helpful for planners and policy makers to allocate groundwater resources in different sectors including agriculture, drinking and industry.

Prediction of monthly discharge volume by different artificial neural network algorithms in semi-arid regions

Prediction of monthly discharge volume is important for reservoir management and evaluation of drinking-water supplies. Also, it is very essential in arid and semi-arid regions due to the lack of observed data. This study compared four artificial neural network (ANN) algorithms to predict the monthly discharge volume from Idenak Watershed in Kohkiloye Boier Ahmad Province in southwestern Iran. These algorithms, including resilient backpropagation (ANN_RP), scaled conjugate gradient (ANN_SCG), variable learning rate (ANN_GDX), and Levenberg–Marquardt (ANN_LM), were applied to monthly discharge volume data. The transfer function employed was the tangent sigmoid, and input vectors were constructed in different ways during the algorithm development. The algorithms were trained and tested using a 36-year data record (432 monthly values) selected randomly. Comparison of the algorithms showed that ANN_SCG performed better than the other algorithms, where the values of R2 and root mean square errors during validation were 0.78 and 63 million cubic meters. Furthermore, the input vector consisting of precipitation [P(t)], antecedent precipitation [P(t − 1)], and antecedent monthly discharge volume with one time lag [V(t− 1)] was superior to the other input vectors for monthly discharge volume prediction. Generally, the proposed models are capable for prediction of monthly discharge volume in arid and semi-arid regions.

Multilayer perceptron for reference evapotranspiration estimation in a semiarid region

Calculation of reference evapotranspiration (ETo) is essential in hydrology and agriculture. ETo plays an important role in planning and management of water resources and irrigation scheduling. The results of many studies strongly support the use of the Penman–Monteith FAO 56 (PMF-56) method as the standard method of estimating ETo. The basic obstacle to using this method widely is the numerous meteorological variables required. Multilayer perceptron (MLP) networks optimized with different learning algorithms and activation functions were applied for estimating ETo in a semiarid region in Iran. Four MLP models comprising various combinations of meteorological variables are developed. The MLP model which needs all of the meteorological parameters performed best for ETo estimation amongst the other MLP models. It was also found that the ConjugateGradient, DeltaBarDelta, DeltaBarDelta and Levenberg–Marquardt were the best algorithms for training the MLP1, MLP2, MLP3 and MLP4 models, respectively.

Observed changes in relative humidity and dew point temperature in coastal regions of Iran

The analysis of trends in hydroclimatic parameters and assessment of their statistical significance have recently received a great concern to clarify whether or not there is an obvious climate change. In the current study, parametric linear regression and nonparametric Mann–Kendall tests were applied for detecting annual and seasonal trends in the relative humidity (RH) and dew point temperature (Tdew) time series at ten coastal weather stations in Iran during 1966–2005. The serial structure of the data was considered, and the significant serial correlations were eliminated using the trend-free pre-whitening method. The results showed that annual RH increased by 1.03 and 0.28 %/decade at the northern and southern coastal regions of the country, respectively, while annual Tdew increased by 0.29 and 0.15°C per decade at the northern and southern regions, respectively. The significant trends were frequent in the Tdew series, but they were observed only at 2 out of the 50 RH series. The results showed that the difference between the results of the parametric and nonparametric tests was small, although the parametric test detected larger significant trends in the RH and Tdew time series. Furthermore, the differences between the results of the trend tests were not related to the normality of the statistical distribution.

Extreme streamflow drought in the Karkheh river basin (Iran): probabilistic and regional analyses

Analysis of droughts from both the regional and probabilistic points of view is crucial in drought-prone arid and semiarid lands. In this work, the probabilistic behavior and spatial pattern of extreme hydrological droughts based on the minimum streamflow drought index values at 3-, 6-, 9- and 12-month time scales were analyzed by using L-moments in the Karkheh river basin in the southwest of Iran. The results indicated that for the 3-month time scale, the region was homogenous, while for larger time scales the region was divided into two subregions. For all time scales and subregions, the Pearson Type III and Generalized Pareto distributions provided a better fit to the data than the generalized logistic, generalized extreme value and log-normal distributions. The simulation results of the estimated regional quantiles showed that the accuracy of the quantile estimates decreased as return period increased. Furthermore, higher probability of severe droughts is expected at regional scale compared with site scale.